rafmacalaba/data-use-mentions-extended huggingface dataset:
> countries are subsequently classified in three categories: (a) low growth and low final debt; (b) moderate growth and moderate final debt; and (c) high growth and/or high final debt. Results are shown in Table 2.2.<sup>8</sup> It should be noted that Mauritania is classified in category (c) wholly on the basis of its high initial indebtedness (above 70 percent); the change in the country’s debt was a modest 6 percentage points. Sudan also had high initial debt (above 50 percent) but also demonstrated rapid accumulation.<sup>9</sup> There are important differences between the methodology reflected in Table 2.2 and debt sustainability risk assessments found in DSAs. Table 2.2 highlights countries that have shown rapid external PPG debt-to-GDP accumulation, which may not correspond to debt sustainability risk ratings. Most notably, DSAs may conclude there is heightened risk of debt distress based on debt ratios besides debt-to-GDP. For example, Burundi is high risk of debt distress in its most recent DSA due to a high debt-to-exports ratio (which we don’t consider here), despite its debt-to-GDP ratio remaining relatively low. A similar situation occurs with Ghana. The following section uses the DSA database to further explore the debt dynamics of different country groups, including those classified in category (c) in Table 2.2 below.

> 5 Note that the criteria differs from Figures 2.3 and 2.4, where the initial and final data points are the same for all countries (2007 and 2013, respectively). 6 For external PPG low accumulation is a change in indebtedness of less than 10 percentage points (pp), moderate accumulation is a change in indebtedness between 10 and 15 pp, and high accumulation is a change in indebtedness greater than 15 pp. The selection of cut-offs was sample-driven. 7 We consider external PPG debt to be low if it is less than 30 percent of GDP,
changes in the number of Venezuelans as a share of the local population in a province on a wide-variety of outcomes. We rely on administrative data to measure the number of Venezuelans newly registered in each district in Peru on a monthly basis between January 2015 and December 2020. We aggregate this information at the province level, which roughly corresponds to a labor market. Having a time-varying measure of the presence of Venezuelans in each of the 198 provinces allows us to use repeated cross-sectional data on outcomes for Peruvians and control for location and time fixed effects, as well as, location-specific time-trends. Hence, we identify the impact of the presence of Venezuelans by examining how outcomes for Peruvians change when more Venezuelans arrive in a province, conditional on the trend in that outcome.

However, it is possible that local shocks impact both the destination choice of Venezuelans and outcomes for Peruvians, hence we also use an instrumental variable strategy where we instrument for the number of Venezuelans in a location with the presence of Venezuelans in that 3
.5|2.8|2.1|2.2|
|**All**|0.1|0.3|0.2|3.4|2.3|2.5|

_Source:_ Authors’ calculations.

# **Kenya (KIHBS 2005–2006)**

The data are from the Kenya Integrated Household Budget Survey (KIHBS) administered by the Kenya National Bureau of Statistics (Ministry of Planning and National Development) of the Government of Kenya. The fieldwork for this survey was carried out between May 2005 and April 2006. The total of all non-purchased items (both food and others) was imputed to be equivalent to 51 percent of total cash expenditure. However, about 95 percent of non-purchased
bustion power plants, common in the region and more likely to be air-cooled and less affected by droughts, lead to considerable increases in excess PM2 _._ 5. Overall, our findings complement the U.S. literature by highlighting that the threat to air quality posed by hydrological droughts is more significant for developing economies.

More broadly, our paper speaks to the literature that aims to quantify the economic damage of climate change (see Auffhammer, 2018, and references therein) by providing region-wide estimates for LAC of the societal cost that hydrological droughts impose by altering electricity generation, and by projecting these losses under several climate change scenarios. We also contribute to the literature on environmental inequalities (see Banzhaf et al., 2019, and references therein) by documenting that excess PM2 _._ 5 falls disproportionately among those with lower socioeconomic status. We thereby highlight a novel channel through which droughts reinforce socioeconomic inequalities.

The paper is organized as follows. Section 2 describes the data. Section 3 presents the identification strategy and the results. Section 4 provides counterfactual calculations of the losses created by excess PM2 _._ 5. Section 5 presents projections of losses based on our results and climate models. Section 6 presents robustness checks. Section 7 concludes.

# **2 Description of the data**

We construct a power plant-level panel dataset to study whether hydrological droughts impact PM2 _._ 5 concentrations by shifting electricity production towards combustion power plants. This unique dataset combines market-level measures of the level of distress by hydropower plants, derived from a granular watershedlevel measure of hydrological drought, with information on PM2 _._ 5 concentrations within 50 km of each power plant. This monthly dataset covers all Latin American and Caribbean countries between 2000 and 2020.

The starting point for our panel dataset is the Global Power Plant Database detailed in Byers et al. (2018).<sup>1</sup> This database provides detailed information on the geolocation, capacity, fuel type, and commissioning year for all power
three (four) formal jobs pay 10% (20%) more than informal jobs. The light line shows that when we control for worker characteristics (including gender and education), the same trend holds but the line is shifted downwards implying that the initial wage advantage of informal work is even larger (17%). One explanation is that workers in the formal sector tend to be more educated (Conover et al., 2022). A similar pattern holds when we limit the sample to those who are continuously employed for at least three quarters (Appendix Figure A1), confirming results from IMSS data that fast wage growth is not primarily driven by selection.

Interestingly, the pattern looks different for the cohort of people aged 21 to 24 (Panel B). Starting hourly wages are higher in the formal sector and there is less wage growth over time. One possible explanation is that this older group is less likely to accumulate human capital on the job. The wage patterns for these older cohorts is more similar to aggregate wage differentials in other settings, where formal jobs pay more even after controlling for observable characteristics (e.g. Ulyssea (2010)).<sup>11</sup> However, evidence disaggregated by age and education levels shows that across many LMICs, youths with primary and secondary education earn more in informal than in formal employment (Shehu and Nilsson, 2014).

# **2.3 Characteristics of formal vs. informal employment**

What can explain this substantial wage growth for school leavers, and why is it limited to the formal sector? Table 2 compares job characteristics from employed youths in our study sample (described in more detail below) collected two years after graduating. Among these, 47% and 30% report that they received formal training on the job for formal employment with permanent and temporary contracts, respectively. By comparison, this figure is only 6.5% in the informal sector.<sup>12</sup> A
), which is larger than the 300 million residing in HICs.

- The share of LMICs rises by a factor of 9 (from 3 to 27 percent). This group of countries has 281 million 60+ people, which is similar to the HICs combined. Of the 281 million, India claims exactly half. The 70+ cohorts are over 100 million. As in UMICs, they are highly underrepresented in the share of global COVID deaths relative to what their age distribution would suggest.

- The share of LICs rises by a factor of 22 (from 0.2 to 3.9 percent). LICs are less populous as a group and their age distributions are heavily tilted towards the younger cohorts. While the simulations suggest that the death toll would rise significantly based on demography alone, the increase is from a very low base and in absolute terms the contribution to the global death toll remains limited.

18
# **I INTRODUCTION**

Over the past decade, the number of individuals forcibly displaced worldwide has more than doubled, surpassing 110 million by 2023 (UNHCR 2023). Notably, children and adolescents under 18 of make 45 of international forced years age up percent migrants (UNICEF 2023). Forced displacement exposes these young individuals to severe wellbeing challenges and traumatic experiences, placing them at heightened vulnerability. This is particularly concerning as such events unfold during critical developmental stages that significantly influence their future life paths. Despite the urgency, our understanding of the impact of forced displacement on this demographic is still limited.

This challenge is partly due to data restrictions that obstruct the diagnosis of problems and the design of effective aid strategies. This issue is particularly acute for forced migrants living in local communities in developing countries, where an estimated 80% of the world’s refugees reside (Climate Center 2022). These individuals often face trust issues and may lack the formal documentation needed to establish their legal status within host communities. Consequently, they may fear detection by authorities when asked to respond to surveys or participate in public initiatives.<sup>1</sup> Furthermore, collective representative and data on a with longitudinal forcibly displaced migrants, population high mobility rates, is difficult and costly (Ib ́a ̃nez et al. 2024). This complexity is compounded when focusing on children and adolescents, given the need for enumerators to receive specific training to interact with such a vulnerable demographic and for migrant parents to authorize their children’s involvement despite prevailing distrust issues.

To address this knowledge gap, we launched the Venezuelan Refugee Panel Study for Kids (VenRePs-Kids) in Medell ́ın, Colombia. VenRePs-Kids is a longitudinal study representative of forcibly displaced Venezuelan and Colombian children and adolescents aged 5 to 17. To our knowledge, it is the first study to gather panel data specifically on forcibly

> 1This concern is also prevalent among undocumented migrants in the United States, as highlighted by Amuedo-Dorantes and Lopez (2015)
# **1. Introduction**

Since the 1960s, international institutions active in the field of development and most OECD countries have invested considerable resources in data capacity building in developing countries. For example, just between 2006 and 2015, the total amount spent by the World Bank Group in data capacity building was US$919.4 million, of which 45 percent went to Sub-Saharan Africa (World Bank 2017). This number provided by the World Bank’s independent evaluation group might be on the low side, as it excludes support that may go through development policy financing which does not allow for earmarking.

While it is fair to acknowledge recent progress in household survey data collection in Africa, it is also clear that this massive financial effort has not yielded the expected fruits. To date, there are still very few African countries that can claim to have high-quality data produced regularly and made available to the public in a timely manner. Indeed, Jerven (2013) argues that the world bank GDP data for African economies tends to be underestimated due to outdated data, obsolete methods, lack of data availability and uneven use of methodologies in those countries. Even when data are collected, some countries do choose to keep them confidential.<sup>1</sup> The question is what explains these poor outcomes?

Our conjecture is that the development community has failed in data collection or capacity building for data collection in African countries because this has essentially been a donor-led and supply driven effort. There has been little attention paid to the demand side. In other words, we need to focus more attention on the benefits for countries of producing and sharing data. This could lead to countries’ investing more of their own resources in them, a condition for sustained success. This paper contributes to the literature on the demand side interventions by uncovering the returns from investing in statistical transparency for countries. We focus on economic transparency defined as availability and access of economic information and statistical data (Geraats 2002b).

We explore
productivity.

Exploring the first channel, Mattoo, Rathindran and Subramanian (2006) look at whether the impact of liberalization of services sectors on output growth is different from that of liberalization of trade in goods. The authors found that countries with open financial and telecom sectors grew faster than other countries. Eschenbach and Hoekman (2006b) used an index of quality of policy for different services sectors in 20 transition economies and found that policy improvements attract FDI, and are associated with the post-1990 growth performance in these economies. Previously, Robinson, Wang and Martin (2002) had used a ten region, eleven sector CGE model to evaluate the impact of service sector liberalization on the world economy, finding that the effects were not only directly on increased services production and trade, but also indirect, by affecting production in other sectors of the economy.

Exploring the second channel, Fink, Mattoo and Rathindran (2003) use data for 86 developing countries during the period 1985-1999 and find that privatization and competition improved performance in the services sectors. Comprehensive reforms, they claim, bring the largest gains, and the sequence of reforms matters. In particular, their results suggest that competition policies should be incorporated simultaneously with privatization.

Fink, Mattoo and Neagu (2005) and Francois, Manchin and Pelkmans Balaoing (2009) focused on the effects of service sector reform on manufacturers’ export competitiveness, in particular 4
# **Employment Data in Household Surveys: Taking Stock, Looking Ahead**<sup>**1**</sup>

**Sam Desiere**<sup>**2**</sup> **, Valentina Costa**<sup>**3**</sup>

JEL Codes: C8, J13, J18, J21, J22, J24, J43, J46.

Keywords: Household surveys, Employment, Work, Questionnaire Design, Measurement, LSMS‐ISA, Gender, Youth, SDGs.

> 1 This work was supported by the International Fund for Agricultural Development (IFAD). The authors would like to thank Gero Carletto, Michael Frosch, Talip Kilic, Gayatri Koolwal, Joann Vanek, Kieran Walsh and Alberto Zezza and the participants in the _Dog & Pony Show_ held at the World Bank (October 2018), and in the _‘Improving the Availability and Quality of Individual‐ level Data on Women and Youth in Living Standards Measurement Study (LSMS) surveys’_ workshop held at IFAD (November 2018) for sharing ideas and comments at different stages of the preparation of this paper. We are also grateful to the reviewers Markus Goldstein and Amparo Palacios‐Lopez for their valuable comments and suggestions.

> 2 HIVA, Research Institute for Work and Society, University of Leuven, Belgium. Email: <u>sam.desiere@kuleuven.be</u>

> 3 Consultant, Living Standards Measurement Study (LSMS), Survey Unit, Development Data Group, The World Bank, Rome. Email: <u>vcosta@worldbank.org</u>
disrupted by the crisis).

To put these data in perspective, we compare them to similar data from other countries (Table 3). Correa and Iootty (2010) collected data for 6 countries in Eastern Europe, using a similar survey methodology with a rapid firm survey following a larger survey the year before. The data show that in terms of sales all countries were strongly affected by the global financial crisis. In Cambodia, the percent of firms closed (3.9%) and declines in sales (-30.7%) were relatively high although not the highest. The employment impact in Cambodia has been limited, with relatively few firms (16.6%) reducing their number of permanent workers.<sup>7</sup> However, in terms of total change in employment (-0.8%), employment growth was only lower in Lithuania.

> 5 We have run similar regressions for changes in output prices and find that firms in the bottom tercile of the productivity distribution cut their prices by 4% relative to firms in the top two terciles of the distribution. 6 Weighted figure. This is in line with Ministry of Commerce data, showing that the number of factories declined from 308 in June 2008 to 257 in June 2009 (a fall of 16.3 percent; personal communication to the authors).

> 7 Alternatively, the labor market adjustment could have been made on the wage side in Cambodia. However, survey data do not support that view.

8
||(0.05)|
|Other||0.020|
|||(0.02)|
|Constant|0.177***|0.209***|
||(0.05)|(0.05)|
|Observations|5,916|5,916|
|Region FE|NO|YES|
|Developed countries|YES|YES|
|Developing countries|YES|YES|
|Adjusted R-squared|0.02|0.03|
|Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1|||

16
.847***|0.828***|0.834***|
||(0.084)|(0.158)|(0.242)|(0.244)|
|Observations|275718|275718|275718|268173|
|Mean Violence Index|-0.001|-0.001|-0.001|-0.001|
|C: Any Violence|||||
|ln(U<sup>f</sup>)|-0.088|-0.725***|-0.668***|-0.679***|
||(0.052)|(0.121)|(0.205)|(0.213)|
|ln(U<sup>m</sup>)|0.084|0.642***|0.601***|0.610***|
||(0.066)|(0.111)|(0.206)|(0.212)|
|Observations|274002|274002|274002|266517|
|Mean Violence Index|0.008|0.008|0.008|0.008|
|Year FE||Y|Y|Y|
|Country FE|||Y|Y|
|X controls||||Y|

_Source:_ Authors’ analysis based on data from the Demographic and Health Surveys (DHS).

# _Notes:_

* p<0.10, ** p<0.05, *** p<0.01. Standard errors are robust to arbitrary forms of heteroscedasticity and are clustered at the country level. X is a vector of controls for age and education of respondent, education of her spouse, and urban location. Relative to Table 1, the dependent variables are now
element becomes particularly relevant when investments in the sector are made primarily to boost chances for reelection, without recognition of documented facts on service delivery improvement for the society at large. Moreover, the populist politician’s strong focus on reelection may even reduce his or her propensity to request the background information needed to make informed infrastructure decisions and priorities— or have the results of investment analyses biased.<sup>17</sup> Politicians who are overly focused on reelection have been suspected of accepting weak solutions for projects that will not affect their constituency.<sup>18</sup> For instance, Cadot, Roller, and Andreas (2006), using French regional data, show that infrastructure investment are often a byproduct of a politically driven game in which decisions are likely to depart from efficiency considerations.<sup>19</sup> An overly strong focus on reelection and the associated need for campaign funding may influence the ranking of investment priorities. Extending network coverage may be effective as a political propaganda, but the effect for poor voters may be limited if the policy is not supported with enough budget allocations to reach all segments of users. The regulated price for network connections can be too significant for poor consumers. There are many cases in which networks have been expanded and celebrated as a political decision while in the end consumers cannot afford to pay for access to the extended services.<sup>20</sup>

**_Market reform:_** The speed and timing of decisions on market design may change as the populist’s focus on reelection surpasses welfare-orientations. Politically unpopular layoffs are a likely short-term effect of divesting state-owned or state-controlled monopolies to competition. Political risk and reelection concerns can explain the timing to implement reform and possible delays.<sup>21</sup> Governments with weak political support are more likely to postpone needed reforms. Not only will they have difficulties building consensus; they would also be careful not to put reelection at greater risk.<sup>
banks’ activities. Results are reported in column 4 (labeled “Act restr”). For all measures of market power in the banking sector, we find that the degree of restrictions on banks’ activities enter the model with a statistically significant negative sign. This finding clearly indicates that sectors that depend more on external finance are hurt by restrictions imposed on banks’ activities.

The quality and scope of credit information in a country can affect the impact of bank competition on access to finance. The presence of credit information sharing mechanisms such as credit registry or credit bureaus can help overcome adverse selection and moral hazard problems in credit markets (Padilla and Pagano, 2000; Jappelli and Pagano, 2002). Several empirical studies have found that information sharing results in more lending and better access to finance (see Love and Martinez‐Peria, 2014 for a review of the literature). This means that in an environment of reduced information asymmetry bank will have fewer incentives to invest in private information acquisition and therefore less to lose in the presence of higher competition. We test this hypothesis by interacting our measure of external financial dependence with a variable measuring the depth of credit information. The depth of credit information index, taken from the World Bank Doing Business Database, measures rules affecting the scope, accessibility, and quality of credit information available through public or private credit registries. The index ranges from 0 to 8, with higher values indicating the availability of more credit information to facilitate lending decisions. Results, which are reported in column 6 (labeled “Depth credit”), show that the estimated coefficient is positive and significant in all but one specification (the one with 3‐ bank concentration ratio), indicating that there is strong evidence that availability of credit information has positive effects on the growth performance of more financially dependent sectors.

12