rafmacalaba/data-use-mentions-extended huggingface dataset:
emotional and cognitive stimulation intersects with lack of proper nutrition and other critical inputs to increase the likelihood of long-term risks to development (Yousafzai et al., 2013).

All of these risks operate in conjunction with rising levels of food insecurity, which was estimated to be 37 percent prior to the pandemic and has increased as a result of the pandemic (National Nutrition Survey, 2018; World Bank, 2021a). Many under-resourced households suffer from high rates of malnutrition leading to high rates of child stunting and wasting, with an estimated 40 percent of children stunted and 29 percent of children underweight (National Nutrition Survey, 2018). These health and nutritional risk factors to development are highly associated with other risk factors, such as living in a rural area, and low maternal educational status (Khan et al., 2019), which leads to a cumulative negative impact on development.

# Early learning opportunities outside the home, through early childhood education, are also few

Enrollment in early childhood education is very low for children aged 3-5 in Pakistan, further curtailing access to early stimulation and learning opportunities for young children. According to 2019-20 PSLM data, just 18.9 percent of 3- to 5-year-olds nationally are enrolled in ECE (Tomlinson et al., forthcoming). The greatest level of coverage is seen in Punjab, where just over one-quarter of eligible children (27.0 percent) are enrolled, whereas in Balochistan, only 5.2 percent of eligible children are enrolled (Tomlinson et al., forthcoming), putting 95 percent of children in this province at a considerable disadvantage in terms of school readiness. Low-income and rural children are especially disadvantaged in terms of access to ECE—and gender inequality begins to emerge as well. Children in Pakistan who lack ECE experiences show lower rates of ontrack development than children who attend ECE programs (e.g., 52 percent vs. 74 percent respectively in Punjab, 2017-18) (Tomlinson et al., forthcoming).

Very little is known about the
the East for fear of allowing insurrection to reach the capital. What he feared nonetheless happened, when in 1997 a Rwanda- and Uganda-backed rebel force swept across the country, taking town after town and eventually overtaking the capital Kinshasa. Ever since, eastern Congo has remained mired in conflict, the region’s roads transformed into sinews of war. The number of armed groups has, over time, exploded, with larger groups fragmenting and new militia popping up. By the latest count, there are well over 120 armed groups active in Eastern Congo (Kivu Security Tracker 2021), many of which maintain connections to networks of patronage inside the Congolese army–which has itself been called the country’s largest armed group, for the similarities in its mode of operating on the ground. Over decades of conflict, government forces and militia alike have sustained and enriched themselves as much through extracting rents from artisanal mines and farmers as from road users through roadblocks (Schouten 2022). While dynamics are extremely fluid and can, at times, be volatile, overall, government agents and larger armed groups such as M23 have competed for control over main roads, while government soldiers and smaller militia have tended to dominate rural connections in the countryside. Indeed, in 2017, 70% of the roadblocks with a government/army presence in North Kivu sat on national or provincial roads, while 79% of rebel roadblocks sat along tertiary/feeder roads (Schouten 2022, pp. 117-118). However, such patterns are highly unstable over time, with rebels periodically overtaking control over main roads. Roads are, therefore, magnets of continuous and often violent contestation among different types of authorities and road users over how goods, people, and capital circulate and who gets to benefit. According to the Congo Research Group (CRG 2019, p. 6), almost half of all violent events between 2017 and 2019 took place at or near roads, as opposed to mines or villages (cf. (O’Mealia 2022))
and supply in quantity is constrained.<sup>20</sup> The analysis of wage offers is the most robust among the three variables, considering that the wage offers posted on Babajob are influenced by the labor market for its competitiveness.

# **_Analyzing Wage Trends and Forecasting_**

Areias et al. (forthcoming) analyzed the patterns of wage growth and distribution across different locations using 50,000 job advertisements posted in 20 cities with the largest number of advertisements. Wages were deflated using state-level urban consumer price index (CPI) obtained from the Reserve Bank of India.

> 18 Econometric models can be used to forecast economy-wide employment. See, e.g., ILO (2013).

> 19 “Nowcasting” is used to estimate economic conditions in the present.

> 20 Due to absence of national labor statistics since 2012 at the time of conducting analysis, it was not possible to compare post 2012-period of Babajob data (which this paper mostly uses) with the growth trends of national skills demand and supply.

21
2009, when liquidity was tight. Similarly, Thébaud and Sharkey (2016) show that women-led firms in the United States were more likely than men-led firms to experience difficulties in acquiring funding when lending tightened in 2009 and 2010. In other contexts, however, women-led firms appear to have weathered the global financial crisis better than firms led by men, possibly due to lower levels of risk taking and more prudent management strategies. Palvia et al. (2015) show that among U.S. commercial banks, smaller banks with female CEOs and/or board chairs were less likely to fail during the global financial crisis, which may be explained by the fact that these banks were holding higher levels of equity capital. Cowling et al. (2020) show that among medium sized enterprises in the United Kingdom in the aftermath of the 2008 global financial crisis, women-led businesses were less likely to apply for loans but more often successful in their loan applications, a pattern which may suggest greater risk aversion among women entrepreneurs.

On the COVID-19 pandemic and more directly aligned with the focus of our paper, several recent studies and reports in the popular media suggest that the pandemic has had a disproportionate impact on female entrepreneurs. Evidence from cross-country data sets has been documented by ANDE (2020b) and Facebook et al. (2020). Additional evidence from country-level studies has been documented for Bangladesh (Jaim, 2021); India (ANDE, 2020a; Chawla et al., 2020); the United Kingdom (Reuschke et al., 2021); and the United States (Fairlie, 2020; Manolova et al., 2020; Bloom et al., 2021), among other settings. Many of these studies, however, especially those referring to developing countries, are either based on relatively small samples (typically covering a few hundred firms or even fewer), or use a sampling approach that is not well suited to draw inferences on a target population (Schneider, 2020). Therefore, caution must be exercised
# **1 Introduction**

Government procurement is a major market, accounting for about 12% of world GDP in 2018 (Bosio et al., 2020). Given this important size, public authorities often prefer local to foreign providers in procurement contracts in order to achieve socioeconomic objectives (e.g., promoting “sustainable” local purchases, and the development of small and medium local enterprises).<sup>1</sup> Buy-national provisions are prime examples of measures that explicitly exclude foreign firms from government contracts. The Global Trade Alert (GTA) initiative has collected data since 2009 on the adoption of protectionist measures in government procurement. The data show that 56 new discriminatory measures in government procurement were enacted on average each year between 2009 and 2018.<sup>2</sup> In parallel with this persistent protectionism, governments have committed to greater market access in government procurement through the WTO Government Procurement Agreement (GPA) as well as targeted provisions within other free trade agreements (TAs). Membership to the WTO GPA expanded from the initial 34 signatory countries (including EU member states) in 1996 to a group of 48 as of 2020. Meanwhile, the number of TAs including enforceable provisions aimed at liberalizing procurement markets has been increasing over time. Figure 1 shows that provisions on government procurement have become more common since 2000. In 2017, a quarter of TAs in force had enforceable provisions on government procurement.

> 1We use the terms “government”, “public sector” and “public authorities” interchangeably to indicate public institutions that are buyers in the public procurement market.

> 2We include only policies that the GTA database classifies as “red” (i.e., that almost certainly discriminate against foreign firms) in the following policy areas: “Government Procurement: Domestic Price Preference”, “Government Procurement: Local Content Requirement”, “Government Procurement: Market Access Restrictions” and “Government Procurement: Tendering Process”. These protectionist measures involve around 2500 country
|0.18|0.55|0.71|
|Std Dev|0.50|0.50|0.49|0.49|0.41|0.41|0.50|0.50|
|Sample|Techn.|Gener.|Techn.|Gener.|Techn.|Gener.|Techn.|Gener.|

_Notes:_ Source: IMSS administrative data for formal employment and endline survey data for informal employment, June 2019-May 2021. Robust standard errors are reported in parentheses. We control for demographic characteristics, socioeconomic status, work experience, time and school fixed effects.<sup>_∗_</sup> _p <_ 0 _._ 10, _∗∗ ∗∗∗ p <_ 0 _._ 05, _p <_ 0 _._ 01 To analyze the effect of the wage incentive on job retention, we apply the AG extension of the Cox proportional hazards model for discrete recurrent events, such as losing employment. Figure 5 shows employment survival rates for work spells starting with permanent contracts (Panel 5a) and temporary contracts (Panel 5b). While there is no discernible difference between treatment groups for work spells beginning with permanent contracts, treatment youths who start with temporary contracts face a lower risk of exiting employment. To formally test differences in retention, Appendix Table A3 reports hazard rates separately for the first six months of employment (Panel A) and over the full employment spell (Panel B). There is no change in retention for permanent jobs during the first six months. By contrast, we observe a more than 50% reduction in turnover for jobs with temporary contracts over this period (Panel A, Col. 3). For the full employment period, retention effects decrease. However, hazard rates for temporary jobs continue to be 26.1% lower.<sup>22</sup> This is partly ex-

> 22It is also noteworthy that there
duction of a new establishment is attributable to increasing local house prices. Increased housing demand following migrations of a sizable mass of non-incumbent, especially highskilled, workers in the wake of a new MDP may push up house prices in the winning county. This creates a wealth channel for incumbent residents in the winning county, as in Stroebel and Vavra (2019), which in turn drives up the retail prices. While this mechanism would also be consistent with less consumer price sensitivity, the underlying mechanism is different in that the price sensitivity will be induced by wealth effect and not by income effect. We rule out this explanation in figure 5. In panel A, we analyze whether the winning county experienced a different house price growth trajectory following a new plant introduction. We do not find any differential house price dynamics between the winning and losing counties, mitigating concerns of wealth effect driven increases in retail prices in the winning counties. Panel B refines this analysis by considering only high-skilled zip-codes.<sup>19</sup> We again observe no discernible difference in house price trends between high-skilled winning and losing zip-codes. Finally, in Panel C, we do a within-winner analysis of house price trends between high-skilled and low-skilled places. Again we observe no significant differences in price trends. Taken together, these graphs assuage concerns that the observed patterns in retail prices are driven by differential changes in house prices in winning counties.

# **5 Conclusion**

In this paper, using detailed barcode-level prices, we show that the entry of a Million Dollar Plant in a county increases the barcode-level prices across all types of products in the winning compared to the runner-up counties. However, only the wages of high-skilled workers increase, while the wages of low-skilled workers do not change. Therefore, with rising retail inflation, real wages of low-skilled workers unambiguously decrease. The rise in the number of expensive varieties and varieties mostly consumed by skilled workers is consistent with the fact that the rise in income dominates the price effect for skilled workers, thereby
GDP and wages and salaries estimates are from WB's Statistical Handbook 1995: States of the Former USSR, and relate to 1993. Average Government wages are taken from IMF Report SM/94/81 of March 29, 1994 and relate to 1993. Data on wages in manufacturing (monthly basis) are taken from the International Labor Office's Yearbook of Labor Statistics 1995 and refer to 1993.

# **Georgia**

Unemployment rate reflects only official unemployment for 1994. Data on paid employment in non-agricultural activities are taken from Statistical Handbook 1995: States of the Former USSR and are for 1993. Central Government, Non central, Education and Health employment are taken from Georoia: Issues in Government Pay and Employment Reform, based on the findings of a World Bank mission in June 1995 and relates to the situation as of April 01, 1995.

GDP per capita estimate and Average Government wage is taken from IMF study No. EBS/95/151 of September 25, 1995 and refers to 1994. Estimate of wages and salaries as percent of GDP is from the same IMF study and relates to 1994.

Kazakstan Unemployment rate reflects only official unemployment for 1994. Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and are for 1992.

Central Government, Non Central Government, Education, Health and Armed Forces employment figures are taken from Draft Report 1 5353-KZ Transition of the State. Data refer to 1994. On the matter of Military employment, total armed forces IIIS estimates at 40,000, not including paramilitary units, i.e., the Republican Guard (2,500 people), the Internal Security Troops (20,000), under the control of the Ministry of Interior, and the Border Guards (12,000), under the control of the National Security Committee. The World Bank has a different estimate based on the results of the mission. The authors have preferred to include data from the World Bank mission. However,
these initial decompositions suggest that if households had continued to consume the same share of income in the final period as what was observed in the initial period, then poverty would have fallen even more in the case of Bangladesh and Thailand. In other words, the fact that households are consuming a smaller share of their incomes in the final period implies that poverty—as measured by consumption aggregates—is higher than if the consumption-to-income ratio had remained constant. This result is particularly important in Thailand. In the case of Peru, by contrast, a higher consumption-toincome ratio contributed to the observed decline in poverty.

Results using different decomposition paths are in most cases invariant to the order in which the decompositions are done. The differences are particularly small in terms of the contribution that income per adult has on observed poverty reduction.<sup>12</sup> These results give us some confidence to further decompose total income per adult into subcomponents using a single path, instead of calculating all potential paths—which would require 5,040 separate decompositions.

To explore the multiple channels behind the changes in poverty depicted in Figure 1, we further disaggregate total income per adult into its subcomponents. The key result that emerges is that

> 11 The relevant international poverty lines are US$1.25 and US$2.50 for Bangladesh and US$2.50 and US$4 for Peru and Thailand.

> 12 We found similar small differences in the results of alternative paths when decomposing the poverty gap and the severity of poverty.

9
methods is presented in Section 2. In this section we also offer a methodological contribution by deriving standard error estimates for the remote-sensing based poverty map. Section 3 describes the different data used in this study. Our empirical findings are presented in Section 4. Finally, Section 5 concludes.

# **2 Methodologies**

## **2.1 Population census-based small area estimation of poverty**

Elbers et al. (2003; ELL) pioneered the use of unit record population census data combined with household consumption expenditure survey data for the small area estimation of poverty and inequality. ELL assume the following data generating process (DGP): where _y_ is log per capita income or expenditure, _x_ is a vector with independent variables, and where _v_ and _ε_ are zero expectation error terms that are independent of each other with variances denoted by _σv_<sup>2and</sup><sup>_σ_</sup> _ε,ah_<sup>2, respectively.The subscripts indicate household</sup> level _h_ residing in small area _a_ . The predictors _x_ may consist of variables observed at different levels of aggregation, i.e. household level employment status and local area employment rates may both serve as good predictors of household expenditure.

The model permits heteroskedasticity in the household errors _εah_ to allow for instances where the predictability of expenditures varies between demographics (i.e. elder versus young), employment status (i.e. unemployed versus employed), and sectors (i.e. blue collar versus white collar). Specifically, the variance _σε,ah_<sup>2isassumedtobeafunc-</sup> tion of household and area characteristics, denoted by _zah_ . A logistic transformation of the squared error will serve as the dependent variable (as the distribution of the untransformed squared error tends to be heavily skewed): where the vector _zah_ includes the constant term. An estimate of _σε,ah_<sup>2=</sup><sup>_E_[<
These papers provide a framework to study sovereign defaults but do not provide reasons why do some countries default considerably more times than others given similar fundamentals.

Recent work by Hatchondo, Martinez and Sapriza (2008) and D’Erasmo (2008) take a political economy approach. In their models, international lenders face different types of borrowers. Policymakers of the borrowing country that have different degrees of impatience alternate in power. Lenders cannot directly observe a borrower’s type and, hence, have to infer from the borrower’s actions. My paper differs from these in two ways. First, in the model, default risk does not depend only on the policymaker in power. What is essential is the institutional environment in which these powerful groups take actions. Second, since I focus on explaining the cross-country difference in terms of sovereign default frequencies, I do not try to match with a particular country’s data, as the other papers do.

This paper is organized as follows. Section 2 provides details of the data used in the cross-country empirical analysis and shows the findings. Section 3 presents a two-period model with default option in period two with a closed-form solution. It shows that with weak institutions, default is more likely. Furthermore, default probability is increasing in polarization. In Section 4, the analysis embeds the two-period model into an infinite horizon framework. Section 5 compares simulation results with the data. Section 6 concludes.

# **2 Data and Findings**

This section documents the cross-country differences in terms of institutional quality and sovereign default frequencies, and establishes the facts the paper aims to explain using the models that developed in Sections 3 and 4. The data set covers more than 90 countries. It includes measures of quality of institutions, sovereign default history and economic indicators from 1960 to 2008.<sup>4</sup>

# **2.1. Data Description and Source**

The first key measure of government characteristics is government polarization (polar), which captures how
_Table 2: Occupational clusters used in the experiment_

|**Occupational cluster**|**Share of cluster in**<br>**total number of vacancy**<br>**ads**|**Included occupations (according to online**<br>**portal’s classification)**|
|---|---|---|
|Accounting|9%|Accounting, Audit, Finance|
|Marketing|11%|Marketing, Business Development|
|Sales|19%|Sales|
|IT|20%|IT, Engineering, R&D|

Note: Average shares of cluster in total number of vacancy ads are calculated using the ad counts in the online job portal between January and February 2015.

# 2.2.  Treatments and resume construction

# _Socioemotional skill signals_

A particular challenge associated with this study is the difficulty in defining and measuring socioemotional skills, which also includes personality traits like the Big Five (Heckman and Kautz, 2012). For our purposes, however the Big Five are too general to signal in a CV, and it is not clear whether employers may understand or value a statement that signals, for example, “high conscientiousness." Depending on data availability, some studies use more specific measures, such as misbehavior in childhood (Segal, 2013), leadership positions or behavioral reports in high school (Kuhn and Weinberger, 2005; Protsch and Solga, 2015), and skills such as locus of control, aggression, and withdrawal (Groves, 2005), but most of these studies use information on skills and labor market outcomes for real individuals, which presents a challenge for causal inference.<sup>11</sup> One problem is the potential association between different aspects of an individual’s skill set: for example, there is evidence of a significant correlation between an individual’s IQ (i.e. cognitive skills) and openness to experience
<!-- Start of picture text --> Graph 2 ‐ Rationale<br>30<br>24<br>25<br>20<br>15 12<br>10<br>6<br>5<br>5<br>0<br>Monitor/oversee Determine Determine the net Other<br>over financial mismatches worth<br>position between A/L<br><!-- End of picture text --> _Note: Multiple responses were permitted, and the total is therefore superior to the number of respondents._ Source: Responses to SALM survey Overall the results confirm that sovereign ALM is just one of several objectives of producing a sovereign balance sheet, as reflected by only six countries (25 percent of the respondents to this question) mentioning the determination of mismatches between assets and liabilities as a rationale. It is a reminder of the limited number of countries where ALM considerations are integrated into the assessment of macroeconomic / fiscal policies and in the decision‐making process within the public sector.

It is difficult to see how the determination of sovereign net worth, mentioned twice as often as a rational of the balance sheet, could be of value for implementing ALM practices. The net worth of the sovereign is a value at a point in time which may not be fully relevant for long‐term sustainability. Moreover, it could include theoretical valuation of very illiquid assets that may not be used to cover future expenditures, or it may miss significant contingent liabilities that could undermine fiscal sustainability. In addition, in most cases,<sup>12</sup> the net worth of a sovereign does not take into account the value of future taxes (i.e. the capacity of the sovereign to raise taxes in the future) as well as the value of future expenditures that are unavoidable. It is indeed difficult to assess with precision these elements as they are based on various assumptions (what is the long‐term