rafmacalaba/datause-encoder-data huggingface dataset:
-67-

over one quarter of projects had debt service ratios that were higher at the end of the project than at appraisal and another 40% were about the same. One third of debt service ratios had declined during the course of the project. Of the 50% of projects that had poor debt servlce ratlos at appralsal (2.0 or less), one half had not changed significantly by the end of the project and another quarter had actually deteriorated.

# Davs Receivable

The number of days receivable Is a good Indicator of the collection and commercial operations of a utility. Consequently, It Is as much an Indicator of Institutional performance as an Important component of working capital. For utilitles with a monthly biling cycle (most utilities), the Bank normally regards 60 days as a reasonable value for a wellmanaged power company (although norms may vary according to country) and Indeed the mean value of forecasts made at appralsal was 63 days. Unfortunately, performance by utilities fell far short of this target since the average for recelvables was 96 days. There has been a clear deterloratlon over time, as can be seen In Table 7. Receivables Increased from 77 days In the period 1966-73 to 112 days In the perlod 1980485.

TABLE 7 Days Receivable for Completed Prolects

|Days<br>Receivables|E.Asl.|S.Asla|E. Africa|W. Africa|LAC|EMENA|Averaae|
|---|---|---|---|---|---|---|---|
|At Appraisal|<br>84|86|120|93|73|152|94|
|At Project<br>Completlon|107|97|128|177|91|121|108|

Only 18% of projects actually met the forecast number of days receivable (plus or minus ten days) by the end of the project and another 5% were hligher. Of the 77% of projects that did not meet the forecast level,
CHAPTER 3

# PROBLEMS OF **THE** COTTON EXPORT POLICY

## 3.1. ECONOMIC PROBLEMS

### **_Cotton Farmers'Income Loss_**

The cotton export policy has caused a nurnber of negative consequences. The largest cost imposed by the policy has been borne by farmers. The cotton farmers' income was adversely affected by the cotton price being kept low throughout the 1980s **(Table** 5). Also the suppressed cotton price caused adverse income distribution effects within the cotton production sector; while wealthier cotton farmers who grew more than one type of crop in irrigated areas were less, affected, poorer farmers could not promptly switch from cotton to other new crops, such as castor seeds or sesame seeds when the price of cotton fell.

**Table** 5: **_Movement in Price Indices_**

|Price Index|1981/2 - 1989/90|
|---|---|
|Wholesale Price Index (WPI)|+ 72 %|
|WPI for Manufacturers|+ 71 %|
|Consumer Price Index (CPI)|+ 93 %.|
|CPI for Industrial Workers (General Index)|+ 96 %|
|CPI for Urban Non-manual Employees (General Index)|+ 92 %|
|**Cotton Lint Price**|+ 55 %|
|Cotton Yarn Price|+76%|
|Cotton Cloth Price|+ 40 %|

Source: Chaurdhuri (1994) While microeconomic trade inteivention in the cotton sector acted against cotton farmers, macroeconomic intervention had also been against them until recently in India. As in many other countries, the currency had been overvalued until macroeconomic liberalization began in 1991. This had kept the real farm-gate prices of cotton (in US dollars) al a low level for many years in India. It is only recently that the Rupee became convertible (in the current account) and the real exchange came close to the free market rate, which corrected part of the discrimination against the cotton production sector.

16
**<u>Table 3: Estimated own and cross-price elasticities</u>**

||_Seeds_|<br>Fertilizer|<br>Pesticide.|Bullocks|Mechan.|Irrigation|Hired labor|Fam. labor|
|---|---|---|---|---|---|---|---|---|
|Seeds|**_-0.776***_**|0.149***|0.197***|0.168***|0.101***|0.186***|0.186***|0.164***|
||**_(-81.518)_**|(24.274)|(17.743)|(14.859)|(12.470)|(17.058)|(20.268)|(24.991)|
|Fertilizer|0.234***|**_-0.717***_**|0.282***|0.213***|0.308***|0.252***|0.254***|0.253***|
||(24.274)|**_(-57.633)_**|(14.864)|(9.971)|(20.418)|(13.252)|(14.567)|(21.685)|
|Pest. &Fungic.|0.071***|0.065***|**_-0.755***_**|0.076***|0.036***|0.056***|0.015|0.031***|
||(17.743)|(14.864)|**_(-53.736)_**|(5.704)|(3.984)|(5.265)|(1.414)|(4.932)|
|Bullocks|<br>0.071***|<br>0.057***|<br>0.088***|<br>**_-0.726***_**|<br>0.086***|<br>0.066***|<br>-0.119***|<br>0.085***|
||(14.859)|(9.971)|(5.704)|**_(-16.428)_**|(4.654)|(3.865)|(-4.020)|(6.167)|
|Impl. &mechan.|0.082***|0.159***|0.080***|0.166***|**_-0.771***_**|0.150***|0.242***|0.058***|
||(12.470)|(20.418)|(3.984)|(4.654)|**_(-25.487)_**|(6.905)|(7.789)|(3.650)|
|Irrigation|0.078***|0.067***|0.065***|0.066***|0.078***|**_-0.501***_**|-0.028**|-0.015*|
||(17.058)|(13.252)|(5.265)|(3.865)|(6.905)|**_(-27.752)_**|(-1.965)|(-1.941)|
|Hired labor|0.104***|0.090***|0.024|-0.158***|0.167***|-0.037**|**_-0.299***_**|-0.050**|
||(20.268)|(14.567)|(1.414)|(-4.020)|(7.789)|(-1.965)|**_(-5.172)_**|(-2.394)|
|Family labor|<br>0.251***|<br>0.246***|<br>0.134***|<br>0.311***|<br>0.110***|<br>-0.056*|<br>-0.136**|<br>**_-0.411***_**|
||(24.991)|(21.685)|(4.932)|(6.167)|(3.650)|(-1.941)|(-2.394)|**_(-13.578)_**|

_Note:_ Based on full sample of 4,790 observations 16
# TABLE OF CONTENTS

SUMMARY** * ..... . ...

|Is|INTRODUCTION ......................................................<br>1|
|---|---|
|II.|THE<br>MosEL..o<br>o..........|
|III.|IMPLICATIONS OF THE MODEL FOR THE OPTIMAL CURRENCY<br>COMPOSITION OF EXTERNAL DEBT.........<br>.9 .....................<br>.<br>1|
|IV.|SPECIFIC EXAMPLES .......................**.**......... ..*..............<br>17|
|V.|ESTIMATION OF THE OPTIMAL CURRENCY COMPOSITION OF<br>EXTERNAL DEBT ..................................................28|
|VI.|POLICY RULES FOR THE CURRENCY COMPOSITION OF EXTERNAL DEBT ....... 33|
|-VII.|COMPUTATION .................<br>o*.s*#o9o37|
|VIII.|APPLICATIONS.........<br>*s..<br>.....<br>oo.o....o<br>..38|
|REFERE|NCES..o ...<br>o......*.oo,...<br>.....<br>39|
**Table III.2 Average Coffee Yields, 1981 and 1999**

|Year|Area underCultivation|Yield per|Production Green Coffee|
|---|---|---|---|
||Tareas<br>(Hectares)|Tarea<br>(Hectare)|Hundredweights, qq<br>(MetricTons,MT).|
|1981|2,428,418 tareas<br>(152,731 ha)|0.44 qq/ta<br>350kg/ha|1,075,400 qq<br>(53,770MT)|
|1999|1,883,455 tareas<br>(118,456ha)|0.53 qq/ta<br>420kg/ha|1,004,100 qq<br>(50,205MT)|

1981: National Agricultural Census and Coffee Growing Statistics. 1998: National Producers Registry and Production Statistics.

# **B. Geography and Ecology of Production**

Coffee production is distributed among the Dominican Republic’s eight agricultural regions (see Map Annex), but five major coffee producing zones can be identified. Of these, the four most important are located in the main mountainous areas of the country: the Central Mountain Range, the Northern Mountain Range, the Neyba Mountain Range, and the Bahoruco Mountain Range. These mountainous areas are all located in critical watershed areas, and land use on these mountains has major and far reaching environmental impacts.

The Central and Southern Region agricultural regions, located in the center of the south of the Central Mountain Range and the north of the Neyba Mountain Range represents 44% of the total coffee land area and 37% of the coffee farms (table III.3). The Northern Region, located in the center of the Northern Mountain Range, and part of the northwest slope of the Central Mountain Range, represents 24% of the total coffee land area and about 25% of the coffee farms. The North Central Region, located in the center of the north of the central range, represents 13% of the total coffee land area and 14% of the coffee farms. The Southeastern Region, located in the center of the Bahoruco Mountain Range, and to the south of the Neyba Mountain Range has the least favorable coffee growing conditions and is the only area of Dominican Republic where Robusta coffee is produced.

**Table III.** **<u>3 Coffee Farms and Land Area Under Coffee Cultivation by Agricultural Region</u>**

|<br>AGRICULTURAL<br>|NUMBER OF<br>|FARMS<br>|AREA UNDER<br>|CULTIVA<br>|<br>TION<br>|
|---|---|---|---|---|---|
|REGION|Units|%|Tareas|Hectares|%|
|NORTH|10,986|25.2|450,897|28,358|23.9|
|CENTRAL|10,092|23.1|449,390|28,264|23.9|
|SOUTHERN|6,049|13.9|371,604|23,371|19.7|
|NORTH CENTRAL|6,132|14.1|245,601|15,447|13.0|
|SOUTHWEST|3,969|9.1|168,380|10,590|9.0|
|NORTH EAST|4,148|9.5|105,096|6,610|5.6|
|NORTH WEST|1,383|3.2|72,889|4,584|3.9|
|EAST|855|2.0|19,588|1,232|1.0|
|NATIONAL TOTAL|43,613|100%|1,883,445|118,456|100%|

Source: SEA, National Registry of Agricultural Producers, 1999. Preliminary Data. Agricultural region is an administrative area defined by government Estimates of the number of farms by regions based on the cross of variables and farms by strata and surface area.

Dominican coffee growing areas are mostly classified as subtropical humid forest to subtropical high humid forest. Most soils belong to the land capability class VII, corresponding to steep 31
the test items in two halves and treating them as separate measures of the underlying true concept. The Cronbach’s � is the mean of all possible split-half coefficients resulting from different splits of the test and the reliability ratio can be estimated as the ratio of true variance to observed variance of a given test. In Table 7 below, we report both the point estimates from estimating specification (2), �<sup>�</sup> , and the unbiased effects �<sup>�∗</sup> ��<sup>�</sup> �<sup>�</sup> , where �<sup>�</sup> is the Cronbach’s � estimate of the reliability ratio with first differenced test score data.<sup>21</sup> The within-student within-teacher results are reported in Table 7. As a benchmark, column (1) reports a simple OLS regression of standardized student test scores (z-scores), on teacher quality and a set of country by district fixed effects as well as survey fixed effects.<sup>22</sup> The point estimates on the four teacher quality dimensions we measure – content knowledge, pedagogical knowledge, pedagogical skills, and effort (absence from class) – are quantitatively large and significant. Students taught by the best possible teachers in the sample, i.e., those who have full scores on the teacher knowledge and pedagogy test and display the full set of skills in the classroom have test scores that are almost 1.5 standard deviations higher than those who are taught by the worst possible teachers. While these results are by no means causal, it should be noted that they exploit only within-district variation in test scores. In column (2) of Table (7), we turn to the estimation of the full model specified in equation (1) that also includes student, teacher and school characteristics. While the coefficients on the teacher variables are reduced somewhat, they are still large and significant. A one standard deviation reduction in absence from class is associated with an increase in student test scores of 0.05 standard deviations. Moving a student from a teacher with no content or pedagogical knowledge and no pedagogical skills to a teacher with full scores on these three dimensions would raise student test scores by almost one standard deviation. Looking at a standard deviation change in knowledge and skills, obviously, yields smaller changes (a one standard deviation change in teacher content knowledge, pedagogical knowledge and skills is associated with roughly a quarter of a standard deviation increase in student test scores), but also implies relatively modest changes in teacher quality.

In column (3), we estimate specification (2). As we exploit within-student variation, we can no longer calculate the effect of classroom absence and pedagogical skills as they are

> 21 Metzler and Woessmann (2012) derive a reliability ratio for first-differenced test score measures by assuming that the measurement errors are unrelated across the two subjects. We follow their approach here.

> 22 Student test scores are standardized in each country. For the teacher pedagogy score, we focus on the lesson preparation score and the evaluating students score, as the comparing writing score is more specific to language teachers only.

21
NVRB during the commitment period (Table 2). This could be for one of two reasons: either marginal debtors have access to cheaper forms of credit than overdrafts, or marginal debtors are already maxed out on overdrafts.

While the effect of SSP on the debt stock is not significantly different from zero, the point estimate is economically meaningful and very close in magnitude to the treatment effect on savings. As a result, we find no evidence that SSP increased savings net of debt (Table 3, – – column 8). One caveat here is that our survey-based estimates while close to zero are noisy. The 95 percent confidence interval for the impact of SSP on savings net debt easily includes the analogous point estimate on savings with NVRB.

# **3.4 Heterogeneity by Baseline Overdrafting**

The main effects mask strikingly different treatment effects by baseline overdrafter status. This can be seen in Figure 1, which graphs monthly treatment effects on the average daily balance at NVRB by overdrafter status. Here, the first vertical dashed line demarcates the payout month and the second vertical line demarcates the end of survey coverage. Panel B shows that the treatment effect for heavy overdrafters peaked roughly six months _before_ the SSP payout. This could reflect a combination of program dropout (19 percent of heavy overdrafters dropped out early, as compared to 7 percent of light overdrafters) and drawdown of liquid, non-committed NVRB balances. In contrast, the treatment effect for light overdrafters peaked the month before the payout (Panel A). Moreover, treatment effects for light overdrafters re-emerge in the post-endline period after dropping immediately after commitment amounts are released. These patterns are consistent with the hypothesis that light overdrafters became accustomed to making smaller withdrawals from their NVRB accounts and continued with this behavior after the SSP program ended.<sup>15</sup> These stark differences in treatment effects by baseline overdraft behavior are also apparent in the survey data. We examine heterogeneous treatment effects by augmenting equation 1 to include interactions between the different treatment effects and either a “heavy overdrafter” (above median overdraft score at baseline) or “light overdrafter” (below median overdraft score) dummy.<sup>16</sup> Table 4 shows that during the commitment period, light overdrafters saved more both with NVRB _and_ with other banks and credit unions – commitment crowded additional savings into the banking sector. While there is some crowd out of informal savings during the commitment period, the point estimate is small compared to treatment effects on formal savings. Consequently, we reject the null of no impact on total

> 15In contrast, treatment effects on overdrafts are similar for the two groups – see Appendix Table A6 for detail.

> 16We also include interactions between overdrafter status and time period.

10
decide whether to apply for the subsidy, and the public agency, which must decide which projects to subsidize, is likely to precede the allocation process. This makes public funding an endogenous variable with respect to innovation itself. This is one of the reasons why evaluators tend to (over)estimate impacts, using existing (administrative) data. But these types of models fail to correct for important source bias. Prospectively designed impact evaluations help researchers incorporate other factors that produce biased estimates. This is important because the scale of intervention, size of the firm, type of beneficiary, and project attributes can produce biases in R&D impact estimates.

32
WPS3652

# **Growth Spillover Effects and Regional Development Patterns:**

# **The Case of Chinese Provinces**

## **Xubei Luo**<sup>*****</sup>

### **<u>Abstract:</u>**

This paper discusses regional development patterns in China, and examines effective ways of using development aid to attain regional balanced growth through optimizing growth spillover effects.

Based on provincial panel data from 1978 to 99, the paper constructs an indicator “neighbor performance” to measure the geographic spillover effects of aggregate growth from and to different provinces according to their relative richness and geographic position. Analysis of a Solow-type growth model suggests that positive spillover effects dominate negative shadow effects at the national level as well as the regional level; and some coastal provinces provide growth pull and growth push forces for their neighbors and serve as locomotives.

The results show that the rapid take-off of the coastal provinces has the largest spillover effects on the entire Chinese economy, but at the expense of a widening regional gap. A policy of encouraging the growth of the non-coastal regional hubs would have strong forward and backward linkages with the inland/western regions and thus reduce the regional development gap without sacrificing much aggregate growth. The paper offers support for the policy of developing inland hubs, and argues that directing development aid to Hubei and Sichuan would optimize the growth spillover impacts on inland regions.

#### **JEL** : O18, O47, R11

**Key words** : Spillover effects, Regional development pattern, Balanced growth,

China

### World Bank Policy Research Working Paper 3652, June 2005

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> * Email: <u>xluo@worldbank.org; mailing address: World Bank, Office of Senior Vice President and Chief</u> Economist, Development Economics, MSN MC4-404, 1818 H Street NW, Washington DC, 20433.

1
in the distribution of housing prices may be greater than what would be predicted using standard metro-level estimates. Ouazad and Rancière's (2016a) estimates of supply elasticities using 30m by 30m satellite data also display a positive correlation between supply elasticities and the distance to the central business district, a potential evidence of the importance of within-city distribution of elasticities in driving changes in wealth inequalities. Elasticities of the provision of higher education services are also likely far from the perfectly elastic benchmark. Heckman, Lochner, and Taber (1999) makes the point that general equilibrium endogenous responses to tuition subsidies can offset the estimated partial equilibrium effects and calls for a revision of microeconomic studies that do not estimate price responses. Recent developments in the use of micro data now enable a microeconomic analysis of such general equilibrium effects. Overall, as in Ouazad and Rancière (2016a), financial deepening and the rise of credit are unlikely to yield uniformly positive welfare impacts, even before the onset of a credit downturn.

Another driver of financial development's welfare and distributional consequences is the presence of social spillovers, peer effects, or social externalities. Krysan and Farley (2002) use the multi-city study of urban inequality in Atlanta, Boston, Detroit, and Los Angeles to show that African-American households prefer diverse neighborhoods with about half African-American residents while White households prefer neighborhoods with greater fractions of White neighbors. With such preferences, market equilibrium in urban location choice models as in Schelling (1971) and Benabou (1996) is perfectly segregated. This is in stark contrast with market equilibrium in labor economics, where Becker (1957) predicts that competition leads to lower discrimination. Moreover, such perfect segregation at market equilibrium in a city with no credit market imperfection can be inefficient. The inefficiency of segregation or of diversity depends crucially on two key parameters: (i) whether low- and high-human capital households’ welfare is equally affected by their peers in neighborhoods and schools, and (ii) whether the marginal impact of peers is concave in the fraction of high-human capital peers.

To make this clear, consider a stylized two-neighborhood city as in Benabou (1996), with two types of households, with high- and low human capital, h ∈ {hA, hB}, hA>hB. The city has a fixed density 1 of housing, and neighborhoods 1 and 2 have the same size 1/2. The total density of 20
**<u>Table 1: Average Population and Geographic Size of European Local Governments</u>**

|**Country**|**Average Population in**<br>**Local Governments**|**Average Area (Km)**|
|---|---|---|
|UnitedKingdom|126,128|533|
|Lithuania|66,300|1,166|
|Yugoslavia|49,500|487|
|Bulgaria|35,000|432|
|Sweden|30,040|1,595|
|Netherlands|27,559|60|
|Denmark|18,760|150|
|Belgium|16,960|na|
|Poland|15,561|130|
|Macedonia|15,800|209|
|Slovenia|10,300|106|
|Albania|10,000|77|
|Finland|10,870|730|
|Norway|9,000|710|
|Croatia|8,800|104|
|Romania|7,600|81|
|Italy|7,105|38|
|Estonia|5,713|178|
|Germany|5,575|na|
|Spain|4,930|60|
|Ukraine|4,600|56|
|Latvia|4,300|115|
|Austria|3,421|na|
|Hungary|3.242|32|
|Switzerland|2,468|na|
|Slovakia|1,900|17|
|Czech Republic|1,659|13|
|France|1,580|15|

Source: Adapted from World Bank (2002) and Swianiewicz (2002).

**<u>Table 2: Average Population and Geographic Size of Selected Local Governments</u>**

|**Country**|**Average Population**|**Average Size (sq. km.)**|
|---|---|---|
|Sudan|293,400|17,400|
|Côte d’Ivoire|221,769|3,740|
|Nigeria|204,267|1,206|
|Kenya|191,916|3,344|
|Somalia|142,464|7,591|
|Uganda|86,222|775|
|Angola|77,822|7,510|
|Rwanda|76,700|236|
|South Africa|69,842|1,771|
|Ghana|26,410|301|
|Algeria|21,124|1,546|
|Mali|15,124|1,771|

Source: en.wikipedia.org/wiki/wikipedia, http://lcweb2.loc.gov/frd/cs/continent_africa.html 3