Targeted poverty investments and economic growth in China

World Development Vol. 26, No. 12, pp. 2137-2151, 1998 0 1998 Elsevier Science Ltd All rights reserved. Printed in Great Britain 0305-750x/98/$ - see front matter PII: s0305-750x(98)00104-1

Pergamon

Targeted Poverty Investments

and

Economic Growth in China SCOTT ROZELLE University of California at Davis, Davis, ZI.S.A. ALBERT PARK University of Michigan, Ann Arbol; U.S.A. VINCENT BENZIGER Stanford Universi& Stanford, U.S.A. and

CHANGQING REN* Chinese Academy of Social Sciences, Beijing, China Summary. - In the mid-1980s, the Chinese government launched its ambitious poor area development policy, which was centered around a series of grant, credit, and Food-for-Work programs. Ironically, for the remainder of the 1980s rural poverty remained at about 90 to 100 million, or approximately 10% of the rural population. The lack of progress cannot necessarily be blamed on ineffective poor area policies, since much of the agricultural economy was mired in a deep recession between the mid-1980s and the early 1990s. By the mid-1990s substantial additional poverty reduction had been achieved. Even in the late-1980s, farmers in many poor counties did better than the national average in terms of income growth. After accounting for the effects of macroeconomic elements, what factors can help explain the differences in performance among poor regions and between poor areas and rich ones? Can part of these differences be accounted for by poor area policies, in general, or by the way local and regional officials allocate their poor area investment funds, in particular? The overall objective of this paper is to analyze the effectiveness of Chinese poor area policy. Specifically, the paper seeks to meet three objectives. First, we want to understand the evolution of poor area policy since the mid-1980s, trying to deduce the true goals of central and regional poor area officials, as well as how these policies have been implemented in the provinces. Next, we want to understand the magnitude and scope of investment into poor areas, and examine if changes in these policies have affected the uses of the investment funds. Finally, we want to determine the effectiveness of the investment of poor area funds, analyzing which types of investments have generated growth, and which ones have not. 0 1998 Elsevier Science Ltd. All rights reserved. Key words -

China, anti-poverty policy, rural poverty, regional

1. INTRODUCTION In the mid-1980s, the Chinese government began an aggressive regionally-targeted investment program aimed at eliminating rural poverty, recognizing the potential danger of social and political instability due to rising inequality accompanying economic reforms.. Led by the interministerial Leading Group for Development in Poor Areas Economic (LGEDPA), the new program designated poor counties throughout the country for support through subsidized loans (tiai daikuan),

differences

budgetary grants @~zhan z&n), and public employment projects (Food-for-Work projects, or yigong daizhen). Nearly all of these funds were intended to support productive investments that would promote regional economic growth, and *The authors thank Fan Chen, Gang Fan, Carl Riskin, Sangui Wang, participants in the Harvard Conference on “Unintended Social Consequences of Economic Reforms in China” (May, 1997), and one anonymous referee. The support of the Ford Foundation, Beijing, is gratefully acknowledged. Final revision accepted: April 27, 1998.

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were clearly distinguished from welfare and relief programs administered by the Ministry of Civil Affairs and fiscal subsidies to cover overall budgetary shortfalls. Despite the important political and welfare implications of these new, large-scale anti-poverty efforts. to date there has been little evaluation of the nature and success of the programs.’ not Although economic growth does automatically lead to poverty reduction or further other development objectives, in China progress in reducing poverty has generally occurred during periods of rising rural incomes. The rapid growth set off by the government’s rural reform program lifted half of China’s poor (125 million people) from poverty from 1978-85, an achievement of historic precedent (SSB, 1996). Progress slowed in the second half of the 1980s as rural incomes stagnated (World Bank, 1992). But rapid improvement resumed in the 199Os, in step with the renewed increase of rural incomes. The poverty count fell significantly each year during 1991-96, to about 57 million (Park et al., 1998). This pattern of poverty reduction does not coincide with changes in the government’s poverty investment programs, which were introduced in 1986. This does not mean, however, that poverty policies were ineffective Had the programs not been in place, it is possible that poverty would have been even greater or that poor areas would have grown at an even slower rate. Indeed, while some poor areas have others have enjoyed remained very poor, considerable success, sometimes contrary to overall trends (Wang, 1990; Fan, 1994; Li, 1994)’ Real income grew by 7.5% per year in China’s 664 poor counties versus just 2.5% annually in non-poor counties during 1983-87 (Tong et ul., 1994). During 1985-92, the slowest growing provinces were in China’s agricultural heartland (e.g., Hunan, Hubei, Jiangxi, Henan, Shanxi), not those in the poor northwest and southwest regions of the country (Rozelle, 1996). There is evidence that households in poor counties in southern China enjoyed more rapid growth in consumption expenditures than those in non-poor counties, other factors being held constant (Jyatna and Ravalian, 1997). The goal of this paper is to assess the extent to which China’s poverty investment program has achieved its stated goal of promoting economic growth in poor areas.j First, we describe the area policy since the of poor evolution mid-1980s including the goals of the central government as well as the implementation of these policies at local levels. Next, we quantify

the magnitude and scope of investment into poor areas, and see if changes in policies have affected the sources and uses of poor area investment funds. Finally, after disaggregating growth into its three maJor components-agriculture. rural industry, and state-owned industry-we present results from growth regressions to evaluate the extent to which poverty investments in each sector have promoted economic growth. The paper uses county-level data on the sources and uses of poverty investment funds, sectoral growth rates, and other determinants of growth from Shaanxi Province, a poor province in northwest China that lies in one of China’s main poverty belts. Poor counties in Shaanxi, like many poor counties in other parts of China, are located in remote regions with unfavorable natural environments (the Qingling Mountains in the south and the Loess Plateau region in the north) and face problems such as a severe shortage of fiscal resources, growing population pressure on land. low levels of rural industry, and pressures to meet local demand for food and other necessities (Park et ul., 1996). Changes in provincial poverty policies follow closely those announced at the national level. Despite Shaanxi’s representativeness across these dimensions and because of the great diversity of China’s provinces, especially in the local implementation of policies, caution much be exercised in generalizing results for Shaanxi to all of China. More work using data from other provinces is necessary to develop a fuller picture of the impact of China’s poverty programs.

2. NATIONAL

POOR AREA

POLICY

While leaders had not formulated an explicit national policy on poverty alleviation prior to 1986, this did not mean that the rural poor did not receive special attention. With little to lose given the marginal contribution of poor areas to the overall economy, reformers permitted communes in poor areas to decollectivize earlier than other areas (as early as the late 1970s). The central government was already subsidizing poor areas, both through direct budgetary transfers (Park et al., 1996) and through subsidized grain sales and other assistance to farmers in need (Park et ul., 1994). With fiscal decentralization and increasing scarcity of budgetary resources, however, these forms of support were reduced over time.J Market reforms led to widespread increases in production specialization, yields, and income (Rozelle and Weersink, 1996). These changes helped many escape poverty, especially those in central and coastal China that had been

TARGETED

POVERTY

hurt most by grain-first policies because they lived in areas not well-suited for grain production (Lardy, 1988). (a) Eurly programs-grants and loans to households After the first years of reform, national leaders began to realize that redressing poverty of resource-poor households living in remote, isolated regions required more than favorable macroeconomic conditions and decentralized decision-making (State Council, 1989a-e, 1991). In 1986, the State Council formed an interministerial Leading Group for Economic Development in Poor Areas at the national level to administer a new investment program allocating four billion yuan per year to 300 nationally designated poor counties. Provincial governments also designated additional poor counties to receive provincial support. By 1988, 698 counties received public help through these programs. Poor Area Development Offices (PADOs) were established at the provincial and county levels to administer funds from both national and provincial sources. China’s multilevel poverty alleviation network was funded by grants and loans approved by the State Council.’ In the first years, most funds came from programs such as luoshuo hianqiong diqu d&ban (subsidized credit for supporting old revolutionary bases, minority areas, and remote areas) and bu fada diqu fuzhan z&n (development capital funds, DCF, for supporting underdeveloped areas). As the real value of grants declined due to inflation (the nominal level of funding for DCF remained largely unchanged over 1986-93) new subsidized loan programs became the main form of poverty investment. In its initial years local PAD0 offices controlled most of the loan portfolios (Li and Li, 1992). Agricultural Banks, which disbursed and collected the loans, exercised little decision-making authority according to interviews with PAD0 and banking officials. Policies during the 1986-88 stressed that funds should be allocated directly to poor households to support agricultural production and other income-generating projects (State Council, 1989a-e; Chen, 1991; World Bank, 1992). Program rules often required local poverty officials to allocate up to 80% of their funds for agricultural loans to households (Li and Li, 1992). After these first few years of implementation, poor area policies came under closer scrutiny. There was a general perception that much of the

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assistance was not spurring growth but was being used to support consumption directly (State Council, 1989a-e). As is typical for formal subsidized loan programs, loan repayment rates were low. Estimates of timely repayment on subsidized poverty loans range from 30 to 65% (Park et al., 1998). The main problem, as described in an important policy document, was that the long term development of “social productive forces” in poor areas was being neglected (Zhou, 1988; State Council, 1989a-e). In 1989, even after four years of targeted programs, there were still over 100 million rural poor. Interestingly, these conclusions were drawn on the basis of limited data and observation. No systematic, large-scale evaluation of China’s poverty program was ever conducted. (b) Policy reform-targeting development projects By the late 198Os, the widespread belief that economic growth in poor areas had stagnated led to a major policy shift. New directives ordered local officials to turn their attention away from poverty alleviation through direct transfers to households in favor of projects that would better promote economic development (State Council, 1991). The new measures encouraged poor area officials to direct loans away from households and toward “economic entities” Cjingjishiti) that could better coordinate activities requiring new technology, greater input use, and marketing support (State Council, 1991). In recognition of the need for local governments to generate fiscal revenues, local officials were allowed to allocate more funds to enterprises. A new poverty loan program was established to support county stateowned enterprises. The Food-for-Work Program to support basic infrastructure construction, which had previously focused on roads and drinking water, was expanded to include infrastructure projects in agriculture designed to help spur long-term productivity increases (State Planning Commission, 1989). Local officials welcomed these changes. By the end of the 198Os, fiscal decentralization, which tied budgetary expenditures more closely to local revenues, had created a budgetary crisis in nearly all poor counties (Park et al., 1996). This created a strong incentive for local officials in poor areas to invest in revenue-producing enterprises rather than other growth-oriented activities, or to divert earmarked investment funds to meet fixed expenditure obligations such as wage payments to government cadres (Wu, 1994; Park et al., 1996). Investment funds and loans were commonly channeled to county-run enterprises

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regardless of the investment’s projected rate of return; the infusion of cash allowed government officials to transfer funds to the county budget (through fees and remittances or tax payments) or to shift fiscal burdens to enterprises (such as for hosting guests or paying travel costs). Banking reforms in the late 1980s also affected the allocation of investment funds. Reforms made bankers more responsible for their profits and losses (Park and Rozelle, 1997; Li and Li, 1992). In response. bank managers began to assert more influence on loan decisions, including subsidized poverty loans. Conflicts arose between bank managers and PAD0 officials over proposed projects. Given the excess demand for low-interest loans, bankers also began to target borrowers with less risky projects or larger projects that made loans less costly to administer (Du, 1994). As is common in rural settings, banks sought to ration out the poor, who have fewer assets, demand small loans with high transaction costs, and face greater uncertainty over crop yields (Carter, 1988). Thus. the poverty policy shift in emphasis from household to enterprise, and from agriculture to industry, was reinforced by changing incentives caused by fiscal and banking reforms.

3. SOURCES AND USES OF POVERTY FUNDS IN SHAANXI PROVINCE Shaanxi Province is typical of the agricultural provinces in China’s interior. Growth in output and income was very high in the early 1980s. After the mid-1980s real per capita income growth slowed, rising by only 1.3% per year during 1986-91 (ZGTJNJ, 1987-93). Over 55% of the gross value of rural social product came from agriculture in the early 1990s about average for non-coastal provinces, but much higher than in rapidly developing coastal provinces. Since the mid-1980s the province’s industrial output increased by more than 10% every year except 1989 (a recession year). Industrial output started from a low base and was concentrated around several urban centers. In most outlying regions, rural industry is underdeveloped, producing labor-intensive products with simple technologies. Of the 9.5 counties in Shaanxi in 1992, poverty officials had designated 34 counties as “national poor counties,” and 12 as “provincial poor counties.” Data for a sample of 43 poor counties which received targeted investment funds in 1992 were provided to the authors. The study counties are located in the Loess Plateau region in Yulin and Yanan prefectures (northern Shaanxi) and

in Shangluo Prefecture in the Qinling mountains south of Xian (southern Shaanxi). Data from “other” poor counties scattered throughout the province are also used.

(a) Sources of poverty filnds Figure 1 shows that during the first three years of the poverty program, the total amount of poverty alleviation funds more than tripled in real terms, rising from 4.5 yuan per capita in 1986 to 14 yuan per capita in 1988 (Panel A).6 Nearly 65% of the poverty funds in the first year came from the DCF, a grant fund administered by PADOs in each county (Panel B). When the national subsidized loan program began in 1987, however, agricultural loans rose to 7.5 yuan per capita, surpassing the funds available from the DCF, which fell in real terms. Subsidized loans accounted for more than 50% of poverty funds in 1987 before falling to 45% in 1990. While the aggregate funds available for poverty alleviation remained fairly constant in nominal terms during 1988-92, inflation eroded the real level of funding (Figure 1, Panel A). Except for a small rise in 1990, total real funding fell, dipping to 7.5 yuan per capita in 1991, about half of the 1988 level, and only slightly above the level of the first year of the program. The real value of DCF funds fell continuously after 1988. Interviews with finance bureau officials revealed that at about this time many of the funds originally distributed as grants began to be loaned out instead. The change in the composition of loan sources reflects other policy changes in the late 1980s. As described earlier, to help local governments expand their revenue bases and keep them from diverting loans earmarked for households. a special loan fund targeted at county state-owned enterprises (SOE) was created in 1989. The sources of funds differed only slightly in northern and southern Shaanxi. Being old revolutionary base areas, Yulin and Yanan prefectures had a higher fraction of their funds come from budgetary grants administered through the DCF program. Southern Shaanxi counties, the site of many third front industrial concerns, received significantly more earmarked funds for county enterprise development. (b) Uses of poverty fimds There is no direct correlation between the sources of poverty funds and the sector in which they are ultimately used. Money from subsidized

TARGETED

POVERTY

INVESTMENTS

2141

Panel A: Poverty Investment

0

I

I

I

I

I

I

1986

1987

1988

1989

1990

1991

Year 1 -t-

I

Total Investments

+

DCF Grants

++-

Other Sources

I

I

Panel 6: Sources of Investment

1m

Source: Notes: Figure

Sub. Loans

Shaanxi

m

Cty Firm Loan m

Poor Areas Development

DCF means development

1. Poverty alleviation

investment

DCF Grants*

0

Other Source

Office

capital funds

per capitu and propohon of investment Shannwi Province, 1986-91).

by sotuce

(43 sample

counties

in

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loan programs, DCFs, and other sources can be used for activities in any sector. Even when national rules govern expenditure, (e.g., in the late 1980s guidelines set by the Leading Group stated that agricultural households should receive 80% of subsidized loans-Li and Li, 1992) local leaders still have discretion over the use of investment money, as seen by the varied pattern of fund use (Figure 2). Local and national policy changes, however, did affect the pattern of investment. In the first years of the program, agricultural loans and grants to households rose faster than those of other categories (Panel A). In response to poverty policy reforms, funds targeted at agricultural households fell beginning in the late 1980s (although the proportion of agricultural loans remained around 40%-Panel B). Officials also used poverty alleviation funds for industrial projects in the early years even though national guidelines made it difficult to do so. In the first years of the program, loans and grants to TVEs were about equal to those to county-run those to other firms and exceeded non-agricultural uses. Although many county enterprises lost money and, as a group, never contributed more than 11% of poor county output value, the amount of funds devoted to county enterprises did not fall as did those for TVEs and agricultural production. This was due in large part to the new county enterprise subsidized loan program. The fraction of poverty funds spent on county-run SOE increased from 15%’ in 1986 to more than 25% in 1990 (and rose to 30% in 1992).

4. GROWTH

IN SHAANXI’S COUNTIES

POOR

During the initial three years of Shaanxi’s poverty program, 1986-88, the province’s poor counties registered positive growth, although different regions and sectors grew at different rates. Agricultural output value in the 43 sample poor counties grew at an annual rate of around 7.1%; the value of output produced by township and village enterprises (TVEs) increased by 14.2% per year; and county SOE output value rose by 7.1% (Table 1)’ The counties in northern Shaanxi enjoyed the highest rate of annual income per capita growth (X.0%) and most rapid expansion of TVE output value (16.6%). County-run SOE output value grew the fastest (X.2%) in the “other” poor counties, while the fastest growth in agricultural output (12%) was in the southern counties. Benefiting from strong economic performance in all sectors,

growth in income per capita for the whole sample was robust during this first period, averaging 4.1% per year (Table 1, column 3). Even so, this rate was still less than the 7.5% income growth for 19X4-88 in all of the country’s poor counties (Tong et al., 1994). Such vigorous performance may be surprising given the critical assessments of the poverty program during these years, which led to the major policy shift in 19X9. Why did poverty alleviation officials make such fundamental changes when growth had been so strong‘? Were leaders comparing growth to coastal China? Did they feel that positive performance was due to factors other than poverty policies, especially given the poor repayment rates on poverty loans? Unfortunately, the literature in China is silent on the underlying motivation for the policy shift, perhaps lending credence to the conjecture that local officials sought changes that would allow them to use poverty funds to combat other more immediate problems such as growing fiscal deficits (Wu, 1994; Park et ul., 1996). Whatever the reason for the change in policy, poor areas faced a harsher macroeconomic environment after the 1989 poverty policy changes. Plummeting grain prices and rising urban unemployment caused incomes to fall in the rural economies of nearly all of the poorer provinces (Rozelle, 1996). Even so, rural incomes in Shaanxi recovered sharply during 1989-91, and the poor counties continued to grow faster than the province as a whole (Table 1, column 3). The value of non-agricultural activities in the Shaanxi grew more slowly than in the earlier period (Table 1, columns 5 and 6) while the sample poor counties actually saw output growth accelerate slightly in the latter period. Overall, for the entire five-year period (1986-91) income and the value of output for each sector grew significantly faster for the poor counties than for Shaanxi Province. While there may have been other forces affecting poor area economies during the late 1980s and early 1990s poverty policy very likely played a significant role in the region’s above average performance. But which policies? Did the growth arise from lagged effects of the earlier household-oriented, pro-agriculture grant and loan programs? Or, did the 1989 policy changes that increased resources for the state and rural industrial sectors boost growth? 5. MODELING

GROWTH PROVINCE

IN SHAANXI

We examine economic growth in three sectors of Shaanxi’s rural economy: agriculture, rural

TARGETED

POVERTY

INVESTMENTS

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Panel A: Poverty Investment

l3-‘F 1986

O-

-

X

I

‘F 1987

1988

I

1989

1990

1991

Year -C-

County

Enterprises

+-

Ag-Infrastructure

-I-

NE

Enterprises

*

Education

++

Production

Agri.

Panel B: Uses of Investment

1986

1987

1988

1989

1990

1991

Year

Source:

Shaanxi

Figure 2. Poverty alleviafion

Poor Areas Development irwestment

Office

per capita and proportion of invectrnmt Shaumi Psor?nce. 1986-91).

by uses (43 sunzple counties

in

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industry (township and village state-owned industry. For each that the rate of growth in output in year t is a linear function factors:

DEVELOPMENT

enterprises), and sector i, we posit value per capita of the following

XYonJth rate: = J’(poverty investment.& poverty output investments: _ 1. government expenditures,, value;_ ,, human cupitul, other county variables,, county and time - reluted fixed effects) The growth rate in period t is the percentage change in output value per capita from year t- I to year t. The poverty investment values include funds from subsidized loan programs, the DCF, and other sources, but omit funds from the Food-for-Work program (see Table 2 for a list of variables). Poverty investments in agriculture are types: of three investments in cropped agriculture (mainly loans to farm householdsAGCINPC); investments in other agricultural activities (e.g., raising livestock--ilGOZNPC); and investments in agricultural infrastructure (AGBZNPC). Poverty investments in rural

industry and state-owned industry are each aggregated into single measures (TVEINPC and CTYINPC).Recognizing that the effects of some investments take time to manifest themselves, current-year investments and investments lagged one year both are included as explanatory variables.’ We attempt to control for other investments by including government expenditures per capita (EXPPC).Most other investments are public investments, which WC expect to be highly correlated with total government expenditures. For state-owned industry, we have specific information on fixed investments in the assets of stateowned enterprises (NHXPC) (from both retained earnings and other budgetary channels), which we include in the growth equation for that sector in both current and lagged-year form.’ To control for private investment, we include rural income per capita (ZNCPC)among our other county variables as a proxy for private wealth. Systematic differences in other public or private investments that vary by county or by year are

Table 1. Shaanwi rural income und .yectoralreal per capita growth ratcJ, 1986-91" Years

Counties

Shaanxi province (n = 93)

Poor (n = 43)

1986-88

Rural income 1.12

Agricultural output value

TVE output value

County-run SOE output value

1988-89 1989-91 I’)%-91

- 7.77 6.32 1.29

5.79 -5.76 6.00 2.79

12.96 0.65 8.87 8.76

5.15 -0.01 4.89 4.00

1986-88 1988-89 1989-Y 1 1986-91

4.57 -7.33 7.25 3.11

7.12 px.13 7.94 4.20

14.19 3.07 17.77 13.27

7.09 4.35 7.55 6.72

Northern

Poor (n = 20)

1986-88 198X-89 1989-91 1986-91

8.01 -12.14 7.43 3.41

3.67 - 18.57 5.12 -0.07

16.59 3.87 26.71 17.78

5.36 4.69 6.12 5.53

Southern

Poor (n = 6)

1986-88 1988-89 1989-91 1986-91

4.58 PI.33 9.11 5.14

11.Y5 -11.88 6.49 4.60

11.95 - 1.0.5 14.46 10.1’)

6.00 10.29 5.12 6.49

1986-88 1988-89 1989-91 lY86-91

2.64 -6.11 6.69 2.40

8.07 0.25 10.13 7.26

13.84 4.25 14.08 11.95

8.23 3.28 9.16 7.59

Other Poor (n = 17)

Scources: Shaanxi totals for TVE and county-level SOE growth are from SXTJNJ (1987-92). Other county data are from Ministry of Agriculture (1985-92) database. “All values are deflated by provincial retail price index. Sectoral output per capita is calculated by dividing real output value by total population. Mean growth rates weight grrowth in scctoral output per capita by population and growth in rural income per capita by agricultural population. Data on SOE value are missing for one county in the ‘Other Poor‘ region.

TARGETED

POVERTY

Table 2. List of variables used in agricultural, TVE and county-run Variable

Variable

name

AGCINPC AGBCINPC AGOINVPC TVEINPC CTYINPC LAGCINPC LAGBCINPC LA GOINPC LTVEINPC LCTYiNPC INCPC INCPC POPDEN ELIU XCULTPC EXPPC INCPCHA T EXPPCHAT LAGVOPC LTVEVOPC LCTYVOPC NFIXPC LNFIXPC

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enterprise growth equations

description

FAP investment per capita for crop PAF investment per capita for agricultural infrastructure construction PAF investment per capita for agricultural other than crop and infrastructure construction PAF investment per capita for TVE PAF investment per capita for county-run enterprises Lagged PAF investment per capita for crop Lagged PAF investment per capita for agricultural infrastructure construction Lagged PAF investment per capita for agricultural other than crop and infrastructure construction Lagged PAF investment per capita for TVE Lagged PAF investment per capita for county-run enterprises Farmers’ income per capita Lagged farmers’ income per capita Population density Persons with at least a high school education Change of cultivated land per capita of two period County financial expenditure per capita Fitted value of income per capita Fitted value of financial expenditure per capita Lagged gross value of agriculture output per capita Lagged gross value of TVE output per capita Lagged gross value of county-run enterprise output per capita New added fixed assets per capita Lagged new added fixed assets per capita

also controlled for by inclusion of county and time dummy variables. There remains the possibility that poverty investments are correlated with other omitted investments, but to the extent that such investments also are correlated with the size of the sector, government expenditures, income per capita, unchanging county characteristics, or pure time effects, they should not bias the coefficient estimates. Another factor whose affect on growth has recently received much attention is human capital (Romer, 1990). Our measure of human capital is the share of the labor force that had graduated from middle school in 1985 (EDU), the beginning of the period under consideration. Lagged output value is included as a regressor because the rate of growth may depend on the initial size of the sector. Neoclassical growth models that assume decreasing returns to scale predict a negative relationship, while the newer endogenous growth literature argues that this need not always be the case (Sala-i-Martin, 1997). The final county-level time-varying variable included in all the specifications in population density (POPDEN), which we take as a proxy for the relative abundance of labor, which may reflect the allocation of labor across sectors. Population pressure on the land should negatively affect agricultural growth per capita

given diminishing marginal returns and a tendency for rural labor in more crowded areas to leave agriculture in favor of off-farm work and temporary migration. In the agricultural growth equation, we also include as a regressor changes in the availability of agricultural land (XCLILTPC). Li (1994) finds that this variable is an important factor in his analysis of productivity and investment in Shaanxi’s wheat, corn and soybean economies (in both poor and well-off counties). On average, total cultivated land fell in the province by 5% between the early 1980s and 1991. The north experienced expansion while cultivated land in other regions fell. The data used in this study were collected by the authors during fieldwork in the province’s poor northern and southern regions. Extensive field interviews allowed the authors to verify the content and biases of the data. Data are secondary in nature, having been collected and organized by different government agencies, including the bureaus of statistics, agriculture, township and village enterprises, grain, industry, price, finance, education; Poor Area Development Offices; and Agricultural Banks. The content and construction of the poverty investment variables are discussed earlier. The data for these variables come from the Poor Area Development Offices (PADOs) in each of the 43 sample counties. Provincial officials can

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cross check these data since most of the funds are issued on a project basis, and the supervising agencies keep track of the original project proposals and cvaluatc selected projects. These and all other data which arc measured in value terms are deflated by the provincial consumer price index (ZGTJNJ, 1987-93). Agricultural and TVE growth data come from a county-level data bank that has been collected and organized by the Ministry of Agriculture (MOA) since 1980. The data mostly come from year-end reports on agricultural production and income which originate at the village and are aggregated up to the county level. The data on county-run enterprises (output and fixed investment) arc from a statistical volume published by the Shaanxi Statistical Bureau (SXSLSTJZL, 1991). Growth is measured as the annual percentage increase in the gross value of sectoral output. Agriculture is broadly defined to include production of crops, livestock. forest products, aquaculture, and light handicrafts. Township and village enterprise output includes the production of all firms owned and operated by local governments and private individuals in the rural economy. County-run enterprises include stateowned firms directly under the control of the county (both those operated directly and those contracted out). These same data are also used for the variables used to control for the level of development of the county--LAG VOPC. LTVEVOPC, LCTYVOPC. The data for cultivated area, population density (rural population divided by cultivated area), and per capita income are also from the MOA data set. The rural income variable measures full household income, and is made up primarily of income from agricultural outputfor both home use and market sales. off-farm income, and income earned from self-managed activities. Expenditure per capita, a proxy for all other investments made by the government into poor areas, comes from budgetary data of each county’s finance bureau. The education variable is from a national publication on the educational attainment levels of all of China’s 2000-plus counties (ZGFXJYHB, 1992).

6. TARGETED POVERTY INVESTMENTS AND SECTORAL GROWTH The main finding of the growth regressions is that loans to households for agricultural activities positively affect agricultural growth in poor counties while all other uses of poverty funds (agricultural infrastructure: rural enterprises, and

state-owned enterprises) did not have discernible positive effects on growth. The growth equations for the three sectors-agriculture, rural industry, and state-owned industry-generally performed well (Table 3). The adjusted R-square statistics ranged from a high of 0.58 for agriculture to a low of 0.1 I for county-run firms. The signs on the control variables were mostly as predicted and results were robust to alterations in specification and functional form.“’

(a) Growth in agriculture The agricultural sector growth model provides strong evidence that the emphasis of early poor area policy on household loans was an effective way to promote growth in poor areas. Investment in cropping agriculture going directly to households (AGCINPC and LAGCINPC) significantly and positively affects agricultural growth. While the sign of the coefficient of the contemporaneous variable (AGCINPC) is positive, its marginal t-ratio shows only a weak association between current loans to households and agricultural growth. The relationship, however, is particularly strong when the investment variable is lagged (LAGCINPC); farm loans to households increase agricultural output in the following year. Lagged loans for non-cropping activities (LAGOZNPC) also positively affect agricultural growth. Most of these loans, which supported activities such as livestock, fishery, and individual businesses, also went to individual households. The importance of lagged effects may be due to the fact that many household loans disbursed toward the end of the calendar year will not have effects until the following year. Investment programs targeted at infrastructure projects. even those focused on agriculture (AGBINPC and LAGBINPC), not only did not increase growth, they detracted from it.” This result was surprising since many leaders, project officials, and farmers complained that marketing constraints, frequently identified with poor infrastructure, were holding back growth in agriculture and in other sectors. One explanation for the result is that in Shaanxi, most of the investments were used not for roads or communication, but for terracing and planting trees.” According to Li (1994) such investments rely on labor supplied by farmers (funds support only non-labor inputs), typically organized by the village collective. They are perceived to be of low return in mountainous areas because of the high labor input cost of building terraces on steeply sloped land, the long time horizon and limited economic return to planting trees, and the lack

TARGETED

Table 3. Agricultural,

7’VE, and county-run

INTERCEPT Investment Variables Household-Cropping (AGCINPC) Household-Other Ag. (AGOIWPC) Project Loans (AGBINPC) Lagged Cropping (LAGCINPC) Lagged Other Ag. (LAGOINVPC) Lagged Project (LABINPC) TVE Grant/Loan (i’%‘EZNpC) Lagged TVE (U’VEINPC) County SOE Invest (C7’YINpC) Lagged County (K7YINpC) Budget-Fixed Invest (NFZXPC) Lagged Fixed Invest (LNFIXPC) Control Variables Income Per Capita (INCPC) Size of Ag. Sector (LAGVOPC) Size of TVE Sector (LWEVOPC) Size of County SOEs (LCTYVOPC) Cultivated Land (XCLILTPC) Population Density (POPDEN) Education Level (EDU) Budgetary Investments (EXPPC) R’ in parentheses

are t-values.

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enterprise growth models, estimated by ordinaly least squures Agriculture

Variables

“Figures

POVERTY

TVE

0.65 (0.05)” 0.37 -0.20 - 2.29 0.82 1.58 -3.48

-27.76

(-1.84)

County-run -8.23

enterprise (-0.50)

(1.67) (0.36) (2.3) (3.46) (2.90) (2.00) 0.08 (0.17) - 0.25 (0.56) -0.23 (~ 1.15) 0.26 (1.19) 0.007 (0.32) 0.03 (1.14)

0.22 (6.57) -0.12 (-6.74)

0.05 (1.38) -0.05

0.05 (1.31)

(-3.06) -0.12

-79.35 -0.06 -0.13 ~ 1.05 0.58 Prefectural

(-4.16) (-2.31) (-0.70) (-0.93)

and year dummies

of clearly defined property rights. If this is the case, households may lose income by being forced to participate in such projects and will have little incentive to maximize effort, which leads to low survival rates of tree plantings and poorly constructed terraces. It also may be that some funds for these investments have never been actually invested but have, as observed by Park et al. (1996) been diverted to other uses. Most of the coefficients on the control variables had the expected signs and many were statistically significant. The coefficient of the lagged value of agricultural output (D1GVUK) is negative and significant. Agriculture is growing slower in those areas where the sector is already highly developed, consistent with decreasing returns to production. The results also show that after controlling for other factors agriculture grows slower in poorer areas, where income per capita (INCPC) is low, consistent with the hypothesis that the rich are more able to self-finance profitable investments. The importance of the ability to self-finance suggests that households are credit-constrained, which helps explain why provision of household loans had a positive effect on growth. Population density (POPDEN) is negatively related to agricultural growth, perhaps an indica-

0.07 (2.50) 0.40 (2.32) 0.28 (4.45) 0.36

(-3.36)

0.04 (1.11) 0.0001 (0.00s) 0.06 (0.70) 0.11

not shown.

tion that in China’s more liberal reform environment, labor abundant regions may turn to non-agricultural employment activities. These results are similar to those found in studies by Lin (1992) and Huang and Rozelle (1996) which demonstrate that farmers in China are increasingly allowing relative factor scarcity to guide their production decisions. Changes in cultivated land per capita (XCULTPC), after controlling for land-to-labor ratios (POPDEN), are negatively related to growth in farm output. Although this may at first seem counterintuitive, it is consistent with findings from other recent research in China. First of all, after the end of grain-first policies in the 1970s and early 1980s some of China’s most productive areas were able to reduce cultivated area, removing less productive, fragile land that may have been unprofitable to farm (Weins, 1987 and Huang et al., 1996). Moreover, while a lot of good land was taken out of production for construction of housing, factories, and roads, in the mid-1980s three-quarters of the reduction in “cultivated” land was actually conversions into pasture, fishponds, and orchards (Smil, 1993, p. 56). This is undoubtedly the major reason why losses of cultivated land are associated with higher growth rates in farm output.

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DEVELOPMENT

(b) Growth in industry For both rural and state-owned industry, none of the coefficients of the poverty investment variables are significantly related to growth in output value. The contemporaneous and lagged variables for TVE investment (TVEZNPC and LTVEINPC) both suggest that when poverty officials provide funding for rural industrialization, there are no current or lagged impacts on growth. Likewise, funds for county-owned firms (CTYINC and LCTYZNC) do not contribute to sectoral growth, either in the short or long run. While these results may be disturbing, interviews with officials in poor areas reveal reasons for such outcomes. Many local leaders believe that enterprise development is the key to generating fiscal revenues and achieving rapid economic development. The failure rate of enterprises in poor areas, however, is very high given the poor quality of managers and workers, poor infrastructure, high transport costs, lack of capital, and limited access to markets. In many counties, repayment rates on enterprise loans are lower than those for household loans (Park et al., 1998). Nonetheless, despite low expected returns. local leaders prefer enterprise investments because of their effects on fiscal revenues and employment. The fact that TVE growth accelerated in the sample regions likely reflects success in more affluent townships in poor counties whose success does not depend on poverty funds. Like agriculture, industrial growth is negatively correlated with initial levels of output, ceteris paribus (LTVEOPC and LC7’YVOPC). Government expenditures (EXPPC) and education (ED!/) have positive coefficients in both industrial sector growth equations, are only significant in the TVE growth equations (both the EXPPC and EDU variables are negative-although insignificant-in the agricultural growth equation). The positive sign of the coefficient on the income variable (ZNCPC) also suggests a propensity for those areas with higher incomes to be able to generate faster growth (although the lower t-ratios implies that this relationship is more tenuous). Finally, the positive signs on the population density variable (POPDEN) differ from that in the agricultural model, but this is reasonable for non-agricultural activities.

7. CONCLUSIONS This study utilizes a unique data set to examine the sources, uses and effectiveness of targeted poverty investments in 43 poor counties

of Shaanxi Province during the years 1986-91. The overall goal of the study is to understand China’s poverty alleviation policy, tracing changes over time, assessing the motives for the changes, and evaluating the effectiveness of the various strategies pursued. For this sample, overall funding increased in the early years of the program and then fell significantly in real terms after 1990. There was also a major policy change in the middle of the sample period, characterized by a redirection of spending after 19%) away from households and toward economic “entities” and TVE and county-run enterprises. According to the results of the sectoral growth analyses, targeted investment funds allocated directly to households for agricultural activity have a significant positive effect on growth, while investments in township and village enterprises or county state-owned enterprises do not have a discernible effect on growth. Investment5 in agricultural infrastructure do not positively affect growth rates in agricultural output. suggesting that other types of basic investments (e.g.. roads and education) should receive higher priority. These results suggest that the program’s initial emphasis on household lending was most appropriate for furthering economic growth in poor areas. To the extent that these results can be generalized to the whole of China, the change in poverty alleviation strategy and the preference to allocate scarce resources to revenue-generating (if not growth-enhancing) enterprises has a clear rationale if one considers the dwindling fiscal resources in poor arcas that forces local leaders to pursue every possible source of funds to finance public expenditures and fulfill mandatory government functions. A viable local government surely plays an important role in the process of economic development, both in promoting growth and pursuing equity concerns. The lack of discernible impacts of industrial investments on growth in part reflects the failure to solve the fiscal problems that have emerged in poor areas, which increases the incentives to distort resource allocation decisions. This linkage suggests that addressing the fundamental problems in the fiscal system could lead to a more effective poverty investment program. This study suffers from several limitations that call for additional work. First, it is based on data from only one province. Work using data from other provinces is necessary to have confidence that the results for Shaanxi can be generalized to other parts of China. Second, the study focuses

TARGETED

POVERTY

narrowly on economic growth. The goals of poverty investment programs include many other objectives as well. For example, other work has pointed to serious concerns about increasing inequality (Khan and Riskin, 1998) poor targeting (Park et al., 1998) and serious environmental problems in poor areas (Rozelle et ul., 1997). To fairly evaluate the success of poverty

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investments in China, more research is needed to look at other dimensions of economic and social development. Finally, China’s poverty program has continued to expand and change since 1991. Work examining the effectiveness of poverty investments in more recent years will be important to assess the proper course for future changes in poverty policies.

NOTES 1. See stud&.

Park

et ul. (lYY8) for a review

of previous

2. The growth experience of counties within the province used for the case study in this paper, Shaanxi, very much fits this pattern of heterogeneous growth among regions. Shaanxi as a whole is considered a poor province, and grew only 1.3% per year during 1986691. While growth in the other poverty counties (2.4%) was a little higher, growth in the southern poverty counties (5.1%) was very much higher (see Table 1). It is precisely the part of the differences caused by poverty policies that the growth analysis will attempt to explain. 3. In focusing narrowly on growth outcomes due to data limitations, we are unable to directly assess the effect of poverty investments on important development goals such as equity; improved nutrition, educahealth; social empowerment; and tion, and environmental sustainability. 4. Park et al., 1996 describe greater fiscal self-reliance among poor counties. In 1993, the state eliminated national subsidies resold grain. 5. In this paper we are primarily concerned with the policies of the Leading Group. The State Council also funds a laree Food-for-Work (FFW) nroeram aimed at the constrtction of roads and drinking water delivery projects. Analysis of this program is beyond the scope of this paper, and the interested reader should see Zhu and Jiang (1996). Total funding on combating poverty (including FFW, subsidized loans and budgetary grants) expanded very rapidly in the lY9Os, from 4,600 million in 1990 to 10.800 current yuan in 1996 (Park CI fll., 1998). ,l

6.

All values in this section

are deflated

u

to IY86 yuan

using the provincial

consumer

price index.

7. The per capita growth rates for our sample were calculated from deflated aggregate totals for income and the values of agricultural, TVE, and county-firm output. From these the growth rate of sample population from 19X6-88 (1.6%) was subtracted. in order to arrive at per capita growth rates: this was done because there some missing data for county population figures in 1991. 8. A number of specifications were used with varying lag structures. Lags of more than one year typically were not significant either by themselves (replacing the one year lag used in this specification) or simultaneously with other lags. Most importantly, the coefficients of the contemporary and single-lag variables did not change significantly when other variables were added. 9. These tixed investments do not overlap with poverty investments, which are mainly in the form of working capital loans. Lack of data precluded using a similar variable in the TVE equation. To the extent that poverty alleviation funds invested in TVEs are positively (negatively) correlated with the township’s own investment. the coefficient on TVEINVEST will be over (under) stated. 10. A seemingly unrelated regression model was also estimated for the three equations, with little effect on the coefficient estimates.

I I. The authors also want to stress caution in interpreting this particular result, since it might be that there are longer-term lag effects that are not being captured here. 12. Most road and community infrastructure projects are funded by the Food-for-Work Program, an important poverty program.

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