rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 10584

# **Abstract**

Using official employment surveys for 45 advanced economies and Latin American countries, this paper shows that the positive cross-country correlation between business size and GDP per capita is tighter than previously found using firm-level datasets and finds a close negative business size-Gini relationship. The paper also finds a closer connection between individual income and business size for workers in less developed countries compared with those in advanced economies. Because employment data address the bias against the smallest productive units that characterize firm-level datasets, our approach uniquely assesses and highlights the dominance of the left tail of the business size distribution in less developed countries.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean Region. 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://www.worldbank.org/ prwp. The authors may be contacted at at mmelendez@worldbank.org, meslava@uniandes.edu.co, n.urdaneta@duke.edu, lauratenjo@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank. org, click **here** for direct access.

_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._ Produced by the Research Support Team
>(0.106)|
|Post_×_ODS|_−_1.401<sup>_∗∗∗_</sup><br>(0.381)|_−_0.381<sup>_∗∗_</sup><br>(0.190)|_−_0.043<br>(0.078)|_−_0.434<sup>_∗_</sup><br>(0.251)|_−_0.287<sup>_∗_</sup><br>(0.170)|_−_0.005<br>(0.212)|0.023<br>(0.280)|
|Observations|6,118|6,118|6,118|6,118|6,118|6,118|6,118|
|R<sup>2</sup>|0.526|0.551|0.390|0.484|0.602|0.642|0.528|
|Mean(ODP tons)|-1402|-261|-3|-551|-30|-116|14|

FE triple-difference regressions on a stacked country-level panel with a _±_ 3 year event window around RTA signature. All outcomes are the inverse hyperbolic sine of the difference between ODS emissions and emissions targets under the Montreal Protocol. All models include country-event fixed effects. Robust standard errors are (two-way) clustered at the country-event and country-year levels. We report the mean deviation from target across the entire sample in tons of ozone depletion potential (ODP). Statistical significance from two-sided _t_ tests are denoted by<sup>_∗_</sup> p _<_ 0.1; _∗∗_ p _<_ 0.05; _∗∗∗_ p _<_ 0.01
vehicles that are distinguishable from the ICE models. In addition to displaying the conspicuous adoption of energy-efficient vehicles, the green plate also helps make EVs easily recognizable for relevant road traffic and parking infrastructure privileges. The policy was promoted through three waves starting from December 2016. The enrollment was restricted to five pilot cities in 2016. The coverage expanded further in 2017 to 20 cities and was implemented at national scale by the end of 2018 (effectively 147 cities in our sample) as shown in Figure 6.

In addition to the incentives above that explicitly target the demand side, the central government also issued policy plans to address energy efficiency in production activities. In September 2017, MIIT issued the mandate on new energy vehicles using dual credit point system for both corporate average fuel consumption (CAFC) for ICE models and new energy vehicles (NEVs), which could be considered as a modified version of California’s Zero Emission Vehicle (ZEV) mandate.<sup>22</sup> As an addition to the existing regulation on fuel consumption of ICE models, NEV production and importation will result in credits accumulation for the corresponding manufacturers. Manufacturers with production and import volume more than 30,000 are subject to the dual credit point system, and the credit proportion for NEVs are set at 10% and 12% for 2019 and 2020 respectively. The mandate is considered as a continuation of the subsidy policies with less financial investment from the government, and the incentives are injected through the supply channel instead.

# **2.3 Data Description**

**Vehicle data** We obtain EVs sales at the city-quarter-trim level from 2015 to 2018. Our sample of analysis has 150 cities. This list of cities includes (1) the top 40 cities with the largest aggregate EV sales during our data period, and (2) their neighboring cities in order to conduct city-border analysis. In 2018, the total sale of passenger EVs in these 150 sample cities were 0.82 million, accounting for 78%
|418|
|**Wau**|1497|0.310|0.016|0.0045|643|

Note: Road length indicates the total length of roads in kilometers within each state; road grade is the fraction of primary roads in all roads. Road area is the density of roads per square kilometer in the state; road density is the density of kilometers of roads per capita in the state. Available data are constant for the period 2008-2013. Distance is the road distance from the indicated state capital to Juba.

Source: Authors’ calculations based on World Bank 2014a

# **<u>Box 1: Checkpoints in South Sudanese Routes</u>**

Price differences across food products across a country are not only driven by fuel prices, distances, quality of networks, and demand and supply conditions, but also by a variety of restrictions to trade and transit along routes, which are often difficult to quantify systematically.

A survey implemented in November-December 2010 and commissioned by the Ministry of Finance and Economic Planning of South Sudan quantified the time and monetary costs of authorized and unauthorized check-points along the major trade routes in South Sudan. To do this, authors made 147 journeys in commercial vehicles along major roads in South Sudan and collected detailed information on the number and location of check-points, payment and time waited, and officials present. A summary of the main results is presented here.

There are many check-points along major trade routes through South Sudan, both authorized and unauthorized. These check-points are justified on grounds of ensuring security of citizens, among other reasons. However, unauthorized payments and time consuming stops along trade routes can inflate prices for consumers. In addition time consuming processes at the road blocks adds to the delivery time of products and further raises the costs incurred by final consumers. The survey provides detailed information on the location of checkpoints along major routes, as well as time spent waiting at checkpoints and charges paid.

Over all routes surveyed, there was an average of
The Worldwide Governance Indicators (WGI) by the World Bank (2017) offer a consistent and quantifiable measure of governance standards. These indicators report estimated governance standards for over 200 countries and territories along six dimensions: (i) voice and accountability, (ii) political stability and absence of violence, (iii) government effectiveness, (iv) regulatory quality, (v) rule of law, and (vi) control of corruption. For each country, these governance indicators reflect surveys of a large number of enterprises, citizens, and experts, based on over 30 individual data sources.

<mark>To illustrate the correlation between infrastructure quality and governance, Figure 2 presents the relationship between the WGI sub-index on corruption, in comparison to the Infrastructure Quality Index of the Global Competitiveness report (WEF, 2018). The same positive correlation between corruption and infrastructure quality can be observed for infrastructure in general, electricity, and roads. Similar patterns can be observed for different WGI sub-indices.</mark> _Figure 2 Infrastructure quality correlates strongly with governance standards. The WEF Infrastructure Quality Index ranks countries on a scale from 1 (worst) to 7 (best). (Data source:_ WEF, 2018) <!-- Start of picture text --> 3<br>Infrastructure<br>2<br>1<br>0<br>-1<br>-2<br>1 2 3 4 5 6 7<br>WEF Infrastructure Quality Index (1-7)<br>3<br>Electricity<br>2<br>1<br>0<br>-1<br>-2<br>1 2 3 4 5 6 7<br>WEF Infrastructure Quality Index (1-7)<br>3<br>2 Roads<br>1<br>0<br>-1<br>-2<br>1 2 3 4 5 6 7<br>WEF Infrastructure Quality Index
Financial Regulation and Development Table 3: Profits, Branches and Regulation: Annual Reports

|Variables|(1)<br>Real Profts<br>(million birr)|(2)<br>Number of<br>Employees|
|---|---|---|
|Policy _×_ Big Bank|24.47*<br>(14.86)|393.3***<br>(114.6)|
|Bank FE|Yes|Yes|
|Year FE|Yes|Yes|
|Observations|52|52|
|Adjusted _R_<sup>2</sup>|0.938|0.971|
|Mean Dep. Var.|126.2|1,461|
|SD Dep. Var.|96.42|976.7|

_Note:_ This table reports OLS estimates. The unit of observation is bank level, and bank and month fixed effects are included. Davidson and McKinnon (1993) robust standard errors are reported in parentheses, which for this case are more conservative than bootstrapped bank-level clustered. Real Profits is the difference between the asset-generated income and liability-induced costs after taxes; it is continuous and measured in million birr. The number of branches denotes the total number of branches as shown in banks’ annual reports. The variables are made inter-temporally comparable using the core inflation figures from National Bank of Ethiopia (2012, 2013). Their means and standard deviations are reported in the final two rows. These variables are regressed over a Policy dummy, which takes unit value after the introduction of the regulation policy and a dummy for Big Banks, taking unit value for the six banks classified as large in Figure 4. Bank and Year fixed effects are included. The adjusted _R_<sup>2</sup> of these regressions is reported in the row “Adjusted _R_<sup>2</sup> ”. ***, *
Section J provides concluding remarks.

# **2 Background**

In India, limited infrastructure and transport services restrict mobility for both men and women. Still, women frequently experience extra socio-cultural and economic factors that negatively affect their commute patterns (Srinivasan and Rogers, 2005; Tripathi et al., 2017; Alam et al., 2021). Given the sizeable gender wage gap (Duraisamy and Duraisamy, 2016; Deshpande et al., 2018) and the additional barriers in accessing the financial system compared to men (Khera, 2018), paid access to transportation is plausibly harder for Indian women. Besides, the low rate of female usage of public transport might raise a perception problem since more female presence in public transportation makes women feel safer (Sajjad et al., 2017). In a survey of 3,800 students at Delhi University, Borker (2021) found that women are willing to travel 27 minutes more per day or 40 % more than their daily travel time if they can use a perceived safer transport route. These factors put women at a disadvantage regarding access to transport services and infrastructure (Astrop et al., 1996; Dominguez Gonzalez et al., 2020), potentially affecting their participation in labor markets (Patacchini and Zenou, 2005; ILO, 2017; Sajjad et al., 2017; Martinez et al., 2020).

In light of these challenges, the Delhi government introduced a scheme offering free bus rides to all women in the city from November 2019 onward (Kejriwal, 2019). The scheme makes bus travel free for women in all Delhi Transport Corporation (DTC) and Cluster buses. On each ride, bus operators provide a pink ticket to each woman. Afterward, Delhi’s government compensates the bus operators with |10, the equivalent of $0.14 (all currency conversions use the 11/2019 exchange rate), per pink ticket ride (The Economic Times, 2019; Durai, 2021). The program showed an early
as data on the presence of several organized crime groups from the Armed Conflict Location and Event Data and the Global Terrorism Database.

Our first key result is that municipalities affected by forced displacement experienced sharper declines in economic development relative to other municipalities. We estimate that nightlight density declined by 7.5 percent for every additional percentage point of the municipal population displaced from Venezuela to Colombia, representing an estimated 2.1 percent decline in GDP (Henderson et al., 2012). Considering that approximately 14 percent of Venezuela’s 1990 population is registered as living in Colombia, our estimates imply that total displacement outflows of this magnitude would translate into a GDP contraction of roughly 29 percent. This amounts to about half of the total GDP contraction Venezuela experienced during our period of analysis. Our results are not mechanically driven by population loss since data from ENCOVI show consistent increases in poverty rates and unemployment. Moreover, inequality also rose markedly in municipalities with higher displacement outflows, although these coefficients are estimated with less precision.

How does an autocratic regime remain in power during such a profound economic and social crisis? Our second key finding is that forced displacement contributed to the survival of autocratic leadership by simultaneously weakening political opposition and expanding organized crime, which provided illicit income and coercive capacity to the regime.

We find that displaced individuals disproportionately backed opposition candidates and that their departure also changed the composition of remaining voters.<sup>3</sup> For a municipality experiencing a 14 percent population decline, our estimates imply a reduction of 21.1 percentage points in opposition support and 39.3 percentage points in electoral turnout.<sup>4</sup> These results are corroborated by the fact that only 60,000 out of approximately eight million Venezuelans abroad were permitted to vote in the 2024 presidential election. Our findings include the

> 3This is consistent with the evidence in Holland et al. (2024), in which 12.1 percent of displaced Venezuelans in Colombia supported the left and only 0.1
# **B. Tobacco Price Elasticity by Decile**

Let Qid be defined as the average quantity smoked per day by individual i in income decile d; P the average price per cigarette (unit value of tobacco use); Di the consumption decile of individual i; and Xid the individual characteristics. Then, the smoking intensity equation is written as follows: ln Qid = β0 + β1lnP∗Di + β3Xid + μid (B.1) The empirical analysis of equation (B.1) assumes a log-log relationship among smoking intensity, price, and income. lnQid is observed if and only If the individual from a given decile d is a current smoker.

Several models were tested before deciding on the final elasticities used. All relied on the RLMS-HSE in 2010–16. Table B.1 considers the main equation using cross-sectional estimates and some that use the panel feature of the data. All the models were tested without controls and then adding demographic controls and year fixed effects. Among control variables included in the household level model were urban status, macroregion and year fixed effects. For the individual level cross-section and panel models, the controls included age, education, number of years smoked, urban status, macroregion, and year fixed effects.

**Table B.1 Regression Results, by Model**

|_Both_|<br>_1_|<br>_2_|<br>_3_|<br>_4_|<br>_5_|<br>_6_|<br>_7_|_8_|_9_|_10_|
|---|---|---|---|---|---|---|---|---|---|---|
|Cross-section (ind level)|-0.25|-0.23|-0.22|-0.21|-0.20|-0.21|-0.20|
AL, although the unconditional relationship in Figure 1 partly reflects demographic composition.

The finding that TEE is falling in household size is notable given the observation by (Deaton and Paxson 1998) that for several developing countries, measured food expenditure per capita declines in household size. A similar pattern holds for measured caloric intake. (Deaton and Paxson 1998) argue that this is a “puzzle” in the sense that larger households with a given demographic composition should benefit from economies of scale that increase their per capita consumption of relatively private/rival goods like food. They – suggest that “caloric overheads” energy expenditures that only need to be incurred by one household member on behalf of the entire household – could be one possible explanation. The TUS data is rich enough to capture differences in time spent on domestic chores, travel, etc. that would be hard to detect in most household surveys.

To examine this further and to also control for demographic composition, Table 2 estimates a log-log regression specification similar to the one used by (Deaton and Paxson 1998). The dependent variable is log household average TEE, REE or AL. Detailed demographic controls for age/sex ratios, log household size, log per capita expenditures, and mean years of adult education are included. When including all household sizes (1-14), there is close to a zero partial elasticity of TEE to household size, resulting from a perfect offset of REE and AL. Larger households economize on energy, but their members are also physically “larger” on average. When restricting to household sizes between 2 and 8, which make up 92% of the sample, there is a significant negative partial elasticity of about 0.017, due to a negative effect on AL that is much larger than the positive effect on REE. Both expenditure and education reduce _TEE_ holding constant demographics and appear to have independent effects.

Columns 7 and 8 of Table 2 estimate the same specification but replace TEE with caloric intake estimated for the 1993-94 NSS 50th round Schedule
of external validity. Corruption research comes from a tradition of macro-level studies with large country-level data sets, where „internal validity‟ – the causal relationships among variables – can be questioned. However, the contribution of these studies is not to demonstrate causality (though quite a number of them attempt it), but rather to document various institutional factors that contribute to, or discourage, corruption. The cost to external validity looms even larger when dealing with inferences from country-level experimental studies. A number of studies covered in this paper describe results that are very specific to a particular culture; whether lab results from Karachi or

> 4 For the purposes of this paper, we focus exclusively on corruption in the public sector.

3
and labor market outcomes back home, much more comprehensive information on past migration overseas needs to be collected in nationally representative surveys of South Asian countries, in the spirit of the Egyptian Labor Market Survey (Wahba 2015). In addition, dedicated migrant surveys that collect detailed retrospective information on employment and migration history, such as the World Bank Bangladesh Return Migrant Survey (BRMS), are well-suited for that purpose, but remain quite scarce globally (Bossavie, Gorlach, et al. 2021; Bossavie and Ozden 2022).

19
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third wave of the Tanzania National Panel Survey and was collected between October 2012 and November 2013. The NPS sample used a multi-stage clustered sampling design covering a total is 5,015 urban and rural households. With households selected to be interviewed over the course of 14 months to account for seasonality in consumption, the data have variation in the recall period, which can be exploited to assess the impact of recall decay on data quality. The reference agricultural season is the long rainy season of 2012, which all farms in the sample reported on.

The IHS4 2016/17 includes both a cross-sectional and a panel component. The cross-sectional sample includes 12,480 households surveyed in 780 enumeration areas. Households were visited once throughout the 12 months of fieldwork between April 2016 and April 2017. The IHS4 data were collected in the same way as the NPS 2012/13 and used for analysis following the same rationale. For our analysis, we use the 2015/16 rainy season as the reference agricultural season. Data collection began in April 2016, at which point not all households had finished the 2015/16 rainy season harvest. These households instead reported on the harvest of the previous, 2014/15 rainy season. For comparability, we dropped these households from the analysis. In addition, some households stated having harvested in the 2015/16 season, but harvest dates revealed respondents were referring to the previous season, and vice versa. We dropped these households as well.

The IHPS 2016/17 sample includes 1,989 households which were interviewed twice between April 2016 and April 2017, in one post-planting and one post-harvest visit relative to the 2015/16 rainy season. Given 5
’s development level (Loayza et al. 2012). Some studies have even focused on narrower sets of disasters, such as storms (Ishizawa and Miranda 2019), hurricanes (Strobl 2012), and earthquakes (Webb et al. 2002).

Studies based on this approach have shown that severe disasters trigger significant contractions in output and trade (Gassebner et al. 2010). However, their estimates are quite sensitive to assumptions, especially because observed income in the years following a disaster may be boosted by reconstruction-related investments. Moreover, with few exceptions (Kashiwagi et al. 2021), estimates based on national accounts fail to provide a breakout of the cost by geographic areas, which is critically important to target support. Importantly, waiting for the release of reliable national account data may not be practical when quick estimates are needed to guide assistance and reconstruction efforts on the ground.

Rapid field surveys like those conducted in the context of PDNAs address some of these concerns. Field surveys typically focus on the geographic areas most affected by disasters. Teams on the ground take stock of existing public and private assets, assess their condition in the aftermath of the disaster, and collect qualitative input from local economic agents. The result is a rich information set that is used to forecast the impact of the disaster on local economic activity and 4