rafmacalaba/data-use-mentions-extended huggingface dataset:
# 3 Data

We now turn to the empirical evidence and analyze both price and quantity data. The price data are used to estimate risk premia on short and long-term bonds, and to characterize the behavior of the implied term premium. The quantity data are used to analyze the comovement between the maturity structure of bond issuance and the observed term premium.

We conduct the empirical analysis by collecting data on sovereign bonds from the early 1990s up to 2003 for eight emerging economies. These countries are Argentina, Brazil, Colombia, Mexico, Russia, Turkey, Uruguay, and Venezuela. To calculate the term premium, we also collect data on sovereign bonds for Germany and the U.S., which are assumed to be free of default risk. The choice of emerging markets is constrained by data limitations. To estimate time series of the term premium, we need enough foreign currency denominated bonds of different maturities at each point in time. Therefore, our sample corresponds to those emerging economies that borrowed heavily in foreign currency, generating a rich enough pool of bonds. Furthermore, we restrict the sample to sovereign bonds because they constitute the most liquid debt instrument in most emerging markets, and because private debtors in emerging markets do not issue enough bonds to compute the term premium.

We collect weekly (end-of-week) time series of bond prices, using all available bonds for each country.<sup>21</sup> We also collect other information on these bonds, including currency denomination, coupon structure, and maturity. In addition, we compile time series of bond issuance in foreign currency. For each bond, we collect the amount issued, currency denomination, and maturity date. With this information, we construct weekly time series of amount issued valued in U.S. dollars. We exclude from the sample the bonds with collateral and special guarantees, such as collateralized Brady bonds and those issued by Argentina during the large pre-default swap. We also exclude bonds issued during forced restructurings, like those issued by Argentina and Russia
– a city with a disproportionately low share of employment in tradables; or a _neutral city_ – a city in which the shares of employment in tradables is neither disproportionately low nor disproportionately high.<sup>7</sup>

**Data.** For 76 countries and 191 country-years (1960-2015), we have IPUMS census microdata with information on the administrative unit in which the respondent lives, the respondent’s sector, _and_ whether the respondent lives in an “urban” area.

We process the data following several steps. First, we select “urban” observations.<sup>8</sup> Second, we have information on the resident’s second level administrative unit. Such units are typically smaller than FUAs.<sup>9</sup> Third, IPUMS classifies employment into 16

> 6These units correspond to _counties_ in the U.S. and _municipalities_ in most Latin American countries. 7We interchangeably use the words “FUAs”, “agglomerations” and “cities” in the rest of the analysis. 8When the urban identifier is unavailable, we identify urban residents from information on the locality. 9In some cases, information is only available for first level administrative units, which leads to coarser
_of education or simply lack of labor demand), they are a good income choice for many members of this group, and for their households._ The positive correlation between income and HE ownership does not imply causality; in particular not if the rich were already rich and had more personal capital and better access to infrastructure. Tracking of consumption growth over time in panel data provides better evidence, but to date there are not very many panel data sets for SSA. There is still only limited panel data evidence on the role of NFE and changes in consumption. Mead and Lindholm used tracer studies and area panels of enterprises to track the progress of micro and small enterprises in Eastern and Southern Africa in the 1990s, and found positive results for enterprise survival on household incomes. Barrett et al (2001) was one of the first studies to use household panel data to analyze the evolution of livelihoods in rural SSA and how adding nonfarm activities helped households reduce poverty. While finding a positive effect of adding an HE, they also found that better off households were more able to take advantage of opportunities both within and outside the agricultural sector.

There are many reasons why adding an informal non-farm income appears to be a welfare increasing strategy, especially in rural areas. Evidence from Uganda shows that for men, where self-employment in farming is the main activity of the individual and no secondary activity is reported, hours worked per month are on average about 100, well below the 160-180 which would constitute full time employment, indicating the presence of hours-based underemployment. But where the reported main activity is HEs, reported hours worked per month were on average are over 200, exceeding the definition of full the work. (Fox and Pimhidzai, 2011).

Increasing the hours of productive work is not the only benefit of HE ownership in countries such as Uganda. The same analysis suggests that the expansion into non-farm enterprises helped raise agricultural productivity and vice versa. The diversification appears to have provided extra liquidity, thus compensating for the failure of rural
..|<br>.<br>. ..|2|
|Educational Policy Context .... . . . . . . . .<br>..|.......<br>.|6|
|III.**Framework,**Data,**and**Variables<br>.<br>....<br>.<br>.<br>.<br>.|.<br>.<br>.|12|
|The Schooling Decision. ....<br>.<br>....<br>.. .. .<br>.|.<br>.|12|
|Empirical Model<br>.<br>..<br>. . . . . . .<br>.|......|16|
|Variables<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.........<br>...<br>.|....<br>.<br>.<br>.|18|
|IV. Data DescriptionandSample Profiles.......<br>.<br>Sample Distribution by Gender, Cohort, and Residence<br>EducationalProfiles by Gender and Residence..<br>Changesin Educational Attainment Over Time<br>.<br>.<br>.<br>.|.<br>.<br>.<br>.<br>.<br> .<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<
system<sup>2</sup> helps reduce inequality.

Our fiscal incidence analysis uses a partial equilibrium approach. It does not model the behavior of agents in response to different government fiscal interventions and does not consider general equilibrium effects. The data used came from the latest Integrated Survey on Agriculture ( _Enquête Agricole de Conjoncture Intégrée_ , EACI) and ( _Enquête nationale sur les Conditions de Vie des Ménages et Agriculture_ (ECVM/A), from 2014/15, and the national budget for 2014. We use simple notions of progressivity and the pro-poor nature of spending and taxes. A public expenditure (or tax) is progressive, in relative terms, if the proportion of expenditure (or tax) in relation to income decreases (increases) with household income. A public expenditure is pro-poor if it is progressive in absolute terms—in other words, if the absolute amount (e.g., per capita) of public expenditures decreases with household income.

The analysis had three broad results.

- (a) In both countries, poverty is high, but the gap between poor and wealthy is small, so rapid improvements could be made. The amount needed (under perfect targeting) to fully

> 2 The fiscal system includes taxes and public expenditures.

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Among the rest, each hypothesis points to one or more potential confounders in estimating the SPEC, which we use in testing for OVB. We also point to some likely theoretical ambiguities in how the confounders can influence SP spending; for example, having a more capable government can be expected to reduce the unit cost of attaining a given level of social protection (thus reducing SP spending), but also increase the desired level of protection (increasing spending).

The paper uses two new data sets on public spending on SP. The first spans 142 countries over 1995-2020, combined with data on identified covariates. The second data set focuses on the 4
0.13|0.34|

# **3.3. Initial Conditions and Complementary Policies and Institutions**

The initial conditions in the country are captured through the country area, country gross domestic product (GDP) per capita<sup>10</sup> at time of loan appraisal, terrain ruggedness, and whether the country is landlocked. All initial conditions, except terrain ruggedness, are measured at the country level. Terrain ruggedness— measured at the level of our geographic unit—was extracted from the data set created by Nunn and Puga (2012). Corridors located in areas with greater terrain ruggedness may find it more difficult to generate and spread WEBs along the corridor than corridors where the surrounding area is flatter. And richer countries may not see their local economic activity boosted by as large a margin as poorer countries that still have far to go in income convergence.

The complementary policies and institutions are captured using measures of trade openness<sup>11</sup> of the country at the time of project appraisal and indicators on whether there were policy and institutional reforms in the five years preceding the signing of the loan agreement for the project. We use World Bank Development Policy Operations (DPOs) as a proxy for policy and institutional reforms in the country. Development policy operations support a program of policy and institutional actions that promote growth and enhance the well-being and increase the incomes of poor people. For example, some DPOs aim to improve the investment climate, diversify the economy, and create employment (World Bank 2004). Clearly, not all policy and institutional reform programs are supported by the World Bank, so the use of DPOs is an

> 10 From the World Development Indicators, World Bank.

> 11 Measured using imports, exports, and GDP data from the World Development Indicators, World Bank.

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sample bias. The source of data on India is the 1999 Rural Economic and Demographic Survey done by the National Council for Applied Economic Research, and the data on Bangladesh comes from the 1996 Matlab Health and Socioeconomic Survey (MHSS). The Bangladesh survey collected information on household head and spouse’s all children (including from past marriages) irrespective of their residency status from 4538 households in Matlab thana of Chandpur district.<sup>16</sup> The India survey also collected information on all of household head’s children from current marriage but not non-coresident mothers of children from earlier marriage(s). We utilize these information to create data sets containing education and other personal characteristics of parents and children. Both of these surveys focus on rural areas in respective countries. An advantage of rural samples is that the bias from censoring due to possible non-completion of younger children may not be as important, because only few go on to have more than middle school (or high school) education. The children who go for more than high school education (10 years of schooling in Bangladesh and India) are also the children who leave the village household, because the “colleges” (for grades 11 and 12) and universities (for three-four year undergraduate, and graduate study) are not located in villages.

Our estimation sample consists of household head and spouse, and their children, including those from other marriages in the case of Bangladesh. For the empirical analysis, we use alternative samples defined by different age ranges for the children. Our main results are based on a sample of children aged 13-60 years. To test the sensitivity of our conclusions with respect to the specific age cutoffs, we estimate the IGRC and IGC for a number of alternative age ranges; 16-60, 20-69 and 13-50 years.

Table A.1 reports the summary statistics of the relevant variables for both the Bangladesh and India data sets for our main estimation sample (children in the age range 13-60 years). Several interesting observations and patterns
and company websites (FactSet, 2021).<sup>6</sup> FactSet analysts map the resulting information in consistent records of normalized relationship types, taking into account links detailed in a firm’s own records and the records of its partners (“reverse” links). By monitoring additional sources and “reverse” matching links, the database provides an exceptionally comprehensive view of supply chain linkages relative to other databases such as Compustat Segment (Huang et al., 2023).

As of March 2024, the relationship record of more than 54,000 “source” or reporting companies has been actively monitored, yielding a dataset of more than 2 million relationship links that involve approximately 360,000 entities. Table A.3 presents an overview of the list of variables obtained from FactSet. For each link, information on the start and end date is included, with historical data available from 2003. Global coverage starts from 2014, with the latest addition of Latin America in 2016.<sup>7</sup> Furthermore, the nature of each relationship is specified. For our study, we focus on customer and supplier relationships, which we supplement for some of the analysis with information on ownership ties and other firm characteristics. At each point in time, we observe the parent and ultimate parent firm of a company, and if applicable, any subsidiaries.<sup>8</sup> Additionally, the dataset provides information on firm name, industry affiliation, headquarter country, and entity type which we leverage throughout our analysis. Information on the firm’s annual sales, converted to constant 2020 U.S. dollars, is added whenever available.

We construct the agribusiness supply chain network by keeping all direct customer or supplier links that involve at least one agribusiness company. We identify this latter based on their primary NAICS 6-digit code, including relevant industries from the agriculture,

> 6In financial reporting, a 10-Q is a company’s quarterly report, containing unaudited financial statements,
with each cohort. Again, a result that is not consistent with a presence of increasingly less skilled college educated workers.

The paper is organized as follows. Section 2 introduces the data analyzed in this paper as well as some descriptive statistics. Section 3 presents the analysis of the evidence. Section 4 summarizes the results. Section 5 concludes.

# **2. Data**

We use the Labor Force Survey for each quarter starting in the second quarter of 2000. The Mexican labor force surveys are very similar to the Current Population Survey applied in the United States or other labor force surveys around the world. From 2000 to 2004 the survey used is the “ _Encuesta Nacional de Empleo_ ”, and from 2005 to 2014 it is the “ _Encuesta Nacional de Ocupación y Empleo_ ”.<sup>5</sup> Even though the questionnaire is slightly different, most of the key labor questions are the same across surveys: earnings, hours of work, occupation, etc., while the sampling is the same in both. The labor force surveys are representative at the national and state levels as well as by metropolitan area. In each quarter, approximately 120,000 households are interviewed. The

> 5 Both surveys have the same sample design. Although it is not common to employ the two surveys, we use them to provide a complete picture of wages in Mexico since 2000. Other studies that use both surveys are Campos, Hincapie and Rojas (2012) and Calderon (2014).

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one-quarter. The 2001 LICs that became MICs also had a geographical advantage, as around one-third were landlocked compared to almost half of today’s LICs. Furthermore, the 2001 LICs that were landlocked but became MICs had, on average, neighbors with per capita incomes that were 52 percent higher than current incomes among the neighbors of today’s landlocked LICs.

# **_LICs that achieved MIC per capita income levels_**

Between 2001 and 2020, 35 LICs achieved middle-income status (Figure 5). The progress made by LICs that have become MICs has helped lift 20 percent of the global poor in 2001 out of poverty by 2016, more than offsetting the increasing poverty headcount among the countries that remained LICs.<sup>7</sup>

- _Favorable initial conditions._ The 2001 LICs that have become MICs were some of those countries closest to the middle-income threshold to begin with: their average per capita

> 6 Exchange rate regimes are grouped according to the classification in Ilzetski, Reinhart and Rogoff (2017), with the only exception that freely falling currencies are also regarded as flexible exchange rate arrangements.

> 7 Due to data limitations, poverty headcount data exclude the following 11 LICs of 2001: Afghanistan, Azerbaijan, Haiti, Cambodia, Myanmar, Democratic People’s Republic of Korea, Somalia, Sudan, Turkmenistan, Uzbekistan, and Zimbabwe. In 2001, their combined population accounted for 8 percent of the total 2001 LICs population.

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the limited government financial resources, even basic infrastructure was not made available in many of these zones. Governments did not proactively play their indispensible facilitating role: they did not provide some basic industry-specific infrastructure and often waited (in vain) for qualifying firms to finance investment in electricity, water, or telecommunication within the zone. They did not coordinate the design and implementation of the investment needed and used collectively by firms in their industries (storage facilities for example). Again in retrospect, it may have been beneficial to public finance that governments did not spend even more money financing sub-optimal and unprofitable infrastructure.

As shown in investment climate surveys carried out in Africa, government officials running SEZs also did not realize that successful integration into the world economy increasingly requires the realization of behind-border measures that fall under the heading of trade facilitation. They failed to alleviate the burden of red tape and provide the type of efficient services such as customs and port efficiency. In countries such as Senegal (where an SEZ was established as early as 1974) or Cameroon, it often took more than a year for a foreign firm to obtain necessary permits to operate. They also had to deal with heavy and complex bureaucratic rules and procedures, a very high cost of infrastructure (communications, energy, water), and constraining labor regulations. In addition, they had to agree to unrealistic job creation goals and high requirements for initial investment. In many other African countries, qualifying firms that
variables to indicate the size of the household, the presence of children, the age of the head, and the region. The budget constraint for household _k_ is: _k k k_ Let _wi_  _pi ci_ / _Mk_ denote the share of expenditure allocated to bundle i. Applying Roy’s Identity we get the demand share vector: The aggregate demand is obtained by summing over all household types. Let _nk_ be the number of households of type k; the aggregate share vector is then: The above equations (A52) and (A53) are estimated simultaneously, with (A52) estimated over one year of cross-sectional consumer expenditure data, and (A53) estimated using time series national prices and aggregate consumption expenditures.

To use the estimated equation (A53) in the model that include projections into the future we make some modifications. Firstly, the consumer survey data does not include some items that are in the National Accounts such as imputed rentals for owner-occupied housing and FISIM. We make some adjustments to the  _p_ ’s to scale the shares to match the consumption in the Input-Output table for our base year 2010. We project the distribution and demographic terms to account for the aging impact and thus re-write the share demand system as: Next, the aggregate expenditures on the 4 bundles are allocated to the 27 commodities according to the tier structure in Table 1. This is done with a linear logarithmic function that allows the shares to ‐55‐
reforms is gathered from IMF Article IV reports, program and other documents, and Haver Research and other market-oriented websites. We rely on IMF’s AREAER to code changes in exchange rate arrangements, changes in capitalaccount liberalization and restriction measures, and macroprudential policy measures.<sup>20</sup> A first pattern in Table 9 is that a majority of countries experiencing sudden stops between 1991 and 2014 in fact eased monetary policy in response, whereas a majority tightened fiscal policy. Countries experiencing sudden stops need to simultaneously do something to reduce the level of spending relative to income when foreign finance becomes more difficult to tap, while at the same time taking other steps to support economic activity and aid the financial system.<sup>21</sup> Fiscal tightening evidently is the preferred policy to pursuing the former, while monetary easing the preferred instrument for achieving the latter. Governments could conceivably adopt the opposite policy mix, but in only 1 of 44 episodes do we observe this response. Budget deficits become more difficult to finance in the wake of sudden stops, especially if monetary policy is tightened, making some degree of fiscal consolidation inevitable for countries with preexisting fiscal deficits. Monetary tightening could reinforce the

> 20 For macroprudential policy initiatives, we utilized AREAER information under heading XII: Provisions specific to the financial sector, supplemented with information from IMF Article IV reports.

> 21 One is reminded, for example of Brazil’s response to its sudden stop in 2015, which entailed fiscal consolidation and a reluctance to tighten monetary policy (keeping central bank interest rates on hold in a period when inflation was rising).

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The estimate of _γ_ represents how much larger the share of the population responding ”yes” to the IPV indicator increased in disaster-affected regions than in the rest of the country. Importantly, these results are a likely underestimate of the impacts in the worst-affected regions given the omission of the worst-affected areas from the fourth phase of the survey and the delay in interviewing households in the region on which this analysis focuses.

# **Section 5b: Robustness Checks of the Identification Strategy**

As discussed in the Introduction, there are three critical assumptions for _γ_ to identify the arguably causal impact of the earthquake. Specifically, there cannot be substantial differences between urban and more remote populations across provinces, the survey design and implementation needs to be roughly identical across provinces, and the IPV change in the rest of the country need to be a good counterfactual for the change in disaster-affected regions in absence of the earthquake. A variety of robustness checks illustrate that these conditions are likely met and that the results are being driven by the earthquake.

First, we illustrate that the empirical strategy uncovers no significant differences in individual characteristics that would be impossible to be affected by the disaster, where differences in the sampling strategy or in how urban and more remote regions varied by province would result in different samples where such differences would likely be evident. Tables 1 and 2 reports the changes in each individual control variable used in the baseline specification in disaster-affected regions in column (1) following the disaster, the change in the rest of the country in column (3), and reports the difference between the changes in column (5). Table 1 reports the changes for the Men’s Survey and Table 2 reports the changes for the Women’s Survey.

Consistent with the identification strategy, the results illustrate that there were few differences in the changes between disaster-affected regions. As expected by going from urban to rural and remote areas over time, the estimates illustrate that households are socioeconomically worse off following the earthquake in the fourth phase in both the