rafmacalaba/data-use-mentions-extended huggingface dataset:
from the Centre for Monitoring Indian Economy (2014-2016).

This research makes several significant contributions to understanding rural economic resilience in the context of weather variability. As climate change intensifies the frequency and severity of weather shocks, understanding their comprehensive economic impacts becomes crucial for policy design. The study’s findings on how weather variations affect both farm and non-farm sectors provide valuable insights for developing integrated rural development policies, particularly important for regions like Rajasthan where agricultural vulnerability to climate variability is high (Auffhammer and Carleton, 2018). By quantifying the relationships between agricultural conditions and non-farm enterprise performance, it provides evidence-based guidance for designing interventions that can enhance rural economic resilience, addressing a critical gap identified by Pingali et al. (2019) regarding the need for policies that support both agricultural adaptation and rural economic diversification.

New insights are added locally: The current study supports the existing literature on the interdependence of the rural economy with evidence on the specific channels for and magnitude of agricultural-nonagricultural linkages affected by weather shocks. Most notably, past studies ascribed this commonality to weather variables in general. The current study moves forward by examining potentially-weather dependent relationships, such as those related to agricultural economics and climate adaptation strategies. In addition, significant methodological contributions are made through the linking of multiple data sources to produce a nuanced understanding of rural economic development under climate conditions. These insights hold potential for informing climate adaptation strategies in rural economies, filling a “critical gap” identified by Barrett et al. (2017) in the need for combination strategies relying on evidence that relate solely to agricultural activities but also to climate developments in sectors engaged in non-grain-related activities. The ultimate translated goal supports the need to inform policymakers of the need to rethink the clear economic interdependence of rural activity with climactic realities.

# **2 Data and Summary Statistics**

This paper combines data from three sources: namely data on non-farm enterprises based on a nationally representative enterprise survey; agricultural statistics and rainfall data from the district-level ICRISAT database; and household consumption data from a
the individual data in the DHS.

# **4. Evidence from individual welfare indicators**

We have seen the difficulty of making assessments of women‘s welfare with standard household level data sources. The paper now turns to a source of data rarely used in poverty measurement but with some advantages.

We know that widows and divorced women usually remarry in Mali, typically into polygamous households as second, third or fourth wives. As a result of large age gaps between

> 16  Here, the 2% difference with the estimate in Table 3 is due to the omission of urban male divorcee headed households from the comparison group.  I also tested effects for divorced FHH of which there are 19 in the ELIM‘s urban sample.  The estimated coefficients are negative and large (-0.20) but vastly attenuated once demographics are included in the regressions.  However, they are never statistically significant, possibly due to the limited sample size.

15
asset ratio of 13.2%, compared with 11.7% for non-African banks. African banks were also substantially smaller. The median bank held $455 million in assets in 2020, compared with $1.2 billion for the median non-African bank. African banks also lent less: non-interest income was 24.9% of total income for the median African bank and only 13.9% of the income of the median non-African bank. These comparisons give a picture of African banks that are well-capitalized, over-liquid, and provide only limited lending to the real economy, as highlighted previously by Honohan and Beck (2007), Beck et al. (2011), and Beck et al. (2019). Moreover, there has been improvement among African banks on some of these indicators – in 2010, the median African bank held only $302 million in assets, non-interest income was 36.5% of total income, and liquid assets were 26.7% of total assets. There was not enough progress, however, to close the gaps with banks in non-African developing economies.

The bank-level data also permit an analysis of the drivers of high operating costs in African banking. More specifically, Table 1 uses regression analysis to relate bank-level variation in overhead costs to bank- and country-level characteristics and compares banks in Africa with banks in non-African developing countries. For a cross-country sample of banks, overhead costs in 2020 are regressed on (1) the share of non-interest income, (2) the equity-asset ratio, (3) the liquidity ratio, (4) loan growth over the previous year, (5) the log of total assets, (6) the inflation rate and (7) the Kaufmann, Kraay and Mastruzzi (2011) indicator of Rule of Law. The results in Table 1 indicate the extent to which these different factors contribute to substantially higher overhead costs of banks in Africa (3.44% in the regression sample) than in banks outside Africa (1.87
provided by the WHO. The distribution of known deaths due to Ebola by May 2014 across prefectures is highly varied with the majority of cases were in one prefecture (168 cases in Gueckedou), and just over 65% of the prefectures had no known cases (Figure A.2).

We perform a range of tests to demonstrate the internal validity of the study.<sup>10</sup> The sample is balanced for a range of student and school-level characteristics, including student baseline learning, number of students assessed, and exposure to Ebola (Table A.7). Furthermore, school-level attrition in student assessment data is balanced (Table A.8), and the sample remains balanced if restricted to schools for whom we have midline and endline data (Tables A.9 and A.10, respectively). Finally, there is no _reduction_ in the number of students assessed at midline and endline in the respective treatment groups, suggesting that teachers did not strategically keep under-performing students from attending school on the day of assessment to inflate their scores (Tables A.11 and A.13).

> 10Section A.2 provides a more detailed discussion.

8
Sah and Stiglitz 1985). Yet empirical analysis of the distributional impact of land taxes has received little attention (Norregaard 2013). Notable exceptions have found that area-based land taxes are regressive in Rwanda (Ali et al. 2020; Kalkuhl et al. 2018), in Indonesia, Peru, and Nicaragua (Kalkuhl et al. 2018), and in Ethiopia (Hill et al. 2017; Mesfin and Gao 2020).

Survey data on tax payments and individual disaggregated land ownership has also been scarce to better understand the distributional analysis of land tax policy. This includes understanding how tax burdens differ for the groups that are most economically vulnerable, such as women. This paper examines the gender implications of the tax incidence of the rural land use fee and agricultural income tax in light of expanded recognition of land rights on the one hand and the regressivity of area-based land taxation on the other. To estimate tax incidence, we use new data on household taxation and individual land ownership in the Ethiopian Socioeconomic Survey (ESS4) 2018/2019, part of the World Bank Living Standards and Measurement Study (LSMS). With what we know about the regressivity of these taxes in Ethiopia (Hill et al. 2017; Mesfin and Gao 2020), we assess horizontal equity by looking at tax burdens across gender-disaggregated households and individuals. Individual tax incidence is imputed in proportion to the amount of the household land a person owns, using self-reported ownership data.

There are two key findings from our study. First, the rural land use fee and agricultural income tax are regressive in that poorer households face a larger tax burden than wealthier households. Second, the tax burden of femaleheaded and female-only households (with no male adults) is 37 percent higher than for male-headed and dual adult households (with both male and female adults present), which violates the horizontal equity principle. The gender differences in household tax incidence persist when we impute tax liabilities using land area and regional tax schedules. There is also a gender difference in individual tax
and 73 percent for the treatment GPs. In the weighted data, access to toilets in the control GPs increased from 80 percent of households at the baseline to 84 percent after the completion of project interventions. A relatively larger improvement was noticed in the treatment arms, where access to toilets improved from around 74 percent of households at the baseline to around 82–83 percent of households after implementation. Improvement in access in treatment GPs was mostly due to the expanded coverage of safely managed facilities, which increased from around 59–61 percent at the baseline to 66–68 percent after project completion. There was also an increase in “limited” sanitation facilities—that is, toilets shared among households—in the treatment GPs after completion. For the control GPs, most of the improvement in access seen between the baseline and midline is due to limited sanitation facilities, whose coverage increased from 6 to 11 percent. Coverage of basic and safely managed sanitation facilities in the control GPs mostly remained unaltered (table A.7). A relatively larger proportion of households initially without toilets in the treatment GPs also reported having toilets under construction during the midline compared with the baseline. At the baseline, 4.3 percent of households without toilets in arm 1, 2.3 percent in arm 2, and 7.3 percent in arm 3 reported having toilets under construction. The respective percentages increased to 10.2 percent, 12.2 percent, and 19 percent at the time of the midline. In contrast, the percentage of households initially without toilets that reported having toilets under construction in control GPs declined from 2.8 percent at baseline to 1.2 percent at midline.

In the unweighted data, we observed a reduction in open defecation, as reported by any member of a household, across the treatment and control groups. Reduction in open defecation was relatively larger for the treatment arms (ranging from 8 to 10 percentage points) compared with control GPs, where open defecation declined from 42.6 percent at
have meaningful variation in project outcomes across both countries and task managers. Our headline finding here is that task manager fixed effects are at least as important as country fixed effects in accounting for the variation in project outcomes, suggesting a strong role for various task managerspecific characteristics in driving project outcomes.

The results in this paper have important implications for aid effectiveness in general, and for IDA in particular. The first is basic and not very new, though it is confirmed by the updated and expanded work in this paper: targeting aid to countries with better policies and institutions pays off, as rates of project success are significantly higher in countries with good policy, as measured by the CPIA ratings. However, the very large heterogeneity in project performance within countries suggests that policies to improve aid effectiveness could focus more on project-level factors as opposed to country-level factors. These include those that make individual projects difficult to restructure or cancel outright even after early indications of problems arise; those that contribute to project size and complexity; and those that underlie the large differences in project performance across task managers that we observe in the data.

The rest of this paper proceeds as follows. In the next section we review related literature that has also considered the World Bank project-level outcome data we work with here. In Section 3 we describe the project-level data in detail. Section 4 contains our main empirical results, and Section 5 discusses countries, projects, and task managers. Section 6 offers policy implications and conclusions.

# **2.  Related Literature**

This paper is not the first to study the correlates of individual World Bank project outcomes. In earlier contributions, Isham, Kaufmann and Pritchett (1997) and Isham and Kaufmann (1999) studied the determinants of project-level estimated ex-post economic rates of return. Both of these papers focused primarily on country-level factors affecting project returns, notably the role of democracy and civil liberties in the first, and the role of sound macroeconomic policies in the second. Subsequent papers have similarly focused on country-level determinants of
# **2 Data**

## **2.1 Patterns in temporary migration**

Many migrant workers return home for a range of reasons. First, returning may be optimal and part of the migrants’ original plan even if they have the option to stay abroad; or because they are not legally permitted to stay in the destination country beyond a specified time frame. In contexts where permanent stays are possible, workers may choose to return because of location-specific preferences for consumption and amenities, such as being near their families or cultural characteristics (Hill 1987; Djaji ́c and Milbourne 1988). They may also choose to return because the income earned abroad and the resulting savings may have higher purchasing power and lead to higher consumption levels (Dustmann 1995; Dustmann 1997; Djaji ́c 1989; Dustmann 2003; Stark, Helmenstein, and Prskawetz 1997). Finally, migrants may have accumulated significant human capital (Dustmann, Fadlon, and Weiss 2011; Dustmann and Kirchkamp 2002; Stark 1991) or financial capital (or both) which may yield higher returns as entrepreneurs, self-employed professionals or investors at home (Mesnard et al. 2004; Bossavie, Gorlach, Ozden, and Wang 2021; Dustmann and Kirchkamp 2002).

To provide insights on the incidence of temporary migration, we first report evidence on the extent of return migration from United States where permanent migration is not only allowed, but tends to be the norm. As in most destination countries, most of the data come from standard nationally representative cross-sectional surveys or censuses. The main issue with these data sources is that returning migrants are simply not observed since they drop out of the sample. These data constraints limit the ability to draw precise conclusions, which happens to be the disclaimer in most of the papers reviewed here.

While we do not directly observe return migration, we proxy for the extent of returns by following a given arrival cohort of immigrants over different waves of the survey or the census. Figure 1 focuses on the different immigrant
# **A2:  Education**

# **A2.1  Data on Expected Years of School**

**E** xpected number of years of school ( EYS ) that a child who starts school at age 4 would attain by her 18<sup>th</sup> birthday is calculated using the methodology described in UNESCO et. al (2014), Section 2.2 and Annex 2.2.  Conceptually, expected years of school achieved by age A is simply the sum of age‐specific enrollment rates over all ages in this age range, i.e.

where ENR� is the enrollment rate of children aged a . Unfortunately however, age‐specific enrollment rates are not systematically available for a broad cross section of countries. Instead, more readily‐ available data on enrollment rates by level of school are used to approximate enrollment rates in different age brackets. Specifically, pre‐primary enrollment rates approximate the age‐specific enrollment rates for 4 and 5 year‐olds; the primary rate approximates for 6‐11 year‐olds; the lower‐ secondary rate approximates for 12‐14 year‐olds; and the upper‐secondary rate approximates for 15‐17 year‐olds. Naturally, cross‐country definitions in school starting ages and duration of different levels of school imply that these will only be approximations to the number of years of school a child can expect to complete by age 18.

The ideal measure of enrollment rates for this calculation is the “total net enrollment rate” (TNER), which measures the fraction of children in the theoretical age range for a given level of school, who are in school at any level. For example, if the theoretical age range for lower secondary school is 12 to 14 years, then the TNER measures all children aged 12 to 14 who are enrolled in any level of school as a fraction of all children aged 12 to 14. In this way, the TNER best approximates the age‐specific enrollment
to measure a country’s sustainable, long-term growth potential. Wealth accounting is necessary to have a complete picture.5 The World Bank (Hamilton et al., 2006; Lange et al., 2021; Lange et al., 2018; Lange et al., 2011) has compiled data on the wealth of 141 nations, divided into three major categories: human capital (discounted lifetime earnings), produced capital (e.g. machinery, buildings, urban land) and natural capital (e.g. forests, cropland, protected areas, subsoil assets).

> 5Lange et al. (2018) draw the illustrative parallel between a country’s GDP growth and its wealth, on the one side, and a company’s income statement and balance sheet, on the other side.

5
>2004 2005 2006 2007 2008 2009<br>2004 2005 2006 2007 2008 2009<br>PAK BGR COL ECU PER ZAF PAK BGR COL ECU PER ZAF<br>Source: Authors’ calculations based on data from the Exporter Dynamics Database (World Bank).<br><!-- End of picture text --> It appears that the lower concentration of exports in Pakistan is due to a very large presence of small exporters. Indeed, while the average size of exporters in Pakistan is lower than in countries such as Bulgaria, Colombia, Ecuador, Peru and South Africa the median is higher, suggesting that there are more important non-linearities in the distribution of exports in Pakistan than in the distribution of exports in comparable countries, with large exporters determining the bulk of exports (see Figure 1 9 ).

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**Table E.6:** Effect on Probable and Suspected Cases

||Control Mean|Pooled|CM|NFA|Difference|N|
|---|---|---|---|---|---|---|
|Ebola Cases|||||||
|Probable and Suspected|0.029<br>(0.208)|0.003<br>(0.008)|0.015<br>(0.011)|-0.007<br>(0.009)|0.022<br>(0.011)*|5,440|
|Log(Ebola Cases + 1)|||||||
|Probable and Suspected|0.018<br>(0.123)|0.002<br>(0.005)|0.009<br>(0.007)|-0.004<br>(0.005)|0.013<br>(0.007)*|5,440|
|Linear Probability Model|||||||
|Probable and Suspected|0.022<br>(0.148)|0.002<br>(0.006)|0.011<br>(0.008)|-0.005<br>(0.006)|0.016<br>(0.008)*|5,440|
|IHS(Ebola Cases)|||||||
|Probable and Suspected|0.023<br>(0.159)|0.002<br>(0.006)|0.012<br>(0.008)|-0.005<br>(0.007)|0.017<br>(0.
regulatory frameworks, in contrast with vulnerability to political pressure and lower levels of autonomy—ERC’s tariff regime has been stable, if not exemplary.

ERC performs poorly in quality regulation, computed as the average of quality-of-service standards and quality-of-service enforcement. The main reason for the low score is the lack of enforcement of quality-of-service standards. Moreover, ERC does not specify how technical performance data are to be collected and does not independently review or validate technical performance data. Measurements of reliability of the power supply are not publicly available, and there are no incentives for utilities to increase customer satisfaction.

**Table 11. Formal regulatory substance in Kenya and comparators, 2015**

|**Indicators**|**Kenya**|**Senegal **|**Tanzania **|**Uganda**|**International**<br>**benchmark**|
|---|---|---|---|---|---|
|**Regulatory Substance**|81%|73%|83%|78%|76%|
|**_Tariff Regulation_**|88%|76%|88%|92%|77%|
|_Regulatory Framework for Tariffs_|_100%_|_86%_|_100%_|_100%_|90%|
|_Determination of Tariffs_|_75%_|_67%_|_75%_|_83%_|64%|
|**_Quality Regulation_**|54%|42%|63%|58%|75%|
|_Quality of Service Standards_|_75%_|_50%_|_75%_|_50%_|82%|
|_Quality of Service Enforcement_|_33%_|_33%_|_50%_|_67%_|68%|
|**_Market Entry Regulation_**|100%|100%|100%|
_“Our hardship is like time, it never stops.”_ – A Nepali woman in Makawanpur, Nepal, in a conversation with “Stories of Nepal”

# **1. Introduction**

In this paper, we explore some aspects of economic and social mobility in Nepal. The data required for this analysis come from a variety of national surveys conducted in the past two decades as well as from a nationally representative survey that was implemented in 2014 to specifically collect information on social and economic mobility as it is perceived in Nepal.

Nepal has undergone some dramatic political, social and economic changes in the past couple of decades. In a single generation, the country has gone from being a unitary, Hindu state ruled by an absolute monarch to a secular republic that has adopted a federal structure of governance. More than 14,000 Nepalis lost their lives between 1996 and 2006 in a violent Maoist conflict that took the country to the brink of a civil war that has fundamentally transformed the common Nepali psyche. This experience has not only altered the relationship between the state and its citizens but continues to have a profound influence on an evolving social contract.

Despite these changes, there is an enduring perception of “structural inequalities,” some of which are deeply encoded in the legacy of the country’s social structure (e.g., the caste system) and some are driven by what has been widely acknowledged to be a spatially imbalanced development experience. A discussion of mobility becomes pertinent because in many ways mobility is the fundamental characteristic of any development process that can neutralize these deep inequalities. From an intergenerational perspective, a society in which children can aspire to achieve and indeed can achieve levels of education, jobs, and living standards that are materially different from the levels enjoyed by their parents is a society that will automatically begin to redress some of these inequalities. Mobility is not just important from the point of view of equity, it also has efficiency benefits. If talent is distributed more equally in
Poisson regression model. It is calibrated using values and weights data for 25 commodities obtained from ECLAC (Economic Commission on Latin America and Caribbean) and the Eurostat database on exports. This model has developed to take into account the impact of transport costs for different commodities on commodities weight in (Halim et al., 2018). Cristea et al. (2013) constructed a database of weight-value ratios using U.S. imports and exports of merchandise; Eurostat Trade (covering the imports and exports of 27 EU countries), and the ALADI (The Latin American Integration Association) trade database.

## **Mode choice models**

Several global level models have been developed to estimate the impact of changes in socio-economic variables (such as GDP, trade agreement, transport costs, travel time, shared language, land contiguity, etc.) on the modal choices of international freight transport. A modeling study conducted by (Sou & Ong, 2016) makes use of a mode choice model to estimate the substitution between maritime and air transport modes for international freight. Martínez et al. (2015) and (ITF/OECD, 2018) developed a mode choice model for international freight transport that covers four major modes of transport: air, rail, road, and sea. The model takes into account commodity specific variables, travel time, and distance, as well as socio-economic indicators such as contiguity and trade agreement between countries. A cost-based mode choice model, which is built on this model, was introduced by (Halim et al., 2018). The model is based on a multinomial logit model and has been used to investigate the impact of increased maritime transport costs on the modal choice for freight transport for China-Europe and globally. Studies by (Tanabe et al., 2016; Tavasszy et al., 2011) specified network choice models, which make use of transport costs to estimate both mode and route choices for global cargo transport.

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