rafmacalaba/data-use-mentions-extended huggingface dataset:
efforts are constrained by the lack of data (Kjellgren et al. 2014). The lack of data on measures of household welfare, prevalence of shocks, and coping strategies used to mitigate the impact of shocks significantly constrained the analysis of household vulnerability and resilience to shocks and its implications on the effects of shocks on their welfare. Recently, the Central Bureau of Statistics began conducting a household survey collecting data on these variables—the National Baseline Household Survey (NBHS) in 2009 and the National Household Baseline and Poverty Survey (NHBPS) in 2014/15. Both datasets contain information on household demographics, assets, consumption/expenditure, shocks experienced, coping strategies used, and agricultural activities making it possible to describe the prevalence of idiosyncratic shocks and the types of coping strategies used to mitigate them in each period and discuss changes overtime. However, it is not be possible to examine the dynamics in the effects of shocks on household welfare at household level. This is because both surveys have difference sampling frameworks in addition to political and geographic differences between the two periods- one being before the secession and the other being after. However, using cross-section regression techniques on the latest survey, it is possible to examine the effects of shocks on the welfare of Sudanese households to draw insights for policy.

This paper applies this framework to examine the impact of idiosyncratic and covariate shocks on the welfare of Sudanese households. Data on idiosyncratic or household level shocks such as floods/drought, sickness, crop/livestock loss, death of family member, fire, robbery, etc. are self-reported and provided in both surveys. We complement this data with information on covariate shocks such as flood/drought and conflict<sup>4</sup> obtained from additional data sources. In particular, state-level rainfall and conflict data is extracted from geo-referenced data overtime. Deviations of observed rainfall and conflict in specific years (such as the years for which there is household data) from historical levels can be constructed and used

> 4 A separate
on the basis of surveys. More recently, data have been collected on the spread of microfinance following CGAP efforts and Microcredit Summit. These cover the number of people with access to a savings account. For some countries, there are data from household surveys, such as the Living Standard Measurement Survey (LSMS)-type. Of these LSMS-surveys, some 27 have covered some dimensions of households’ use of financial services (see Honohan, 2004c). Still, and with the exceptions of some developed countries such as Sweden, much of the data collected in these general households surveys is very basic and limited in terms of the various dimensions of use and access (quantity, costs, quality). Access by households to credit, although typically only one-quarter in terms of number of access to savings and arguably less important in terms of growth and development, has been equally difficult to document at the level of households. Many countries, for example, do not even have data on the aggregate level of consumer credit, in part, as not just banks are providing that, but also non-bank financial institutions.

Data on firms’ use and access to financial services are equally limited. While there is much information on listed firms’ financial structure and their access to (some forms of) external financing, there is much less information on the unlisted firms and especially limited information on small firm finance access. Mostly data come from surveys, such  as those conducted by the World Bank (World Bank Economic Survey WBES, Investment Climate Assessments  ICAs), or by national agencies such as the US Federal Reserve Boards, UK Bank of England, EU, etc. Some data come from central bank statistics and advocacy groups (e.g., US Small Business Administration, chambers of commerce, and equivalents). Again, the data are basic and limited in terms of various dimensions of access (quantity, costs, quality). Access to credit dominates the data collection efforts, with access to savings services less of
considered the most “exogenous” variable, with the implicit view being that it is largely under the control of (risk-averse) banks.

# **IV.  THE DATA**

We begin by examining the data on private and public investment in the three countries in our sample. Next, we consider the evolution of public capital expenditure on infrastructure and describe how these flows are converted into stocks. We then explain how our basic indicators of the quality of public capital in infrastructure are constructed, and how they are used to derive a composite indicator.

# **1.  Overall Trends**

Figure 1 shows the evolution of public and private investment ratios to GDP since the mid-1960s in Egypt, the mid-1970s in Jordan, and the early 1970s in Tunisia. The share of public investment in GDP has displayed substantial volatility over time in all three countries, but has been on a downward trend in Egypt and Jordan since the late 1980s. Private investment ratios have at the same time been subject to large fluctuations, most significantly during the 1980s and 1990s—a period characterized also by large fluctuations in GDP in the region as a whole. In Egypt, following a steady increase from the mid-1960s to the late 1980s, the share of private investment in GDP has averaged 10 percent. In Jordan and Tunisia, private investment ratios have declined significantly since the peaks of the early 1990s, fluctuating in recent years between 12 and 15 percent.

# **2.  Flows and Stocks of Public Infrastructure**

National Accounts data on public investment in infrastructure are generally not available. For the purpose of our study, we used government budget data published in the IMF’s _Government Finance Statistics_ (GFS) Yearbook to build an estimate. Specifically, as discussed in Appendix B, we calculated capital expenditure on infrastructure by adding capital outlays on various categories, including construction, 13
component in determining food security in developing economies. Market concentration in the food production chain has been growing, driven by retail chains, raising concerns of higher commodity prices for consumers (McCorriston 2016). Mexico is no exception to this trend, with high market concentration among key food items such as corn grains. In 2014, the four largest firms amassed 31.2 percent of total gross production per INEGI’s economic census. A closer look at corn flour, an important Mexican dietary staple used to make tortillas, reveals that the firms Gruma and Minsa produced more than 90 percent of corn flour (Zahniser 2004). This concentration resulted in a 26 percent price margin increase due to high market concentration in the tortilla market (Aradillas 2018). Figure 4 shows that Mexico’s maize and maize tortilla consumer price index has been consistently greater than the total consumer price index. Since 2010, both have increased significantly in Mexico, suggesting an increase in the consumer food basket.

13
exports to the rest-of-the-world (i.e., excluding the United States). This methodology was chosen to avoid simultaneity issues between the exporters’ organization dummy and the tariff preferences.<sup>36</sup> This mapping process was repeated for each entry found on the FARA report for 33 countries in Latin America. We used the FARA reports for 1997, 1998, 1999 and 2000. Finally, we calculated the average lobbying expenditure over this four year period by 3-digit ISIC industry and by country. Trade related FARA contributions by Latin American exporters reached 120 million dollars during this period when both fi rms’ and governments’ contributions are included.

After selecting the contributions related to trade, a series containing exporters’ political contributions from private fi rms and Latin American governments was organized at the ISIC 3-digit level. Political organization of exporters is determined using quartile thresholds of the distribution of political contributions. After determining the exporters’ political organization at the ISIC 3-digit level we replicated the value of the variable Ik<sup>iattheHS8-digitlevel.</sup> Elasticities of Export Supply Data on elasticities of export supply was kindly provided by Marcelo Olarreaga. The elasticity data are described in Kee, Nicita and Olarreaga (2004). They estimate elasticities of export supply at the HS 6-digit and ISIC 3-digit levels for about 118 countries. They present elasticities of export supply for most Latin American countries. Dividing Latin America in regions<sup>37</sup> allow us to use the regional export weighted average of the export elasticities in a region to fi ll the gap for those Latin American industry codes missing in their study. In the cases a regional weighted export elasticity is not available to substitute the elasticity for a particular commodity, we use the export weighted average elasticity of Latin America instead. The elasticities of export supply at the HS 8-digit level used in this study are replications of the values obtained by Kee, Nicita and Olarreaga (2004) at the HS 6-digit level.

> 36More details about possible endogeneity of the
of the population.

Once income distributions are available, identifying households that would be eligible for assistance in any likely supply conditions, i.e. those facing a binding demand side constraint is the first step. These households will need direct assistance. We can define this group as those currently living in slums who could not possibly afford the _construction cost_ (at current market prices) of a minimum one story simple dwelling of say 130 square feet<sup>10</sup> with access to water and toilet. The problem of the land cost affordability must be treated separately after addressing the supply side and regulatory constraints. Very roughly, we calculate

> 9 Annex 2 provides details of methodology and sources for developing this income distribution data. 10 This cost represents the price of building a dwelling of decent quality in wood and brick. The cost of a high rise apartment meeting formal standards would be much higher per square foot. In any case, such a small home would not meet formal standards, but it would provide much improved accommodation for poor slum dwellers.

34
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hard core" poverty has not changed dramatically.

Both UNICEF and ILO have been concerned with developing poverty analyses. The former has been mainly interested in the qualitative side of the poverty analysis, while the latter has done some of the early work on poverty using survey data, as noted above.

In addition, there have been various other small scale studies or revisions of poverty lines which led to different proportions falling below poverty lines. For instance a World Bank(1994) study, constructed a poverty line which was based on earlier studies, and concluded that 57.7 percent of the urban population and 66.2 percent of then rural population lived under poverty.

# **3.4 Basic Needs Approach to Poverty**

As mentioned earlier many studies have attempted to understand poverty through indicators of basic needs. One of the first issues that needs to be resolved is what is covered by "basic needs". In the Nigerian context there have been some lengthy exchange of views among scholars and policy makers on this issue. One of the early definitions of basic needs was put forward by the ILO (1981) entitled "First things First" which included food, water, shelter, health

> 4/ The present analysis is based on the same data set, except that now it is processed, edited and allowance has been made for regional price differences and so forth before the poverty analysis. According to the current estimate 49.5 percent of the rural population and 31.7 percent of the urban population fell below the poverty line, resulting in around 43 percent of the national population being poor. It is worth noting that poor households are much larger than non-poor households and this also contributes to the difference in household versus population percentage under poverty line, apart from the differences in poverty lines used.
, confidence, and life satisfaction. Nearly 80% of survey respondents report a negative change in income in round 1 of the survey. Furthermore, more than 40% lack confidence in maintaining their living standards, and less than 20% report being satisfied with life. There are some improvements in subsequent months, but the overall levels still remain quite low. Nearly 20% of respondents even report further negative changes to income over the declines in June. Combined, these findings show that the effects of the COVID-19 crisis were hard hitting and immediate on both business and individual outcomes.

# **<mark>5. Estimation and Results</mark>**

## **<mark>5.1. Estimation Strategy</mark>**

We estimate the causal impact of grants and BDS with two rounds of pre-COVID data (baseline and February recall) and three rounds of post-COVID data, using the following ANCOVA specification: 11
<u>(1.418) (0.966) (1.005) (1.181)</u> Same set of controls in Table 5 Yes Yes Yes Yes Panel B and Panel C of Table 6 replicate the econometric model shown in Tables 4 and 5, respectively. In Panel B, the interaction between the four governance variables and _investor_ are all negative and statistically significant. This confirms our earlier findings that use the baseline governance variables. The instrumental variables estimation of the same regression, whose output is in Panel C confirms that the negative coefficients of the interaction terms are robust to alternative econometric modeling that takes into account endogeneity. Overall, we conclude that there is a negative relationship between governance and the use of short-term debt by firms, and this negative association is robust to the way governance variables are defined.

# **4. Conclusion**

In this paper, we use international corporate governance data and show that the association between short-term debt and governance varies across countries. The firms with good governance do not rely on the external monitoring of the creditors and so they do not incur the costs of shortterm debt such as liquidity risk. However, we show that institutional environment plays a key role in determining the association between short-term debt and governance. As there is a trade-off between the benefits of short-term debt in reducing agency problems and its costs associated with liquidity and distress, firms with better governance choose higher amounts of short-term debt when it is more effective. In particular, we find that short-term debt and governance act as substitutes in high investor protection countries because the benefit for costly external monitoring using shortterm debt is lower.

To address potential endogeneity concerns, we use legal origin as an instrument for investor protection and find that our results continue to hold. We take an additional step and create a number of alternative governance quality measures and test whether the baseline results are specific to the initial set of governance variables. Our findings indicate that there is a strong negative relation between governance
sri lanka country risk profile 12 Clearly, a more disaggregated approach will be required. To do so, the rest of this analysis goes deeper in the distributional analysis, moving from the regional scale to the household level. In the next section, we merge regional asset loss data with household-level socioeconomic characteristics to examine disaster impacts on household income and consumption. This novel approach will allow us to develop estimates of the number of individuals pushed into transient poverty each year by disasters. Subsequently, we study how exposure to flooding affects households’ consumption and wellbeing and use these insights to assess new policy tools for managing the risk of the poor and vulnerable.

# 2.2 Income and consumption poverty

The disaggregation of asset losses to the household level allows us to move from expected asset losses to the secondary effects, including income and consumption losses, that must be expected when households are affected by shocks. From this perspective, we begin to see disaster losses not just in terms of economic costs, but as an obstacle to poverty reduction and other development imperatives.

The technical details of asset loss disaggregation to the household level are discussed in the technical appendix to this report. In this section, we present the main insights generated by the union of the SSBN model results with the household income & expenditures survey (HIES), focusing on how individual households’ socioeconomic characteristics can mitigate or magnify the impact of disasters.

# _Income poverty_

When disasters damage or destroy the assets on which individuals rely for their livelihood — including not only their own shop or field, but also somebody else’s factory — affected households face income losses. Some may receive extraordinary public assistance or additional remittances, which supplement their regular income while they rebuild, while others will be left to their own devices. Net income losses incorporate these socioeconomic characteristics, which influence households’ recovery pathways.
TABLE 6. Contribution of Raw Bilateral Data to the Total

|**Census round**|**Accounted for by**<br>**“raw” data(%)**|
|---|---|
|**1960**|95.9|
|**1970**|92.5|
|**1980**|92.5|
|**1990**|92.1|
|**2000**|93.6|

_Source:_ Authors‘ calculations based on data described in text.

41
group, and the difference. The difference is our list randomization estimate of the incidence of illegal migration. We estimate that 8.4 percent of the households in the Mexican sample have an illegal migrant abroad, as does 8.6 percent of the Philippines sample, 38.5 percent of the Ethiopian sample, and 41.5 percent of the Moroccan sample.

> 3 There are two important reasons why the rate is greater than the national average for the Filipino sample. First, the World Bank data is based on destination country census reports of where individuals live, which can miss a lot of the temporary migration from the Philippines. Second, our definition of migrant for this sample includes both a direct household member being a migrant as well as an immediate family member.

9
_Monitoring_ aims to ascertain whether the program actually achieves its objectives and whether it is doing so at low cost. To this end, it describes what is happening on the ground, compares it to original objectives, and uses the information to update original parameters. The information provided can also be used to (i) assess whether the mechanisms utilized do reach target populations and areas (i.e. poor rural dwellers and underutilized lands); (ii) provide an _ex-ante_ estimate of the expected benefits of land reform, both in terms of productivity and poverty reduction; and (iii) provide information on whether implementation is progressing as expected. _Impact assessment,_ in turn, is concerned with the ultimate impact, both direct and indirect, of the program on household well-being, agricultural productivity, environmental sustainability, and institutional strengthening. To this end a nation-wide LSMS-type household survey, and a farm survey, will be used as a control group, to be compared to a panel of beneficiaries and lands that will be established under the program.

# 4 COMPARISON **WITH** OTHER COUNTRIES

In addition to Colombia, Brazil and South Africa have, under different conditions, recently initiated programs of negotiated land reform. With an institutional background very similar to that of Colombia (presence of land reform legislation and a central land reform institute dating from the early 1960s), negotiated land reform in Brazil has been driven by individual states' initiative. The purpose of the Brazilian interventions is to establish cheaper, more agile policy alternatives to centralized land reform in an environment where the issue of land reform is high up on the political agenda and potential beneficiaries have at least some idea of what to do with the land.

21
00)|(0.00)|(1.75)|
|Household Size||0.00193<br>(0.23)|-0.0033<br>(0.0087)||0.000380<br>(0.06)|<br>-0.00167<br>(-0.27)|
|WB Law Index|||0;0534**<br>(0.0174)|||0.0189**<br>(6.40)|
|Observations|10124|10124|10124|19295|19295|19295|
|Mean of outcome variable|0.135|0.135|0.135|0.137|0.137|0.137|
|WB p-value|0.368|0.336|0.314|0.372|0.398|0.0940|
|N cohorts|42|42|42|59|59|59|
|Time trends|No|No|Yes|No|No|Yes|
|Controls<br>Source: Authors Calculations|Yes<br>- Demographic|Yes<br>and Health Su|Yes<br>rvey (DHS): Jord|Yes<br>an (1990, 1997|Yes<br>, 2002, 2007, 200|Yes<br>9, 2012).|

Source: Authors Calculations - Demographic and Health Survey (DHS): Jordan (1990, 1997, 2002, 2007, 2009, 2012). Notes: The dependent variable is a binary indicator for whether the interviewed woman is currently working. Columns 1 to 3 report results for a restricted period representing 10 years around the reform year between 1997 and 2007. Columns 4 to 6 report results for the full covered period between 1992 and 2017. The estimation samples are restricted to married women whose youngest child's age is around
Policy Research Working Paper 9001

# **Abstract**

Mexico’s conditional cash transfer program, PROSPERA, has demonstrated short- and medium-term positive effects on health and education, including: increased children’s height; decreased risky behaviors among adolescents, including the postponement of parenthood; and increased years of schooling. This paper explores whether these effects lasted in the long-term and translated into positive changes in outcomes across generations. This study uses the most recent PROSPERA Evaluation Survey (ENCEL 2017) and combines it with previous waves and with the 1997 Socioeconomic Characteristics Survey (ENCASEH). Using intergenerational mobility analysis and quasi-experimental methods, this study finds strong evidence of positive absolute intergenerational mobility in height and years of schooling. The findings show that, on average, male offspring are 2.8 centimeters taller and have 5.3 more years of schooling than their providers (usually their parents), while female offspring are 4.1 centimeters taller and have 5.7 more years of schooling than their providers. These intergenerational gains are relevant not only because they reflect improvements in human capital, but also because these improvements have a positive return to investment. The study finds that a 1 percent increase in height is associated with a 10.7 and 8.8 percent increases in hourly wages for men and women, respectively. The analysis finds that a one-year increase in schooling is associated with 3.4 and 4.8 percent increases in hourly wages for men and women, respectively. These results show that PROSPERA has been successful in helping children and youth build human capital through better health and education, which has led to positive returns in the labor market.

This paper is a product of the Social Protection and Jobs 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