rafmacalaba/datause-encoder-data huggingface dataset:
1 Table 8 Pathway through which maternal social participation affects child nutritional status at age <!-- Start of picture text --> y<br><!-- End of picture text -->

|**Child HAZ at Age 1: Support Pathwa**|**y**|
|---|---|
|(1)|(2)|
|No education* member<br>0.458**|0.504**|
|(0.195)|(0.182)|
|Primary* member<br>-0.083|0.009|
|(0.149)|(0.173)|
|Secondary* member<br>-0.160|-0.055|
|(0.115)|(0.150)|
|More than secondary* member<br>-0.055|0.051|
|(0.235)<br>External Support<br>.|(0.241)<br>-0.204|
|.<br>Internal Support<br>.|(0.127)<br>0.106*|
|.|(0.057)|
|N<br>1295|1295|
|R-squared<br>0.38|0.38|

Note: Significant at * 0.1, ** 0.05, ***0.01 level. Child HAZ at age 1 has been estimated by OLS. Model (1) and Model (2) includes all controls. Model (2) includes also the support received. Standard errors in parenthesis (clustering at community level). Mothers with no formal education as reference category. All variables are measured at wave1. Controls: paternal education, cognitive social capital, wealth index, child's age and age squared, birth weight, gender, ethnicity, maternal and paternal age and age squared, mother's height, maternal employment status, maternal depression, rural/urban, negative economic shocks, breastfeeding length, migration background, language, single parent, only child, household size and age composition, household member with disability).

26
# **Table** **<u>3: Benefit-Cost Ratios for Teaching at the Right Level, Fourth and Fifth Grades</u>**

||Test scores to<br>|labor earnings<br>|Test scores to ye<br>to labor<br>|ars of schooling<br>earnings<br>|
|---|---|---|---|---|
||Teacher-only<br>training<br>(1)|Teacher, head<br>teacher, and<br>circuit supervisor<br>training<br>(2)|Teacher-only training<br>(3)|Teacher, head teacher,<br>and circuit supervisor<br>training<br>(4)|
|**Cost per student, 2017 PPP $**|69|124|69|<br>124|
|**Test score effect size, s.d.’s**|||||
|Combined math/English|0.108|0.107|0.108|0.107|
|English|0.065|0.076|0.065|0.076|
|**Benefit per student, 2017 PPP $**|||||
|Combined math/English|465|461|953|<br>945|
|English|280|327|574|<br>671|
|**Benefit-cost ratio**|||||
|Combined math/English|6.8|3.7|13.9|7.6|
|English|4.1|2.6|8.4|5.4|

_Note_ : See appendix B for details on the calculations. s.d. = standard deviation. PPP = purchasing power parity.

28
- 137 -

situation, rather than narrower agronomic assessment of soil characteristics, is appropriate.

5.27 At the margin, investment allocation can shift from irrigation works to more general, and less crop-specific, rural and marketing infrastructure. The Thai Irrigation Study notes the importance of marketing as a constraint on diversification, even when the irrigation system is designed to permit growing upland crops in the dry season. The same point holds in Indonesia, Malaysia, **and** the Philippines. When there is substantial uncertainty about which crop to invest in or how flexible to make an irrigation investment, some of the investment resources can be devoted to roads, telephones, rural electrification, market centers and rural credit programs, ports, and rail facilities.

5.28 Sometimes design of programs is more critical than funding levels, although the two often go together. Schemes to improve the access of small farmers to rural credit systems are an obvious example. Subsidies on interest rates and bureaucratic restrictions on who may participate in the program can be very costly to the budget but provide few benefits in terms of increased agricultural production or improved flexibility of crop choice. By contrast, charging interest rates that cover the costs of servicing small loans as well as the opportunity costs of the money opens the potential to reach a far broader clientele. In fact, if diversification is a major governmental goal, restricting credit programs to farmers will be nearly self-defeating. The primary bottleneck, as stressed often in this report, is usually in marketing the output, not in getting farmers to grow the crop when profitable. Credit programs that make it much easier for small-scale traders to obtain working capital, much of which they will on-loan to small farmers, are likely to be a highly efficient mechanism for speeding the diversification process. Traditionally, project evaluations for a broad range of investments in rural infrastructure, credit programs, and marketing development have not included two potential benefits gained from diversification: increased flexibility of the agricultural system; and the possibility that price fluctuations would have less severe consequences for income distribution.
Policy Research Working Paper 6078

# **Abstract**

This paper analyzes the impact of large-scale expansion of biofuels on the global income distribution and poverty. A global computable general equilibrium model is used to simulate the effects of the expansion of biofuels on resource allocation, commodity prices, factor prices and household income. A second model based on worldwide household surveys uses these results to calculate the impacts on poverty and global income inequality. The study finds that the large-scale expansion of biofuels leads to an increase in production and prices of agricultural commodities. The increased prices would cause higher food prices, especially in developing countries. Moreover, wages of unskilled rural labor would also increase, which slows down the rural to urban migration in many developing countries. The study also shows that the effects on poverty vary across regions; it increases in South Asia and Sub-Saharan Africa, whereas it decreases in Latin America. At the global level, the expansion of biofuels increases poverty slightly.

This paper is a product of the Environment and Energy Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at gtimilsina@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table 8 presents the results of an analysis of the probability of receiving supplier credit in our four countries. We begin by estimating a probit regression to look at the effects of networks on access to credit. We then examine the determinants of access to supplier credit for networked SMEs and indigenous-African SMEs separately.

The role of the network in facilitating access to credit is considered in model I, where firm size (log empt) is included to control for the fact that larger firms have better access to credit. The results show that networked SMEs are much more likely to receive supplier credit than indigenous-African SMEs. As expected, the coefficient on firm-size is positive and significant, indicating that larger firms are more likely to get supplier credit than smaller firms. These findings confirm those of other researchers using the RPED data who have noted an ethnic bias in supplier credit access in SSA. (Biggs, Raturi and Srivastava 2002; Fisman 2002; Fafchamps 2004).<sup>10</sup> Adding the length of relationship with the supplier (log yrssrel) to the probit in model II, we see that the number of years a firm has known its supplier is significant in determining access to supplier credit. This validates the notion that information gained from repeated interactions plays an important role in the willingness of suppliers to extend credit. However, the fact that the network coefficients change only marginally in this regression indicates that the information provided by repeated interactions and firmsize is not enough to explain credit access. SMEs in networks are still much more likely to receive supplier credit than indigenous-African SMEs because of network externalities.<sup>11</sup> 10 Fafchamps (2004) also analyzed financial case study data from about 40 firms and suppliers, collected by RPED teams in Zimbabwe and Kenya as part of the survey work in those countries. He tries to explain why ethnic networks matter so much in getting access to supplier credit by using a "socialization" variable in his regressions, which measures the degree of networking among “insiders.” Although the sample is quite small and the data are noisy, he tentatively finds that the network effect (socialization) is important, but ethnicity on its own still remains an important part of the ethnic impact on access to credit after controlling for networking. He argues that the remainder of the effect might simply be statistical discrimination or other factors.

11 Fisman (2002) finds in some of the RPED countries that firms are about twice as likely to obtain credit from suppliers from within their own ethnic community than from outsiders.

28
39 (real income of approximately $1 a day),<sup>66</sup> the World Bank (2003a) estimates that the fraction of households living in absolute poverty in China has fallen from about 50 percent in 1981 to about 7 percent in 2002. This implies a reduction in the number of individuals in absolute poverty by about 400 million during the reform period. If the poverty line is instead drawn at about $2 a day,<sup>67</sup> the reduction is from about 88 to about 45 percent of the population – thereby illustrating the obvious fact that China is still a country with widespread poverty.

Against the background of China’s fast GDP growth, the reduction in absolute poverty is no mystery. However, international experience shows that there is not always a tight relation between aggregate economic growth and the incidence of poverty (Sen, 1985; Bardhan, 1996; Islam, 1990; Lipton and Ravallion, 1995).<sup>68</sup> In particular, the sector composition of growth is important. An illustration is that nearly half of the decline in absolute poverty in China (by the one-dollar definition) took place in the early 1980s, basically as a result of the increased productivity, and the improved terms of trade, for agriculture in connection with the shift from collective farms to family farms. Indeed, Ravallion and Chen (2006) calculate that 75-80 percent of the drop in national poverty incidence since the beginning of the reform period is a result of the reduction in poverty among the rural population.<sup>69</sup> It is, however, also well known that _relative_ poverty has increased from the mid-1980, in the sense that per capita income of the poorest sections of the population has increased less than the income of other groups. Indeed, the economic situation of the shrinking group of individuals remaining in severe poverty seems to have improved only modestly. For instance, according to Ravallion and Chen (2003), the mean growth rate for the poorest decile during the 1990s was only 3.6 percent, while per capita GDP growth was above 8 percent. As a result, while the poorest 10 percent of households earned 2 percent

> 66 US $ 1.08 a day in 1993 PPP.

> 67 US $ 2.15 a day in 1993 PPP.

> 68 Time-series regressions reported by the World Bank (2001, pp.17-18) suggest that each additional percentage point of nationwide growth in per capita GDP in China has been associated with a fall in the fraction of individuals living in absolute poverty in rural areas by 0.8 percentage points during the 1990s (one-variable regression). Cross-provincial regression indicates an even stronger relation: 1.8 percentage points. Rather similar results are recorded by Ravallion and Chen (2004).

> 69 Fan, Zhang and Zhan (2004) have hypothesized that the heavy infrastructure investment in interior regions over several decades prior to the economic reforms finally paid off in terms of higher productivity in the agricultural sector, when economic incentives in that sector were drastically improved by the shift to family farms.
10

# **Figure 3**

**Civil** Servants **per 100** in<sup>the Population</sup><sup>**in**Selected</sup><sup>**OEC1D**CountTles</sup>

<!-- Start of picture text --> 376 36<br><!-- End of picture text --> <!-- Start of picture text --> Earliest period<br>1989<br><!-- End of picture text --> <!-- Start of picture text --> !.26 1.24 1.0<br>So~~~ce S42uMI.I $:. CECPS~~~a<br><!-- End of picture text -->
<!-- Start of picture text -->
-0.3%<br>Total Import -1.2%<br>-1.9%<br>Total Export -6.9%<br>0.4%<br>Total Investment<br>1.8%<br>-0.7%<br>Gross Output -2.5%<br>-0.8%<br>Household Consumptioin -2.8%<br>-0.9%<br>Household Income<br>-3.1%<br>2.6%<br>Government Revenue<br>8.0%<br>-12.3%<br>Emission Intensity -29.2%<br>-12.5%<br>Total Emissions<br>-25.3%<br>-0.20%<br>GDP<br>-0.9%<br>-0.9%<br>Welfare<br>-3.2%<br>With Linkage No Linkage<br><!-- End of picture text -->

The carbon tax required to meet the NDC is ¥372/tCO2 when the models are not linked; it drops by 70% to ¥110/tCO2. As the rate of carbon tax decreases, its impacts on the economy also decrease no matter the direction of the impacts. The drop in the carbon tax rate can be explained by the marginal cost curve of CO2 emission reduction in China presented in Figure 5. This curve is plotted by running the CGE models at various carbon tax rates, 100, 200, 400, 500 yuan per ton of CO2 against the corresponding percentage reduction of CO2 emissions from the baseline in 2030. The shape of the marginal abatement curve indicates that the shadow price of carbon would be around ¥100/tCO2 for about 10% reduction of CO2 from the baseline. If 17
Policy ReseaRch WoRking PaPeR 4917

# **Abstract**

Much attention has focused on the impact of the current crisis on goods trade; hardly any on its impact on services trade. Using new trade data from the United States, and more aggregate data from other OECD countries, the authors show that services trade is weathering the current crisis much better than goods trade. As of February 2009, the value of US goods imports had declined year-onyear by 33 percent and the value of goods exports by 21 percent; services imports and exports each had declined by less than 7 percent. Within services, interesting patterns are emerging. Trade in goods-related transport services and crisis-related financial services has shrunk, as has expenditure on tourism abroad. But trade in a range of business, professional, and technical services is still increasing, with US exports growing even faster (at 10 percent) than US imports (at 7 percent). Developing countries like India, which are relatively specialized in business process outsourcing and information technology services, have suffered much smaller declines in total exports to the United States than countries like Brazil and China and regions like Africa, which are specialized in exports of goods, transport services, or tourism services. On the basis of new evidence from Indian services exporters, the authors suggest that services trade is buoyant relative to goods trade for two reasons: demand for a range of traded services is less cyclical, and services trade and production are less dependent on external finance. Even though few explicitly protectionist measures have so far been taken in services, the changing political climate and the widening boundaries of the state in crisis countries may introduce a national bias in firms’ procurement and location choices This paper—a product of the Trade Team, Development Research Group—is part of a larger effort in the department to improve our understanding of trade in services. Policy Research Working Papers are also posted on the Web at http://econ. worldbank.org. The authors may be contacted at iborchert@worldbank.org and amattoo@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
In this paper, I use non-TEEP divisions where SBM was introduced after 2006. SBM was scaled up to non-TEEP divisions after the completion of TEEP. However, the implementation had lags due to uneven distributions of preparedness across divisions. TEEP divisions were partnered to their neighboring nonTEEP divisions to introduce SBM.<sup>11</sup> At that time, there was not a systematic large-scale school intervention, such as TEEP, so I can assume that the major change introduced in non-TEEP schools after 2006 was SBM.

As discussed below, I use two groups of schools: those schools that received SBM grants in 2006, and those received the grant in 2009. The distinction between them is simply the lag in receiving the SBM grant, which is correlated with school-level preparedness for SBM, reflected in the school improvement plan (5 years) and the annual improvement plan.

# **4. Data**

The analysis uses the Basic Education Information System (BEIS) database for elementary schools, national achievement test score data, and SBM grant information. They are panel data, so it is possible to estimate the effect of SBM introduction on changes in test scores, controlling for changes in school conditions.

To increase homogeneity, I restrict our sample to non-city divisions among non-TEEP divisions. First, schools from non-city divisions are used in the analysis since there are not enough numbers of treatment and control schools within a city division (relatively small area). Treatment and control schools are therefore compared within each division, excluding city divisions. By doing this, I can also avoid contagion effects of early SBM practitioners to latecomers in the same city.

> 11 However, there were provinces not located close to TEEP provinces. Examples include provinces in Central Luzon, Bocol and some parts of Mindanao.

8
# **Tax Capacity and Tax Effort: Extended Cross-Country Analysis from 1994 to 2009**<sup>*1</sup>

## Tuan Minh Le<sup><u>a</u></sup>

aSenior Economist, PREM World Bank, 1818 H Street, NW Washington, DC 20433, USA Email: tle@worldbank.org Tel: +1-202-473-8485

## Blanca Moreno-Dodson<sup>b</sup>

bLead Economist, PREM-CICTI World Bank, 1818 H Street, NW Washington, DC 20433, USA Email: bmorenododson@worldbank.org Tel: +1-202-458-8047

## Nihal Bayraktar<sup>c</sup>

<u>2</u> cAssociate Professor of Economics Penn State University, 777 W. Harrisburg Pike Middletown, PA 17057, USA Email: nxb23@psu.edu Tel: +1-240-461-0978

**JEL codes:** H20, E62, O23 **Key words:** Taxable capacity, tax effort, tax policies, economic development

* We thank Pierre-Richard Agenor (Manchester University), Daniel Alvarez (World Bank), Roy Bahl (Georgia State University), Alberto Barreix (Inter-American Development Bank), Michael Engelschalk (World Bank), PierrePascal Gendron (Humber Institute of Technology and Advanced Learning), Christopher Heady (Center for Tax Policy and Administration, OECD), Eduardo Ley (World Bank), Anand Rajaram, (World Bank), James A. Brumby (World Bank), and Jacqueline Coolidge (World Bank) for helpful comments and suggestions. Errors remain our own.
representative of poverty in the poorest countries, it was computed using data from national poverty lines of 33 of the poorest countries. The international poverty line is expressed in terms of purchasing power parity (PPP) rather than traditional currency exchange rates to compare both poverty and GDP across countries (Beegle et al., 2016).<sup>30</sup> The value of the poverty line has been revised through the years and adjusted to reflect welfare conditions of low‐income countries. In 2008 this international line was estimated at $1.25 per capita per day at 2005 prices. In 2015 the line was updated to its current level at a daily value of US$ 1.90 (2011 PPP) per person (World Bank, 2016b).

# _Poverty and inequality measures_

The poverty measure is primarily based on the three standard poverty measures following Foster, Greer, and Thorbecke (1984). These measures are derived from the following general function: Here y� denotes the consumption of individual i , n the total population, p the poor population and z the poverty line. The poverty headcount ratio is obtained when the parameter α takes the value of 0, the poverty gap and severity when this parameter is set to 1 and 2 respectively. The poverty headcount ratio or poverty incidence is the most common poverty measure. It is the share of population in a given region that is poor by virtue of having a total consumption lower than the poverty line. With α�0 , the poverty headcount ratio can be expressed as the sum of poor individuals ( p ) over the total population ( n� , such that

# (10)

The poverty gap, obtained when α takes the value of 1, measures how far households or individuals are from overcoming poverty, by measuring the distance poor households are from the poverty line. It captures the difference between poor households’ current consumption and the poverty line as a proportion of the poverty line. It can be interpreted as the minimum amount of resources that would have to be transferred to the poor, under a perfect targeting scheme, to eradicate poverty (Deaton, 2006). This measure is obtained by adding up all the shortfalls of the poor relative to the poverty line and dividing the total by the population: The poverty severity index measures the level of inequality among the poor. This measure is estimated as the square of the poverty gap. It attributes a larger weight to the poorest among the poor, with the formula given by:

> 30 The poverty line was derived considering the regression‐based PPP estimate for Somalia, which corresponds to a private consumption conversion factor of US$1 PPP (2011) worth 10,731 SSh.

30