rafmacalaba/datause-encoder-data huggingface dataset:
for per capita expenditure implies that a one percent increase in agricultural yield increases per capita expenditure of all households by 0.5 percent and of poorer households by 0.52 percent. The implied causal e¤ects of agricultural productivity growth on agricultural wages, labor reallocation to own farming and poverty are thus numerically substantial.

# (6) Conclusions

This paper provides a theoretical and empirical analysis of the e¤ects of agricultural productivity on the rural labor market and poverty using an upazila (subdistrict) level panel data set from Bangladesh. The focus is on the response of agricultural wage, and labor allocation between home production and agriculture (own farming and hired labor), and their implications for poverty measured in terms of per capita household consumption. The theoretical model emphasizes the role of sensitivity of labor allocation between home production and market activities and the importance of self-employment in agriculture. The predictions from the theoretical analysis highlights the limitations of focusing on the e¤ects of agricultural productivity change on the standard labor market indicators: wage and hired labor. The positive e¤ects of a productivity increase on the wage rate may be muted if the labor supply response from home production is high enough. The e¤ect on hired labor is not unambiguous, a higher agricultural productivity may even reduce the amount of hired labor in agriculture. Even when the impacts on both wage and hired labor are positive, the main margin of adjustment for many households may be between home production and own farming. The important point here is that one can have signi...cant positive e¤ect of agricultural productivity on household consumption through a positive response of labor hours devoted to market activities as opposed to home production, even

> activities–between own farming and hired labor (see columns 2 and 3 in Table 3).

31
|Reweighting||survey and admin<br>data|Use admin data to adjust<br>sample weights so that<br>shares of high earners in the<br>surveys match the admin<br>data. Requires common<br>support.|Campos-<br>Vazquez and<br>Lustig (2019)|
|---|---|---|---|---|
|Replacing-<br>imputation|Item non-<br>response|survey data only|Use incomes of those who<br>do respond to impute<br>incomes for those who don't|Campos-<br>Vazquez and<br>Lustig (2019)|
|Replacing-<br>parametric<br>distribution<br>Replacing|Various<br>Various|survey data only<br>survey and admin<br>data|Estimate parameters for (for<br>example) a Pareto<br>distribution using the<br>bottom X% of the survey<br>distribution, impute earnings<br>for the top (1-x)%<br>Replace the top X% of the<br>survey distribution using<br>admin data and either<br>parametric or non-<br>parametric methods.<br>Endogenously determine a<br>merging income value,|Jenkins<br>(2017),<br>Hlasny and<br>Verme<br>(2018)<br>Alvaredo<br>and<br>Londono<br>Velez (2013),<br>Bach et al.<br>(2009),<br>Burkhauser<br>et al. (2018),<br>Czajka<br>(2017), Van<br>der Weide et<br>al. (2018)|
|Reweighting and<br>Replacing|Various|survey and admin<br>data|above which use admin data<br>to reweight survey data and<br>then replace survey with tax<br>data to allow representivity<br>at the very top|Blanchet et<br>al. (2022)|

# _Sparseness_

One solution to the lack of sampled households or individuals at the very top of the distribution is to oversample such households. The US Survey of Consumer Finances aims to obtain estimates of the wealth of households in the US. The survey has two independent samples – one that has a positive probability of selection for all US households and a second that includes only the rich in the sample frame, which is derived from tax filings (Kennickel, 2019). Although such a sample design does improve the sample size at the top of the distribution and thus potentially solves the sparseness problem, the response rates for the survey drawn from the rich sample frame survey are still very low, less than 10% for the wealthiest (Kennickel, 2019). The very low response rates implies that sparseness could 12
Policy Research Working Paper 5465

# **Abstract**

Low student learning is a common finding in much of the developing world. This paper uses a relatively unique dataset of five semiannual rounds of standardized test data to characterize and explain the short-term changes in student learning. The data are collected as part of the quality assurance system for a public-private partnership program that offers public subsidies conditional on minimum learning levels to low-cost private schools in Pakistan. Apart from a large positive distributional shift in learning between the first two test rounds, the learning distributions over test rounds show little progress. Schools are ejected from the program if they fail to achieve a minimum pass rate in the test in two consecutive attempts, making the test high stakes. Sharp regression discontinuity estimates show that the threat of program exit on schools that barely failed the test for the first time induces large learning gains. The large change in learning between the first two test rounds is likely attributable to this accountability pressure given that a large share of new program entrants failed in the first test round. Schools also qualify for substantial annual teacher bonuses if they achieve a minimum score in a composite measure of student test participation and mean test score. Sharp regression discontinuity estimates do not show that the prospect of future teacher bonus rewards induces learning gains for schools that barely did not qualify for the bonus.

This paper—a product of the Education Unit, South Asia Region; and the Education Unit, Human Development Network—is part of a larger effort of the departments to rigorously evaluate innovative government programs supported by World Bank lending operations. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The authors may be contacted at fbarrera@worldbank.org and draju2@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
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goals of the system if the online vote empowered traditionally advantaged demographic groups at the expense of the disadvantaged groups the PB process aimed to help.<sup>1</sup> Several studies have examined the Rio Grande do Sul PB vote, with studies covering the 2012 (Spada _et al._ , 2016) and 2014 (Halkin, Sjoberg and Mellon, 2017; Mellon, Peixoto and Sjoberg, 2017; Peixoto, Sjoberg and Mellon, 2017) votes. These studies allow us to track whether inequalities in Internet access and participation affect demands made through the PB process, and the outcomes of the PB process – that is, which investment projects get finally selected for implementation.

# **Digital divide**

Using the framework above, we first examine whether there is a digital divide in Brazil. Internet access in Brazil was 54% in 2015 (Statista, 2019), with notable demographic gaps in access. Using the 2005 census, Olinto (2009) found substantial education (more educated have greater access), age (younger people have greater access) and racial (white people have much greater access) gaps in Internet access but modest gender (men have slightly greater access) divides.

# **Digital divide → participant profile**

Both Spada et al. (2016) and Mellon, Peixoto and Sjoberg (2017) concluded that online voting’s availability substantially changed the electorate rather than merely providing a convenient option for voters who would have participated anyway. This means the online electorate’s composition affects the overall composition of participants.

The Internet access divide among Brazilians creates a potential for inequality, but this does not entail that participatory inequality will occur along the same lines. In this case, however, Mellon, Peixoto and Sjoberg (2017) show the differences between the online and offline voters in the 2014 PB vote (using online and offline exit polls) closely track the digital divide. They found online voters skewed younger than both offline voters and the general population, and that online voters were more educated and male than either the general population or offline voters (see Figure 2 using the same data). We

> 1 The fieldwork for this study was conducted with the assistance of Aptivate.

12
||**_Iraq_**|**_Philippines_**|**_Uga_**|**_nda_**|
|---|---|---|---|---|
|Enumeration Year|2019|2010|2007|2010|
|Sample Size|5,409|3,263|1404|2,498|
|% Ever Displaced in Sample|81.8%|45.2%|92%|80.5%|
|Relevant Control Groups|Mosul residents|Central Mindanao Population|Acholi Population|Acholi Population|
|Geographic Scope|Nineveh/Mosul|Central Mindanao|Acholi|Acholi|
|V-Dem Categorization|Electoral autocracy|Electoral democracy|Electoral autocracy|Electoral autocracy|

Table 1: Descriptive Characteristics of Contexts in Year of Survey In northern Iraq, forced displacement at the time of the survey in this population was the result of the Islamic State (IS) targeting of civilians, particularly minorities, for forced conversion, mass killing, abduction, and sexual slavery. Between 2014 and 2017, jihadists from IS seized vast areas of northern Iraq before, ultimately, being defeated by an Iraqi-led coalition after a brutal civil war. We focus especially on IDPs area around Mosul in Iraq, where most of the IDPs stayed within city limits throughout the conflict (Revkin 2021) or went to displacement camps outside city limits. Despite the territorial defeat of IS, sectarian divisions and mistrust in the Iraqi Government persisted and undermined return and resettlement efforts (Al-Saiedi, Coughlin, Irwani, Khalil, Pham & Vinck 2020). As such, at the time our survey, a large proportion of IDPs remained displaced both in camps and within Mosul.

In the Philippines, we focus on Central Mindanao. Muslim-dominated conflict-affected areas in the Mindanao have the highest poverty levels and the lowest levels of human development in the Philippines (Adriano & Parks 2013). A longstanding conflict between the Government of the Philippines and the Moro Islamic Liberation Front over demands of self-determination, and complicated by political power struggles at the local level, often in the form of clan conflicts known as _rido_ , created cyclical IDP flows. In the immediate period before our surveys, renewed military operations in 2003 displaced at least 411,000 individuals and then, in 2008, the collapse of GPH-MILF negotiations over the Memorandum of Agreement on Ancestral Domain (MoA-AD) once again displaced hundreds of thousands individuals (Vinck & Bell 2011). Our surveys were conducted in the immediate aftermath of this period.

In northern Uganda, forced displacement at the time of the survey was driven by the conflict between the government and the Lord’s Resistance Army (LRA), a violent rebel group known for its atrocities and abductions. The conflict with the LRA began in 1987 as a rebellion against President Yoweri Museveni’s National Resistance Movement (NRM) and its efforts to consolidate control over northern parts of the country. Rejected by the population and local leaders, the LRA increasingly turned violent against the civilians (Pham, Vinck & Stover 2008). Predatory behaviors by government forces and an encampment policy contributed to mass displacement (JIPS 2012).

4
(vii)

# ApRendix Tables

|Table|.~~~~~~~~~<br>Description|_<br>Page|
|---|---|---|
|Al-I|Air and Vessel Freight Rates for Low Income East and Southern Africa's Major Export<br>Products to the United States|88|
|A1-2|Air and Vessel Freight Rates for Low Income West Africa's Major Export Products to the<br>United States|90|
|Al-3|Air and Vessel Freight Rates for Middle Income East and Southern Africa's Major Export<br>Products to the United States|91|
|Al-4|Air and Vessel Freight Rates for Middle Income West Africa's Major Export Products to<br>the United States|92|
|A1-5|Air and Vessel Freight Rates for African Oil Exporting Countries' Major Export Products<br>to the United States|93|
|A1-6|Air and Vessel Freight Rates for the Republic of South Africa's Major Export Products|94|
|A2-1<br>to<br>A2-40|OECD Uruguay Round Tariff Concessions for Products of Export Interest to Sub-Saharan<br>African Countries|97|
SOUTH SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Current transport conditions are a major impediment to South Sudan’s economic and social 70 60 activity. On average, around 60 50 percent of South Sudanese firms 40 30 rated transport as a major-to-severe 20 obstacle to doing business. 10 Transport concerns are especially 0 acute in Malakal, where over 60 percent of firms reported that transport is a major obstacle. In contrast, a smaller percentage of firms (15–21 percent) in Sudan (excluding Nyala in the Darfur region) reported that transport impeded their ability to conduct business (figure 5).

**Figure 5. Transport is a major obstacle in parts of South Sudan**

<!-- Start of picture text --> 70<br>Sudan South Sudan<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>firms)<br>Transport is a major constraint (% Red Sea Khartoum River Nile Gezira North Kordofan Nyala Juba Malakal<br><!-- End of picture text -->

**Table 2. Benchmarking Sudan and South Sudan’s national network with African aggregates for regional corridors**

Fragmented and underdeveloped corridors, high costs, and complicated trade logistics contribute to the difficulties associated with transport.

|Corridor|Road in good<br>condition (%)|Implicit<br>velocity (km<br>per hour)|Freight tariff<br>(US cents per<br>tonne-km)|
|---|---|---|---|
|Western|72|6|8|
|Central|49|6.1|13|
|Eastern|82|8.1|7|
|Southern|100|11.6|5|
|Sudan|26|8.5–13.5|8–10|
|South Sudan|0|6.4|20|

# **Corridors**

The absence of a regional transport backbone connecting South Sudan and its neighbors hinders growth and regional integration. On the border with Uganda and Kenya, the regional road network is confined to one main artery. The road connecting Juba with East Africa is the most travelled and the only road network that is generally in good or fair condition. Connectivity to Sudan is underdeveloped; the relevant regional corridor is in bad condition and records very low traffic volumes. During the rainy season (between April/May and October/November), a majority of the roads in South Sudan are impenetrable.

_Source:_ Teravaninthorn and Raballand 2009; Nathan 2010; UNLJC and FAO 2005; Yoshino 2010. _Note:_ Estimates for Sudan based on routes: Khartoum to Kosti and Khartoum to Port Sudan. For South Sudan, costs are based on varying estimates for travel between Juba to Nimule. Implicit velocity is the total distance divided by the total time taken to make the trip, including time spent stationary at ports, border crossings, and other stops.

As a result of very poor road quality, South Sudan’s corridors record among the slowest-moving traffic rates in the world. Freight movement in Africa as a whole is no faster than the pace of a horse and buggy, and performance in South Sudan is even grimmer. The average pace of 6.4 kilometers an hour (kmph) is comparable to what is observed in landlocked central Africa (table 2). In part, these slow travel times are due to the absence of well-developed road transport infrastructure.

10
WPS4096

# **Rural Poor in Rich Rural Areas:**

# **Poverty in Rural Argentina**

By

**Dorte Verner**<sup>1</sup>

World Bank

## World Bank Policy Research Working Paper 4096, December 2006

_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._ 1 I am very grateful to CIET and PROINDER for assistance on data and information on rural Argentina; Carter Brandon, McDonald Benjamin, Robert Schneider, José María Caballero, Estanislao Gacitua-Mario, Elsie Garfield, and Jesko Hentschel for invaluable suggestions and comments; Sergio España and Luis Orlando Perez for discussions and information on education and health; Michael Justesen and Marisa Miodosky for excellent research assistance; and other team members for suggestions. Additionally, I would like to thank participants in the Rural Strategy Workshop held in Buenos Aires in December 2004 for comments on a previous draft of this paper.
Section I uses a conceptual framework and a database created by the Bank's Operations Evaluation Department (OED) to describe the characteristics of financial sector loans as they have evolved over time. Although FSALs have always tended to cover more reform areas than other Bank financial sector adjustments, their size (as measured by loan funds per resident, or as a percentage of total funds lent to that country by the Bank) has increased substantially relative to their levels in the 1980s. That increase coincided with both greater financial insolvency problems in many countries<sup>3</sup> and a shift in emphasis in the Bank's loan conditionalities. Whereas the FSALs of the 1980s focused primarily on incentive distortions that could be righted through legislative actions ( g., non market-based interest rates), the emphases in the 90s -- bank privatization and recapitalization required something more. Ownership changed hands and bank capital was replenished but, unless bankers' underlying incentives changed (either through improvements in supervision or other regulation), these interventions often were ineffective in spurring financial depth in comparison with the earlier interventions.

Sections II and III, which evaluate the overall success of recent FSALs relative to both previous ones and to other types of financial sector interventions, attempt to control for factors other than the nature of the reform so as to better isolate the independent effects of changed FSAL emphasis. Section Im, for example, uses regression analysis to hold constant factors such as financial sector and macroeconomic conditions at the time of the loan (including deposit bank credit to the private sector, inflation, and openness to 3 Caprio and Klingebiel (1996).

2
# **Table 14: Welfare Equations (OLS): FSU Countries** Dependent Variable = In (household expenditure per equivalent **adult)**

||_Esto_<br>|_nia_<br>|_Kyrgyz R_|_epublic_<br>|_Rus_|_sia_|
|---|---|---|---|---|---|---|
||_Parameter_<br>_Estimate_|_Standard_<br>_Error_|_Parameter_<br>_Estimate_|_Standard_<br>_Error_|_Parameter_<br>_Estimate_|_Standard_<br>_Error_|
|Intercept|7.301|0.089|9.627*|0.191|1.068*|0.120|
|Number of children|-0.080*|0.012|-0.060*|0.013|-0.123*|0.015|
|Number of male adults|-0.057*|0.021|-0.029|0.023|-0.030|0.022|
|Number of female adults|0.011|0.017|0.025|-0.022|0.019|0.020|
|Number of elderly|-0.047|0.022|-0.061|0.041|-0.021|0.025|
|Education of head: primary|-0.076*|0.023|0.045|0.060|-0.084*|0.036|
|Education of head: vocational/technical|...|...|0.102*|0.059|0.034|0.030|
|Education of head: university<br>|0.214*|0.028|0.158*|0.060|0.147*|0.037|
|Age of head<br>|-0.003|0.004|0.017*|0.009|0.010*|0.004|
|Age of head squared|0.000|0.000|-0.000*|0.000|-0.000|0.000|
|Female head|-0.103*|0.028|-0.191*|0.056|-0.141*|0.032|
|Household owns enterprise|0.174*|0.022|0.160*|0.042|0.235*|0.041|
|Household owns land|0.160*|0.021|0.249*|0.040|-0.043|0.029|
|Household is renter|-0.122*|0.019|0.064|0.070|0.024|0.026|
|Share of wages in household income|0.329*|0.032|0.205*|0.066|0.297*|0.040|
|Number of unemployed in household|-0.189*|0.026|...|...|-0.240*|0.038|
|Head is unemployed<br>.|...|...|-0.148*|0.071|...||
|Head is inactive|-0.119*|0.026|-0.056|0.061|-0.149*|0.037|
|Location: non-capital city<br>|-0.196*|0.022|-0.338*|0.059|-0.365*|0.037|
|Location: village|-0.311*|0.028|-0.504*|0.059|-0.428*|0.043|
|Number of observations<br>|2817||1929||5147||
|R<sup>2</sup>(adjusted)<br>|0.256||0.103||0.115||
|F-statistic|57.95*||13.26*||38.08*||

_Note:_ * Indicates that estimated parameters are significantly different from zero at the 90% confidence level.

71
most recent DHS survey for each country. These numbers are, in general, very similar to the mortality numbers published by the WHO and the World Bank with the notable exception of Niger and Liberia, where the mortality estimates from the DHS appear significantly lower than the official WHO figures.<sup>17</sup> In column 3 of Table 6, we show the calculated child mortality rates for counterfactual scenario 1 (basic improved water and sanitation), while we show imputed child mortality rates under counterfactual scenario 2 (privately piped water and flush toilets) in column 4 of the table.

The average expected decrease in child mortality obtained from upgrading households to at least basic improved water and sanitation (counterfactual scenario 1) is 11 deaths per 1000 in Sub-Saharan African countries, and approximately 4 deaths per 1000 children born in the other developing countries in our sample (Table 6, column 5). Under scenario 2 – upgrading all households to private water connections and flush toilets – the effects are substantially larger. On average, we get a reduction in under-5 mortality of 31 deaths per 1000 in SubSaharan Africa, and a reduction of 14 deaths per 1000 children born for other countries in our sample (Table 6, column 6).

Table 6 also highlights the heterogeneity in the mortality effect of water and sanitation upgrades across regions and countries. The potential of reducing child mortality through improved water and sanitation technologies does not only depend on the existing stock of water and sanitation infrastructure, but also on the child mortality levels in the country of interest. As shown in columns (5) and (6) of Table 6, the highest absolute decreases in mortality are predicted for Nigeria and Chad, with predicted reduction in under-5 mortality of 46 and 50 deaths per 1000, respectively. These numbers are the result of both low levels of existing infrastructure and high current mortality rates. In comparison, the predicted improvements appear small for countries like Peru or the Philippines, where baseline child mortality levels are relatively low, and water and sanitation coverage rates are already relatively high.

> 17 The under-5 mortality estimates reported by the WHO in 2005 are 262 for Niger and 235 for Liberia. But these estimates appear to be based on earlier survey data. See <u>http://www.who.int/healthinfo/statistics/mortchildmortality/en/index.html</u> for details.

19