rafmacalaba/data-use-mentions-extended huggingface dataset:
demonstrates that improving women’s empowerment can have limited impacts on total household income and household public good provision if noncooperation within the household still prevails. Under other conditions, moving to cooperation will have limited impacts on household good provision, though total income will always increase.

Given the emphasis on women’s empowerment in the development discourse, it is useful to clearly capture the different impacts one would expect from increasing women’s bargaining power versus increasing intra-household cooperation. Thus, our first contribution to the literature is the development of a stylized model that captures these differences, and that characterizes the conditions under which we would expect greater gains from increasing bargaining power versus increasing intra-household cooperation. In reality, even though increasing women’s empowerment may increase intra-household cooperation, there may still be losses from not additionally focusing on gains from fostering cooperative decision-making within the household.

Our second contribution is to empirically test the model hypotheses using the sub-sample of rural households compromised of married couples that were interviewed in 2010 and 2013 by the Malawi Integrated Household Panel Survey (IHPS). The IHPS solicited information on whom in the household controls income from each source of income, allowing for both individual and joint 3
.

**In this paper, we apply an innovative methodology to identify a forward-looking (ex-ante) measure of vulnerability to poverty in El Salvador, a country marked by high exposure to natural and human hazards.** We follow an innovative approach to vulnerability developed by Gao et al. (2020), and estimate the vulnerability rate in El Salvador. The innovative approach applied in this paper follows a probabilistic understanding of vulnerability, which takes into account the average level of welfare across many periods and the deviation of welfare across these periods (that is, the variance of welfare). Studying vulnerability to poverty from an ex-ante perspective is highly relevant in the context of El Salvador. El Salvador is characterized by high risk exposure. It scores 4.7 out of 10 in the INFORM Global Risk Index 2021 and ranks 58th (DRMKC, 2021). It is especially vulnerable to natural hazards, such as tsunamis, earthquakes, and volcanos (ibid), but also faces a number of human hazards, such as high crime rates, a high population density, and one of the highest emigration rates. Given the significant number of shocks to which the population is exposed, studying vulnerability to poverty is highly relevant in the context of El Salvador.

**Using household survey data from El Salvador, we show that the country's vulnerability rate is higher than its poverty rate; our analysis also reveals important differences between rural and urban areas.** We estimate a vulnerability rate of 23.7 percent for 2019. This number is higher than the poverty rate, standing at 21.7 percent.<sup>2</sup> In addition, vulnerability patterns differ for the rural and urban populations. While the overall vulnerability rate is only slightly higher than the overall poverty rate, the rural population is especially prone to falling back into poverty. Among the rural population, 41.2 percent is affected by vulnerability to poverty, while this only applies to 13.5 percent of the urban population
Policy Research Working Paper 6052

# **Abstract**

This paper analyzes the distributional effect of public spending in Zambia using the most recent data from the 2010 Living Conditions Monitoring Survey. The analysis focuses on both the “traditional” social sectors, such as education and public healthcare, as well as other spending areas less thoroughly studied, such as agricultural support programs. Ultimately, this benefit incidence analysis addresses the extent to which spending is pro-poor and progressive; that is, it primarily benefits the poor and does so at an increasing rate as welfare levels decrease.

The results indicate that overall public education spending in Zambia is neither pro-poor nor progressive, but while this is true for the system as a whole it is not true for all of its parts. The net unitary benefits of primary and secondary education are clearly both propoor and progressive. However, their progressivity is ultimately outweighed by the extreme concentration of tertiary education benefits among the wealthiest members of Zambian society. Health spending is also regressive and not pro-poor. Although unitary net benefits are slightly progressive, unequal access remains the key constraint. In contrast, the benefits of agricultural-input subsidy programs follow a somewhat progressive pattern (for each beneficiary in the top quintile there are almost two beneficiaries in the poorest quintile) but clearly suffer from targeting problems. Consequently, without better-designed and more conscientiously implemented targeting mechanisms, public spending on health, education, and fertilizers will not be able to further the government’s larger objectives for pro-poor and progressive development policy.

This paper is a product of the Poverty Reduction and Equity Unit, Poverty Reduction and Economic Management Network. 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 jcuesta@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the
The matching analysis draws only from the baseline survey which interviewed 6,722 households in 57 treatment municipalities and 4,562 households in 9 control municipalities.<sup>11</sup> The subsample for this analysis includes only children who were born during 1975-1994, and who may have graduated from high school between 2003 and 2009.<sup>12</sup> For instance, a child that had completed grade 6 at baseline (either in a treated or control area) was expected to finish high school (grade 11) by 2007 if the child progressed on schedule. In contrast, a child starting primary school (grade 1) in 2002 (baseline) will not be able to finish high school at least before 2013. Therefore, the relevant cohorts of children to estimate the average impacts of the program are those who at baseline had 4 to 10 years of schooling, and who were 18 years old or younger (called ―PSM data‖). The baseline survey is also used to construct most of the pre-program covariates for the matching procedures.

The samples of analysis for the RDD approach are constructed with two different administrative sources of data. The first is the monitoring and evaluation system, SIFA, created for administrative and monitoring purposes at the onset of the FA program. The system is a longitudinal census of program beneficiaries from 2001 to present. To date, there is information on approximately 2.8 million families currently participating in the program. The second source of information is the data from a census of the poor ( _Sisben_ ) carried out between 1994 and 2003 to construct the poverty index score for the proxymeans test. Questions were asked regarding households‘ demographics, structure, durable goods, housing characteristics, human capital, labor force participation, income, and access to basic services. By 2003, the surveys covered over 25 million individuals.<sup>13</sup> Data from SIFA and _Sisben_ were carefully merged using confidential information on date of birth, full name, and
towards addressing this concern, I use readily-available data on interest payments on external public and publicly-guaranteed debt to net out this portion of interest expenditures from total government spending.<sup>21</sup> I then re-estimate the spending multipliers using this proxy for government non-interest expenditures. The results are presented in the second set of three columns in Table 6. Doing so weakens identification somewhat, particularly in the full sample, in which the firststage F-statistic now falls below the Staiger and Stock (1997) threshold of 10. However, in the remaining two samples, the first-stage F-statistics remain respectable at 19.9 and 16.0, respectively, and the point

> 21 Specifically, I use data on interest payments on public and publicly-guaranteed external debt as reported in the World Bank's Global Development Finance publication.  Aggregate interest payments from this source are based on loan-level transactions reported in the DRS database that I also use to construct my instrument.  This is only an imperfect adjustment, since subtracting interest payments on external debt from total government will not correct for interest payments on domestic debt, or net transfers.

20
Policy Research Working Paper 9653

# **Abstract**

This paper provides novel evidence on the economic impact of industrial automation in a large developing economy. It combines labor force survey and manufacturing plantlevel data from Indonesia over 2008–15, when the country experienced a rapid increase in imports of robots. The findings show a positive impact of robots on various measures of plants’ performance and integration into global value chains. In contrast to existing evidence on advanced and emerging economies, these plant-level impacts result in an increase in manufacturing and services employment at the local level. Such employment effects are consistent with evidence of positive employment spillovers from downstream robot-adopting plants, which help extend the benefits of automation to non-adopting plants. The spillover effects may provide a rationale to incentivize manufacturing firms to adopt industrial robots. The results also suggest that the gains from automation are not equally shared: adoption of robots is associated with a reduction in the labor share in value added and an increase in skill wage premia.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. 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/prwp. The authors may be contacted at mcali@worldbank.org and giopresidente@gmail.com.

_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
# **5.3 Evidence from Administrative Data**

Table 5 shows the campaign’s impact on malaria incidence, expressed as the number of diagnosed monthly cases per million people recorded by health facilities for each sub-district-month. We report the interaction term (Treatment _×_ Post) as the analysis of the administrative data is based on a difference-in-difference specification (see equation 2). We show a parsimonious specification with no controls, as well as with the results with the inclusion of district-level variables, and additionally controlling for subdistrict-level characteristics.<sup>35</sup> Table 5: Malaria monthly incidence rates <u>(N.</u> of monthly cases/1M <u>people)</u>

||Over|all incid|ence|Ur|ban incide|nce||Rural inci|dence|
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Treated _×_ Post|-3.12|-5.66|-4.68|-6.10<sup>_∗_</sup>|-6.96<sup>_∗∗_</sup>|-6.21<sup>_∗∗_</sup>|0.99|-1.47|-2.24|
||(6.37)|(6.25)|(5.37)|(3.41)|(2.92)|(2.86)|(6.02)|(6.37)|(5.79)|
|Subdistrict
is a similar decrease in inequality for the two medium-term periods, but this is unsurprisingly smaller since inequality measures are averaged over a longer time span for the medium-term periods compared to the shorter periods. Second, long-term inequality is less than

> 15 Increased minimum wages may have some moderate impacts on reducing poverty in these periods; see Calvo, Lopez-Calva, and Posadas (2015) and Kapelyuk (2015) for recent discussions on the role of higher minimum wage on poverty and inequality reduction. A recent study by Aristei and Perugini (2015) also suggests that the income growth component in the Fields-Ok index is relatively more important for income mobility for the period 2004-06 for most former centrally planned economies in Eastern Europe.

> 16 We use the balanced sample for each period for the estimates in Table 6, which varies from period to period due to attrition. Another approach is to use the fully balanced sample for the whole 1994-2015 period. We provide estimation results using this approach in Table 1.3 in Appendix 1, which are largely qualitative similar to the results in Table 6.

20
count of existing contributions and the differing capacities and resources among States.” It formally “intends to provide a basis for predictable and equitable burden- and responsibility-sharing among all United Nations Member States, together with other relevant stakeholders as appropriate.” Underpinning the global debate on responsibilitysharing is the assumption that “the grant of asylum may place unduly heavy burdens on certain countries” (UN General Assembly 2018), typically countries neighboring a conflict area. In this perspective, the number of refugees a country is to host is simply a function of its geography.

This paper examines empirically the proposition that the hosting of refugees falls disproportionately on neighboring countries, which in most cases are in the developing world. To do so, we use data on worldwide bilateral refugee stocks compiled by UNHCR to examine the spatial distribution of refugees and its evolution over time. Our period of analysis is 1987-2017.

Our main findings can be summarized as follows. While refugees still remain overwhelmingly in a country neighboring their country of origin, the past decades have seen a trend towards greater geographic diffusion. We begin by showing that the global population of refugees has been increasingly dispersed across host countries, as captured by a falling Herfindahl index of host-country shares.

We then document several features of the data underlying this greater diffusion, using four outcome measures of refugee spatial distribution. First, we compute the average distance refugees have traveled between their country of origin and their country of destination. The average distance traveled by refugees has increased substantially over time. Next, we look at the probability that source and host countries are contiguous, and show that the share of refugees going to an adjacent country has fallen. Third, we construct a measure of refugee spatial dispersion by computing the Herfindahl index of refugee shares by source country. The Herfindahl index of refugee shares fell substantially over time, indicating that refugees from a given conflict are now more dispersed across host countries.

These results paint a picture of a more globalized and far-reaching refugee network and imply a more equal distribution of
**MONGOLIA'S INFORMAL SECTOR** - **Appendix B: Methodologies and Descnptive Statistics**

_38_ 115. Third, the income numbers were adjusted to reflect a 200 hour month. This adjustment was particularly important for the full-time drivers, the most relevant for the estimation of incomes, since some drive many more hours than formal sector wage-earners work.

116. Lastly, the median adjusted income was calculated as a supplement to the mean. The two statistics, taken together, provide a picture of the income of the "typical" full-time taxi driver in Ulaanbaatar. Estimates of mean and median adjusted income for taxicabs in Ulaanbaatar are provided in the Table B 1.

Table **B1. Monthly Income of Taxis**

||**observations**|**mean**|**median**|
|---|---|---|---|
|Overall sample|165|189,200|_158,300_|
|those who drive less than 25 hours per week|51|41,500|117,700|
|those who drive 25-45 hours per week|60|176,100|170,500|
|those who drive more than 45 hours per week|_54_|343,300|159,000|

_Source:_ Survey of taxicabs conducted by the author.

Incomes have been adjusted for full-time equivalent, and return on capital.
contracted crop (paprika), which we interpret as a reduction in moral hazard. When we compare these benefits to fairly conservative costs of implementation, we find that adoption of fingerprinting is cost-effective (the benefit-cost ratio is 2.27).

The key contribution of the paper is that, to our knowledge, it is the first randomized field experiment examining the impact of a technology that improves the effectiveness of dynamic incentives in a credit market. Our analysis is further distinguished by the fact that, in addition to measuring impacts on borrowing decisions and repayment (using the lender’s administrative data), we also estimate impacts on specific behaviors related to moral hazard using a detailed follow-up survey of borrowers.

Substantively, our intervention most closely resembles the promise of a future lower interest rate conditional on current loan repayment in Karlan and Zinman (2009), henceforth “KZ”, who find evidence of moral hazard and weaker evidence of adverse selection in an experiment with a South African provider of consumer loans. Our experiment differs from KZ’s in several important respects. First, our experiment is concerned with lending for productive investment in rural areas, while KZ’s involves consumer loans for urban customers. Second, our experiment estimates the impact of manipulating the ability to impose dynamic incentives via a technological innovation. KZ, on the other hand, measures the impact of informing borrowers of the existence of a dynamic incentive. Third, we implement a follow-up survey of borrowers to provide additional insight into the specific behaviors that are changed by the intervention and that result in higher repayment. KZ, by contrast, relies exclusively on the lender’s administrative data for analysis and so cannot shed light on what borrower behaviors may have changed.

The fourth and final key difference is in the timing of the intervention relative to the borrowing decision. In KZ, the dynamic incentive is announced _after_ clients have agreed to borrow (and all loan terms have been finalized). As a result, differences in repayment can only be due to moral
br>(0.036)|
|City Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Leader Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|City FE|||Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Province-Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|# of Observations|3642|3642|3642|3642|3642|2887|2887|2890|3361|3361|
|# of Cities|281|281|281|281|281|281|281|281|281|281|
|Adj_R_<sup>2</sup>|0.56|0.56|0.65|0.72|0.78|0.74|0.78|0.65|0.62||

_Notes_ : This table presents several robustness checks for the political cycle of GDP manipulation. The dependent variable is the difference between reported real GDP growth and predicted real GDP growth imputed based on the nighttime lights data of prefecture-level cities. City controls include city-level timevarying economic controls (the share of industrial and tertiary value added in GDP, log population, and growth of nighttime lights (except for columns 4 and 5)) and leader controls are the same as the ones displayed in the table of baseline results. Standard errors clustered at the city level are reported in parentheses. ***, ** and * denote significance at 1%, 5% and 10%, respectively.

53
iii) Non-green occupations in green sectors (NOGS), and (iv) Non-green occupations in non-green sectors (NONS). Green sectors would be less vulnerable to an increase in the cost of carbon. As a result, workers in green occupations in such sectors would face the lowest risk of displacement and highest re-employment likelihood in a greener economy in case of job loss. In contrast, workers with non-green occupations in non-green sectors would face the highest labor market vulnerability in terms of both job-loss risk and re-employment opportunities.

Using data from countries in Latin America and the Caribbean (LAC)—because of better availability of granular occupation data at the ISCO08 2-digit level—we analyze the relationship between green occupations and sectors. The first finding is that the concentration of employment in non-green occupations and non-green sectors – i.e. the most vulnerable group during a green transition – is

> 6 The focus on GHGE implies that the paper focuses on mitigation, rather than adaptation, in defining green sectors. We note here that this is not the only way green sectors can be defined. For instance, an alternative definition of sectors that considers more prominently the adaptation angle would define green sectors not only based on the level of emissions, but also on the potential to create green jobs and/or foster green technologies in the longer run. Examples of sectors that would be affected by this alternative definition include water management and waste management.

3
1,000 registered firms. Second, for each transacted item on a receipt, we observe a product-classification UNSPSC code and a string description. We use two product classifications in our analysis—we identify textile products as having UNSPSC codes 601058, 5310, and 2312, and we identify masks as having string descriptions containing _mask_ in English, French ( _masque_ ), or Kinyarwanda ( _agapfukamunwa_ ), and remove any misclassified products (e.g., masking tape). Masks are then classified as manufactured when sold by a firm that was issued a mask manufacturing license from the FDA. In Section 3.1.1, we use this data on mask and textile purchases and sales to construct sub-District mask manufacturing exposure; our primary analysis sample comprises the 86 sub-Districts for which we observe non-mask textile purchases necessary to construct mask manufacturing exposure.

Third, we observe a timestamp for each receipt, and prices and quantities for each transacted item. Lastly, each unique combination of selling firm and product string description identifies a firm-product. We present additional details on the coverage and construction of the EBM data in A.1.

Basic descriptive statistics from the EBM data are presented in Appendix A.1.3. We observe over 30,000 transactions covering over 2,500,000 masks, approximately 0.9 masks per adult in our primary analysis sample. On average, prices are 650 RwF (0.63 USD) per mask. Production of masks is spatially concentrated relative to consumption; aggregating to the sub-District level, the average purchasing (selling) sub-District buys (sells) from 2.4 (11.3) sub-Districts.

**Customs** We construct measures of border prices of masks using customs data containing the universe of imports by Rwandan firms. Just as for EBM, we identify mask imports using a combination of product codes and product string descriptions, and all imports are timestamped. We then use the combination of the point of entry

6
crisis that has the largest number of empirical studies in Sub-Saharan Africa thanks to the considerable number of household surveys that have been conducted in the affected region over the years.

Alix-Garcia and Saah (2009) was one of the first studies that looked at the impact of the Burundian and Rwandan refugees in Tanzania using a mix of USAID, WFP and DHS data sets on food aid, prices and household assets. They find positive effects on prices of non-aid foods and smaller but positive effects on aid-related food items. This is one of the few studies that looked at both aid and refugee effects separately finding that the aid effect is considerably smaller than the refugee effect. The authors also find a positive 59