rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 7968

# **Abstract**

Using a new database of household surveys, this paper examines inequality among all individuals living in developing East Asia regardless of their country of residence. The East Asian Gini index increased from 39.0 in 1988 to 43.3 in 2012. Inequality increased during the initial decade, regardless of the choice of inequality measure. The trend appears to have reversed in the mid-2000s. Regional inequality is now almost entirely explained by within-country differences, while gaps in average income across countries have become unimportant. This reversal has been driven by rising national inequality especially in populous countries, counteracted by catch-up growth in average incomes, particularly in China. Interpersonal differences in income at the regional level have thus become internalized within national boundaries.

This paper is a product of the Poverty and Inequality Team, Development Research Group. 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 authors may be contacted at ljirasavetakul@imf.org and clakner@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/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
mentioned above, we expect stock prices to fall more for levered oil firms in response to the 2020 oil crash.

To test whether debt levels mediate the effect of oil price fluctuations on stock returns, we use daily data on oil prices and stock prices of U.S. oil firms from January to June 2020—a period of great upheaval for the oil market. Taking the issue of endogeneity seriously, we establish a causal relationship between changes in oil prices and changes in stock prices of oil firms by following a heteroskedasticity identification strategy developed by Rigobon (2003).<sup>9</sup> Specifically, we instrument for changes in oil prices with plausibly exogenous news that might exclusively affect current or future oil supplies, coined “oilspecific news.” We note that current oil demand by investors may increase if oil-specific news makes them worry about the availability of future oil supplies. We call days with oil-specific news “event days” and days without oil-specific news “non-event days.” The results are twofold. First, a decline in oil prices statistically significantly reduces oil stock returns. A 1 percent decline in oil prices leads on average to a 0.44 percent decline in stock prices. Furthermore, the elasticity becomes smaller with lower oil prices. Second, our empirical results point to the “irrelevance” of debt in mediating the effect of oil prices on oil stock returns. The role of total debt becomes smaller and statistically insignificant after standard firm characteristics are progressively controlled for. The liquidity backstop provided by the Federal Reserve appears not to have muted the role of debt for oil firms.

This paper contributes to the rapidly growing strand of the finance literature exploring the effect of COVID-19 on firm performance. Notably, Ding et al. (2021) evaluate the connection between corporate characteristics and the reaction of stock returns to COVID-19 cases for firms across over 60 economies. The authors find that the pandemic-induced drop in stock returns was milder among firms with stronger pre-2020 finances and less exposure to COVID-19 through global supply
poor; it could also contribute to financial deepening and inclusion. This improvement of financial deepening is critical for poverty reduction in SSA.

_Housing finance could increase investment in education and reduce vulnerability for the poor._ Becker (1975) and Atkinson (1975) studied the link between investment in human capital and wealth distribution. An implication of these models is that income inequality will decrease as access to finance improves and that housing improves homeowner’s borrowing capacity. They also suggest that housing finance could lead to more investment in human capital. To the extent that improved housing affordability benefits the poor, housing finance may improve education opportunities for the poor. Jacoby (1994) finds that lack of access to credit perpetuates poverty in Peru because poor households cannot afford to provide their children with appropriate education. Jacoby and Skoufias (1997) find that without access to finance, income shocks cause poor families to discontinue schooling for children; housing provides an asset that can be used to smooth income shocks.

_A more inclusive housing financial system can improve access to housing finance for poor and low-tomoderate-income households_ . Consistent with this view, Malpezzi (1999) suggests shifting from a housing finance perspective—where special circuits are used to mobilize short-term household deposits for longterm mortgages—to a perspective where housing finance is integrated with broader capital markets. Singh and Huang (2011) analyze data from Africa between 1992 and 2006 and find that financial deepening (as measured in part by credit to the private sector as a percent of GDP) is associated with less poverty and reduced income disparities in African countries, and that this is most important in the early stages of financial development. Stronger property rights strengthen this relationship. Beck, Demirguc-Kunt, and 5
|662|Diatomite|Yes|Kenya and Mozambique|
|---|---|---|---|
|681|Platinum|Yes|Botswana, Ethiopia, South Africa, and Zimbabwe|
|683|Nickel|Yes|Zimbabwe|
|686|Zinc|Yes|Algeria and South Africa|
|687|Tin|Yes|Nigeria, Rwanda, and Uganda|
|971|Gold|Yes|Algeria, Burundi, Cote d'Ivoire, Ethiopia, Kenya, Liberia, Mali, Republic<br>of Congo, South Africa, Tanzania, Uganda, Zambia, and Zimbabwe|
|_282_|_Steel_|_No_|_Kenya, Mozambique, Nigeria, South Africa, Sudan, and Uganda_|
|_289_|_Gemstones_|_No_|_Botswana, Ethiopia, Madagascar, Mozambique, and Rwanda_|
|_321_|_Coal_|_No_|_Botswana, Burundi, Ethiopia, Malawi, Morocco, Mozambique, Niger,_<br>_Nigeria, South Africa, Tanzania, Zambia, and Zimbabwe_|
|_525_|_Uranium_|_No_|_Namibia and Niger_|
|_663_|_Chromite_|_No_|_South Africa, Sudan, and Zimbabwe_|
|_684_|_Aluminum, ammonia, and bauxite_|_No_|_Cameroon, Mozambique, Nigeria, and South Africa_|
|_728**_|_Asbestos_|_No_|_South Africa_|

Source: Authors’ construction using USGS data.

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(38.5 percent higher in treatment areas). For female members of productive age (15-64 years old) the results are similar (33 percent higher in treatment areas).

Next, we inspect the nature of livelihoods activity that is causing this increase. Again, field notes and project monitoring suggest that program beneficiaries are moving away from being daily wage laborers (casual labor) to self-employment, thus initiating more businesses. The survey data confirm the same. The increase in livelihoods is predominantly due to the large and significant increase in the number of self-employment livelihoods activities in farm (5.4 percent more women employed) and non-farm activities (0.7 percent more women employed) with an overall increase of 5.8 percent. Most of this increase is coming from households moving away from casual farm labor towards self-employment such as farm and non-farm businesses. Furthermore, the results indicate that there is a 8.4 percent increase in the number of farming households who have transitioned to high-value agricultural crops.<sup>15</sup> This is another indicator of improved diversification and additional source of employment opportunities. Lastly, the data also indicate a small but significant increase in the number of formal jobs within the households. It is possible that this increase could be because of the program’s focus on skills and job placement (in select areas) or because of more businesses starting and employing more people.

Last, we analyze whether the increase in the number of livelihoods and diversification has influenced women’s participation in the labor force. The survey data allow us to look at the work participation rate (WPR)<sup>16</sup> and we focus on the female household members as they are the primary beneficiaries of the program. In 2011, as per the population census data in the sample areas, there was almost no difference in the treatment and control villages(see Table A 5)<sup>17</sup> . However, in 2016-17 we estimate that
from Staiger and Wolak (1994) which has shown that even an investigation can have trade-destroying effects. Nevertheless, in unreported results available from the authors, we confirm that the evidence below extends to a redefinition of the dependent variable to counts of products under investigations that only conclude with formal trade barriers being imposed.

11 This measure is carefully constructed for each policy-imposing economy by trading partner and by quarter in a conservative way that does not allow for redundancy. At any point in time in the sample period under the Harmonized System, there are roughly 5000 HS-06 imported products that could be imported from any particular trading partner. In terms of policy, governments impose these import restrictions at the 8- or 10-digit product level; unfortunately the HS-06 level is the most finely disaggregated level of data that is comparable across countries. First, so as to avoid double counting in cases in which new import protection at the 8-digit level falls into the same HS-06 category as a previously imposed barrier, we do not include such products. Second, for our baseline import protection measure that expansively covers all four TTB policies, we also do not include products that were subject to a simultaneous or previously imposed barrier under a different policy. This phenomenon is particularly relevant for countervailing duties as most have been imposed simultaneously with antidumping duties on the same products against the same trading partners. For a discussion, see Bown (2011a).

12 We are forced to use year-over-year changes in these variables as opposed to quarter-to-quarter changes due to how quarterly real GDP for China, one of the key trading partners in the analysis, is defined and available in the underlying data. Thus each of the key macroeconomic determinants is defined as a year-over-year change; i.e., quarter 1 of year _t_ over quarter 1 of year _t-1_ , quarter 2 of year _t_ over quarter 2 of year _t-1_ , etc.

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not included in the LIS data set, e.g. Morocco, Jordan and Egypt.<sup>8</sup> The comparison with

> 4 We follow the most recent (2020) World Bank classification that uses the Atlas method to estimate the size of economies in terms of gross national income (GNI) per capita: Low income below US$1,036; Lower-middle income between US$ 1,036 and US$4,045; Upper-middle income between US$4,046 and US$12,535; and High income above US$12,535.

> 5 “Disposable household income” is usually the preferred measure for income distribution analysis, as it is the income available to households to support their consumption expenditure and savings during the reference period (Canberra Group, 2011). According to the LIS documentation (https://www.lisdatacenter.org/data-access/key- <u>figures/methods/disposable/), this measure includes income received from work, wealth, and from direct government</u> benefits, such as retirement or unemployment benefits. The measure then subtracts direct taxes paid, such as income taxes. The definition does not include sales taxes or non-cash benefits, such as government or employer provided health care. The income definition also does not include income from capital gains, a significant source of non-salary income for many wealthy individuals. As a result, many top incomes are probably underestimated.

> 6 The publicly accessible country data sets are listed on the LIS website at: <u>https://www.lisdatacenter.org/our-data/lisdatabase/. To preserve confidentiality, access to the micro-data is remote, meaning that the program code is sent to</u> LIS rather than being run by the user directly on the data.

> 7 The figures in parentheses indicate the year of the survey.

8 Egypt is included in LIS but does not have the female wage variable. In order to keep
This apparent convergence, however, might be transitory. Figure 7 shows the same scatterplot during the slowdown period but with a different classification of countries: “the Reformers”. These are countries for which the Global Competitiveness Index (published by the World Economic Forum) has increased at a faster pace than the worldwide average increase from 2010 to 2015. Under this definition, the reformers have become more competitive by improving a set of institutions, policies, and factors (e.g., infrastructure, health, and education; macroeconomic stability; and well-functioning labor, financial, and human capital) that determine the level of productivity in their economies (WEF 2016). These reformers appear to have achieved larger poverty gains that non-reformers during 2012-14. This classification suggests a new split in the region that is worthy of analysis, and leaves behind the old “commodity”–“non-commodity” classification.

_Figure 7: Is there a new split in the region in terms of poverty performance?_ Slowdown (2012-14 annual average) <!-- Start of picture text --> 2<br>0<br>-2<br>-4<br>"Reformers"<br>-6<br>-1 1 3 5 7 9<br>GDP Growth (%)<br>Poverty change (ppts)<br><!-- End of picture text --> Source: LAC Equity Lab tabulation using SEDLAC data (World Bank and CEDLAS). “Reformers” are defined as countries which saw their Global Competitiveness Index (World Economic Forum 2015) increase at a faster pace than the worldwide average from 2010 to 2015. The countries identified as reformers are Colombia, Dominican Republic, Ecuador, Mexico, Nicaragua, Peru, and Paraguay.

# **3. What is behind recent trends?**

While the previous section focused on exploring what has been happening with poverty and inequality in LAC since the beginning of the slowdown, this section explores some of
responsibilities can take up the majority of women’s time in rural Honduras, the interviews suggest that it is probable that many of these women are also including agriculture and market activities in their daily domestic tasks and not reporting them.

Based on the interviews conducted for this study, more detail should be added to the agricultural activity in the follow-up question to explicitly include the gathering and sale of forestry products (like the blackberries picked by women in the communities) and rearing animals to sell (like chickens and eggs). If the survey is attempting to capture production for own consumption, then the question should also explicitly mention animals and vegetables usually grown by women close to the home. In addition, it is likely that the listing of the task does n o t overcome the bias by respondent or interviewer that a job needs to be full time and/or outside the home. To address this issue requires also training interviewers to engage with the respondents and explain that activities can be undertaken in the home or irregularly.

Using the EPHPM 2018 data, we can simulate what rural labor force rates would look like if these activities were accurately measured. Figure 3 presents three simulations. Each of these simulations assign employment for women who fit the following conditions: 1) she reports being too busy with household tasks as the reason for not searching for work, 2) she is not enrolled in school, and 3) there are no children under the age of 1 in the household. Under these conditions and given the results of the interviews, it is likely that these household tasks include market-oriented activities or agriculture for own-consumption. The most conservative is Simulation 1, which allocates employment to women in agricultural households, specifically those who satisfy the above conditions and are the spouse/partner of the household head and he reports running an agricultural enterprise (either as an employer or self-employed). These are households where it is probable that the wife is contributing to the agricultural enterprise. This would increase female labor force participation rates by 6
Kelly Hall, Miriam King, and Chad Ronnander. 2009. Integrated Public Use Microdata Series: Version 4.0 [Machine-readable database]. Minneapolis, MN: Minnesota Population Center [producer and distributor].

Schoeni, Robert. 1997. “New Evidence on the Economic Progress of Foreign-Born Men in the 1970s and 1980s.” The Journal of Human Resources 32(4): 683-740.

Schoeni, Robert. 1998. “Labor Market Outcomes of Immigrant Women in the United States: 1970 to 1990” International Migration Review 32(1): 57-77.

Verbeek, M. and T. Nijman. 1992. “Can Cohort Data Be Treated as Genuine Panel Data?” Empirical Economics, 17: 9-23.

Warren, R. and E. Kraly. 1985. “The Elusive Exodus: Emigration from the United States.” Population Trends and Public Policy Occasional Paper no. 8, Population Reference Bureau. Washington D.C.

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<!-- Start of picture text --> 6000 6000<br>5000 5000<br>4000 4000<br>3000 3000<br>2000 2000<br>1000 1000<br>0 0<br>0 1 2 3 4 0 1000 2000 3000 4000 5000 6000<br>BWI<br>Predicted Flow<br>Active Model<br>Figure 1.3 : Average of all flow (m3/s per month, Figure 1.4 : Predicted (m3/s per month, x-axis)<br>y-axis) and wetness index (index, x-axis) in sample versus calculated average flow (m3/s per month, y-<br>catchment area of the Zambezi river basin in a axis) in sample catchment area of the Zambezi river<br>nonlinear regression. basin from BWI model.<br>2500<br>2000<br>1500<br>1000<br>500<br>0<br>-500<br>-1000<br>-1500<br>-2000<br>-2500<br>-3000<br>Average Flow Average Flow<br>Residuals<br><!-- End of picture text -->

**Figure 1.5: Observations in years (x-axis) and the residuals (m3/s per month, y-axis) of the Zambezi river basin BWI model. SSM/I data is not available from mid-1990 through 1991, thus no residuals are in the figure.**

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obtained, often by aggregating up from regional registries, we find support for the notion of a recent and marked increase in land transfers (Table 2). Total confirmed land transfers over the 2004-2009 period amounted to 4.0 million ha in Sudan, 2.7 million ha in Mozambique, 1.2 million ha in Ethiopia and 1.6 million ha—although mainly through renegotiation of existing agreements—in Liberia. Comparing these figures to the estimated total available area in each country reveals that this respectively amounts to 8.6%, 16.6%, and 25.4% of the total suitable non-forested non-protected area with a population density of less than 25 inhabitants per km<sup>2</sup> for Sudan, Mozambique, and Ethiopia, respectively.

Given the difficulties of obtaining consistent data from official sources, we use information from press articles to get a global picture of the recent demand for large scale land acquisition. Our sample is based on such reports published between Oct. 1, 2008, and Aug. 31, 2009, as reported by the NGO GRAIN.<sup>16</sup> Figure 1 plots the evolution of the IMF food price index and the number of recorded press reports on cross-border land acquisitions. The ―land rush‖, or at least media awareness of it, started with the 2007 2008 commodity price boom. While commodity prices soon returned to more moderate levels, investors‘ interest in land persisted. Using data on the size of projects where available in such reports, Table 3 compares regional rates of land expansion in the 1961-2007 period to demand for land by investors. Press reports suggest that such demand disproportionately focused on Africa where almost 70% of the area of interest to investors was located but also that it was quantitatively large: compared to an annual rate of area expansion in Africa of some 1.8 million ha in 1961-2007, demand for land in Africa in 2009 alone amounted to some 39.7 million hectares--greater
# **V. Plant Panel Analysis**

Using plant panel data, this section explores two additional elements of the adjustment process to greater connectivity. _First_ , we ask how much of the observed response is due to adjustments on the extensive (exit) versus the intensive margin (changes within firms). _Second_ , we explore how the capabilities of incumbent plants, this time measured at the plant level, may affect their process of adjustment. Our baseline specification takes the form of difference-in-difference estimation where the _plant-level_ outcome variable in district _d_ at time _t,_ is estimated as: = ∑ ∗G pp,dd,tt D D D D ∗G D s D D + Γ + Ω + ε (3) YY pp,dd,tt is the outcome measure for plant dd∈DD dd _p_ ii,dd in district _d_ in year _t_ , Γ captures the vector of time-invariant plant dd,tt pp tt pp,dd,tt YY ββ bbD dd DD DDccssc DDdd specific traits (e.g. location, industry, ownership and other attributes of the plant) and pp,dd,tt pp Ω captures yearYY specific factors as well as secular trends. The estimate of 1 is the focal parameter, which captures the plant-tt level difference-in-differences effect of the nearest GQ highway segment opening among those plants ββ located in non-nodal districts within 10km of the GQ network, relative to plants more than 50km away (the reference group).<sup>16</sup> Preliminary analysis presented in Table 4 reveals that the sample focusing more on smaller plants (sample scheme) yielded the most provocative results, while those for the larger sample (census scheme) are presented in Annex Table A.6. We summarize the latter, when germane. Because the Annual Survey of Industries is structured as a rotating panel for the majority of the plant size distribution, we limit the sample to the set of plants observed in the data in the five years prior to their nearest GQ segment being completed