rafmacalaba/data-use-mentions-extended huggingface dataset:
spending pattern at the school level is quite stable across the first and second year of the grant. Many of these items could be provided directly by parents for their children, suggesting a high potential for substitution.

# **4.3  Data**

Data on household expenditure on education was collected from a household survey that attempted to cover every household with a child in a treatment or comparison school and administer a short questionnaire on education expenditures on the concerned child during the previous school year.<sup>29</sup> Data on household spending was collected at three points in time – alongside the baseline tests for spending incurred in the pre-baseline year (Y0), during the second year of the program about spending during the first year (Y1), and after two full years of the program about spending during the second year (Y2). Data on household education spending was collected retrospectively to ensure that this reflected all spending during the school year.<sup>30</sup> The outcome data used in this paper comprise of independent learning assessments in math and language (Telugu) conducted at the beginning of the study, and at the end of each of the two years of the experiment. The baseline test (June-July, 2005) covered competencies up to that of the previous school year. At the end of the school year (March-April, 2006), schools had two rounds of tests with a gap of two weeks between them. The first test covered competencies up to that of the previous school year, while the second test covered materials from the current school year's syllabus. The same procedure was repeated at the end of the second year, with two rounds of testing. Doing two rounds of testing at the end of each year allows for the inclusion of more overlapping materials across years of testing, reduces the impact of measurement errors specific to the day of testing by having multiple tests around two weeks apart, and also reduces sample attrition due to student absence on the
% in quintiles 2 to 4, while the</mark> largest change was a reduction of 1.1% in quintile 1.

<mark>Overall, the shock caused income declines across the distribution, but with no pattern across countries. This suggests that differences in the structure of the labor market captured by the household survey as well as the sectoral growth patterns taken from National Accounts lead to</mark> diverse patterns of projected distributional impacts. This would require further study.

<mark>We also study how income changes correlate with the educational level and occupation of the household head. Panel A of Table 1 shows that in four out of five countries —Türkiye, Philippines, South Africa, and Brazil—income losses were larger the lower the educational level of the household head. The magnitude of the losses, however, differ between countries. For instance, household where the head has up to complete primary education faced an average income loss of 8.3% in South Africa and of 11.6% in Brazil. For households where the head has tertiary education, the loss was of 6.3% in South Africa and 8.4% in Brazil. Sri Lanka is the only exception to the negative correlation between education of the household head and magnitude of the income loss. In this country, households where the head has up to complete primary education experienced an</mark> income increase, while households with more educated household heads faced income reductions.

<mark>Panel B of Table 1 presents income changes by initial occupation of the household head. We do not find a clear pattern in this case. For instance, in Philippines, households where the head was initially an unpaid worker or a wage employee suffered the largest income losses (10.6% and 7.8% respectively). In South Africa, the largest income losses were for households where the head was</mark> self-employed, unemployed or wage employee (19.1%, 7
13

|**Classification**|**Oil trade status**|**No. of countries**|**Median V**|**Median**∆**V**|
|---|---|---|---|---|
|Developingcountries only|||||
|East Asia and Pacific|Importers|14|5.9|4.1|
||Exporters|3|-3.0|0.9|
|Europe and Central Asia|Importers|17|5.0|1.8|
||Exporters|5|-15|-2.7|
|Latin America and the Caribbean|Importers|23|8.0|5.9|
||Exporters|7|-2.7|-3.4|
|Middle East and North Africa|Importers|9|5.4|6.4|
||Exporters|3|-31|-1.2|
|South Asia|Importers|8|5.4|3.0|
||Exporters|0|n.a.|n.a.|
|Sub-Saharan Africa|Importers|34|5.4|4.0|
||Exporters|11|-19|-3.8|

_Sources_ : Author’s calculations based on data from EIA (2013a) and WDI. _Notes_ : Results by region exclude high-income countries. V = vulnerability; ∆ V = change in vulnerability; n.a. = not applicable.

Among net importers, high-income countries had the lowest median vulnerability and the lowest median increase in vulnerability. Upper-middle-income countries had the highest median vulnerability and increase in vulnerability. By region, Latin America and the Caribbean had the highest vulnerability and second highest increase in vulnerability. Among exporters, as expected, the Middle East and North Africa, with some of the major oil exporters,
private firms in the national total and the total sample size. The numbers of firms in each city, county, and sector were likewise determined. The survey thus covers firms in 19 sectors ranging from agriculture to service industries in 31 provinces, municipalities, autonomous regions in China. And consequently, NSPF is the most comprehensive survey on private firms and their BEs in China.

The survey was then conducted through interviews with firm owners. The survey covers owner attributes (i.e., family background, human capital, political connections, and occupational experiences) and firm attributes (i.e., size, age, and basic financial background). More importantly, the survey has information on firms’ external environments such as court

> 9 Data sources: Penn World Table version 9.0.

14
|31|Kenya receives the most aid in health and populations services (China excluded)<br>(2013)||
|---|---|---|
||OECD DAC aid database 2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|32|
|32|Ministry of Energy and Petroleum receives most loans from China (2014)<br>Estimates of Development Expenditure Government of Kenya (2014)<br>. . . . . . . . . . . .|33|
|33|Kenya’s debt to China is growing quickly (2010-2014)<br>Kenya National Bureau of Statistics Economic Survey 2015 . . . . . . . . . . . . . . . . . .|37|
|34|Kenya owes most debt to China (2015)<br>The National Treasury of Kenya (2015)<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . .|38|
|35|Kenya’s debt to China outpaces the rest (2015)<br>The National Treasury of Kenya (2015)<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . .|38|
|C36|Trade and Distance Kenya and Rest of the World (1948-2014)<br>CEPII Gravity Database 2010 and UN Comtrade 2015||
|C37|Trade and Importer GDP per capita Kenya and Rest of the World (1948-2014)<br>CEPII Gravity Database 2010 and UN Comtrade 2015||
|C38 <br>**st o**|Trade and Importer GDP per capita Kenya and Rest of the World (2014)<br>CEPII Gravity Database
goods to the consignee’s warehouse once they have been cleared, blurring the specific role of ports and government border agencies in generating delays. Therefore, the fact that the coefficient on the LPI index never enters with a statistically significant sign contrary to the one on import uncertainty seems to suggest that the efficiency of Customs and other border agencies matters more than the performance of transportation companies. Second, to the best of our knowledge, this variable is the only available cross-country measure of uncertainty to date, and its statistical insignificance seems to imply that a more refined measure such as the one we use here may be necessary to fully grasp the implications of import uncertainty for export dynamics.

We also check whether our results are affected by the inclusion of proxies for access to finance. As mentioned in Section 2.1, liquidity-constrained firms are likely to face difficulties paying on time the duties and fees associated with the import process, thus leading to longer clearance times and higher uncertainty as measured by the interquartile range of the time to import. On the other hand, the micro-literature provides ample evidence of the detrimental impact of financial constraints on export dynamics.<sup>34</sup> If access to finance affects the ability of firms to enter export markets and survive while also influencing the time required to clear foreign inputs at the border, failing to properly control for its effect would yield biased estimates of the impact of import uncertainty on export dynamics. To address this concern, we consider two variables drawn from the WBES. To capture the burden imposed by loan requirements, we include the proportion of firms that had to provide collateral to obtain their most recent loan or line of credit. Alternatively, we use a broader indicator of the difficulties faced by firms to access the financing required to support their export activity, computed as the proportion of firms that needed a loan but did not apply for it owing to complex application procedures, unfavorable interest rates, excessively high collateral requirements, insufficient maturity and loan size, and anticipation of
# **2.  The Global Distribution of Methane Emissions**

The global standard for “bottom-up” methane emissions reporting is the Emissions Database for Global Atmospheric Research (EDGAR (2022)), which combines detailed sectoral activity data with emissions factors that are adjusted for broad regional differences (Crippa et al. 2020; Solazzo et al. 2021). EDGAR reports estimated emissions<sup>4</sup> by sector in raster files with a resolution of 0.10 degrees (approximately 10 km), yielding information for 6,480,000 grid cells. Table 1 reports global totals from these files by sector in 2018, the latest year for which complete estimates are available. Table entries are displayed in descending order of emissions, which total 375.2 million tons.<sup>5</sup> For expositional clarity and simplicity, we confine our analysis to the seven sectors that account for 90% of global methane emissions. Two are in agriculture (Enteric Fermentation, Agricultural Soils), three in energy (Gas, Coal, Oil Exploitation) and two in the waste sector (Waste Water Handling, Solid Waste Landfills).

Priority site selection is most effective when the spatial distribution of emissions is dominated by relatively few large sources. We assess the potential effectiveness of priority-setting in this case by calculating total methane emissions for countries’ Level 1 administrative units (GADM1s). We expand the dataset to include emissions from countries’ marine exclusive economic zones (EEZs), because many oil and gas operations are offshore. We tabulate estimated emissions in descending order, calculate the cumulative percentage of total emissions, and divide the results into deciles. For example, the first decile comprises the largest-emitting areas whose total emissions account for 10% of the global total.

Figure 1 provides a summary table and graph for emissions from 3,812 areas that comprise Level 1 ADM1 and EEZ units. The tabIe reveals a highly-skewed spatial distribution; in the top decile, 7 areas account for 10% of
13 the availability of bank loans over those reporting an improvement rose to 20 percent from 14 percent in the previous survey, which covered the first half of 2011 (figure 12b). While the deterioration was linked largely to the general economic outlook, companies also complained about the increased unwillingness of banks to provide loans, as well as increased borrowing costs and collateral requirements (figure 12c).

**Figure 11. European Bank Lending Conditions and Drivers**

<!-- Start of picture text --> a. Net Percentage of Banks Reporting Tight b. Factors Affecting Credit Standards<br>Conditions<br>80 200<br>Weighted Net Percentage, Jan 2003 - Apr 2012 Weighted Net Percentage, Jan 2003 - Apr 2012<br>60<br>150<br>40<br>100<br>20<br>50<br>0<br>0<br>-20<br>-50<br>-40<br>Firms Household Mortgages Economic Outlook Liquidity position<br>Other Household Credit Access to Market Financing Capital Positions<br>Jan 03 Jun 03 Nov 03 Apr 04 Sep 04 Feb 05 Jul 05 Dec 05 May 06 Oct 06 Mar 07 Aug 07 Jan 08 Jun 08 Nov 08 Apr 09 Sep 09 Feb 10 Jul 10 Dec 10 May 11 Oct 11 Mar 12 Jan 03 Jun 03 Nov 03 Apr 04 Sep 04 Feb 05 Jul 05 Dec 05 May 06 Oct 06 Mar 07 Aug 07 Jan 08 Jun 08 Nov 08 Apr 09 Sep 09 Feb 10 Jul 10 Dec 10 May 11 Oct 11 Mar 12<br><!-- End of picture text --> Source: ECB Lending Survey.

# **18. Available information suggests that certain areas of banking have been hit harder than others.**

- Less profitable, capital-intensive projects are disproportionately affected, including infrastructure finance and loans to SMEs. As discussed, the most recent ECB SME lending survey suggests that SMEs are experiencing difficulties in accessing
average share of five OECD countries (OECD 2010), as shown in Table 4.

It is noticeable that the average share of SME lending is consistently low across all GCC countries, while there is more variation among non-GCC countries. The low share of SME lending in the GCC reflects to a large extent the structure of oil-based economies – less diversified, dominated by very large enterprises, and characterized by appreciated exchange rates and small non-oil traded sectors. These factors imply a more narrow space for SMEs to flourish, especially in non-oil sectors producing traded goods. Moreover, GCC countries tend to have small populations, and the nationals tend to find attractive positions in the public sector, which may also discourage risk-taking in the SME sector. By contrast, in the non-GCC countries there is probably scope for more SME growth across a wider range of economic sectors, including traded sectors, and also as part of supply chains linked to large enterprises.

In the case of non-GCC countries, three sub-groups can be identified: a first group with SME lending below 10% of total lending (Syria, Egypt), an intermediate group with SME lending between 10%-15% of total lending (Palestine, Jordan), and a third group with SME loans between 15%-24% of total loans (Tunisia, Lebanon, Yemen, and Morocco). There could be an element of error in country comparisons, related to different definitions of SMEs adopted by the banks. However, Figure 3b indicates that there is broad consistency between the results of enterprise surveys and the results of this survey (the comparison is only made where both survey results are available). The countries with the highest shares of enterprises with a loan tend to be the countries with the highest share of SME loans in total loans – Morocco, Lebanon, and Yemen, while the countries with the lowest shares of enterprises with a loan tend to also have small shares of SME loans – Syria, Egypt, and the Palestine (the correlation between the two variables
<br>(0.053)|
|Placebo|coef.|0.023|0.022<br>-0.009|
||s.e.|(0.022)|(0.015)<br>(0.023)|

_Notes: Bootstrapped standard errors (200 replications) in parentheses._<sup>_∗_</sup> _p<0.1,_<sup>_∗∗_</sup> _p<0.05,_<sup>_∗∗∗_</sup> _p<0.01. (E): estimated effect of the treatment at the time period of the infrastructure provision._ _(D): estimated effect one period after the infrastructure provision._

# **4.3 Causal Mechanisms**

We now investigate in greater detail the possible mechanisms that can explain how infrastructure provision affects output and productivity. Assuming a Cobb-Douglas aggregate production technology at the municipal level, we focus specifically on the capital/labor ratio (capital intensity) and the total factor productivity (TFP), which captures technological improvements (see derivation and estimation procedure of TFP in Appendix 1). Capital and labor data are available only for the period 1970-1980, which, fortunately, is a period when significant investment was made. However, as we explain in section 2.2, the data are only available for some municipalities, leaving us with a smaller sample of municipalities during that period. Therefore, we first apply the DIDM estimator to this restricted subsample to make sure that the main results in the second row of Table 2 are unchanged. Appendix Table A2 shows that this is indeed the case. Because the subsample does not include municipalities with highway access that were subsequently electrified during the period, we cannot estimate 17
their experience abroad once they return home at an older age. We report, however, that the minority of return migrants who find employment in the construction sector back home - the main sector of employment at destination - experience a large and statistically significant wage premium compared to nonmigrants employed in the same sector.

The paper is organized as follows. Section 2 provides some contextual background on temporary migration from South Asia. Section 3 introduces the data for Bangladesh, Nepal and Pakistan used in the paper and reports descriptive statistics. Section 4 provides details on our empirical estimation specification. Section 5 reports and discusses our descriptive results. Section 6 concludes.

4
that attitudes to and experiences of IPV differ between religious and cultural groups<sup>2</sup> (Odimegwu and Okemgbo 2003; <mark>Antai and Antai 2008;)</mark> . IPV is still widely condoned in Nigeria with 35 percent of women and 25 percent of men believing a husband is justified in beating his wife for any of the five reasons asked about in the survey<sup>3</sup> (NPC 2014).

Table 1: Women who have ever experienced physical or sexual violence by a husband or partner in Nigeria, by location and region, percent.

||**National**|**Urban**|**Rural**|**North-**<br>**Central**|**North-**<br>**East**|**North-**<br>**West**|**South-**<br>**East**|**South-**<br>**South**|**South-**<br>**West**|
|---|---|---|---|---|---|---|---|---|---|
|**2008**|18|18|19|26|17|7|24|39|14|
|**2013**|16|18|15|21|21|6|20|28|20|

Source: 2008 and 2013 Nigerian Demographic and Health Survey reports Between 2008 and 2013, a time period that corresponds to the onset of the BH insurgency, the national rate of IPV fell from 18 to 16 percent. However, this progress was uneven and in two regions, North East and South West, IPV prevalence rates increased. The BH
to spend more time hauling water on average (e.g., Northern, Upper East, Volta) is reduced in the restricted sample. This is not surprising knowing that the average distance between matched clusters was higher in these regions (Table 1). Some statistics on the distribution of the time to the water source in both samples are given in Table E.2 (Appendix E).

In general the estimated coefficients are of the same sign and of similar magnitude in the two models, but fewer coefficients are significant in the restricted sample. In particular the coefficient for hauling time greater than 20 minutes is found not significant at usual levels of significance. This result is probably driven by the lack of variability of hauling times, which itself is a consequence of the removal of the poorest clusters from the restricted sample.

We perform a final robustness check by excluding the last round of observations (2008). As discussed earlier, there was a significant increase in the proportion of girls attending school in this year (Table 2). It is thus important to check that our main findings are not driven (mainly) by what happened at the end of the period. Estimated coefficients of all variables are similar as the ones shown in Table 4. The coefficients of the time variables are found negative and significant. For hauling time greater than 20 minutes, the estimated parameter is now somewhat greater in absolute value, -0.20 (whereas it was -0.14 when the 2008 data were included), not statistically different from the parameter estimated on the full sample.

# **7. Conclusion**

Using four rounds of the DHS from Ghana, we provide some new insights into the relationship between households’ water access and girls’ school attendance in this African country. Our main methodological innovation is to build a panel of clusters based on GPS coordinates. The latter is combined with the use of panel data techniques suited to the analysis of fractional response data allows us to control for endogeneity
# **References:**

Adams, Alayne, Jindra Cekan and Rainer Sauerborn. 1998. ―Towards a Conceptual Framework of Household copping: Reflections from Rural West Africa,‖ _Africa_ 68(2): 263-283.

Appleton, Simon. 1996. ―Women-Headed Households and Household Welfare: An Empirical Deconstruction for Uganda.‖ _World Development_ 24(12): 1811-1827.

Backiny-Yetna, Prospere, Franck Adoho, Issa Bouaré, Virginie Briand, Kassim Dabitao, Assa Gakou, Estelle Sommeiller, Dramane Traoré, Clarence Tsimpo, amd Quentin Wodon. 2007. ―Tendances et Déterminants de la Pauvreté au Mali 2001-2006,‖ World Bank.

Buvinic, Mayra and Geeta Rao Gupta. 1997. ―Female-headed Households and FemaleMaintained Families: Are they Worth Targeting to Reduce Poverty in Developing Countries?‖ _Economic Development and Cultural Change_ 45(2): 259-280.

Case, Anne, Christina Paxson and Joseph Ableidinger. 2004. ―Orphans in Africa: Poverty and School Enrollment,‖ _Demography_ 41(3): 483-508.

Chen, Martha. 2000. _Perpetual Mourning: Widowhood in Rural India_ . New Delhi: Oxford University Press.

Christiaensen, Luc and Richard Boisvert. 2000. ―On Measuring Household Food Vulnerability: Case Evidence from Northern Mali,‖ Working Paper 2000-05, Cornell University, Department of Applied Economics and Management, Ithaca, NY.

Croson, Rachel and Uri Gneezy. 2009. ―Gender Differences in Preferences,‖ _Journal of Economic Literature_ 47(2): 448-474.

Dercon, Stefan and Pramila Krishnan. 2000. ―In Sickness and In Health: Risk Sharing within Households in Rural Ethiopia,‖ _Journal of Political Economy_ 108(4): 688-727.

Doepke, Matthias and Michele Tertilt. 2010. ―Does Female Empowerment Promote Economic Development?‖ Policy Research Working Papers, World Bank, forthcoming.

Drèze, Jean and P. V. Srinivasan. 1997. ―Widowhood and Poverty in Rural India: Some Inferences from Household Survey Data,‖ _Journal of Development Economics_ 54: 217-234.

Duflo,
|133,566|238,340|
|Haiti|1971|434,869|51,096|101,984|35,014|58,427|Venezuela|1990|1,800,000|199,055|445,482|149,752|269,185|
|Haiti|1982|128,770|15,471|36,494|8,349|15,840|Venezuela|2001|2,300,000|234,403|534,204|204,784|394,511|

Notes: This table reports the total sample size by country-year Census, and for restricted population by age and keeping only observations with information of education for children and parents.

36