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Unemployment effect of WTO ascension: Evidence from a natural experiment
International Economics 159 (2019) 48–55
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Unemployment effect of WTO ascension: Evidence from a natural experiment Chinedu Increase Onwachukwu a, *, Ekene Francis Okagbue b a b
Department of Economics and Finance, City University of Hong Kong, Hong Kong School of Public Affairs and Administration, University of Electronic Science and Technology of China, China
A R T I C L E I N F O
A B S T R A C T
JEL codes: E24 F00 F13
Because international trade affects the rate of unemployment, ascending the World Trade Organization (WTO) established to improve the outcomes of trade will impact employment. By using a dataset covering 175 countries between 1991 and 2017 and exploiting the exogenous variation in the ascension years of countries, we estimate the causal effect of ascending WTO on the unemployment rate. Difference-in-difference estimates imply that unemployment rate reduced by 13.7 percent. Countries that ascended between 2011 and 2017 had the highest reduction in unemployment compared to those that joined between 1995 to 1999 and 2000 to 2010. Also, the effect of ascension is more for developing countries than developed countries. Our results are robust to the matching of countries by ascension period and are not influenced by any simultaneous shock or pre-existing political and economic conditions. These results point to the need for non-member countries to join the organization.
Keywords: World Trade Organization Ascension Unemployment rate Difference-in-difference
1. Introduction The work of Ricardo (1817), Smith (1887), Heckscher (1991) and a few others laid the theoretical foundations for examining the outcomes of trade between two or more countries. Recent studies have relied on the postulations of these theories to derive interesting results on how trade influences employment. For instance, Brecher (1974) and Davis (1998) modify the Heckscher-Ohlin model by including workers' minimum wages. Their results suggest that free trade worsens the problem of unemployment. Similarly, Larch and Lechthaler (2011) identify three mechanisms through which free trade impacts employment. Workers who have competitive advantage, a unique area of specialization, and can switch easily from one sector to another have a higher probability of getting employed. Also, adopting a theoretical model in which returns to scale is increasing and introducing the idea of equal wages shows an increasing effect of removing trade barriers on unemployment (Egger and Kreickemeier, 2009). Going further, studies on trade-employment relation have been extended by several researchers by allowing for wage inequality. Haouas et al. (2005), and Ferreira et al. (2007) find that the effect of free trade on employment is more than the impact on wages if adjustments could be made on employment. Helpman et al. (2010) also investigate the interplay between unemployment, wage inequality and international trade. While the effect of trade on employment is ambiguous, they find that increase in free trade gradually raises disparity in wage until a certain point. Some empirical studies have also examined how trade liberalization affects employment in the presence of search friction. Felbermayr et al. (2011) examine how trade openness influences unemployment and incorporate work search friction in their model. Their
* Corresponding author. E-mail address: [email protected] (C.I. Onwachukwu). https://doi.org/10.1016/j.inteco.2019.04.003 Received 7 December 2018; Received in revised form 20 February 2019; Accepted 23 April 2019 Available online 4 May 2019 2110-7017/© 2019 CEPII (Centre d'Etudes Prospectives et d'Informations Internationales), a center for research and expertise on the world economy. Published by Elsevier B.V. All rights reserved.
C.I. Onwachukwu, E.F. Okagbue
International Economics 159 (2019) 48–55
results suggest that the liberalization of trade reduces unemployment. Also, the work of Davidson et al. (1999) which also incorporates friction shows that the effect depends on the ratio of capital to labour across countries. The observed mixed results could be attributed to a lot of reasons, of which one of them is the fact that the direction of the impact depends on whether a country is an importer or exporter, and if the local industries of the importing country produce the imported goods.1 Empirical evidence shows that while export increases employment and factor earnings, the effect of import is the direct opposite. World Trade Organization (WTO), established in 1995 seek to achieve many objectives some of which will foster trade among countries through liberalization.2 Because trade affects employment, as confirmed by previous studies, it makes sense to believe that the establishment of WTO will impact employment in different countries. Some studies have attempted this kind of analysis (Bhalla and Qiu, 2002; Zhu and Warner, 2005; Cling et al., 2009; Gnevko et al., 2016). While the study of Cling et al. (2009) and, Gnevko et al. (2016) concern the implications of joining WTO on the economies of Vietnam and China respectively which are individual countries, the work of Bhalla and Qiu (2002), and Zhu and Warner (2005) focus on the effect on employment in China. To the best of our knowledge, this is the first empirical study to document a relationship between the decision to join WTO and employment level using cross-country data. As a result, this study is significant in three ways. First, it is a cross-country study covering almost all the countries of the world. A given percentage of the countries are WTO members, and their years of ascension are different. This suggests that a natural experiment has taken place so that countries are categorized into treatment and control groups. These type of data make it possible to investigate the causal effect of ascending WTO on unemployment using the difference-in-difference (DD) estimation method. Second, while countries such as the United States, United Kingdom, Australia, etc. are developed countries with good institutions, most African and Asian countries are developing countries with the high problem of corruption and unemployment. Given the disparities, it is possible that the effect of ascension defers between developed and developing countries. Finally, we classify countries in the treatment group into three groups based on the timing of ascension. This classification allows us to examine how timing augments the effect of joining the organization. Our results reveal that ascension reduced the unemployment rate and that the effect is more for developing countries. Also, the reduction in unemployment is more for countries that joined the organization very late. Section 2 introduces the econometric method of this study. The empirical results are discussed in Section 3, and Section 4 concludes the study. 2. Econometric method 2.1. Baseline regression model We started by obtaining data on the countries that have joined the World Trade Organization, and those that are yet to join. Though we identified a total of 164 members and 23 non-members, we dropped countries that have no observations in our variables of interest. This reduces the total number of countries to 175. Out of these 175 countries, 154 are members of WTO, so they are categorized as the treatment group in this study. The remaining 21 countries are non-members, and they are called the control group. It is worthy of note that the international organization in question came into existence in 1995. Thus, our data cover the period from 1991 to 2017. We are unable to cover periods before 1991 because of data unavailability. Since we take ascending WTO as a shock to investigate the effect on unemployment, the difference-in-difference specification is below. lnUit ¼ α þ θMi þ ϑAt þ βMi *At þ δXit þ εit
(1)
Where i and t indicate country and year respectively. Mi is a dummy variable that is equal to one for countries that are WTO members. At is equal to one if a country has joined WTO in year t. It is the dummy variable for after joining the organization. The coefficient of the interaction, β is the DD estimate of the effect of ascension on unemployment rate. Uit is the dependent variable, it is the unemployment rate of country i in year t. Xit is a vector of control variables. Data for all the control are from the World Bank Website. They include GDP growth rate, FDI, lagged trade openness, inflation rate, and population. Estimating the above model will not provide the correct estimate of the effect of ascension for two reasons. First, it does not take into consideration the fact that our dataset is panel since it does not include controls for fixed effects. Second, since At only has a time subscript, it means that all the countries in the treatment group joined WTO in the same year. This is obviously not correct because countries in the treatment group joined the organization in different years. We address these problems by including fixed effects and allowing the At to vary across countries. Adding country (ωi ) and year (π t ) fixed effects requires dropping Mi and At to avoid redundancy. The implication of allowing At to vary by both country and year is that the definition of the control group changes. More specifically, the control group is no longer countries that have not ascended WTO. For every year, the control group is now all the countries that are not yet members of WTO. Model 2 below is now what we want to estimate.
1 Revenga (1992) finds that competition because of increased import dampens wage and employment in manufacturing industries in the United States. Also, the effect on wage is relatively small compared to that of employment. Similar study by Autor et al. (2014) reveal that competition because of trading with China reduces employment and labour earnings. Tacero et al. (2017), on the other hand, employ input-output analysis and show that increased importation of goods will create jobs in Spain. The study of Martincus et al. (2017) also reveal that programs that brings about road construction have bolstering impact on export and employment. 2 See the link for the six main goals World Trade Organization seeks to achieve. https://www.britannica.com/topic/World-TradeOrganization#ref224477.
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C.I. Onwachukwu, E.F. Okagbue
International Economics 159 (2019) 48–55
lnUit ¼ ωi þ π t þ βMi *Ait þ δXit þ εit
(2)
Now, we neglect the definition of the control group in the preceding paragraph and identify one issue in our dataset. The problem is that there is a significant difference between the number of countries in the treatment group and the control group. Specifically, while there are 154 countries in the treatment, only 21 countries are in the control group. The implication is that the estimate of β may just be reflecting such dominance of the treatment over the control. To fix this issue, we identify three different periods during which countries in the treatment group entered WTO – 1995 to 1999, 2000 to 2010, and 2011 to 2017 - and group the countries accordingly. We then estimate model 2 for each group using the same control group. By doing this matching of countries in the treatment group, we bridge the gap between the number of countries in each group and the same control group. The result of this matching will further provide evidence as to whether the timing of ascension is of any importance by revealing the difference in the unemployment rate of the three groups. An alternative way of examining the effect of ascension given timing is to create a dummy variable that takes the value of one for countries in the treatment that joined WTO between 1995 and 2010 and zero for those that entered between 2011 and 2017. Here countries that take the value of one joined earlier than those that take zeros. Including an interaction of the dummy with Mi * Ait in model 2 will show how the unemployment rate of countries that joined early differ with that of those that joined late. If the estimate is positive (negative) it means that early ascension is associated with higher (lower) unemployment than late ascension. We also group the countries into developed and developing countries and estimate model 2 for each group. This helps answer our third research question. We understand that the matching of both countries in the treatment group and control group is necessary because it helps mitigate disturbances coming from other variables in our model. However, we are faced with the problem of matching countries in the control group according to the year of ascension because they have not joined the organization. The good news is that ascending WTO is a very long process and it starts with the creation of a working party which will handle the ascension process. We use the year a working party was created as a pseudo ascension year and match countries in the control group. The implication is that if we want to identify all the countries that joined WTO between the period from 1995 to 1999, countries in the control group that created their working parties during this period will be included. The same thing applies to identifying countries that ascended between 2000 to 2010 and 2011 to 2017. Furthermore, DD estimation requires there be no shock affecting both treatment and control groups that is coincidental with ascension. If this happens, our estimate of the effect of ascension on unemployment could be reflecting the effects of both the shock and ascension, so that it is difficult to disentangle the exact impact of ascension on unemployment. Since the shock needs to contemporaneous with ascension to become problematic, it calls for no worry because the year of ascension varies across countries in the treatment group. Specifically, it is not possible that the same shock took place each year that a country ascends WTO. Despite this, we still move to solve the problem. One reasonable way of tackling this problem is to ensure that the coefficient estimates of all the control variables change over time. If there is no significant change in the estimate of the effect of ascension on unemployment because of allowing time-varying coefficient estimates for the control variables, it means that no other shock is driving our results. Finally, we worry that ascension may not be exogenous because it could have been motivated by the prevailing economic cum political conditions in each country, which could be correlated with the unemployment rate. That is, year of ascension could be correlated with the error term in time t, tþ1, tþ2, … To be more specific, suppose that the countries that ascended WTO did so because they were experiencing high unemployment rates, the estimate of the effect of ascension on unemployment will be highly biased. We employ two approaches to tackle this issue. First, if the existing movement in unemployment necessitated ascension, by allowing the same movement in countries that joined WTO, we expect a significant change in our coefficient estimate. By including a trend for countries in the treatment group, we show that ascension is exogenous because there are no substantial changes in our results. Second, if ascension was motivated by transitory changes in the state of the economies, it is possible to see some effect of ascension before ascension actually takes place. We, therefore, add major dummy variables for years countries in the treatment group joined WTO and years before they entered. The non-significance of these dummies would further suggest that ascension is exogenous. 2.2. Descriptive statistics Here, we show that there is a need to investigate the issue of how ascending WTO affects unemployment rate. Fig. 1 displays the
Fig. 1. Trend in unemployment rate for treatment and control. 50
C.I. Onwachukwu, E.F. Okagbue
International Economics 159 (2019) 48–55
trend in the unemployment rate of the treatment group and the control group. The first observation is that the unemployment rate of countries in the treatment group is always lower than that of the control group. After 1995, the distance between the two curves begins to widen, and we show in this study that this is because of ascension. We document the descriptive statistics of the full sample in Table 1. The average unemployment rate is 8.5 percent. Before ascension, the average unemployment rate was 8.9 percent, but after ascension, it decreases to 8.4 percent. The average unemployment rate for ascension between 1995 and 1999, 2000–2010 and 2011–2017 is 8.8 percent, 8.2 percent, and 7.7 percent respectively. This shows that late ascension is associated with a higher reduction in unemployment. We present further evidence in Table 2 by comparing characteristics for the treatment group and the control group. The unemployment rate of the treatment is less than that of the control by 26.9 percent, and the difference is significant. 3. Results 3.1. Main results The main estimation results are in Tables 3–6 contain the results of our robustness checks. For Table 3 the first two columns contain the results for the pooled sample, column three, four and five are the results after matching countries, column six is for developed countries, and column seven shows the results for developing countries. All the regression results include controls for country and year fixed effects. The coefficient associated with Mi *Ait is the DD estimate of the impact of WTO ascension on the unemployment rate. The estimate in the first column is 0.114, and it is significant at the one percent level. This value means that ascension significantly reduced the unemployment rate by 11.4 percent. As pointed out from the outset, developed countries have high growth rate, with good institutions and high saving and investment, so it is possible that these factors explain the negative estimate in column one. Also, it is has been shown in the literature that opening up to trade brings about an increase in export and reduction in unemployment. As a result, we control for income levels by adding the growth rate of GDP and FDI, and trade dependence measured by lagged openness to trade. We also control for inflation rate and population of each country. GDP growth rate and FDI both have negative signs and are significant, affirming that unemployment reduces as they go up. Trade openness is positive and insignificant, but the DD estimate goes up from 11.4 to 13.7 percent. One possible mechanism of transmission is through an increase in export and reduction in import. Growth in export demand encourages higher production, and this could be met by increasing employment. Increase in import, on the other hand, crowds out local production of the imported goods in the importing country, thereby decreasing employment. So, the opposite effect is expected if the demand for import decreases (Revenga, 1992; Autor et al., 2014; Martincus et al., 2017). Another mechanism of transmission is through changes in the tariff rate which affects import and export, thus affecting employment. According to Queen's University (1997) if wages and prices are flexible, increase in the tariff rate will decrease wage and raise the demand for labor. The ultimate impact is a reduction in the rate of unemployment. The findings of Eichengreen (1981) also support the results of Queen's University (1997). Matching countries by the period of their ascension help mitigate the influence of having more countries in the treatment group than in the control group. This also provides an insight as to whether the timing of ascension influences the effect of ascension on unemployment. The results are in column three, four and five of Table 3. They show the results of countries that ascended between 1995 and 1999, 2000–2010, and 2011–2017 respectively. Countries that ascended between 1995 and 1999 had a significant reduction in unemployment by 7.7 percent. For those that ascended between 2000 and 2010, the reduction in unemployment rate is by 9.2 percent, but those that ascended last had a decrease in unemployment by 29.6 percent. These results point to one thing, countries that ascended late had the highest reduction in the unemployment rate and these countries are mostly developing countries. This is in line with existing argument that developed countries do not benefit much from WTO because they had already achieved their status of development before joining the organization. Developing countries, on the other hand, benefit a lot by enjoying larger markets for their export in developed countries (Pugel, 2007). Table 1 Descriptive statistics of the sample. Variable
N
Mean
SD
Min
Max
Unemployment Before Establishment After Establishment Treated 1995–1999 Treated 2000–2010 Treated 2011–2017 GDP Growth Rate Inflation Rate lnFDI lnOpennesss lnPopulation
4725 700 3850 770 1694 1078 4725 4725 4725 4725 4725
8.509 8.866 8.447 8.848 8.164 7.744 3.760 27.788 22.719 0.285 15.800
6.329 6.769 6.247 6.570 6.126 5.874 6.883 402.919 24.175 0.537 1.842
0.122 0.300 0.122 0.523 0.190 0.122 64.047 18.109 – 6.348 0
44.157 38.527 44.157 44.157 38.040 31.380 149.973 195.024 27.322 1.392 21.050
Notes: Though FDI has both positive and negative values, the mean, standard deviation, and maximum values are all positive, so their natural logarithm are taken to keep the numbers small. There is nothing in the minimum value because it is negative, and log of negative is not defined. Unemployment rate is not logged here. 51
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International Economics 159 (2019) 48–55
Table 2 Country characteristics of treatment and controls. Country Characteristics
lnUnemployment Rate GDP Growth Rate lnFDI Inflation Rate lnOpenness lnPopulation
Treatment
Control
Difference
A
B
A-B
1.824 (0.816) 3.551 (5.307) 22.822 (24.230) 26.991 (420.529) 0.291 (0.544) 15.846 (1.816)
2.093 (0.799) 5.562 (14.471) 20.515 (20.958) 35.175 (166.921) 0.239 (0.473) 15.516 (1.757)
0.269*** (0.036) 2.011*** (0.613) 2.307** (0.957) 8.184 (9.575) 0.052** (0.021) 0.330*** (0.079)
Notes: Column A and B contain averages for countries in the treatment and control respectively. Standard deviations are in parenthesis. Column A – B provides information on the difference between A and B. FDI has both positive and negative values, but since the averages and standard deviations in A and B are positive, natural log of these values are taken to keep the values small.
Table 3 Unemployment effect of WTO ascension. Variable
Pooled Sample (1)
(2)
(3)
(4)
(5)
(6)
(7)
Mi * Ait
0.114*** (0.025)
0.137*** (0.033) 0.003** (0.001) 0.000** (0.000) 0.012 (0.027) 0.000 (0.000) 0.002 (0.107) Yes Yes 0.8575 3160
0.077** (0.036) 0.004*** (0.001) 0.000** (0.000) 0.069*** (0.024) 0.000 (0.000) 0.082 (0.130) Yes Yes 0.8534 2771
0.092* (0.050) 0.001 (0.001) 0.000*** (0.000) 0.157** (0.067) 0.000*** (0.000) 0.197 (0.135) Yes Yes 0.9112 618
0.296*** (0.081) 0.000 (0.001) 0.000** (0.000) 0.174*** (0.050) 0.000*** (0.000) 0.524*** (0.136) Yes Yes 0.9436 349
0.017 (0.068) 0.018*** (0.005) 0.000** (0.000) 0.035 (0.074) 0.000 (0.000) 0.676*** (0.247) Yes Yes 0.7150 901
0.116*** (0.035) 0.002* (0.001) 0.000 (0.000) 0.011 (0.030) 0.000 (0.000) 0.077 (0.157) Yes Yes 0.8861 2259
GDP Growth Rate FDI lnOpenness-1 Inflation lnPopulation Country Fixed Effect Year Fixed Effect R-Squared Observations
Yes Yes 0.8447 4725
Ascension Timing
Developed
Developing
Notes: For all the regression results in this table, the dependent variable is the natural log of unemployment rate. Column three, four and five display results of countries that ascended between 1995 and 1999, 2000 to 2010, and 2011 to 2017 respectively. All the estimations include controls for country and year fixed effects. 1% significance ¼ ***, 5% significance ¼ ** and 10% significance ¼ *. Robust standard errors are in parenthesis.
As pointed out in the previous section, we identified the years countries in the control group created working parties that will handle the ascension process. We use them as pseudo ascension years and group both countries in the treatment and control according to the period of ascension. Column two, three and four of Table 4 display the results. The results in column one is the same as the pooled sample results in the second column of Table 3. The results for developed countries in column six show that unemployment reduced by 1.7 percent, but it is insignificant. On the other hand, the unemployment rate of developing countries decreased by 11.6 percent, and it is significant at the one percent level. This implies that the effect of ascension on unemployment is more for developing countries than in developed countries. These numbers further strengthen the argument in the preceding paragraph. For the matching in column two, three and four, we still document similar results with the matching results in Table 3. More specifically, countries that entered between 2010 and 2017 had the highest reduction in the unemployment rate. It is important we show that our significant results are because of ascension. As a result, we document our placebo results in the last two columns of Table 4. Because our dataset only covers four years period before ascension (1991–1994), we are only able to use only four years data for our placebo. However, because countries in the treatment group joined WTO in different years, it is difficult to make them have different ascension years. Thus, the results in column 5 and 6 are obtained using 1992 and 1993 as the common ascension year respectively. The coefficient of interest is positive and insignificant, suggesting that our main results are as a result of ascension. 3.2. Robustness check Of great importance is the need to demonstrate the robustness of our results. DD estimation requires there be no shocks affecting both 52
C.I. Onwachukwu, E.F. Okagbue
International Economics 159 (2019) 48–55
Table 4 Unemployment effect of WTO ascension. Variable
Mi * Ait GDP Growth Rate FDI lnOpenness-1 Inflation rate lnPopulation Country Fixed Effect Year Fixed Effect R-Squared Observations
Pooled Sample
1995–1999
2000–2009
2010–2017
Placebo Results
(1)
(2)
(3)
(4)
(5)
(6)
0.137*** (0.033) 0.003** (0.001) 0.000** (0.000) 0.012 (0.027) 0.000 (0.000) 0.002 (0.107) Yes Yes 0.8575 3160
0.111** (0.044) 0.008*** (0.002) 0.000** (0.000) 0.053** (0.025) 0.000 (0.000) 0.119 (0.145) Yes Yes 0.8523 2655
0.137*** (0.051) 0.001 (0.001) 0.000*** (0.000) 0.244*** (0.080) 0.000*** (0.000) 0.346** (0.150) Yes Yes 0.8624 972
0.189** (0.081) 0.003 (0.002) 0.000 (0.000) 0.009 (0.068) 0.000** (0.000) 0.941* (0.480) Yes Yes 0.9407 126
0.013 (0.049) 0.001 (0.001) 0.000 (0.000) 0.027 (0.038) 0.000*** (0.000) 2.195*** (0.474) Yes Yes 0.9813 419
0.067 (0.088) 0.001 (0.001) 0.000 (0.000) 0.028 (0.037) 0.000*** (0.000) 2.203*** (0.471) Yes Yes 0.9814 419
Notes: For all the regression results in this table, the dependent variable is the natural log of unemployment rate. Column contain the pooled sample results. Column two, three and four ascension between 1995 and 1999, 2000–2009 and 2010–2017 respectively. The placebo results are in the last two columns. All the estimations include controls for country and year fixed effects. 1% significance ¼ ***, 5% significance ¼ ** and 10% significance ¼ *. Robust standard errors are in parenthesis.
Table 5 Robustness checks. Variable
1
2
3
4
5
6
7
Mi * Ait
0.136*** (0.033) Yes No No No No No 0.8576
0.131*** (0.033) No Yes No No No No 0.8581
0.132*** (0.033) o No Yes No No No 0.8580
0.137*** (0.033) No No No Yes No No 0.8575
0.137*** (0.033) No No No No Yes No 0.8587
0.135*** (0.035) No No No No No Yes 0.8576
0.138*** (0.035) Yes Yes Yes Yes Yes Yes 0.8601
GDP Growth Rate*YD FDI*YD lnOpenness*YD Inflation*YD lnPopulation*YD M*Year R-Squared
Notes: The dependent variable is the natural log of unemployment rate. All the results include GDP growth rate, FDI, lagged openness, inflation rate, population, country fixed effects and year fixed effects. 1% significance ¼ ***, 5% significance ¼ ** and 10% significance ¼ *. Robust standard errors are in parenthesis.
Table 6 Robustness checks. Variable
1
2
3
Mi *Ait Mi * Y1
0.077* (0.045) –
Mi * Y0
–
0.219 (0.365) 0.374 (0.371) 0.081 (0.073) –
0.243 (0.369) 0.379 (0.371) 0.062 (0.083) 0.012 (0.081) 0.032 (0.081) 0.029 (0.069) 0.8577
Mi * Y
þ1
Mi * Yþ2 Mi * Yþþ2 R-Squared
0.047 (0.054) 0.091 (0.055) 0.087* (0.044) 0.8577
– – 0.8576
Notes: The dependent variable is the natural log of unemployment rate. All the results include GDP growth rate, FDI, lagged trade openness, inflation rate, population, country fixed effects and year fixed effects. Y1 is a dummy that takes one for years before ascension. Y0 is a dummy that takes one for the actual years of ascension. Yþ1 is a dummy that takes one for one year after ascension, Yþ2 is a dummy that takes one for 2 years after ascension. Yþþ2 is a dummy that takes one for more than two years after ascension.1% significance ¼ ***, 5% significance ¼ ** and 10% significance ¼ *. Robust standard errors are in parenthesis.
53
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International Economics 159 (2019) 48–55
treatment and control groups which are coincidental with ascension. If this happens, our estimate of the effect of ascension on unemployment could be reflecting the effects of both the shock and ascension, so that it is difficult to disentangle the exact impact of ascension on unemployment. To show that no coincidental shock is driving our results, we allow the coefficients of GDP growth rate, FDI, lagged trade openness, inflation rate, and population to change with time. The point is to see if there will be a significant change in our main coefficient of interest. As can be seen in the first five columns of Table 5, the DD estimate almost did not change. Thus, we conclude that no shock is influencing our results. Going forward, we worry that ascension may not be exogenous because it could have been motivated by the existing economic cum political conditions in countries in the treatment group. We, therefore, present two pieces of evidence suggesting that this is not the case for our results. First, if movements in unemployment necessitated joining WTO, allowing the same movement for countries that have ascended will produce a sharp change in our coefficient of interest. In the last two columns of Table 5, we include a trend for countries in the treatment group and show that ascension is exogenous because there are no significant changes in our results. Second, if ascension was motivated by transitory changes in the state of the economies, it is possible to see some effect of ascension before ascension takes place. We, therefore, add major dummy variables for the exact years countries in the treatment group joined WTO and years before they joined. If at least one of them is significant, it implies that ascension is endogenous. From the three columns of Table 6, these dummies are not significant, so ascension is exogenous. 4. Conclusion In this study, we estimate the causal effect of joining the World Trade Organization on unemployment using data covering the period of 1991–2017 for 175 countries. While the 154 countries that are members of WTO are the treatment group, the control group includes the 21 countries that are non-members. Using the difference-in-difference estimation method, we find a significant reduction in the unemployment rate by 13.7 percent. The unemployment rate of developing countries reduced significantly by 11.6 percent. That of developed countries is lesser and insignificant. This means that developing countries had a higher reduction in unemployment because of joining WTO. Also, grouping countries according to the period they joined the organization revealed that the countries that joined late have the highest reduction in unemployment. These results confirm the idea that WTO is an avenue for developing countries to enjoy larger markets for their products in developed countries (Pugel, 2007). We performed some robustness checks to verify the consistency and originality of our results. We first carried out placebo estimations and showed that there is an insignificant effect on unemployment rate in periods before ascension. We then allowed the coefficients of the control variables to change over time and confirm that no shock coincidental with ascension is driving our results. Finally, we present evidence that supports our arguments concerning the exogeneity of ascension. Our results all point to need for non-member countries of WTO to join the organization. Conflicts of interest There are no conflicts of interest to disclose. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.inteco.2019.04.003.
References Autor, D.H., Dorn, D., Hanson, G.H., Song, J., 2014. Trade adjustment: worker-level evidence. Q. J. Econ. 129 (4), 1799–1860. Bhalla, A.S., Qiu, S., 2002. China's accession to WTO: its impact on Chinese employment. In: United Nations Conference on Trade and Development. Brecher, R.A., 1974. Minimum wage rates and the pure theory of international trade. Q. J. Econ. 98–116. Cling, J.P., Marouani, M.A., Razafindrakoto, M., Robilliard, A.S., Roubaud, F., 2009. The distributive impact of Vietnam's accession to the WTO. Econ. Int. (2), 43–71. Davidson, C., Martin, L., Matusz, S., 1999. Trade and search generated unemployment. J. Int. Econ. 48 (2), 271–299. Davis, D.R., 1998. Does European unemployment prop up American wages? National labor markets and global trade. Am. Econ. Rev. 478–494. Egger, H., Kreickemeier, U., 2009. Firm heterogeneity and the labor market effects of trade liberalization. Int. Econ. Rev. 50 (1), 187–216. Eichengreen, B.J., 1981. A dynamic model of tariffs, output, and employment under flexible exchange rates. J. Int. Econ. 11 (3), 341–359. Felbermayr, G., Prat, J., Schmerer, H.J., 2011. Globalization and labor market outcomes: wage bargaining, search frictions, and firm heterogeneity. J. Econ. Theory 146 (1), 39–73. Ferreira, F.H., Leite, P.G., Wai-Poi, M., 2007. Trade Liberalization, Employment Flows, and Wage Inequality in Brazil. The World Bank. Gnevko, V., Shahina, N., Kirsanov, S., 2016. Implications of the Russian Federation's accession to the WTO. Procedia Eng. 165, 1039–1045. Haouas, I., Yagoubi, M., Heshmati, A., 2005. The impacts of trade liberalization on employment and wages in Tunisian industries. J. Int. Dev.: J. Develop. Stud. Assoc. 17 (4), 527–551. Heckscher, E.F., 1991. Heckscher-Ohlin Trade Theory, Translated, Edited, and Introduced by Harry Flam and M. June Flanders. Helpman, E., Itskhoki, O., Redding, S., 2010. Inequality and unemployment in a global economy. Econometrica 78 (4), 1239–1283. Larch, M., Lechthaler, W., 2011. Comparative advantage and skill-specific unemployment. B. E. J. Econ. Anal. Policy 11 (1). Martincus, C.V., Carballo, J., Cusolito, A., 2017. Roads, exports and employment: evidence from a developing country. J. Dev. Econ. 125, 21–39. Pugel, T., 2007. International Economics. McGraw-Hill, New York. Queen's University (Kingston, Ont.). Institute for Economic Research, 1997. Tariffs, Unemployment, and the Current Account: an Intertemporal Equilibrium Model. Institute for Economic Research, Queen's University, Kingston, Ont. 54
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Revenga, A.L., 1992. Exporting jobs? The impact of import competition on employment and wages in US manufacturing. Q. J. Econ. 107 (1), 255–284. Ricardo, D., 1817. The Principles of Trade and Taxation. London. Smith, A., 1887. An Inquiry into the Nature and Causes of the Wealth of Nations. T. Nelson and Sons. Tacero, M.D., de Pablos, M.I.H., Benito, S.M.R., 2017. Exports and employment in the Spanish economy: a repetitive pattern. Investig. Econ. 76 (301), 137–169. Zhu, Y., Warner, M., 2005. Changing Chinese employment relations since WTO accession. Person. Rev. 34 (3), 354–369.
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International Economics journal homepage: www.elsevier.com/locate/inteco
Unemployment effect of WTO ascension: Evidence from a natural experiment Chinedu Increase Onwachukwu a, *, Ekene Francis Okagbue b a b
Department of Economics and Finance, City University of Hong Kong, Hong Kong School of Public Affairs and Administration, University of Electronic Science and Technology of China, China
A R T I C L E I N F O
A B S T R A C T
JEL codes: E24 F00 F13
Because international trade affects the rate of unemployment, ascending the World Trade Organization (WTO) established to improve the outcomes of trade will impact employment. By using a dataset covering 175 countries between 1991 and 2017 and exploiting the exogenous variation in the ascension years of countries, we estimate the causal effect of ascending WTO on the unemployment rate. Difference-in-difference estimates imply that unemployment rate reduced by 13.7 percent. Countries that ascended between 2011 and 2017 had the highest reduction in unemployment compared to those that joined between 1995 to 1999 and 2000 to 2010. Also, the effect of ascension is more for developing countries than developed countries. Our results are robust to the matching of countries by ascension period and are not influenced by any simultaneous shock or pre-existing political and economic conditions. These results point to the need for non-member countries to join the organization.
Keywords: World Trade Organization Ascension Unemployment rate Difference-in-difference
1. Introduction The work of Ricardo (1817), Smith (1887), Heckscher (1991) and a few others laid the theoretical foundations for examining the outcomes of trade between two or more countries. Recent studies have relied on the postulations of these theories to derive interesting results on how trade influences employment. For instance, Brecher (1974) and Davis (1998) modify the Heckscher-Ohlin model by including workers' minimum wages. Their results suggest that free trade worsens the problem of unemployment. Similarly, Larch and Lechthaler (2011) identify three mechanisms through which free trade impacts employment. Workers who have competitive advantage, a unique area of specialization, and can switch easily from one sector to another have a higher probability of getting employed. Also, adopting a theoretical model in which returns to scale is increasing and introducing the idea of equal wages shows an increasing effect of removing trade barriers on unemployment (Egger and Kreickemeier, 2009). Going further, studies on trade-employment relation have been extended by several researchers by allowing for wage inequality. Haouas et al. (2005), and Ferreira et al. (2007) find that the effect of free trade on employment is more than the impact on wages if adjustments could be made on employment. Helpman et al. (2010) also investigate the interplay between unemployment, wage inequality and international trade. While the effect of trade on employment is ambiguous, they find that increase in free trade gradually raises disparity in wage until a certain point. Some empirical studies have also examined how trade liberalization affects employment in the presence of search friction. Felbermayr et al. (2011) examine how trade openness influences unemployment and incorporate work search friction in their model. Their
* Corresponding author. E-mail address: [email protected] (C.I. Onwachukwu). https://doi.org/10.1016/j.inteco.2019.04.003 Received 7 December 2018; Received in revised form 20 February 2019; Accepted 23 April 2019 Available online 4 May 2019 2110-7017/© 2019 CEPII (Centre d'Etudes Prospectives et d'Informations Internationales), a center for research and expertise on the world economy. Published by Elsevier B.V. All rights reserved.
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International Economics 159 (2019) 48–55
results suggest that the liberalization of trade reduces unemployment. Also, the work of Davidson et al. (1999) which also incorporates friction shows that the effect depends on the ratio of capital to labour across countries. The observed mixed results could be attributed to a lot of reasons, of which one of them is the fact that the direction of the impact depends on whether a country is an importer or exporter, and if the local industries of the importing country produce the imported goods.1 Empirical evidence shows that while export increases employment and factor earnings, the effect of import is the direct opposite. World Trade Organization (WTO), established in 1995 seek to achieve many objectives some of which will foster trade among countries through liberalization.2 Because trade affects employment, as confirmed by previous studies, it makes sense to believe that the establishment of WTO will impact employment in different countries. Some studies have attempted this kind of analysis (Bhalla and Qiu, 2002; Zhu and Warner, 2005; Cling et al., 2009; Gnevko et al., 2016). While the study of Cling et al. (2009) and, Gnevko et al. (2016) concern the implications of joining WTO on the economies of Vietnam and China respectively which are individual countries, the work of Bhalla and Qiu (2002), and Zhu and Warner (2005) focus on the effect on employment in China. To the best of our knowledge, this is the first empirical study to document a relationship between the decision to join WTO and employment level using cross-country data. As a result, this study is significant in three ways. First, it is a cross-country study covering almost all the countries of the world. A given percentage of the countries are WTO members, and their years of ascension are different. This suggests that a natural experiment has taken place so that countries are categorized into treatment and control groups. These type of data make it possible to investigate the causal effect of ascending WTO on unemployment using the difference-in-difference (DD) estimation method. Second, while countries such as the United States, United Kingdom, Australia, etc. are developed countries with good institutions, most African and Asian countries are developing countries with the high problem of corruption and unemployment. Given the disparities, it is possible that the effect of ascension defers between developed and developing countries. Finally, we classify countries in the treatment group into three groups based on the timing of ascension. This classification allows us to examine how timing augments the effect of joining the organization. Our results reveal that ascension reduced the unemployment rate and that the effect is more for developing countries. Also, the reduction in unemployment is more for countries that joined the organization very late. Section 2 introduces the econometric method of this study. The empirical results are discussed in Section 3, and Section 4 concludes the study. 2. Econometric method 2.1. Baseline regression model We started by obtaining data on the countries that have joined the World Trade Organization, and those that are yet to join. Though we identified a total of 164 members and 23 non-members, we dropped countries that have no observations in our variables of interest. This reduces the total number of countries to 175. Out of these 175 countries, 154 are members of WTO, so they are categorized as the treatment group in this study. The remaining 21 countries are non-members, and they are called the control group. It is worthy of note that the international organization in question came into existence in 1995. Thus, our data cover the period from 1991 to 2017. We are unable to cover periods before 1991 because of data unavailability. Since we take ascending WTO as a shock to investigate the effect on unemployment, the difference-in-difference specification is below. lnUit ¼ α þ θMi þ ϑAt þ βMi *At þ δXit þ εit
(1)
Where i and t indicate country and year respectively. Mi is a dummy variable that is equal to one for countries that are WTO members. At is equal to one if a country has joined WTO in year t. It is the dummy variable for after joining the organization. The coefficient of the interaction, β is the DD estimate of the effect of ascension on unemployment rate. Uit is the dependent variable, it is the unemployment rate of country i in year t. Xit is a vector of control variables. Data for all the control are from the World Bank Website. They include GDP growth rate, FDI, lagged trade openness, inflation rate, and population. Estimating the above model will not provide the correct estimate of the effect of ascension for two reasons. First, it does not take into consideration the fact that our dataset is panel since it does not include controls for fixed effects. Second, since At only has a time subscript, it means that all the countries in the treatment group joined WTO in the same year. This is obviously not correct because countries in the treatment group joined the organization in different years. We address these problems by including fixed effects and allowing the At to vary across countries. Adding country (ωi ) and year (π t ) fixed effects requires dropping Mi and At to avoid redundancy. The implication of allowing At to vary by both country and year is that the definition of the control group changes. More specifically, the control group is no longer countries that have not ascended WTO. For every year, the control group is now all the countries that are not yet members of WTO. Model 2 below is now what we want to estimate.
1 Revenga (1992) finds that competition because of increased import dampens wage and employment in manufacturing industries in the United States. Also, the effect on wage is relatively small compared to that of employment. Similar study by Autor et al. (2014) reveal that competition because of trading with China reduces employment and labour earnings. Tacero et al. (2017), on the other hand, employ input-output analysis and show that increased importation of goods will create jobs in Spain. The study of Martincus et al. (2017) also reveal that programs that brings about road construction have bolstering impact on export and employment. 2 See the link for the six main goals World Trade Organization seeks to achieve. https://www.britannica.com/topic/World-TradeOrganization#ref224477.
49
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International Economics 159 (2019) 48–55
lnUit ¼ ωi þ π t þ βMi *Ait þ δXit þ εit
(2)
Now, we neglect the definition of the control group in the preceding paragraph and identify one issue in our dataset. The problem is that there is a significant difference between the number of countries in the treatment group and the control group. Specifically, while there are 154 countries in the treatment, only 21 countries are in the control group. The implication is that the estimate of β may just be reflecting such dominance of the treatment over the control. To fix this issue, we identify three different periods during which countries in the treatment group entered WTO – 1995 to 1999, 2000 to 2010, and 2011 to 2017 - and group the countries accordingly. We then estimate model 2 for each group using the same control group. By doing this matching of countries in the treatment group, we bridge the gap between the number of countries in each group and the same control group. The result of this matching will further provide evidence as to whether the timing of ascension is of any importance by revealing the difference in the unemployment rate of the three groups. An alternative way of examining the effect of ascension given timing is to create a dummy variable that takes the value of one for countries in the treatment that joined WTO between 1995 and 2010 and zero for those that entered between 2011 and 2017. Here countries that take the value of one joined earlier than those that take zeros. Including an interaction of the dummy with Mi * Ait in model 2 will show how the unemployment rate of countries that joined early differ with that of those that joined late. If the estimate is positive (negative) it means that early ascension is associated with higher (lower) unemployment than late ascension. We also group the countries into developed and developing countries and estimate model 2 for each group. This helps answer our third research question. We understand that the matching of both countries in the treatment group and control group is necessary because it helps mitigate disturbances coming from other variables in our model. However, we are faced with the problem of matching countries in the control group according to the year of ascension because they have not joined the organization. The good news is that ascending WTO is a very long process and it starts with the creation of a working party which will handle the ascension process. We use the year a working party was created as a pseudo ascension year and match countries in the control group. The implication is that if we want to identify all the countries that joined WTO between the period from 1995 to 1999, countries in the control group that created their working parties during this period will be included. The same thing applies to identifying countries that ascended between 2000 to 2010 and 2011 to 2017. Furthermore, DD estimation requires there be no shock affecting both treatment and control groups that is coincidental with ascension. If this happens, our estimate of the effect of ascension on unemployment could be reflecting the effects of both the shock and ascension, so that it is difficult to disentangle the exact impact of ascension on unemployment. Since the shock needs to contemporaneous with ascension to become problematic, it calls for no worry because the year of ascension varies across countries in the treatment group. Specifically, it is not possible that the same shock took place each year that a country ascends WTO. Despite this, we still move to solve the problem. One reasonable way of tackling this problem is to ensure that the coefficient estimates of all the control variables change over time. If there is no significant change in the estimate of the effect of ascension on unemployment because of allowing time-varying coefficient estimates for the control variables, it means that no other shock is driving our results. Finally, we worry that ascension may not be exogenous because it could have been motivated by the prevailing economic cum political conditions in each country, which could be correlated with the unemployment rate. That is, year of ascension could be correlated with the error term in time t, tþ1, tþ2, … To be more specific, suppose that the countries that ascended WTO did so because they were experiencing high unemployment rates, the estimate of the effect of ascension on unemployment will be highly biased. We employ two approaches to tackle this issue. First, if the existing movement in unemployment necessitated ascension, by allowing the same movement in countries that joined WTO, we expect a significant change in our coefficient estimate. By including a trend for countries in the treatment group, we show that ascension is exogenous because there are no substantial changes in our results. Second, if ascension was motivated by transitory changes in the state of the economies, it is possible to see some effect of ascension before ascension actually takes place. We, therefore, add major dummy variables for years countries in the treatment group joined WTO and years before they entered. The non-significance of these dummies would further suggest that ascension is exogenous. 2.2. Descriptive statistics Here, we show that there is a need to investigate the issue of how ascending WTO affects unemployment rate. Fig. 1 displays the
Fig. 1. Trend in unemployment rate for treatment and control. 50
C.I. Onwachukwu, E.F. Okagbue
International Economics 159 (2019) 48–55
trend in the unemployment rate of the treatment group and the control group. The first observation is that the unemployment rate of countries in the treatment group is always lower than that of the control group. After 1995, the distance between the two curves begins to widen, and we show in this study that this is because of ascension. We document the descriptive statistics of the full sample in Table 1. The average unemployment rate is 8.5 percent. Before ascension, the average unemployment rate was 8.9 percent, but after ascension, it decreases to 8.4 percent. The average unemployment rate for ascension between 1995 and 1999, 2000–2010 and 2011–2017 is 8.8 percent, 8.2 percent, and 7.7 percent respectively. This shows that late ascension is associated with a higher reduction in unemployment. We present further evidence in Table 2 by comparing characteristics for the treatment group and the control group. The unemployment rate of the treatment is less than that of the control by 26.9 percent, and the difference is significant. 3. Results 3.1. Main results The main estimation results are in Tables 3–6 contain the results of our robustness checks. For Table 3 the first two columns contain the results for the pooled sample, column three, four and five are the results after matching countries, column six is for developed countries, and column seven shows the results for developing countries. All the regression results include controls for country and year fixed effects. The coefficient associated with Mi *Ait is the DD estimate of the impact of WTO ascension on the unemployment rate. The estimate in the first column is 0.114, and it is significant at the one percent level. This value means that ascension significantly reduced the unemployment rate by 11.4 percent. As pointed out from the outset, developed countries have high growth rate, with good institutions and high saving and investment, so it is possible that these factors explain the negative estimate in column one. Also, it is has been shown in the literature that opening up to trade brings about an increase in export and reduction in unemployment. As a result, we control for income levels by adding the growth rate of GDP and FDI, and trade dependence measured by lagged openness to trade. We also control for inflation rate and population of each country. GDP growth rate and FDI both have negative signs and are significant, affirming that unemployment reduces as they go up. Trade openness is positive and insignificant, but the DD estimate goes up from 11.4 to 13.7 percent. One possible mechanism of transmission is through an increase in export and reduction in import. Growth in export demand encourages higher production, and this could be met by increasing employment. Increase in import, on the other hand, crowds out local production of the imported goods in the importing country, thereby decreasing employment. So, the opposite effect is expected if the demand for import decreases (Revenga, 1992; Autor et al., 2014; Martincus et al., 2017). Another mechanism of transmission is through changes in the tariff rate which affects import and export, thus affecting employment. According to Queen's University (1997) if wages and prices are flexible, increase in the tariff rate will decrease wage and raise the demand for labor. The ultimate impact is a reduction in the rate of unemployment. The findings of Eichengreen (1981) also support the results of Queen's University (1997). Matching countries by the period of their ascension help mitigate the influence of having more countries in the treatment group than in the control group. This also provides an insight as to whether the timing of ascension influences the effect of ascension on unemployment. The results are in column three, four and five of Table 3. They show the results of countries that ascended between 1995 and 1999, 2000–2010, and 2011–2017 respectively. Countries that ascended between 1995 and 1999 had a significant reduction in unemployment by 7.7 percent. For those that ascended between 2000 and 2010, the reduction in unemployment rate is by 9.2 percent, but those that ascended last had a decrease in unemployment by 29.6 percent. These results point to one thing, countries that ascended late had the highest reduction in the unemployment rate and these countries are mostly developing countries. This is in line with existing argument that developed countries do not benefit much from WTO because they had already achieved their status of development before joining the organization. Developing countries, on the other hand, benefit a lot by enjoying larger markets for their export in developed countries (Pugel, 2007). Table 1 Descriptive statistics of the sample. Variable
N
Mean
SD
Min
Max
Unemployment Before Establishment After Establishment Treated 1995–1999 Treated 2000–2010 Treated 2011–2017 GDP Growth Rate Inflation Rate lnFDI lnOpennesss lnPopulation
4725 700 3850 770 1694 1078 4725 4725 4725 4725 4725
8.509 8.866 8.447 8.848 8.164 7.744 3.760 27.788 22.719 0.285 15.800
6.329 6.769 6.247 6.570 6.126 5.874 6.883 402.919 24.175 0.537 1.842
0.122 0.300 0.122 0.523 0.190 0.122 64.047 18.109 – 6.348 0
44.157 38.527 44.157 44.157 38.040 31.380 149.973 195.024 27.322 1.392 21.050
Notes: Though FDI has both positive and negative values, the mean, standard deviation, and maximum values are all positive, so their natural logarithm are taken to keep the numbers small. There is nothing in the minimum value because it is negative, and log of negative is not defined. Unemployment rate is not logged here. 51
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Table 2 Country characteristics of treatment and controls. Country Characteristics
lnUnemployment Rate GDP Growth Rate lnFDI Inflation Rate lnOpenness lnPopulation
Treatment
Control
Difference
A
B
A-B
1.824 (0.816) 3.551 (5.307) 22.822 (24.230) 26.991 (420.529) 0.291 (0.544) 15.846 (1.816)
2.093 (0.799) 5.562 (14.471) 20.515 (20.958) 35.175 (166.921) 0.239 (0.473) 15.516 (1.757)
0.269*** (0.036) 2.011*** (0.613) 2.307** (0.957) 8.184 (9.575) 0.052** (0.021) 0.330*** (0.079)
Notes: Column A and B contain averages for countries in the treatment and control respectively. Standard deviations are in parenthesis. Column A – B provides information on the difference between A and B. FDI has both positive and negative values, but since the averages and standard deviations in A and B are positive, natural log of these values are taken to keep the values small.
Table 3 Unemployment effect of WTO ascension. Variable
Pooled Sample (1)
(2)
(3)
(4)
(5)
(6)
(7)
Mi * Ait
0.114*** (0.025)
0.137*** (0.033) 0.003** (0.001) 0.000** (0.000) 0.012 (0.027) 0.000 (0.000) 0.002 (0.107) Yes Yes 0.8575 3160
0.077** (0.036) 0.004*** (0.001) 0.000** (0.000) 0.069*** (0.024) 0.000 (0.000) 0.082 (0.130) Yes Yes 0.8534 2771
0.092* (0.050) 0.001 (0.001) 0.000*** (0.000) 0.157** (0.067) 0.000*** (0.000) 0.197 (0.135) Yes Yes 0.9112 618
0.296*** (0.081) 0.000 (0.001) 0.000** (0.000) 0.174*** (0.050) 0.000*** (0.000) 0.524*** (0.136) Yes Yes 0.9436 349
0.017 (0.068) 0.018*** (0.005) 0.000** (0.000) 0.035 (0.074) 0.000 (0.000) 0.676*** (0.247) Yes Yes 0.7150 901
0.116*** (0.035) 0.002* (0.001) 0.000 (0.000) 0.011 (0.030) 0.000 (0.000) 0.077 (0.157) Yes Yes 0.8861 2259
GDP Growth Rate FDI lnOpenness-1 Inflation lnPopulation Country Fixed Effect Year Fixed Effect R-Squared Observations
Yes Yes 0.8447 4725
Ascension Timing
Developed
Developing
Notes: For all the regression results in this table, the dependent variable is the natural log of unemployment rate. Column three, four and five display results of countries that ascended between 1995 and 1999, 2000 to 2010, and 2011 to 2017 respectively. All the estimations include controls for country and year fixed effects. 1% significance ¼ ***, 5% significance ¼ ** and 10% significance ¼ *. Robust standard errors are in parenthesis.
As pointed out in the previous section, we identified the years countries in the control group created working parties that will handle the ascension process. We use them as pseudo ascension years and group both countries in the treatment and control according to the period of ascension. Column two, three and four of Table 4 display the results. The results in column one is the same as the pooled sample results in the second column of Table 3. The results for developed countries in column six show that unemployment reduced by 1.7 percent, but it is insignificant. On the other hand, the unemployment rate of developing countries decreased by 11.6 percent, and it is significant at the one percent level. This implies that the effect of ascension on unemployment is more for developing countries than in developed countries. These numbers further strengthen the argument in the preceding paragraph. For the matching in column two, three and four, we still document similar results with the matching results in Table 3. More specifically, countries that entered between 2010 and 2017 had the highest reduction in the unemployment rate. It is important we show that our significant results are because of ascension. As a result, we document our placebo results in the last two columns of Table 4. Because our dataset only covers four years period before ascension (1991–1994), we are only able to use only four years data for our placebo. However, because countries in the treatment group joined WTO in different years, it is difficult to make them have different ascension years. Thus, the results in column 5 and 6 are obtained using 1992 and 1993 as the common ascension year respectively. The coefficient of interest is positive and insignificant, suggesting that our main results are as a result of ascension. 3.2. Robustness check Of great importance is the need to demonstrate the robustness of our results. DD estimation requires there be no shocks affecting both 52
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International Economics 159 (2019) 48–55
Table 4 Unemployment effect of WTO ascension. Variable
Mi * Ait GDP Growth Rate FDI lnOpenness-1 Inflation rate lnPopulation Country Fixed Effect Year Fixed Effect R-Squared Observations
Pooled Sample
1995–1999
2000–2009
2010–2017
Placebo Results
(1)
(2)
(3)
(4)
(5)
(6)
0.137*** (0.033) 0.003** (0.001) 0.000** (0.000) 0.012 (0.027) 0.000 (0.000) 0.002 (0.107) Yes Yes 0.8575 3160
0.111** (0.044) 0.008*** (0.002) 0.000** (0.000) 0.053** (0.025) 0.000 (0.000) 0.119 (0.145) Yes Yes 0.8523 2655
0.137*** (0.051) 0.001 (0.001) 0.000*** (0.000) 0.244*** (0.080) 0.000*** (0.000) 0.346** (0.150) Yes Yes 0.8624 972
0.189** (0.081) 0.003 (0.002) 0.000 (0.000) 0.009 (0.068) 0.000** (0.000) 0.941* (0.480) Yes Yes 0.9407 126
0.013 (0.049) 0.001 (0.001) 0.000 (0.000) 0.027 (0.038) 0.000*** (0.000) 2.195*** (0.474) Yes Yes 0.9813 419
0.067 (0.088) 0.001 (0.001) 0.000 (0.000) 0.028 (0.037) 0.000*** (0.000) 2.203*** (0.471) Yes Yes 0.9814 419
Notes: For all the regression results in this table, the dependent variable is the natural log of unemployment rate. Column contain the pooled sample results. Column two, three and four ascension between 1995 and 1999, 2000–2009 and 2010–2017 respectively. The placebo results are in the last two columns. All the estimations include controls for country and year fixed effects. 1% significance ¼ ***, 5% significance ¼ ** and 10% significance ¼ *. Robust standard errors are in parenthesis.
Table 5 Robustness checks. Variable
1
2
3
4
5
6
7
Mi * Ait
0.136*** (0.033) Yes No No No No No 0.8576
0.131*** (0.033) No Yes No No No No 0.8581
0.132*** (0.033) o No Yes No No No 0.8580
0.137*** (0.033) No No No Yes No No 0.8575
0.137*** (0.033) No No No No Yes No 0.8587
0.135*** (0.035) No No No No No Yes 0.8576
0.138*** (0.035) Yes Yes Yes Yes Yes Yes 0.8601
GDP Growth Rate*YD FDI*YD lnOpenness*YD Inflation*YD lnPopulation*YD M*Year R-Squared
Notes: The dependent variable is the natural log of unemployment rate. All the results include GDP growth rate, FDI, lagged openness, inflation rate, population, country fixed effects and year fixed effects. 1% significance ¼ ***, 5% significance ¼ ** and 10% significance ¼ *. Robust standard errors are in parenthesis.
Table 6 Robustness checks. Variable
1
2
3
Mi *Ait Mi * Y1
0.077* (0.045) –
Mi * Y0
–
0.219 (0.365) 0.374 (0.371) 0.081 (0.073) –
0.243 (0.369) 0.379 (0.371) 0.062 (0.083) 0.012 (0.081) 0.032 (0.081) 0.029 (0.069) 0.8577
Mi * Y
þ1
Mi * Yþ2 Mi * Yþþ2 R-Squared
0.047 (0.054) 0.091 (0.055) 0.087* (0.044) 0.8577
– – 0.8576
Notes: The dependent variable is the natural log of unemployment rate. All the results include GDP growth rate, FDI, lagged trade openness, inflation rate, population, country fixed effects and year fixed effects. Y1 is a dummy that takes one for years before ascension. Y0 is a dummy that takes one for the actual years of ascension. Yþ1 is a dummy that takes one for one year after ascension, Yþ2 is a dummy that takes one for 2 years after ascension. Yþþ2 is a dummy that takes one for more than two years after ascension.1% significance ¼ ***, 5% significance ¼ ** and 10% significance ¼ *. Robust standard errors are in parenthesis.
53
C.I. Onwachukwu, E.F. Okagbue
International Economics 159 (2019) 48–55
treatment and control groups which are coincidental with ascension. If this happens, our estimate of the effect of ascension on unemployment could be reflecting the effects of both the shock and ascension, so that it is difficult to disentangle the exact impact of ascension on unemployment. To show that no coincidental shock is driving our results, we allow the coefficients of GDP growth rate, FDI, lagged trade openness, inflation rate, and population to change with time. The point is to see if there will be a significant change in our main coefficient of interest. As can be seen in the first five columns of Table 5, the DD estimate almost did not change. Thus, we conclude that no shock is influencing our results. Going forward, we worry that ascension may not be exogenous because it could have been motivated by the existing economic cum political conditions in countries in the treatment group. We, therefore, present two pieces of evidence suggesting that this is not the case for our results. First, if movements in unemployment necessitated joining WTO, allowing the same movement for countries that have ascended will produce a sharp change in our coefficient of interest. In the last two columns of Table 5, we include a trend for countries in the treatment group and show that ascension is exogenous because there are no significant changes in our results. Second, if ascension was motivated by transitory changes in the state of the economies, it is possible to see some effect of ascension before ascension takes place. We, therefore, add major dummy variables for the exact years countries in the treatment group joined WTO and years before they joined. If at least one of them is significant, it implies that ascension is endogenous. From the three columns of Table 6, these dummies are not significant, so ascension is exogenous. 4. Conclusion In this study, we estimate the causal effect of joining the World Trade Organization on unemployment using data covering the period of 1991–2017 for 175 countries. While the 154 countries that are members of WTO are the treatment group, the control group includes the 21 countries that are non-members. Using the difference-in-difference estimation method, we find a significant reduction in the unemployment rate by 13.7 percent. The unemployment rate of developing countries reduced significantly by 11.6 percent. That of developed countries is lesser and insignificant. This means that developing countries had a higher reduction in unemployment because of joining WTO. Also, grouping countries according to the period they joined the organization revealed that the countries that joined late have the highest reduction in unemployment. These results confirm the idea that WTO is an avenue for developing countries to enjoy larger markets for their products in developed countries (Pugel, 2007). We performed some robustness checks to verify the consistency and originality of our results. We first carried out placebo estimations and showed that there is an insignificant effect on unemployment rate in periods before ascension. We then allowed the coefficients of the control variables to change over time and confirm that no shock coincidental with ascension is driving our results. Finally, we present evidence that supports our arguments concerning the exogeneity of ascension. Our results all point to need for non-member countries of WTO to join the organization. Conflicts of interest There are no conflicts of interest to disclose. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.inteco.2019.04.003.
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