rafmacalaba/datause-encoder-data huggingface dataset:
average of per capita public education spending for the poorest quintile corresponds to 4.7 percent of GDP per capita, against 5.3 percent in the United Kingdom, and 3.1 percent in the United States.<sup>5</sup>

**Figure 5: Universal vs. contributory health spending.**

<!-- Start of picture text --> Argentina Colombia<br>1 2 3 4 5 1 2 3 4 5<br>Income Quintile Income Quintile<br>Mexico Peru<br>1 2 3 4 5 1 2 3 4 5<br>Income Quintile Income Quintile<br>Universal Health Spending Contributory Health Spending<br>.06 .06<br>.04 .04<br>.02 .02<br>% GDP per capita % GDP per capita<br>0 0<br>.06 .06<br>.04 .04<br>.02 .02<br>% GDP per capita % GDP per capita<br>0 0<br><!-- End of picture text --> In terms of progressiveness of spending, however, large variations can be observed: Honduras, for instance, spends in the education of the poorest quintile only 46 percent of what it spends for the richest quintile, while the percentage raises to 168 percent in Colombia. Besides Honduras, however, all countries show a higher level of progressiveness than the Unites States, which spends on the poorest income quintile only 59 percent than what it spends on the richest one, but no country shows higher progressiveness than the United Kingdom (228 percent).

The composition of public education spending also varies a lot by income quintile. Data from CEPAL (2006) show that in Latin America, more than 50 percent of primary education spending is spent on the poorest two income quintiles, while more than 70 percent of public spending on higher education goes to the two richest income quintiles. Although we do not have data on public education spending for the United Kingdom or the United States at this level of disaggregation, it is likely that the composition of education spending is more similar across income quintiles.

# **Direct transfers**

> 5 As in Latin America there are more children in the poorest quintile than in the United States or in the United Kingdom, public education expenditure _per child_ would however show slightly lower figures.

12
# **2.4.1 West Manggarai**

Data on West Manggarai spans from January 2019 to December 2022. It was extracted from administrative data from more than 400 restaurants with a local tax registration number (NPWPD), which reduced to a balanced panel dataset comprising 399 restaurants (NPWPDs) for 48-month period post-cleaning. In the sample, a total of 30 restaurants were given POS units with each of them receiving only one type of POS (RPOS, TMD, or WS)—14 received RPOS, 9 received TMD, and 7 received WS. All of them obtained the units at separate times throughout the observed timeframe. The control group in each estimation is identical and comprises of 369 restaurants (NPWPDs) that did not receive any POS devices.

The data set also includes 225 hotels with 6 of them being facilitated with WS, thus leaving the remaining 219 in the control group. Unlike restaurants, hotels in West Manggarai were not subject to distribution of RPOS and TMD (Table 3). As the enactment of POS distribution program in West Manggarai came in waves, our data naturally carries varying treatment times, which we accommodate in the form of panel data analysis.

# **2.4.2 Gorontalo**

Compared to West Manggarai, Gorontalo’s data is more comprehensive. It spans longer from January 2018 to December 2022 and covers more businesses—up to 1,081 restaurants in total. Among them, 92 received RPOS, 14 received RPOS with added connection to Grab’s system (RPOS-Grab), and 7 received TMD. With 5 restaurants using 2 types of POS, the treatment group reduced to 107 restaurants and left the remaining 974 as controls. In contrast to West Manggarai, we found no information regarding the WS assignment to restaurants in Gorontalo.

There are 400 hotels in the districts included in our Gorontalo observation. Two of them were given access to the PAC Hotel application and 3 were equipped with TMD devices. This boils down to 5 being in the treatment group and 396 in the control one. Intervention time in the district also varies (Table 3).

10
used food access metrics are capturing different aspects of poor food access (e.g., Maxwell et al. 2014). However, these findings build upon previous work and demonstrate that some of these coping strategies do not have the expected relationship with other key welfare indicators, suggesting that it might be unclear exactly what some of these coping strategies are capturing. Therefore, future work needs to be done to unpack the exact causes of these discrepancies and how generalizable the results are to additional contexts.

The rest of the paper is structured as follows. Section 2 presents a brief background on food access indicators and the FIES; Section 3 presents a simple model of consumption to illustrate the relationship between monetary poverty and poor food access; Section 4 describes the data; Section 5 describes the empirical strategy; Section 6 reports the empirical results; and Section 7 concludes.

# **Section 2. Background on Food Access Measures and the Food Insecurity Experience Scale**

Food access is one of the four pillars defining food security agreed upon by stakeholders at the 1996 World Food Summit, with the original formulation ensuring that all individuals have access to ”sufficient, safe and nutritious food.” Given the many different components of food access alone, including consumption of macro-nutrients, food safety, and consumption of micro-nutrients, it is difficult for any single measure to capture all information relevant to policy makers (e.g., Barrett 2010).

Moreover, many dimensions of food access are difficult to measure precisely. Although there are a number of ways to try and estimate the quantity of calories consumed and the nutritional content of consumption, the methods thought to produce the most precise estimates involve individual-level surveys that are complex, expensive, difficult to analyze, and nearly impossible to perform on a large scale (e.g., Gibson 2005; Fiedler et al. 2013; etc.).<sup>7</sup> In the absence of these difficult-to-collect and expensive data, researchers often turn to household consumption and expenditure surveys (HCES’s) to measure the quantity and quality of foods consumed (e.g., Wiesmann et al. 2009; etc.). However, estimates from HCES’s are subject to a substantial amount of measurement

> 7For example, one method is observed-weighed food record data. See Gibson (2005) for details.

6
Policy Research Working Paper 8759

# **Abstract**

- This paper assesses how household context and character istics shape the welfare trajectory and more specifically the accumulation of productive assets among beneficiaries of the Vision 2020 Umurenge Programme in Rwanda, the government’s flagship social assistance program. The analysis is based on a unique data set combining panel household survey data with in-depth qualitative interviews of a subsample of male and female beneficiaries from the survey data collected between 2009 and 2015. By combining quantitative and qualitative information, the paper draws a more nuanced picture of how household characteristics—structural and temporal—contextualize opportunities for poor men and women and their households and shape how well

they can leverage access to the Vision 2020 Umurenge Programme to accumulate productive assets. The mixed method analysis reveals that household composition, gender power dynamics, disability, care responsibilities, marital arrangements, intrahousehold communication, and access to other social programs and institutions play a crucial role in access to the Vision 2020 Umurenge Programme and related asset accumulation. The findings suggest that households would benefit from a broader definition of the eligibility criteria and the availability of flexible and complementary programming, to reap the benefits of the income transfer received from the program.

This paper is a product of the Social Protection and Jobs Unit, Africa Region. 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://www.worldbank.org/research. The authors may be contacted at lrawlings@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
**Indicator 4 (Strengthen capacity by coordinated support):** Afghanistan (-1.9) and Kosovo (- 2.8)

# **Indicator 6 (Strengthen capacity by avoiding parallel project implementation units):** Burundi (-1.9), Haiti (-2.2) and Sudan (-2.9) **Indicator 9 (Use of common arrangements or procedures):** Central African Republic (-1.9).

# **(3) FCS Partner Country Progress between 2005 and 2010**

Poor FCS aid effectiveness performance in 2010 identified in the previous analyses may not be surprising due to well recognized challenges. However, the question on whether FCS performance improved between 2005 and 2010 is unanswered. There are two hypotheses. One is that as FCS started from a weak base, progress would be better than for non-FCS. The other is that the challenges faced by FCS would prevent these countries from improving their performance.

The same methodology as used previously is employed to compare progress between 2005 and 2010. Changes in indicator values are standardized to enable comparison across indicators and countries. Thirty-two countries participated in the PD survey in both 2006 and 2011. Among these, three countries (Afghanistan, Burundi and Democratic Republic of Congo) are FCS and one country (Cambodia) graduated from FCS between the periods<sup>12</sup> . Figure 4 shows performance in 2010 (the horizontal axis) and progress between 2005 and 2010 (the vertical axis)<sup>13</sup> .

**Figure 4: 2010 Performance and Progress between 2005 and 2010**

<!-- Start of picture text --> 20<br>3rd Quadrant: Progress since<br>1st Quadrant: Both2010<br>2005 is better than average, but<br>performance in 2010 is worse Dominican Rep performance and progress<br>since 2005 are better than<br>than average 15<br>average<br>10<br>: FCSs<br>Peru<br>: Non-FCSs<br>Mongolia 5 Rwanda<br>Cambodia<br>Burkina Faso<br>Albania Brundi Benin<br>0<br>-20 -15 -10 -5 0 5 10 15 20<br>DRC<br>Bolivia -5 Bangladesh<br>Afghanistan<br>Cape Verde<br>-10<br>4th Quadrant: Both2010 2nd Quadnrat: 2010 performance<br>performance and progress -15 is better than average, but the<br>since 2005 are worse than progress is worse than average<br>better than average<br>-20<br><!-- End of picture text -->

> 12 Based on FY05 Low-Income Countries under Stress (LICUS) 13 Appendix 3 includes data used for Figure 4.

12
<!-- Start of picture text --> Urban<br>(a) 1972-1984 (N=6,260) (b) 1985–1994 (N=9,247) (c) 1995–2006 (N=7,505)<br>Prob. boy (%) Prob. boy (%) Prob. boy (%)<br>0 5 10 15 20 0 5 10 15 20 0 5 10 15 20<br>Quarter Quarter Quarter<br>Prob. no birth yet Prob. no birth yet Prob. no birth yet<br>0 5 10 15 20 0 5 10 15 20 0 5 10 15 20<br>Quarter Quarter Quarter<br>Rural<br>(d) 1972-1984 (N=2,778) (e) 1985–1994 (N=5,872) (f) 1995–2006 (N=5,592)<br>Prob. boy (%) Prob. boy (%) Prob. boy (%)<br>0 5 10 15 20 0 5 10 15 20 0 5 10 15 20<br>Quarter Quarter Quarter<br>Prob. no birth yet Prob. no birth yet Prob. no birth yet<br>0 5 10 15 20 0 5 10 15 20 0 5 10 15 20<br>Quarter Quarter Quarter<br>75 75 75<br>70 70 70<br>65 65 65<br>60 60 60<br>55 55 55<br>50 50 50<br>45 45 45<br>40 40 40<br>1 1 1<br>.8 .8 .8<br>.6 .6 .6<br>.4 .4 .4<br>.2 .2 .2<br>0 0 0<br>75 75 75<br>70 70 70<br>65 65 65<br>60 60 60<br>55 55 55<br>50 50 50<br>45 45 45<br>40 40 40<br>1 1 1<br>.8 .8 .8<br>.6 .6 .6<br>.4 .4 .4<br>.2 .2 .2<br>0 0 0<br><!-- End of picture text --> Figure 6: Predicted probability of having a boy and probability of no birth yet from time of marriage for women with 8 or more years of education by quarter. Predictions based on age 20 at marriage. Left column shows results prior to sex selection available, middle column before sex selection illegal and right column after sex selection illegal. N indicates the number of women in the relevant group in the underlying samples.

than 51.2 percent.<sup>25</sup> Furthermore, for quarters with more substantial deviations from the natural

> 25 The urban no education group for the 1972-1984 period show two quarters where the predicted percentage boys is just statistically significantly higher than 51.2 percent, but this is likely due to recall error not perfectly caught by the method above and the periods around those two quarters are below the natural percentage boys.

28
# **Using Experimental Evidence to Inform Firm Support Programs in Developing Countries**

Arti Grover and Michele Imbruno<sup>1</sup> JEL classification: D22, L25, L26 Keywords: firm performance, constraints, support program, randomized control trial, entrepreneurship

> 1 World Bank, 1818 H Street, Washington DC, USA. Arti Grover (Finance, Competitiveness and Innovation Global Practice, World Bank), Email: <u>agrover1@worldbank.org; Michele Imbruno (Sapienza University of Rome, Italy, and</u> Nottingham Centre for Research on Globalisation and Economic Policy, United Kingdom), Email:michele.imbruno@uniroma1.it. We would like to thank Miriam Bruhn, Francisco Campos, Justin Hill, Leonardo Iacovone, David McKenzie, Denis Medvedev, Bilal Zia for sharing their knowledge and experience on firm support programs across developing countries. We would also like express special thanks to Gabi Afram, Randa Akeel, Efrem Chilima, Niraj Verma and Greg Toulmin for their guidance on the intervention in Malawi. All remaining errors are our own. 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.
O.A.M.D.G.

|**Dependent Variable**|**Coefficient**|**z-statistic**|
|---|---|---|
|**Migration out of the SISBEN sample**<br>**_municipality level variables_**<br>|||
|no of guerrilleros<br>-|0.00002<br>|*<br>1.49<br>-|
|no of guerrilleros2|2.24E-11|0.30|
|landmine incidents<br>-|0.562<br>|0.45<br>-|
|landmine incidents2|0.831|0.95|
|kidnappings|0.107|1.05|
|kidnappings2<br>-|0.009<br>|*<br>1.53<br>-|
|homicide rate|0.055|*<br>1.48|
|homicide rate2<br>-|0.001<br>|1.28<br>-|
|capitalisation/head|-2.58E-06|1.34<br>-|
|capitalisation/head2|4.31E-12|*<br>1.73|
|constant<br>-|0.581<br>|***<br>3.29<br>-|
|F-test violence var (p value)|0.01||
|Pseudo R2|0.17||
|Log pseudo likelihood<br>-|6,208.72<br>||
|Number of obs|11,380||

Results Table 1: Estimation of the determinants of drop-out out of the SISBEN sample<sup>13</sup> _Note: The regression presents the Probit coefficients of available past violence, and welfare indicators on the probability to leave the SISBEN sample. Stars mark significance at the 10% (*), 5% (**) and 1% or lower (***) levels. Regressions are with robust standard errors, adjusted for clustering on municipality. The F-test examines joint significance of the violence variables._ A higher number of guerrilleros appears to reduce emigration, while kidnappings and homicides encourage it until a certain level. People from very poor, as well as from very wealthy municipalities, as measured by the bank assets per head, seem to emigrate more. When interpreting the results, we need to remember that various motives can prompt emigration. Displacement through violence is only one of them; improvements in wealth, ‘upgrading’ from one neighborhood to the next is also common. To the extent that our limited data reflects both motivations, we observe out-migration rising with kidnappings and the homicide rate (the turning points of the parables are at the very upper end of our sample) on the one hand, and declining with a higher number of guerrilleros and

> 13 The drop in observations is due to an imperfect merge with the _Familias_ dataset. We merge with the National ID number of the household head, which is not reported for all households.
expenditures. In every third year, re-capitalization payments of 20-30 million pesos would more than double the typical 1996 deficit. Even under the assumption that the province manages the residual entity as poorly as possible, the savings from privatization (257 million pesos) would amount to a third of yearly government expenditures and could fulalice 1996 deficits for over twelve years. 9 In short, the potential fiscal savings associated with privatization are large, eveni with counter-factual re-capitalization estimates designed to be conservative. Had we used a discount factor of 5%, for example, the discounted re-capitalization stream would hiave been 4.2 _billion_ pesos -- six years of provincial expenditures, enough to finance the equivalent of a 20 million peso deficit (in 1996) for some two hundred years.

One might argue that public provincial bank quality has improved, or that the remaining banks are of higher quality than those that have already privatized, and thus the 9% loss rate in NW/TL overstates future losses. If the remaining public provincial banks are better ones (and thius have lower loss rates), their quality should be reflected in a smaller residual entity.20 Their discounted re-capitalization payments may be smaller than for the early privatizers but, because these payments are offset by the liabilities of a much smaller residual entity, privatization wi ll also make sense for them -- unless, of course, these banks are of such high quality tilat thley experience no losses in the future. The data on net worth and NW/TL, and that presented in Section IV on operating income and costs, however, make that proposition especially dubious.<sup>21</sup>

> 19 The comparisons with 1996 deficits are for illustrative purposes only. Deficits were, however, relatively small in that year. The average 1995 deficit for privatizers was 140 million pesos; in 1994 it was 84 million. It is unclear to us whether the 1996 deficits or those from prior years are better estimates of future deficits.

- 20 See regression in Section IV on the relationship between public provincial bank portfolio quality and the size of the residual entity.

> 21 Leaving Buenos Aires aside, those public provincial banks that have not begun the privatization process did have slightly better pre-privatization performance than those that eventually privatized. However, the disparities are not particularly striking. Non-privatizers had an average nominal net worth of 66 million pesos, and experienced declines in nominal net worth of 853,000 pesos per year and in NW/TL of 8.2% per year. Their nominal assets grew at a rate of 31.8%. A quick comparison with the figures in Table 11 indicates that non-privatizers are much more similar to public provincial banks that eventually privatized than they are to the private banks.

8
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH

**Table 2.1 Infrastructure endowments by world region**

|Sector and measure|Sub-<br>Saharan<br>Africa|South Asia|East Asia<br>and Pacific|Europe and<br>Central Asia|Latin<br>America and<br>Caribbean|Middle East<br>and North<br>Africa|
|---|---|---|---|---|---|---|
|_Transport_|||||||
|Density of paved road network<br>(km/1,000 km<br>2, 2001)|49|149|59|335|418|482|
|Density of paved road network<br>(km/1,000 arable km<br>2, 2001)|1,087|675|588|1,208|4,826|6,890|
|Density of total road network<br>(km/1,000 km<br>2, 2001)|152|306|237|576|740|599|
|Density of total road network<br>(km/1,000 arable km<br>2, 2001)|2,558|1,400|5,385|2,160|8,850|30,319|
|_Information and communication technology_|||||||
|Density of fixed-line telephones<br>(subscribers per 1,000 people, 2004)|33|39|90|261|197|100|
|Density of mobile telephones<br>(subscribers per 1,000 people, 2004)|101|86|208|489|350|224|
|Density of Internet connections<br>(subscribers per 100 people, 2004)|2.8|1.7|6.6|16.4|14.1|10.1|
|_Energy_|||||||
|Electrical generating capacity (MW per 1<br>million people, 2003)|70|154|231|970|464|496|
|Access to electricity<br>(% of households with access, 2004)|18|44|57|—|79|88|
|_Water and sanitation_|||||||
|Water<br>(% of households with access, 2002)|63|72|75|87|90|85|
|Sanitation<br>(% of households with access, 2002)|35|48|60|78|77|77|

_Sources_ : For transport, Easterly & Serven (2003); for ICT, International Telecommunications Union; for energy, Energy Information Agency, U.S. Department of Energy); for water and sanitation, World Development Indicators.

Africa’s deficit remains even when the countries of the region are compared with others in the same income bracket (table 2.2). It is often assumed that Africa’s infrastructure deficit is largely a reflection of its relatively low income levels. But the comparison with other developing countries in the same income bracket shows that income does not tell the whole story. Africa’s low-income countries (LICs) lag substantially behind those in other regions, while the same is true for lower-middle-income countries (LMCs) and upper-middle-income countries (UMC). The divergence is particularly striking for power and paved roads. Electrical generating capacity and access to electricity in Africa are less than a third of the levels found in other UMCs around the world.

6
_-4-_ employment (mostly In township or village enterprises) or non-farm business activitles.

X1ljlang county Is located In Jiangxi province of Southern China where double-cropping of rice Is predominant. The county Is a major rice producer, witi typical farm sizes double that of Jiangsu province. Farm fragmentatlon In the study areas In Jilin and Jiangsu province Is less severe than the average for China (which Is 9 tracts per farm). However, In Xlajiang county fragmentatlor, Is a serlous problem, (an average of 16 plots per household) and In Jiangsu tracts are extremely small. Table 1 summarizes some key characteristics of the samples from the study areas.

The fow counties covered by the study were purposefully selected from the national sample of 846 countles which are surveyed annually by the State Statistical Bureau. Each of the counties were deemed typical of the agro-clinatic reglon In which It Is located. The samples consisted of severa randomly selected townships within each county, and within each township a number of ranooly selected villages, with a total sample size of about 200 farmers per county. Both Gongzhuling and Xlajlang are surplus grain producing counties, while Tal and Jurong, where population density Is very high, have less grain surplus as farm sizes area very small.

Farmers were asked In the course of Interviews to provide information on their farm operations, assets (at present and In the past), credit markot transactions, and perceptions regarding tenure security.