rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 10036

# **Abstract**

This paper estimates intergenerational mobility in education using data from 91 censuses that span 24 countries in Latin America and the Caribbean over half a century. It measures upward mobility as the likelihood of obtaining at least a primary education for individuals whose parents did not finish primary school, whereas downward mobility is the likelihood of failing to complete primary education for individuals whose parents completed at least primary school. In addition, the paper explores the geography of educational intergenerational mobility using nearly 400 “provinces” and more than 6,000 “districts”. It documents wide cross-country and within-country heterogeneity. The paper documents a declining trend in the mobility gap between urban and rural populations, and small differences by gender. Within countries, the level of mobility is highly correlated with the share of primary completion of the previous generation, which suggests a high level of inertia. In addition, upward (downward) mobility is negatively (positively) correlated with distance to the capital and the share of employment in agriculture, but positively (negatively) correlated with the share of employment in industry.

This paper is a product of the Poverty and Equity 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 author may be contacted at emunozsaavedra@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/
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# **Abandonment of Polluting Fuels**

Abandonment of polluting fuels was examined in the context of adoption of LPG by taking the larger geographical area of the two areas (entire country or urban areas) in which 20 percent or more of the population cited LPG as the primary cooking fuel. There is a trade-off between coverage and representativeness, and the cut-off point of 20 percent was selected as a compromise between the two competing objectives—the lower the threshold level the greater the coverage, but the lower the statistical significance of the data. In the areas so selected, the analysis began with identification of all households using LPG in any capacity, followed by identification of those citing LPG as their primary cooking fuels, and finally those who were not only using LPG but had abandoned all polluting liquid and solid fuels (Figure 9). As mentioned in the study description, abandonment as defined and measured in this study is not confined to cooking and instead represents abandonment of polluting fuels for all household activities. There was considerable variation in the population shares across the areas, ranging from those where only a very small fraction of LPG users had abandoned polluting fuels to those where virtually everyone had abandoned them. The solid blue portion in the figure representing abandonment of polluting fuels varied from 2.6 percent in urban Burkina Faso to 98 percent in Mauritius. Urban Burkina Faso stood out for having a majority of LPG users being incidental users who did not use LPG as the primary cooking fuel. By contrast, in Angola, Gabon, Mauritius, and urban Nigeria, the vast majority of those using LPG at all used it as their primary cooking fuel.

**Figure 9  Breakdown of adoption of LPG: abandonment of polluting fuels, primary use, and all users of LPG**

<!-- Start of picture text --> Primary<br><!-- End of picture text --> _Source_ : World Bank staff calculations using household survey data.

_Note_ : The analysis is confined to those
NFA treatments into one pooled treatment indicator.

When analyzing data at the clinic-level, we drop the indexes for households and villages, estimating: These models include a single observations for each clinic, so there is no need to cluster our standard errors.

In addition to conventional standard errors, we report q-values that control for the proportion of incorrectly rejected null hypotheses (Anderson 2008). Specifically, we control for the false discovery rate within treatment arm (1) across outcome families; and (2) across sub-components within each family.<sup>21</sup> As noted above, we follow Kling et al. (2007) to construct outcome families for the medium-run results. If an individual only has data for some sub-components within a family, this procedure involves imputing those missing sub-components using the mean from that individual’s treatment arm. This leads to high rates of imputation when a family includes sub-components that are not well-defined for a large share of observations (e.g., complications with childbirth can only be measured for mothers). To confirm that such imputation does not influence our results, we follow Kling et al. (2007) and Casey et al. (2012) and employ seemingly unrelated regressions (SUR). In short, we estimate treatment effects on each sub-component of a family without any imputation and then average the coefficients across these stacked regressions, using the delta method to compute standard errors. The tables in Appendix Section D.2 include results for our pre-specified families using SUR, as well as the results from the stacked sub-component regressions.

# **3.5.2 Ebola Case Data**

We assess the impact of the CM and NFA interventions on reported cases for the 160 sections in the Ebola sample (described in Section 3.1.2). We observe counts of reported cases in each section in every week from 10 August 2014 to 18 October 2015. However, we restrict attention to the period from September through April 2015, when Ebola transmission
use a measure of demand that reflects growth in construction and manufacturing industries in the key destination countries. Specifically, I use the growth rate in the levels of carbon dioxide emissions attributed to construction and manufacturing industries in India, Malaysia and the Gulf countries (Qater, Saudi Arabia, and United Arab Emirates). I get this measure from the World Development Indicators database (The World Bank) which reports carbon dioxide emissions calculated by U.S. Department of Energy’s Carbon Dioxide Information Analysis Center (CDIAC) using data from the United Nations Statistics Division’s (UNSTAT) World Energy Data Set and the U.S. Bureau of Mine’s Cement Manufacturing data set. The UNSTAT collects this information from several countries using questionnaires designed to collect information on emissions from various sectors. Cement manufacturing data from the U.S. is used to estimate CO2 emission from construction. Further, CDIAC continually maintains and updates this database based on supplemental information collected and upon availability of new information.

The variation in this dataset comes from the fact that the destination countries are in different cycles of construction and manufacturing. Further, the great recession of 2008 affected these destination countries quite differently, creating a useful variation in the data on the growth of migrant demand.

> 16See Appendix B for details of this estimation.

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In a second exercise, we use estimates from the pre-Great Recession model to then show the expected trade policy response during 2008:Q4-2010:Q4 given the realized macroeconomic shocks. The historical models for the US and EU in particular predicted a sharp increase in import protection taking place beginning in 2009:Q2 and lasting through 2010:Q1, peaking in 2009:Q3. While the increase in import protection during the Great Recession was not as large as the potential additional 15 percentage point coverage of imports that some historical models predicted, arguably the fear of this realization contributed to the pre-emptive “anti-protectionism” G20 declaration of November 2008 and a number of initiatives to actively monitor trade policy (by the WTO, World Bank, and Global Trade Alert) beginning in early 2009.

Third, we re-estimate the models on data from the Great Recession period in order to explain deviations to the realized protectionist response away from historical behavior. We do not find robust evidence of a change to the countercyclical relationship between _domestic_ unemployment and new import protection. However, we do provide evidence that these policy-imposing economies “switched” from their historical behavior; during the Great Recession they shifted new import protection away from those trading partners that were contracting and toward those experiencing relatively stronger economic growth. This evidence, in addition to the sharp and persistent real depreciations of the US dollar and euro after their initial run-up in 2009, were particularly important contributors to the relatively low levels of import protection that arose given that so many trading partners were themselves experiencing periods of severe economic contraction during 2008-2010.

Finally, our results contribute to a substantial literature that has evolved to explain the role of trade agreements and the use of temporary trade barriers under the WTO. Our paper identifies the importance of bilateral channels through which new trade barriers arise, and how these channels differed in 2008-2010 relative to periods before the Great Recession. Nevertheless, even this research is just one additional step in understanding the role of trade agreements, including the potential limits to what trade agreements can accomplish in
Second, our data suggest productivity of farms below 50 ha is low in part due to investment disincentives and transferability restrictions associated with land under permanent use rights, they also show that informal farms are as productive as formally registered ones and that productivity differences between farmers of different size groups above 50 ha are much narrower than policy makers seem to believe. The rapid growth of SAR registrations suggests that farmers will come out of the shadows if doing so provides benefits. Measures to expand this into a digital marketplace and facilitate land transfers and boost mortgage lending using land as collateral, including greater transferability of land given under rights of permanent use rights could thus have a transformative effect by setting in motion a wave of investment and growth in higher value products by small and medium-sized farms to solidify Ukraine’s comparative advantage in agriculture and improve rural living conditions.

Finally, evidence-based decision-making to help the country address the unprecedented current challenges and maximize economic potential and public accountability during reconstruction requires that quality farm survey data like the ones analyzed here will need to be provided regularly, ideally by the country’s State Statistics Service (SSSU) including better coverage of the entire farm size spectrum, and in ways that can be linked to data from other sources. To move in this direction, there is a need to amend the legal basis for agricultural statistics to be in line with global best practice in terms of harnessing satellite imagery, computerized data entry, and dissemination of micro-data for analysis without sacrificing confidentiality. An expeditious move towards such a framework would allow alignment with EU membership requirements and help make information collected by SSSU more representative of Ukraine’s farm sector, timelier, and more meaningful for policy decisions to navigate the profound war-induced challenges to the country’s economic and social fabric in a way that is based on evidence rather than ideology and encourages participation and input by all stakeholders.

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regulation create scope for corruption, connected lending, and gambling for redemption.

- Are stronger prudential measures on the claims of foreign investors justified, assuming that the claims are more volatile?

- Empirical evidence is not clear. Experiences of financial crises in several emerging markets have suggested that domestic residents contributed importantly to the short-term capital outflows that occurred in the run-up to the crises in question. (Frankel and Schmukler, 1996, 1997)

- Fischer, 1998 and Mussa, 1998 suggest that prudential regulation should address threats not only to the stability of the banking system but also to general financial stability created by the assumption of large volumes of short-term debt, particularly that denominated in foreign currency, by financial institutions, firms and the sovereign. This could be justification for extending the standard arguments for prudential supervision and regulation beyond the short-terrn foreign currency exposure of the banking system to at least to the monitoring of other short-term flows, and perhaps to tax-like measures to discourage excessive exposure for the economy as a whole. (Eichengreen, Mussa, et al., 1998, p. 26)

- Can capital controls on capital inflows have a significant effect on the composition of capital inflows?

- The empirical evidence is mixed. Studies that distinguish between short-term and long-term suggest that controls are likely to become progressively less effective the longer they remain in place. Still, a number of studies conclude that measures adopted by countries like Chile, Colombia, and Israel to influence the level and composition of portfolio capital inflows have not been without effect.

- On the other hand, in 1990, Korea started a series of reforms designed to liberalize the capital account. The sequencing of the reforms led to an early liberalization of conmnercial credit and short-term flows, while it left in place significant restrictions on foreign direct investments and portfolio investmnents in domestic equities.<sup>40</sup>

- To some extent, the Korean capital controls program should have had the opposite effect of the Chilean program. Data from BIS confirm this. Chile's
Which households are more likely to be unequal? Is it related to the total amount of expenditure? To the household structure? To the head’s characteristics? In this section, we examine the correlates of being an unequal household focusing on the household structure, and show how the specificity of the PSF questionnaire allows to gain new insights on the determinants of intra-household inequality.

In table 5, we present the correlates of the within household Theil index, both for consumption as a whole and then separately for food and non-food expenditures.

Household structure clearly plays a role in explaining intra-household inequality. It is worth noting that this might be difficult to pin down with standard household surveys that only record the relationship of each household member to the household head. The first 2 columns of table 5 compare the description of the relation between inequality and household structure obtained from a regression using only variables that could be retrieved from a traditional survey, to what can be exhibited with the more detailed information on household structure that PSF contains. Column (1) describes household structure with variables counting the number of children below age 5, those between 5 and 15, the number of adult women between 15 and 65 years of age, and the number of elderly members. It also includes a dummy variable if the household doesn’t embrace any horizontal extension, i.e. if all members are either the wife, the parents, the children or the grand-children of the household head. The first column only indicates that the level of intra household inequality increases with the number of children aged 5 to 14. As this might reflect difference in needs that are not captured by the per capita measure of individual consumption, it is not very revealing. The second column makes clear that, even controlling for the size and the age-gender composition of the household, its precise organisation matters. Extended households with more than two cells, whether they are polygamous households or include cells headed by relatives in addition to the head’s conjugal unit, have more
513***|-0.071***|0.036**|
||(0.015)|(0.031)|(0.013)|(0.017)|
|PSU dummies|Yes|Yes|Yes|Yes|
|Women controls|Yes|Yes|Yes|Yes|
|Partner controls|No|No|No|No|
|Observations|1,561|1,561|1,553|1,460|
|R-squared|0.258|0.380|0.456|0.308|
|Standard errors clustered at the district<br>level.|||||

These results indicate that the main channel for an increase in women’s participation in decision making, particularly in areas that have experienced conflict at a “medium intensity level”, is driven by significant changes to household composition. Potentially due to domestic or international migration of men, as well as the deaths of many men in the battlefields, more households are headed by women than before the war started. While we see better involvement of women in decision making, it should be noted that many of these households remain more vulnerable. Aggregate household consumption per capita, which is measured using the YHDS, is lower among female headed households. The bottom 20 percent and 40 percent of households by consumption per capita are more likely to be female headed households (World Bank, 21
comparison of the 7-day recall module with the consumption incidence recorded in the first week of the personal diaries. The shortfall in incidence is largely the same relative to the first week of the personal diaries as with both weeks. It is impossible to conduct a similar analysis for the usual month as the personal diary was only collected for a two-week period. Nonetheless, combining personal diaries from two households fielded within the same calendar month can simulate consumption incidence over a one-month period. This exercise also reveals higher reported incidence by the usual month than in the personal diaries, suggesting that a significant portion of the higher consumption incidence in the usual month arises because of response error.

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,347|9,347|9,347|9,347|9,347|9,347|9,347|9,347|
|**_Control variables_**|||||||||||
|Food expenditures|||Y||||||||
|Non-food expenditures||||Y|||||||
|Furnishings and durable household expenses|||||Y||||||
|Health expenditures||||||Y|||||
|Education expenditures|||||||Y||||
|Utilities expenditures||||||||Y|Y||
|Household assets & house characteristics||Y|Y|Y|Y|Y|Y|Y|||
|Demographics & employment|Y|Y|Y|Y|Y|Y|Y|Y|Y||

**Note:** Normal linear regression with bootstrapped standard errors is used. The standard errors are calculated using 100 bootstrap replications and are adjusted for complex survey design. All estimates are obtained with population weights. The normal linear regression model with the theoretical distribution of the error terms employs cluster random effects. ’Near poor’ status is defined as living on an income between 100 and 125% of the poverty line. All indicators are expressed in percentage. The true rate is the estimate directly obtained from the survey data. Estimates shown in boldface or with a “*” respectively fall within the 95% confidence interval or one standard error of the true rate.

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|Corporate tax %<br>(kpmg_tax)|%|Corporate tax rate. The above rates do not reflect payroll<br>taxes, social security taxes, net wealth taxes, turnover/sales<br>taxes and other taxes not levied on income.|1997-<br>2003<br>(annual)|**Corporate Tax Rates Survey, KPMG, Switzerland**|
|---|---|---|---|---|
|||When 2 or more rates are reported, the highest number is<br>chosen.||The survey (begun in 1993) currently covers 68 countries,<br>including the 30 member countries of the Organization for<br>Economic Cooperation and Development (OECD), and<br>many countries in the Asia Pacific and Latin America<br>regions. Local KPMG tax offices from these countries<br>have contributed to this survey.|
|1D Top marginal tax<br>rate<br>(efw_d)|0 to 10|Average of 1.D.i. Top Marginal Income Tax Rate and 1.D.ii.<br>Top Marginal Income and Payroll Tax Rate<br>Countries with higher marginal tax rates that take effect at<br>lower income thresholds received lower ratings. The<br>income threshold data were converted from local currency<br>to 1982/1984 US dollars (using beginning-of-year exchange<br>rates and the US Consumer Price Index).|1970-<br>2000<br>(5-year)<br>and 2001|**Economic Freedom of the World, The Fraser Institute**<br>From Section:<br>1: Size of government<br>Based on: Price Waterhouse, Individual Taxes: A<br>Worldwide Summary (various issues)|
|**LABOR**|||||
|*
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- (iii) The data presented in this study on textiles and clothing production (see Tables 6 and 7) indicate more analyses is need on this sector. Specifically, given that the Long Term Textile Arrangement (LTA) and the Multifibre Arrangement were intended to give these industries an opportunity to restructure, why do they remain among the most labor intensive sectors of U.S. industry in the 1980s?

- (iv) This investigation identified specific industries (Table 7) that appeared especially "vulnerable" to increased competitive pressures from developing country exporters. As such, it provides a focus for further analyses aimed at establishing how resources may be best transfered from these sectors to industries where a competitive edge has been maintained. These results (Table 7 and Appendix Table 6) also provide a focus for monitoring future developments concerning market penetration and pressures for protecticni.

- (v) This study employed a measure of comparative advantage based on factor proportions data, but noted that Balassa's (1965) "revealed" comparative advantage (RCA) indices could also be used for the analysis. It would be useful to determine how RCA indices relate to those developed in this study (and by the National Bureau of Economic Research). The objective might be to expand the utility of such statistics for anticipating disruptive imports or structural adjustment problems.