rafmacalaba/data-use-mentions-extended huggingface dataset:
form of the transportation costs and that the freight rate contains the specific component.<sup>20</sup> They also were the first to point out that the assumption of ad-valorem transport costs is not innocuous as it underplays the effects of transportation costs and tariffs on the quality composition of exports and imports. This paper has instigated the importance of the functional form of the transportation cost for multiple aspects of trade. Feenstra and Romalis (2014) used the specific transport costs to estimate traded quality. Due to the presence of the specific component in transport costs, remote economies were predicted and found to specialize in higher quality by Lugovskyy and Skiba (2015) and in lighter varieties by Harrigan (2010) and Lashkaripour (2020).<sup>21</sup> Wong (2021) showed that specific transport costs reduce welfare and trade by more than an equal-yield ad-valorem transport costs because they are more price-distorting. We summarize the advantages and disadvantages of the ad-valorem measure as follows.

**Stylized Fact 14.** _The advantages of the ad-valorem measure of transport costs are: (i) it is in line with many trade models (ii) it is available for many countries and years; and (iii) it is intuitive and comparable with tariffs._

_At the same time, the functional form of the transportation cost contains a significant specific (per unit) component. This fact is important for estimating the traded quality and welfare gains from trade._ The data on the per-unit measure of transport costs are relatively sparse. Earlier papers on the specific freight rates explored the determinants of the per-weight freight rates. Mainly because in the BTI dataset—the pioneering dataset for measuring specific freight rates—other units were not reported. Lashkaripour (2020) made a point that consumers buy units, not kilos of manufactured goods, and thus the per-unit measure is more relevant from the perspective of trade models. The per-unit freight rates are available only in a handful of datasets: e.g.,
respectively. The SCI indicator is positively correlated with income per capita as well as most of the political institution variables.

There are other alternative indicators of statistical capacity and data access. Examples include the Statistical Performance Indicator recently released in the 2021 World Development Report (World Bank 2021) and the Open Data Inventory (ODIN). Our choice of SCI is largely due to the fact that the data is available since 2004, while the SPI is available from 2016 and ODIN is available from 2015, excluding data from 2019. The correlation between the SCI and SPI is about 0.77 for the few years where this overlap. The correlation between SCI and ODIN is 0.56 for the years where there is overlap. The estimations in this study were replicated using the SPI and ODIN instead of the SCI, and the results are largely consistent despite the short time span.<sup>7</sup>

# **IV. Data – Other Variables**

We control for several factors that are typically accounted for in growth regressions. These broadly include human capital, sectoral composition, trade, financial development, commodity-price shocks, external debtfinancing shocks, and quality of political institutions. Given that the sample of analysis is largely based on developing economies, we employ variables that tend to have broader coverage across developing economies. We use exports plus imports over GDP for the measure of trade openness. Sectoral composition is captured through the share of manufacturing value added and the share agriculture, forestry and fishing value added over GDP. We also account for human capital using gross primary school enrollment rates. Financial development is measured as the domestic credit to the private sector as a percentage of GDP, following Levine et al., (2000). These variables are compiled by the World Bank’s World Development Indicators.

> 7 Results are available upon request from the authors.

17
were covered in the government basic urban health-care insurance scheme, which does not cover migrant workers (Hu et al., 2008). Furthermore, migrant workers tend to underuse health services in their destination cities, as almost two-thirds of migrant workers who report illness do not visit a doctor (Gong et al., 2012). Another type of public spending—social protection spending—provides similar evidence.<sup>28</sup> In the 15 cities of our estimation sample, the correlation between social protection spending and public education spending shocks is almost 0 (i.e., -0.06).<sup>29</sup> Nevertheless, we employ two different strategies to provide additional layers of robustness checks on the exclusion restriction. First, we employ different model specifications that control for a number of variables in estimating equations (7) and (8). These include the migrant worker’s (household head’s) demographics, employment, and dummy variables indicating his work industry, province of origin, and whether he migrates within the same province. If somehow there is a reason to believe that the city-level shocks to public education spending can have differential effects on different occupations and migrant workers coming from different locations, these variables can help net out such effects. Furthermore, we explicitly control for social protection spending in a robustness check. If the estimated coefficient on school fees does not lose its statistical significance (or change significantly) when social protection spending is included, this would provide supportive evidence for the validity of the exclusion restriction. Second, we apply a bounding method recently developed by Chernozhukov, Lee, and Rosen (2013) that does not require the exclusion restriction. This second strategy, in fact, generally allows for the violation of the exclusion restriction to occur

> 28 Social protection spending is defined in the China City Statistical Yearbooks as being composed of social security benefits, employment subsidies, and unemployment grants. 29 This result is consistent with the fact that
446.8|(83.2)|-25.4|477.8|(90.4)|<br>491.4|(67.6)|13.6|
|Treated 2|470.4|(76.1)|<br>444.6|(81.4)|-25.8|472.1|(90.2)|<br>486.4|(67.1)|14.3|
|Treated 3|466.5|(73.4)|<br>441.9|(80.0)|-24.6|465.4|(88.3)|<br>481.2|(66.0)|15.8|
|Treated 4|460.8|(72.7)|<br>437.2|(78.5)|-23.6|448.1|(85.9)|<br>469.5|(65.9)|21.4|
|Treated 1,2,3,4|468.7|(75.3)|<br>443.6|(81.3)|-25.1|469.5|(89.8)|<br>484.5|(67.3)|15.0|
|Treated 2,3,4|467.0|(74.5)|<br>442.0|(80.4)|-25.0|464.7|(89.2)|<br>480.8|(66.8)|16.1|

_Note._ Students in the 3rd year of public regular secondary school that participate in ENEM in the years 2009 or 2015. _Source. ENEM_ , 2009 and 2015, microdata.

11
# p(Ỹ|Y) = ∫p(Ỹ|θ) p(θ|Y)dθ.

# Estimating the impact of the pandemic on activity

The evolution of the pandemic can dramatically influence the economy by triggering voluntary social distancing on the part of risk-averse individuals and the imposition of pandemic-control policies by governments. Maloney and Taskin (2020) find that the dynamics of high-frequency mobility indicators are more driven by “voluntary” individual response to increased COVID-19 case incidence, proxying for awareness or fear of the virus, rather than lockdowns. In contrast, Coibion (2020) and IMF (2020) show that consumer expenditures are more affected by strict pandemic-control policies such as lockdowns than voluntary shifts in behavior.<sup>7</sup> Following Coibion (2020) and IMF (2020), the impact of infection and lockdown measures on consumption is estimated based on pooled panel regression as follows: where a country i = 1,. . .21, at a time t = 2020Q1-Q3. FEi,t is the forecast revision of real consumption using the difference between pre-pandemic forecasts and the latest forecast by Oxford Economics; stringencyi,t is <mark>Oxford Covid-19 government response stringency index</mark> ; and covid19i,t is covid-19 cases per capita estimated by Imperial College London and is assumed to proxy voluntary social distancing measures.<sup>8</sup> Estimated daily new case counts are used instead of confirmed case counts to overcome severe distortions caused by limited testing early on in the pandemic. In the United States, for example, large-scale seroprevalence studies as of July 2020 were consistent with a much higher rate of community transmission than suggested by cumulative confirmed case counts (Anand et al. 2020; Demonbruen et al. 2020).

# Table 1. Panel regressions

> 7 The impacts of voluntary shifts in behavior and government-imposed lockdowns on economic activity are likely to vary significantly over time, depending on the severity and duration of the pandemic, as well as household adherence to
when social norms allow the husband to coopt a large fraction of the wife’s income, empowerment programs should perform relatively better in terms of wife’s labor supply. Where norms limit that fraction, and where returns to household goods are high, interventions to foster cooperation among spouses should perform relatively better. Nonetheless, the best possible outcome clearly results when spouses cooperate and where the husband does not control his wife’s income, _C_ , _E_ .

# **4. Analysis**

# **4.1. Data**

In testing the model’s hypotheses, we use panel household survey data from the first two rounds of the Malawi Integrated Household Panel Survey (IHPS) which was implemented by the National Statistical Office (NSO) under the Living Standards Measurement Study – Integrated Household Surveys on Agriculture (LSMS-ISA)<sup>4</sup> program. In 2013, the IHPS attempted to track and resurvey 3,246 households across 204 enumeration areas (EAs) that had been previously surveyed as part of the Third Integrated Household Survey (IHS3) 2010/11. The IHPS sample had been designed in 2010 to be representative at the national-, urban/rural, regional levels, and for the six strata defined by the combinations of region and urban/rural domains. In 2013, IHPS targeted all individuals that were part of the IHS3, including those that moved away from the IHS3 dwelling locations between 2010 and 2013. Once a split-off individual was located, the new household that he/she formed or joined since the IHS3 interview was brought into the IHPS sample. As a result, the overall IHPS database includes 4,000 households, which could be traced back to 3,104 IHS3 households. Attrition was limited to only 3.8 and 7.4 percent, of households and individuals, respectively. Given our focus on intra-household decision-making in rural areas, the analysis sample includes 1,929 households that had the same principal couple in 2010 and 2013,
individual fixed effects and density is 0.21 (Mion and Naticchioni, 2009). There is also evidence of spatial sorting in Spain and in the United Kingdom (de la Roca and Puga, 2012; D’Costa and Overman, 2014).<sup>2</sup>

## _Urban costs_

Systematic evidence about urban costs is nearly absent - most agglomeration studies use nominal wages, rather than real wages that account for local urban costs . Urban costs take a variety of forms. In larger cities, housing is more expensive, commutes are longer, and the bundle of amenities that these cities offer may differ. Residential mobility implies that urban (dis)amenities and commuting costs are reflected into land prices. The elasticity of urban costs with respect to city population is the product of three quantities: the elasticity of unit land prices at the city center with respect to population, the share of land in housing, and the share of housing in consumption expenditure. It is critical to estimate the elasticity with respect to urban cost because, _first_ , city size is an outcome of a tradeoff between agglomeration economies and urban costs (Henderson, 1974; Fujita and Ogawa, 1982). The costs of agglomeration can partly explain why firms and workers do not move to larger cities. _Second_ , urban policies such as barriers to labor mobility and zoning limits (e.g. Duranton, 2008) are imposed to curb population growth and costs such as housing and congestion in cities. An estimation of the association of urban cost with city size helps understand the efficacy of these policies in achieving their stated objectives.<sup>3</sup> The topic remains underexplored because of a lack of an integrated framework to guide empirical work and appropriate data.

Although agglomeration benefits are higher, urban costs may explain why cities do not expand further. Urban costs have been estimated for France and Colombia using land and housing price data (Combes et al., 2013; Combes et al., 2019;
A. Quazi (Eds.), _Making Conservation Work: Linking Rural Livelihoods and Protected Area Management in Bangladesh_ (Vol. 84, pp. 270–271). https://doi.org/10.1890/00129658(2003)084[0270:mcp]2.0.co;2

- Ullah, A. A. (2011). Rohingya refugees in Bangladesh: Historical exclusions and contemporary marginalization. _Journal of Immigrant and Refugee Studies_ , _9_ (2).

- UNDP, B., & UN Women, B. (2018). _Report on Environmental Impact of Rohingya Influx. Dhaka, Bangladesh_ .

- UNEP-WCMC, & IUCN. (2021). Protected Planet: The World Database on Protected Areas (WDPA) and World Database on Other Effective Area-based Conservation Measures (WDOECM).

- UNHCR. (2020a). Bangladesh - Shelter Need Assessment Cox ’ s Bazar 2019.

- UNHCR. (2020b). Rohingya Refugee Response - Bangladesh LPG Distribution Tracking Dashboard 2020, 2021.

40
WPS3756

# Enforcement of Labor Regulation, Informal Labor, and Firm Performance

Rita Almeida ∗ World Bank Pedro Carneiro University College London, Institute for Fiscal Studies and Center for Microdata Methods and Practice

## **Abstract**

This paper investigates how enforcement of labor regulation affects the firm’s use of informal employment and its impact on firm performance. Using firm level data on informal employment and firm performance, and administrative data on enforcement of regulation at the city level, we show that in areas where law enforcement is stricter firms employ a smaller amount of informal employment. Furthermore, by reducing the firm’s access to unregulated labor, stricter enforcement also decreases average wages, productivity, and investment. Our results are robust to several specification changes, and to instrumenting enforcement with 1) measures of access of labor inspectors to firms and 2) measures of general law enforcement in the area where the firm is located.

Keywords: Informal Sector, LaborMarket Regulation, Labor Demand, Flexibility, Productivity.

JEL Classification codes: J3, J6, O17.

World Bank Policy Research Working Paper 3756, October 2005 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ ∗ Corresponding author: Rita Almeida (ralmeida@worldbank.org). We thank the Brazilian Ministry of Labor for providing us the data on enforcement of labor regulation that we use in our paper and important information about the process of enforcement, especially Edgar Brandao, Sandra Brandao and Marcelo Campos.
<br>40<br>30<br>30<br>20<br>20<br>10<br>10 0<br>0 below general above higher<br>16-24 25-34 35-44 45-54 55-64 65+ secondary secondary general<br>secondary<br>both men women both men women<br><!-- End of picture text --> _Source:_ Estimates based on Russia Longitudinal Monitoring Survey–HSE 2010–16. _Note_ : Deciles have been established using per capita household expenditures.

# **Tobacco price elasticity by decile**

Although several models have been tested to estimate elasticities by decile, the pooled cross-sectional model with year fixed effects and demographic controls at the household level is preferred (table 1; see 5
# **4 Methodologies for allocating taxes and transfers**

### **This section describes in detail the allocation methodologies used to model each tax and transfer included in the analysis.**

## **4.1 Pensions**

**A CEQ Assessment has two scenarios for treating pensions.** The first scenario treats pensions as saved private income (in the form of contributions) spent later in the form of Pensions as Deferred Income (PDI). The second scenario treats Pensions as Government Transfer (PGT), a tool for redistribution from one segment of society to another by taxing individuals through pension contributions and transferring benefits through pension income.<sup>35</sup> Both scenarios present extremes and reality is always some combination of the two (see Annex A for comparison).

**This analysis uses the (PGT) as the main scenario.** The reason is that pension expenditures are high and unsustainable in the West Bank and Gaza, with pension contributions funding roughly 45 percent of pension payouts, meaning that the Palestinian pension system redistributes from tax-paying individuals to pension recipients. Nonetheless, treating pensions solely as a government transfer means that those earning a comfortable pension will appear to have zero Market Income, thus overestimating the proportion of the poor (Lustig, 2022).

**The pensions model has two components: (i) pension contributions from the current working population, and (ii) pension income received by the retired population.** For this study, the PECS survey directly identifies pension income recipients and amounts. The survey also directly identifies individuals that make pension contributions, and the amounts are inferred based on the region, public sector status and age. A key assumption of the CEQ Assessment is that employer contributions to pensions are borne by the employee in the form of lower wages—that is, the analysis allocates the total pension rate to employees, including both employee and employer’s contributions. The survey identifies roughly 140,000 pension
# **Table S2: Model Performance (10 Fold)**

||**M**|**ain Result**|**s**|**Check**|
|---|---|---|---|---|
|Dataset:|**Stand**|**ard Indica**|**tors**|**Offset**<br>**Indicators**|
|Performance:|O<br>(|ut of Sampl<br>10 fold CV)|e<br>|Out of<br>Time|
|Sample:|All|Has|No|(train early<br>period, test<br>late)<br>All|
||AUC|Bureau<br>Records<br>AUC|Bureau<br>Records<br>AUC|AUC|
|**Baseline Model**|||||
|**Credit Bureau**|||||
|Random Forest|0.516|<br>0.510|-|0.507|
|Logistic, stepwise BIC|0.566|<br>0.565|-|0.550|
|**Our Models**|||||
|**Phone indicators (CDR)**|||||
|Random Forest|0.716|<br>0.713|0.730|0.631|
|Logistic, stepwise BIC|0.765|<br>0.762|0.789|0.595|
|**Phone**<br>**indicators,**<br>**within-week**|||||
|**variation (CDR-W)**|||||
|Random Forest Weekly Ensemble|0.619|<br
# **_b. Effective tax rates by sector and sex of owner_**

Next, we examine the effective tax rates by sex of the business owner, which are presented in rows 1 - 4 in Table 6. The gender difference in tax burden is significant using the tax code with the most recent data (row 2) where male-owned businesses bear a larger tax burden (at 3.1%) than female-owned businesses (at 0.5%).

Using self-reported taxes and conditional on positive tax payments (row 3), the effective tax rates are very high for both female businesses and male businesses at 12.4% and 5.0%, respectively, in period 2. Such high rates could be detrimental for business growth and could potentially push them into the informal sector. For example, as a comparison in Ukraine, taxpayers deemed a presumptive tax rate of 6 percent on turnover to be too high, causing discontent in this tax regime (Coolidge and Yilmaz 2016).

The results by sector show that the tax burden is low for: (1) the retail trade sector (in rows 7 and 11), as some low revenue firms in this sector (e.g., trading in fruits and vegetables) are exempt, and (2) enterprises that manufacture or sell local beverages (rows 5 and 9) because most fall below the exemption threshold. However, for food and beverage services – a female-dominated sub-sector, female-businesses’ effective tax rates are high (at 13.9% in period 1 and 5.1% in period 2 using the tax code in row 10). Given that low revenue firms are not exempt from the presumptive tax in this sector, the effective tax rate is particularly high. Similarly, low revenue transit services - a male-dominated sector - are not exempt from tax, and so the effective tax rates for male businesses in the transit service sector are also high (at 16.4% in period 1 and 10.3
in each country, which excludes households without access to a phone. Second, in some countries the data was collected by Random Digit Dialing (RDD) while in others the sample was based on a pre-pandemic household survey that collected information on the head of the household, which implies that household heads are over-represented in the second group of countries. Household sampling weights were constructed to adjust for differential response rates among subgroups of the population, in order to approximate nationally-representative estimates. Earlier analysis by Kugler et al. (2021) shows that in countries where surveys collected employment information for all household members, estimates from phone surveys did reasonably well in capturing the disparities and changes in employment outcomes across a number of population groups, including gender, education and area of residence, but fare less well with respect to age group comparisons. Brunckhorst et al. (2022) also compare estimates of employment dynamics from HFPS data with employment estimates from LFS data for a subset of middle-income and high-income countries where LFS data was collected throughout the pandemic and find that employment trends observed in HFPS data are qualitatively similar between HFPS and ILOSTAT estimates with a strong correlation (R<sup>2</sup> =0.882). Moreover, as Brunckhorst et al. (2022) note, the discrepancies in absolute magnitudes of pre-pandemic employment and pandemic job losses between HFPS and LFS data are driven, in part, by the different framing of the prepandemic and pandemic employment questions in HFPS. The analysis in our paper does not rely in any way on those features of the data, as the information we use is limited to the learning modalities of children and parental education.

# **3.3 Simulating the loss in educational attainment from learning losses measured in LAYS**

We undertake three sets of simulations, based on the pre-COVID data on educational attainment and estimates of pandemic-related learning losses, to quantify losses in education attainment and resulting changes in intergenerational mobility for countries.

- (i) <u>Scenario 1 (Global uniform