rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Firm-level dynamics of exports**

The recent public availability of microdata allows crucial evaluation of export performance, that is, the entry, exit, and survival dynamics of firms in global markets. Export survival sustainability—first addressed by the seminal work of Besedes and Prusa (2006)—is a key dimension to understanding export performance, and has become an important factor for policy makers to consider in the design of policy to promote exports.

The World Bank has recently assembled an Exporter Dynamics Database that includes exporter characteristics and exporter growth measures based on firm-level customs information from 38 developing and 7 developed countries, primarily for the period between 2003 and 2010.<sup>5</sup> The use of this database makes it possible to compare Brazilian exports with other middle-income economies in the world (results are shown in figures 11–13).

The survival rate of exporters in Brazil stood at very high levels during 2003–9 (figure 11), only lower than levels observed in Turkey. While this result can be considered positive, it reflects a small and decreasing entry rate of firms into foreign trade. The entry rate of exporters was already low in Brazil, and dropped further to 22 percent, much lower than the values observed in countries chosen for comparison (around 35 percent).<sup>6</sup> That result—a matter of concern—also confirms other indicators recently released by the Ministry of Development, Trade and Tourism for Brazil.<sup>7</sup> Several studies, such as Clerides, Lach, and Tybout (1998) for Colombia, Mexico and Morocco, or Bernard and Jensen (1999) for the United States, present convincing evidence that new exporters are on average more efficient than nonexporters. Low and decreasing entry rates can be associated with low productivity at the firm level and/or high costs to export. In any case, this is a point that requires more detailed diagnosis to guide policy actions.

Lack of integration into
Table 5: Post-Sanction Collaboration among Sanctioned Firms

||(1)|(2)<br>**Aver**|(3)<br>**age Collabor**<br>|(4)<br>**ator per P**|(5)<br>**atent**|(6)<br>|
|---|---|---|---|---|---|---|
||U.S.|Euro+|Adv. Asia|U.S.|Euro+|Adv. Asia|
|Treatment_×_Post|-0.115***|-0.041***|0.012|0.257**|-0.070**|0.305***|
||(0.038)|(0.009)|(0.052)|(0.091)|(0.023)|(0.124)|
|Treatment_×_Post_×_pre-US||||-0.446***|0.035*|-0.351**|
|||||(0.134)|(0.019)|(0.158)|
|No. Obs.|5,442|5,442|5,442|5,442|5,442|5,442|
|R-squared|0.367|0.174|0.398|0.379|0.198|0.399|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector-year FE|Yes|Yes|Yes|Yes|Yes|Yes|

Note: We estimate the coefficients using OLS on a set of firms that had positive patenting. Robust standard errors clustered at the 2-digit ISIC sector level are
constraints on female entrepreneurs are driven by concerns over their visibility and interactions with strangers, regardless of medium, then virtual spaces might fail to expand their markets. Second, mobility constraints are not randomly assigned. They likely result from norms and preferences that also constrain women’s skills, aspirations, resources, and time (Martinez Dy, Martin, and Marlow 2018). Indeed, if home-based entrepreneurs are subsistence entrepreneurs with limited scope for growth, then the promise of digitalization might fail to materialize among them (Schoar 2010).

In this paper, I shed light on these issues by experimentally investigating the promise of online market access for female entrepreneurs in conservative settings. I do so in the context of Jordan, which, at 15%, has the world’s third lowest female labor force participation rate<sup>6</sup> and one of the highest gender gaps in entrepreneurship<sup>7</sup> . While access to e-commerce has been growing in emerging markets, I focus on the promise of Facebook as a marketplace. With more than 2.93 billion users worldwide<sup>8</sup> , Facebook has become one of the most accessible digital platforms in low and middle income countries<sup>9</sup> and is often used by business owners to reach customers<sup>10</sup> .

To understand how female entrepreneurs use virtual markets in conservative settings, I start by

> 1Brussevich, Dabla-Norris, and Khalid 2020

> 2Bank 2023

> 3Hardy, Kagy, and Jimi 2022

> 4OECD 2022, Pergelova, Manolova, Simeonova-Ganeva, and Yordanova 2019

> 5Poushter, Bishop, and Chwe 2018

> 6World Bank, ILOSTAT database (last accessed in November 2023)

> 7World Bank, We-Data: Measuring the gap in female entrepreneurship around the world (2022)

> 8Meta Reports, First Quarter of 2022

> 9Pew Research Center (2016)

> 10The use of social media for ‘informal online commerce’ has been documented in Pakistan, Bangladesh,
# **Table 3.5 WSS Tariffs, Cost Recovery, and Access for Selected Countries**

GPP per capita World Bank, 2008 World Development Indicators.

|**Water charges**|Brazil|China|France|Philippines|Turkey|UK|Spain|India|
|---|---|---|---|---|---|---|---|---|
|Tariff (in $US/m3, 2001) without<br>sanitation|0.39|0.2|1.23|0.2|0.3-1.0|1.18|0.57|0.01-0.09|
|% Recover|||||||||
|* O&M|~100%|not recover|75-100%|Low cost recovery|100%|Not always recover|100%|Low cost recovery|
|* Capital|not recovered|not recovered|>80%|Low cost recovery|not recovered|high recoverylevel|not recovered|not recovered|
|GDP/capita(in $ PPP converted)|10,296|5,962|34,045|3,510|13,920|35,445|31,955|2,972|
|% water coverage(Broad Definition)|||||||||
|* Urban|96%|93%|100%|87%||100%|100%|90%|
|% sanitation coverage (Broad<br>Definition)|||||||||
|* Urban|83%|69%||80%|||100%|62%|
|% sanitation coverage(Sewerage)|||||||||
|* Urban|53%|50%||7%|||98%|
# **6. Conclusion**

This paper analyzes the incidence and determinants of _harmful_ child labor (defined as child labor that conflict with human capital accumulation of the child) in Ghana applying a new data set, in an attempt of identifying the most vulnerable groups, thus possibly enabling appropriate actions to be taken by policy makers. While several interesting results are obtained, the major conclusions emerging from this study are (1) we reinstate the positive relationship between poverty and child labor, a conjecture that has been questioned by recent literature, and (2) we find evidence of a gender gap in child labor linked to poverty, since girls as a group as well as across urban, rural and poverty sub-samples consistently are found to be more likely to engage in harmful child labor than boys (3) there exist structural differences in the processes underlying harmful child labor in Ghana across gender, across rural/urban location as well as across poverty quintiles of households. The established gender gap need not necessarily imply discrimination but rather reflect cultural normns. A further exploration of this issue seems to be a potentially fruitful avenue for further research.

**References:**

- Basu, Kaushik and Pham Hoang Van (1998), "The Economics of Child Labor", _American Economic Review,_ 88, 3, pp. 412427.

- Canagarajah, S. and H. Coulombe (1997), "Child Labor and Schooling in Ghana", Policy Research Working Paper No. 1844, World Bank, Washington, D.C.

- Chao, Shiyan and Omer Alper (1998), "Accessing Basic Education in Ghana", Studies in Human Development, No. 1, June 1998, Human Development III, Africa Region, World Bank, Washington, D.C.

- Easterly, W. and R. Levine (1997), "Africa's Growth Tragedy: Policies and Ethnic Divisions", _Quarterly Journal of Economics,_ 112, 4, 1203-50.

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8 Parameter estimates from all the first-stage models are then taken, in the second stage, to the population census. Since predicted household-level per capita consumption in the census is a function not only of the parameter estimates from the first stage consumption models estimated in the survey, but also of the precision of these estimates and of those parameters describing the disturbance terms in the consumption models, we do not produce just one predicted consumption level per household in the census. Rather, _r_ predicted expenditures are simulated for each household (typically around 100 simulations). The full set of simulated household-level per capita expenditures are then used to calculate estimates of the welfare estimates of each target population. Demombynes, Elbers, Lanjouw and Lanjouw (2007) describe a variety of simulation approaches that are available and document that these all yield closely similar welfare estimates. Validation studies of the poverty mapping methodology remain rare; in settings where one can rigorously check the method, it is likely that it was not needed in the first place. However, a few such studies have been conducted and have yielded encouraging findings (see for example Demombynes, et al, 2007, and Elbers et al, 2008). We examine in Section 7 whether there are grounds for suspecting that our broad findings concerning the relationship between urban poverty and city size are due to our employment of small-area estimates of poverty as opposed to direct measures.

# **3. Data**

Poverty-mapping exercises based on the methodology just described have now been conducted in a number of countries. We have selected eight of these countries, on the basis of the availability of the micro-data files and of regional coverage, for analysis in this paper. Table 1 lists the total urban population (at the Census year) in the eight countries, both in absolute numbers and as a share of the total. The sample includes a wide variety of countries, from the relatively small (e.g. Albania) to the relatively large (e.g. Brazil), and from the
types of indexes. We follow the OECD in constructing these indexes in the sense that we use equal weights across indicators, which the OECD does for its overall index as well.

As said, next to the NMR this paper also exploits the OECD’s FDI restrictiveness indicator as put forward in Kalinova _et al_ . (2010). This index ranges from 0 to 1 with also lower values indicating lower levels of restrictions and higher values reflecting greater levels of FDI barriers. This index is made up of several sub‐indicators that measure separately various barriers such as equity restrictions, screening requirements, economic needs test, or movements of key‐personnel. In total, there are 20 different service sectors covered. In the regressions we do not separate them out as they do not make an analytical separation according to a functional type of restriction as in the NMR between entry and conduct and as such the overall index of FDI restrictiveness is employed. Table A2 in the annex also shows in detail which barriers are included for the services sectors taken up in our sample. Note that each barrier, as explained in column 3 in this table, is common across all services sectors presented in column 1 of Table A2.

All reform indexes are rescaled from 0‐1 so as to have consistent interpretation of the coefficient indexes after regressions. In terms of time series, years covered for each index varies. The NMR indexes for telecommunications and post, transport and electricity and gas start in 1975 and are provides on a yearly basis. Retail services have been collected for the years 1998, 2003, 2008 and 2013, while professional services are beginning in 1996 with 2003, 2008 and 2013 with subsequent years. The FDI reform index starts in 1997 up to 2014 with missing years in between but yearly as of 2010. Missing years are interpolated so as to optimize good time series. There are two ways of taking care of this, namely on a linear basis assuming reform is a gradual process that takes place each year, or on
5 THE INTENSIVE MARGIN IN TRADE and Pareto models. In contrast, Melitz and Redding (2015) show that the formula proposed in Arkolakis et al. (2012) to compute welfare changes given changes in trade shares (their formula for “ex-post welfare evaluation”) is no longer accurate if the Pareto productivity distribution is bounded from above. We find Melitz-Pareto a good approximation because variation in the trade elasticity is much smaller in our full Melitz-lognormal model estimated on the EDD data than in their symmetric Melitz model with a truncated Pareto distribution calibrated to match the relative size of exporting and non-exporting U.S. firms.

To recap, this paper makes several contributions to the literature. First, we use the EDD to establish a new stylized fact, namely that between 40% and 60% of the variation in exports across country pairs takes place along the intensive margin, with this margin being important all along the firm-size distribution. Second, we show that the Melitz-Pareto model cannot match this fact, even allowing for ad hoc fixed trade costs, multi-product firms and granularity. Third, we show that a lognormal firm productivity distribution generates a positive role for the intensive margin as required by the data. Fourth, we use likelihood methods and the EDD to estimate a Melitz model with a lognormal distribution for productivity plus idiosyncratic demand shocks and fixed costs. Finally, we extend the exact hat algebra approach to a generalized Melitz model and use it with the estimated Melitz-lognormal model to explore counterfactual trade flow and welfare implications relative to those of the Melitz-Pareto model.

The rest of the paper is organized as follows. Section 2 describes the EDD data and document the importance of the extensive and intensive margins in accounting for cross-country variation in exports. Section 3 contrasts the predictions of the Melitz-Pareto model (with a continuum of single product firms, multi-product firms or a finite number of firms) to the EDD facts. Section 4 shows how the implications of the Melitz model change when we drop the Pareto assumption and instead assume that the firm productivity distribution
to air pollution can result in different labor supply responses to air pollution across households. We also explore whether intra-household substitution in labor supply can dampen the effects at the household level. Finally, the research design also allows us to focus on a somewhat newly monitored pollutant, fine particulate matter (PM2 _._ 5), which has been shown to have some of the most adverse health effects in the medical literature (U.S. EPA, 2009), but for which there is relatively little economic research.<sup>3</sup>

> 1For a review of this literature see Currie et al. (2014) or Graff-Zivin and Neidell (2013). There is also evidence that pollution can affect human capital. For instance, several studies link pollution to poorer school and cognitive outcomes (Almond et al., 2009; Lavy et al., forthcoming), and to increases in school absenteeism (Ransom and Pope, 1992; Gilliland et al., 2001; Park et al., 2002; Currie et al., 2009a).

> 2Previous work using data from U.S. cities also finds a positive and significant correlation between air particulates and work loss (Ostro, 1983; Hausman et al., 1984).

> 3Most of the economic literature focuses on total suspended particulates (Chay and Greenstone, 2003; Sanders, 2012), carbon monoxide (Currie and Neidell, 2005; Currie et al., 2009a; Currie et al., 2009b), and ozone (Lleras-

2
service sector enterprises pursuing the activities of wholesale and retail trade, repair of motor vehicles, motorcycles and personal and household goods, as well as services related to waste collection, treatment and disposal. The NSSO does not survey public administration and defense, private households with employed persons, or extra-territorial organizations and bodies. As with manufacturing, the sample stratification in services also includes both the district level and the two-digit NIC sectors.<sup>33</sup> Although some changes in NIC definitions are evident, we have placed them into a consistent format using 2004 definitions.

Consistent with the manufacturing surveys, the NSSO collects establishment level information for services regarding output, inputs (raw materials and other inputs), fixed assets, value of land and buildings, employment, energy consumption, and so on. Additionally, for our purposes, we observe the gender of the owners of these services establishments through similar categories of ownership structure choices available for unorganized manufacturing. Establishments reporting either female or male proprietors constitute about 95% of the total establishments in the cleaned sample. Additionally, as in both the organized and the unorganized surveys, the services survey by NSSO also collects information on the gender of employees.

Yearly values for variables that are reported on a monthly basis are obtained by multiplying by 12 in the case of perennial and casual enterprises; we multiply by the reported number of months a business is in operation for seasonal enterprises. Monetary values are deflated and converted to 2005 PPP in USD values. Deflators for disaggregate services are calculated from the gross value added (GVA) in current prices relative to the GVA in constant prices, available from the Ministry of Statistics and Programme Implementation (MOPSI) India. These disaggregate services include: (i) electricity, gas & water supply, (ii) construction, (iii) trade, (iv) hotels & restaurants, (v) railways, (vi) transport by other means, (vii) storage, (viii) communication, (ix) banking & insurance
government – for example, by enacting an electricity law giving various powers and duties to a Ministry regulator thereby requiring publicly owned electricity companies to operate in a more commercial way which would, among other things, allow state owned electricity companies to borrow from banks or debt markets on standard, commercial terms.

Investment is encouraged once effective regulation is available to support a workable financial framework. If the electricity industry is in private ownership the owners have the prospect of earning a reasonable return on their investment; if publicly owned, the industry can become independent of tax revenue or continually increasing loans. In addition, the existence of an effective regulatory framework can also encourage the growth of private investment and/or private finance within state systems, as has been happening in recent years in India and China.

These considerations suggest that the presence of an effective regulatory framework should, in general, lead to increased investment in the electricity sector, including the balanced development of generation, transmission, and distribution _ceteris paribus._ Unfortunately, comparable time-series capacity data across countries only exists for generation and it is on this aspect that the present study focuses.

In an unconstrained market economy, per capita generation capacity will adjust to the level of demand, which will depend upon the level of per capita income, the price of electricity, and environmental factors such as climate. The price of electricity will be determined in part by the efficiency of the sector. The latter may depend upon regulatory factors, but also availability of energy sources such as hydro, gas, oil, and coal. (This is most evident in cross-U.S. comparisons of prices.) However, many developing countries with a traditional, vertically integrated and state-owned electricity sector will be constrained not so much by market demand but by the availability of continuing subsidy.

The capacity constraints arise because of either inadequate government revenues for electricity investment or subsidy payments and/or insufficient revenue flow to support viable private investment. A simple diagrammatic version of such a model is shown in Figure 1
to be affected by trauma and distress, since they tend to have recently fled from wars (Moya et al., 2012). These characteristics may complicate the efficient integration of displaced individuals into formal reception markets.

We focus on the case of Colombia because its unique characteristics make it an ideal case with which to pursue this study. The escalation of the Colombian armed conflict in the late 1990s and the 2000s induced large sudden flows of displaced individuals. According to data from the Human Rights Observatory, internal conflict in Colombia between 1995 and 2010 displaced approximately 5.8 million people. Colombia also collects among the most complete and rich firm-level panel data available for the study of firm behavior in developing countries. The Annual Manufacturing Survey, a census of all manufacturing firms of more than 10 employees, includes plant-year level information on sales, wages, employment, and capital as well as product-plant-year information on output and input prices.

To identify causal effects, we use a panel-instrumental variable methodology. We construct the instrumental variable for inflows of IDP following the standard approach in the literature, which combines early settlements of migrants with time trends on migration outflows.<sup>2</sup> Our geographic variation comes from the fact that IDP move disproportionately to municipalities where there are early settlements of populations from their municipalities of origin. Our time variation comes from observed outflows of displaced populations by municipality and year due to conflict shocks. We construct the predicted inflow of immigrants by combining municipal cross-sectional information from the Colombian population census of 1993 (the last population census before the intensification of the conflict) with time-varying data on the total number of individuals expelled from each municipality. Our instrument is a strong predictor of the observed inflows of IDP between 1995 and 2010.<sup>3</sup> We find that large inflows of IDP induce sizable negative effects on formal firms’ intensive

> 2See Card, 2001 and Altonji and Card, 1991 for the pioneer approaches and Lewis and Peri, 2015 for a review of the literature on
Figure 3.4 Student’s own expectation of attending tertiary education (marginal change in probability) <!-- Start of picture text --> Mother's education post-secondary<br>Mother's education sr. sec<br>Mom's education jr sec<br>Family economic condition-well off (student response)<br>Family economic condition-middle (student response)<br>Student performance ranking - high (self assessment)<br>student performance ranking - midde (self assessment)<br>Student standardized test score<br>School is in rural area<br>school is in city<br>-0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35<br><!-- End of picture text --> _Source_ : Authors’ calculation based on the CEPS.

# Financing trends

Financial resources supporting senior secondary schools have been increasing rapidly over the past decade, with an even faster rate for the academic track. Figure 3.5 shows that from 2008 to 2015, total financing to senior secondary education increased rapidly. In 2015, the total amount reached nearly CNY 530 billion (about US$80 billion). The annual growth rate was 15 percent on average. Noticeably, even though the explicit policy of promoting vocational education was in place during this period, the total financing for regular senior secondary schools was growing faster.

Figure 3.5 Total financing is increasing (in CNY) <!-- Start of picture text --> 400,000,000<br>300,000,000<br>200,000,000<br>100,000,000<br>0<br>2008 2012 2015<br>Total vocational Total regular<br>Linear (Total vocational) Linear (Total regular)<br><!-- End of picture text --> _Source:_