rafmacalaba/data-use-mentions-extended huggingface dataset:
taken from IMF Report SW96/212 Recent Economic Developments of August 12, 1996 and relate to 1995. Data on Central Government, Non-central Govemment, Education and Health and State-owned enterprises employment are taken from IMF Report No. SM/96/212 of August 12, 1996 and relate to 1995 (estimates).

# Central African Republic

Paid employment in non-agricultural activities is drawn from the ILO's Yearbook of Labor Statistics 1995 and refers to 1990. Non-agricultural employment is taken from ILO's Yearbook of Labor Statistics 1995 and is for 1992. It reflects data gathered through Establishment surveys, that is, data on the number of workers on establishment payrolls. This in tum may result in an underestimation of employment. Armed forces include 2,300 personnel in the Gendarmerie, a military paramilitary corps entrusted with many domestic policing activities. Education data are given by Herbert Bergmann (AF3PH). Teachers make up 4,314 people and teachers in administrative posts number 486.

Wages and Salaries as percent of GDP is from Antoine Schwartz, Senior Economist, PSP, and is for 1994.

# Chad

Paid employment in non-agricultural activities is drawn from the ILO's Yearbook of Labor Statistics 1995 and refers to 1991. Data on Central Govemment, Education and Health come from IMF Staff Country Report No. 96/61, Statistical Annex IV of July 1996, and relate to 1995.

# Comoros

Population, labor force, and unemployment estimates are taken from IMF Study SM/96/221 of August 16, 1996 and relate to 1991. All other estimates emanate from this data. Estimates on Central Government, Non-central Government, Education and Health employment are taken from Recent Economic Developments, IMF study no. SM 96/221 of August 16, 1996 and relate to 1995.

# Congo

Central Govemment, Education and Health employment are taken from Recent Economic Developments, IMF Report No. SMW941123, and relate to 1993. Public enterprise employment is also from the IMF Report for 1993. It probably understates the actual number of employees
% 36% 35%<br>29%<br>24% 31% 33% 32% 33% 34%<br>29% 29% 28%<br>20%<br>13% 23%<br>15%<br>14%<br>0%<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br>Unit Passthrough Clonflict Adjusted Passthrough Passthrough=0.7<br>Poverty Rate<br><!-- End of picture text --> _Source:_ World Bank staff calculations, based on WDI and CBS data
in informal places, typically within the informal market, often evade VAT payments. Unfortunately, the Bulgaria HBS does not provide data on whether VAT is paid in these informal market transactions, limiting a more detailed assessment of indirect tax evasion.

**It is important to highlight that this fiscal incidence analysis (CEQ) offers insights into how fiscal policy affects monetary income poverty, measured by the at-risk- of poverty (AROP) and international poverty lines; other non-monetary welfare measures are not analyzed.** It is essential to acknowledge that this analysis does not extend to providing evidence on potential effects on non-monetary indicators, such as severe material deprivation, as they are not encompassed within the modeling framework. Consequently, our study lacks simulations or results about the composite indicator of being at risk of poverty or social exclusion, commonly referred to as AROPE. Moreover, it does not look at multidimensional poverty measures.

Identifying the child incidence of fiscal interventions

**Although fiscal incidence analysis is the most widely used methodology to assess the distributional effects of fiscal policies, it is not common to have a child or even a gender lens, given the methodological challenges that emerge once we need to reconcile individual and household-level concepts; this means some assumptions need to be made on how these fiscal resources are allocated within the household** . While examining fiscal incidence by age or gender, it is essential to consider the individual as the primary unit of analysis; however, it is important to acknowledge that most household transfers and expenditures occur at the household level. This introduces a challenge in the analysis, as one must make certain assumptions about how fiscal resources are allocated within the household. Since fiscal policies often target households as a whole, resources may not be evenly distributed among family members. Some individuals within a household might receive more benefits or bear a higher burden than others. For

15
complete panels of 5 periods (i.e., quarters) each, spanning a total of 12 years (48 quarters).

Table I presents a summary description of the workers surveyed by the ENEU. Other aspects of our ENEU data _−_ the evolution of the mean and variance of earnings and returns to education over time (not presented here but available on request) _−_ matched the facts about earnings in the Mexican labor market reported by previous authors.<sup>14</sup> Data on sectoral trade barriers and other sectoral and macroeconomic variables were obtained from the World Bank.

# _II.2. Specification_

Our survey data provide us with earnings (wage rate times number of hours worked) of individuals. As in previous empirical work, we assume that the log of this labor income of individual _i_ employed in industry _j_ in period _t_ , _log yijt_ , is given by: In (1) _αjt_ and _βt_ denote time-varying coefficients, _xijt_ is a vector of observable characteristics (such as age and education), and _uit_ is the stochastic component of earnings. The stochastic component _uijt_ represents individual income changes that are _not_ due to changes in the return to observable worker characteristics. For example, income changes that are caused by an increase in the skill (education) premium are not contained in _uijt_ . In this sense, _uijt_ measures the unpredictable part of changes in individual income. Notice that we allow the fixed effects _αjt_ to vary across sectors, but that the coefficient _βt_ is restricted to be equal across sectors. The latter assumption is made in order to ensure that the number of observations is large compared to the number of parameters to be estimated.

> 14See Hanson (2003) for a broad analysis of wage patterns in Mexico in the 1990s based on population census data.

9
characteristics, which helps mitigate against the concern that our results are potentially biased by the non‐random assignment of interviewers to respondents.

# **4. Discussion**

Our findings illustrate that interviewer characteristics can impact survey data. A methodological implication is that researchers may wish to randomly assign enumerators to respondents, or at a minimum control for interviewer fixed effects in regression analysis of survey data. A substantive implication is that, to the extent that the social interaction during a survey interview echoes social interactions in the real world, our results can offer insights into the psychological mechanisms through which vulnerable adolescent girls perceive others and strategically attempt to manage how they are perceived.

# **References**

- [1] <mark>DUFLO.E, P.DUPAS AND M.KREMER (2015). “Education, HIV, and Early Fertility: Experimental Evidence from Kenya.”</mark> _<mark>American Economic Review</mark>_ <mark>,</mark> _<mark>105</mark>_ <mark>(9): 2757‐97.</mark>

- [2] HAMERMESH.D.S. AND J.E.BIDDLE (1994). “Beauty and the Labor Market.” _American Economic Review_ , _84_ (5): 1174‐94.

- [3] LANGLOIS.J.H., L.KALAKANIS, A.J.RUBENSTEIN, A.LARSON, M.HALLAM AND M.SMOOT (2000). “Maxims or Myths of Beauty? A Meta‐Analytic and Theoretical Review.” _Psychological Bulletin_ , _126_ (3): 390‐423.

- [4] TOURANGEAU.R AND T.YANG (2007). “Sensitive Questions in Surveys.” _Psychological Bulletin_ , _133_ (5): 859‐883.

- [5] WEST.B.T AND A.G.BLOM (2017). “Explaining Interviewer Effects: A Research Synthesis
# _V.1 Income models_

We begin with constructing regression models for (log) income. Also here, we estimate two models for each of the eight regions, a large and a small model specification. The results from the large and small models are very similar. However, the large models produce lower standard errors of estimates. Thus, in this paper we present the estimation results from the large models.

Tables A.2 to A.9 in the Appendix present the GLS estimates for the large models. It shows that all model coefficients make economic sense (have expected signs).

# _V.2 Poverty and inequality estimates_

# _Poverty estimates_

Table 2 reports the estimates of the rural poverty incidence for all eight regions. The estimates are all very close to the estimates based on the 2006 VHLSS. The poorest region is North West with a poverty rate of just below 50 percent. With a poverty rate of around 8 percent, North East South is among the least poor regions.

**Table 2 The income poverty incidences of regions**

|Region|VHLSS 2006<br>Sm|all area estimation|
|---|---|---|
|Red River Delta|15.5|15.3|
||[1.1]|[0.7]|
|North East|22.0|24.4|
||[1.6]|[1.4]|
|North West|48.8|49.2|
||[3.6]|[2.5]|
|North Central Coast|28.2|26.7|
||[1.9]|[1.1]|
|South Central Coast|20.3|18.7|
||[1.9]|[1.4]|
|Central Highlands|24.4|25.4|
||[3.0]|[1
According to the World Food Programme, Afghanistan is among the world’s most vulnerable countries in terms of absorbing food and fuel price shocks; such countries have consistently high levels of food insecurity, are heavily dependent on food and fuel imports, and have large populations of poor people who spend significant shares of their income on food (Sanogo, 2009). Also, mountainous terrain and poor infrastructure, coupled with weak governance, insecurity and corruption, have limited the government’s ability to manage its food distribution and supply networks.

International prices of food commodities increased substantially in 2007 and rapidly in early 2008, peaking around May-July 2008. During this period, Afghanistan experienced several shocks that led to a disruption of its food supply network, causing prices to soar throughout the country. Due to drought and early snow melt, the 2008 wheat harvest of 1.5 million metric tons was the worst since the 2000 harvest (Persaud, 2010). (The harvest period typically falls in the summer months: May-August.) The price impact of the large shortfall in wheat production was magnified by export bans in Pakistan and rising international food prices. In February 2008 the Afghan government eliminated import tariffs on wheat and wheat flour (tariffs had been set at 2.5%), but due to export bans in Pakistan, Iran and Kazakhstan, there was little effect on prices. Between fall 2007 and summer 2008, the prices of domestic wheat and wheat flour increased by over 100%. Figure 1 displays the retail price (Afghani per kilogram) of wheat in four major markets from January 2006 to December 2009.

Total inflation was largely driven by the surge in food prices. Figure 2 presents the consumer price indices (CPI) for food and non-food items in urban areas from 2006 to 2008 constructed by the Government of Afghanistan.<sup>19</sup> During the NRVA survey timeframe (August 2007-September 2008), the urban food CPI increased by nearly 60%, while the nonfood CPI increased by only
in each round with about half of the households in each round also being surveyed in the previous round so that the data set includes a short‐term panel.

These analyses focus on rural households and communes leaving a data set of about 20,000 household observations from ca. 2,250 communes. Each survey round covers about 6,600 and 6,700 rural households. About 1,400 households were interviewed in all three rounds, 1,600 in 2010 and 2012, and 1,400 in 2012 and 2014.

The household surveys include a wide array of socioeconomic data. At the individual level these data cover demographics, education, employment, health, and migration. At the household level the data comprise information on income and expenditures, employment and self‐production, durables, assets, and participation in government programs. Consumption estimates are based on per‐capita expenditure as calculated by the World Bank and GOS to determine the national poverty line. Incomes are calculated based on the raw data in line with classifications from the GSO (Section 3). All consumption and income values are expressed in 2010 prices using data on the Consumer Price Index from the World Development 4
**Figure 14. Moderate poverty headcount rates considering both pre‐ and post‐fiscal income aggregates, separately for households with different compositions in terms of economic agency, presence of child and presence of elderly**

<!-- Start of picture text --> Change in poverty (p.p.)<br>Poverty headcount rate<br>70% 10.0<br>60% 5.0<br>50% 0.0<br>40% -5.0<br>30% -10.0<br>20% -15.0<br>10% -20.0<br>0% -25.0<br>Pre-fiscal poverty rate Post-fiscal poverty rate Change in poverty<br><!-- End of picture text --> Source: authors’ estimations based on PNAD-C 2019. Notes: M stands for single male breadwinner, F for single female breadwinner, CO/M for couple with a man as the main breadwinner, CO/F for couple with a woman as the main breadwinner, and OT for households in which there are either no members with strictly positive habitual labor income or both heads earn the same income amount. Similarly, C stands for “at least one child”, NC for “no child”, E for “at least one elderly member”, and NE for “no elderly”. Family composition is determined by whether there is at least one household member that declared to be the spouse of the household head. Economic agency is determined considering the highest-earning individual in terms of habitual labor income as the household main breadwinner. The household is considered to have a child if there is at least one member aged 15 or less who is neither the household head nor the spouse, and to have an elderly member if there is at least one member aged 60 or more. Pre-fiscal poverty indicates the poverty headcount rate considering market income plus pensions, while post-fiscal poverty indicates
Policy Research Working Paper 6116

# **Abstract**

This paper explores the possibility that universal health coverage may inadvertently result in distorted labor market choices, with workers preferring informal employment over formal employment, leading to negative effects on investment and growth, as well as reduced protection against non-health risks and the income risks associated with ill health. It explores this hypothesis in the context of the Thai universal coverage scheme, which was rolled out in four waves over a 12-month period starting in April 2001. It identifies the effects of universal coverage through the staggered rollout, and gains statistical power by using no less than 68 consecutive labor force surveys, each containing an average of 62,000 respondents. The analysis finds that universal coverage appears to have encouraged employment especially among married women, to have reduced formal-sector employment among married men but not among other groups, and to have increased informal-sector employment especially among married women. The largest positive informal-sector employment effects are found in the agricultural sector.

This paper is a product of the Human Development and Public Services 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 author may be contacted at awagstaff@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._
renewed relatively frequently. This can explain a preference for long-term debt on the part of firms, and increased firm volatility if long-term debt is not available when it is preferable. If long-term finance is undersupplied in a country due to reasons such as poor information or contract enforcement, firms can either reduce investment in long-term assets or bear additional liquidity risk (Aghion et al., 2010). To minimize liquidity risk as well as interest rate risk, firms often match the maturity structures of their assets and liabilities (Hart and Moore, 1995).<sup>4</sup> Limited access to long-term finance inhibits maturity matching when investing long-term, possibly resulting in more volatile firm growth.

In summary, long-term debt may reduce firm-level volatility by reducing roll-over risk, while conversely a lower ability to monitor and enforce debt contracts may lead to greater inefficiency and risk-taking by firms. We empirically explore these relationships by relating measures of firm volatility to proxies for the availability of long-term finance, also controlling for the availability of overall external finance in some specifications.

# **3 The data**

In this study, we relate measures of firm-level volatility to firm debt maturity structure. The sample consists of firms in all sectors with the exception of financial firms and firms in the public sector, as these firms’ capital structure decisions and risk profiles are very different from other firms.

We use two measures of firm volatility. In particular, we construct Asset volatility (book) as the standard deviation of the growth rate of the book value of total assets over the 1995-2013 period using balance sheet information obtained from the Worldscope database.

> 4 For empirical evidence on asset and liability maturity matching by firms, see Schiantarelli and Sembenelli (1997), Stohs and Mauer (1996), Jaramillo and Schiantarelli (2002), and Schiantarelli and Srivastava (1997).

7
tightening of a policy measure, -1 removal or loosening and 0 if no action was pursued. Figure 6 visualizes the policy stance variable, where a red(blue) dot represents a tightening(loosening) measure. Between 2006 and 2013, there are 18 such policy measures in total, but the majority of these measures are announced between 2010 and 2012. Most of these policy measures were actually implemented at or shortly after the announcement, allowing me to focus on announcement dates alone.

# **5.2 Identification assumption**

My empirical findings that capital controls convey negative information about economic fundamentals relies on the identification assumption that a tightening of capital controls does not negatively affect real GDP within the current quarter. Hence, my empirical results are not immune to the criticism that forecasters expect capital controls to decrease real GDP and they are simply incorporating this mechanism into their forecasts. I state several reasons to support my assumption. First, there is no academic consensus on how capital controls affect real output. By improving financial stability, a primary function emphasized by the current literature, capital controls can actually be expected to increase output by avoiding the output cost of a financial crisis. Klein(2012) finds not empirical relationship between capital controls and real output. Second, the Brazilian capital controls considered here focuses on short-term portfolio inflows instead of Foreign Direct Investment(FDI) in the hope of retaining productive capital inflows to the economy. This institutional background mitigates concerns that capital controls negatively affect real GDP by decreasing foreign investment which would help improve the economy’s productive capacity. Last, capital controls and output are unrelated in the model: tradable and nontradable outputs are endowment driven by an exogenous process, and capital controls improve welfare through better intertemporal consumption smoothing.

# **5.3 Capital controls and high-frequency expectations of fundamentals**

My empirical evidence from high-frequency Brazilian survey data suggest that expectations of economic fundamentals respond to capital control announcements. Below, 28
country panel. However this is driven by previously unemployed workers in the low education group starting jobs at a faster rate, rather than those that stopped working since the pandemic returning to jobs more quickly (Table A6.2 and Table A6.3).

To get a cleaner picture of job losses and subsequent recovery, we employ the panel structure of the data to analyze the characteristics associated with employment loss and, separately, with employment recovery. In particular, we draw on a subsample of almost 40 countries with panel surveys to predict conditional probabilities that those who stopped working subsequently returned to work, as reported in a later HFPS wave. Figure 4a shows that those working before the pandemic were significantly more likely to stop working within each country if they were women, lower educated, and during a period of higher policy stringency. Workers in urban places were also marginally more likely to stop work. Figure 4b shows women were also less likely to gain or recover employment during the pandemic period, especially if they had children in their household. Those with lower education were 16
clearing or due to seasonal increases in dust storms. Using this approach yields results that are qualitatively similar to using the average across the full sample but the magnitudes of the correlation are larger in the maximum approach (Table SI-1). Comparing the country specific estimates of the correlation between wealth and pollution exposure, Figure SI-4, indicates broad agreement in the estimated correlation when exposure is measured as median or average levels of pollution. Most countries fall on or near the 45<sup>_◦_</sup> line.

There is less agreement between the maximum measure and the average measure however. At the extremes the qualitative patterns seem to persist - Nigeria for example has a negative correlation with both measures and has the strongest negative correlation in the sample. The notable exception is India and Pakistan (which are treated as a single unit in the RWI data, a choice we preserve), which has a substantially higher positive correlation using the maximum measure rather than the average measure. This is likely driven by very high levels of pollution in some North Indian cities during December. One possible explanation for these somewhat divergent results using the maximum measure rather than the average measure is that the 23