rafmacalaba/data-use-mentions-extended huggingface dataset:
2000-2014 (Elvidge et al. 2014). For the period 2012-2020, we use the average nightlights using Black Marble data from NASA.<sup>11</sup> However, the results show that none of the estimates for the road variables used in the previous specifications are significant (Table A9) but such results should be cautiously interpreted. The use of nighttime lights for rural parts of DRC might be problematic because of the limited satellite coverage for most parts of DRC and the limited insights brought by nighttime lights for rural areas. However, nighttime lights remain the only available data for such geographic coverage and time frequency. The results do not support the assumption that new road projects may reduce violence through a significant increase in economic activities. Given that the estimates vary across periods, we check whether our results on violence reductions with road building are driven by specific periods and find no evidence for this (Table A10). From the previous regressions, we find that the completion of roads when gold mining revenue increases is associated with more violence against civilians and battles. This can be explained by an amplification of rapacity effects.<sup>12</sup> We also know that the opposite is true with roads in areas with high cobalt revenue and in rugged areas, where their violence-reducing effect is amplified. These effects would be aligned with both an opportunity cost channel and a state capacity channel as reported by World Bank practitioners.

The last channel of interest is the adaptation of armed groups from being “roving bandits”, exerting violence to extract maximum benefit in the short term, to “stationary bandits” that set up regular taxation systems and leave a portion of resources for the population to use for accumulation, investment, and subsequent increases in production. Improved roads could reduce violence against civilians and generate taxable traffic that can generate longer-term revenue, through roadblocks for example, rather than through more violent, ‘roving bandit’ tactics. More work and access to more granular data will be needed to fully disentangle
Fitzroy, 2002, Basu et al. 1997, Haltiwanger and Vodopivec, 2003) and in the context of trade liberalization (Revenga, 1997). However, the analyses are not disaggregated by gender and do not encompass a crisis period.

# **2.2 Context: The Indonesian Crisis**

In the early 1990s Indonesia was at the height of an extended period of industrialization with manufacturing being an important engine of growth. (Dwor-Frecault et al. 1999). In the wake of the devaluation of the Thai Baht in July 1997, flows of foreign capital reversed drastically and the rupiah depreciated dramatically. Interest rates were raised to defend the currency, which exacerbated the decline in demand. Inflation rates, which had been 12%, shot up to close to 100% (Hill, 1999). GDP contracted severely in 1997, and fell by over 13% in 1998. Manufacturing output followed a similar pattern, though manufacturing growth had already suffered a mild dip in 1996. The drop in manufacturing output was large relative to the corresponding dip in GDP. Manufacturing, a sector with 46 percent women employees at the inception of the crisis, was one of the hardest hit sectors, making it an interesting case study to examine how employer responses to shocks affected men and women differently.

Estimates based on SAKERNAS indicate the spike in inflation facilitated real wage adjustment by reducing the relevance of nominal wage rigidities and minimum wages. Across the earnings distribution, both men and women experienced real wage cuts on the order of 40%. Yet the changes in total employment rates were relatively small: between 1997 and 1998, female employment in urban areas declined by 0.7% and male employment declined by 2.2% (Thomas et al. 2002). Labor force participation rates increased slightly more for women (1.5%) than men (1.4%) (see also Cameron, 2000).<sup>8</sup> Thus, the impact of the crisis on aggregate
natural next step. Economics research has seen the increasing use of ML techniques in recent years, with proven effectiveness. According to Athey (2018), evaluation of ML’s early contributions to economics, ML techniques have been particularly successful when applied to prediction-based problems, such as the one addressed in our study. In addition, utilizing ML in agricultural economics research is a logical approach to model critical variables, such as crop yield, which involves a complex combination of factors, including soil quality, weather, input timing, and management choices, which have non-linearities and interactions (Storm et al., 2019). Previous research attempting to estimate crop-yields in the context of smallholder farmers using ML techniques often attempt this exercise combining manually-labeled optical imagery, and ground data collection, including as part of household and farm surveys. Azzari et al. (2021) provide examples of these studies and recommendations on how large-scale household surveys should be conducted to generate the data needed to train models for satellite-based crop type mapping in smallholder farming systems. Our study relies essentially on survey data integrated with geospatial variables to make crop yield predictions at a granular level. Yacoubou Djima and Kilic (2024) attempt to apply ML techniques for the prediction of yields, leveraging survey data from Mali, to validate an alternative approach to estimate the relationship between crop yields and inputs. The authors conduct a within-survey imputation exercise that derives predicted, otherwise unobserved, objective crop yields that stem from a machine learning model that is estimated with a random sub-sample of plots for 2
<!-- Start of picture text --> Decrease in firm productivity from 1% increase in power outages<br>4.0 7,000<br>3.5 6,000<br>3.0<br>5,000<br>2.5<br>4,000<br>2.0<br>3,000<br>1.5<br>2,000<br>1.0<br>0.5 1,000<br>0.0 0<br>GDP/cap<br>Percentage decrease in productivity resulting from one percent increase in outages<br>%<br>GDP pc<br>South Africa Angola Nigeria Ghana Zambia Kenya Cameroon Tanzania Mali Rwanda Burkina Faso Uganda DR Congo Malawi<br><!-- End of picture text --> _Figure 2: Decrease in firm productivity from 1% increase in power outages (left axis) and national‐level GDP per capita in 2019 USD (right axis) (Source: Mensah, 2016)_ A meta‐review of literature analyzing constraints to growth in developing countries finds that over 40% of such studies classify electricity as a binding constraint (McCulloch and Zileviciute, 2017). Based on the World Bank Enterprise Survey, Bbaale (2018) assesses the impact of electricity outages on manufacturing firms in 26 African countries. The study shows that power outages lead to self‐ reported losses in sales of 11‐12% for small, medium, and non‐exporting firms. For large and exporting firms, the study observes that the substitution of grid supplied power with generators tends to increase productivity.

Using Enterprise Survey panel data for 15 Sub‐Saharan African countries, Mensah (2016) identifies reductions in firms’ productivity by 0.6% to 1.1% for a percentage increase in outage intensity. A similar study based on a panel of 23 African countries estimates that a one percent
areas, it does show that the recorded distribution of the prevalence of COVID-19 in Mali is dramatically skewed toward Bamako. Second, insights from Google’s Community Mobility Reports, shown in Figure 2, suggest that individuals living in Bamako have experienced relatively large changes in the time spent at various types of places relative to a baseline period in early 2020. Consistent with our claim, these changes in Bamako are larger than reported changes for individuals in all of Mali. Third, as documented in Figure 3 and Figure 4, according to our COVID-19 phone panel survey data individuals in urban areas are more likely to report taking health-related precautions and negative economic impacts due to the coronavirus pandemic than individuals in rural areas. As predicted by Reardon _et al._ (2020), this evidence highlights the reality that although the coronavirus pandemic increased challenges related to food security everywhere, the pandemic is much more disruptive in urban areas than in rural areas in Mali.

The difference-in-differences identification strategy rests on the assumption of parallel counterfactual trends in latent food security between households in urban and rural areas (or, in some specifications, between households in Bamako and all else) in the absence of the coronavirus pandemic. While this identifying assumption cannot be tested directly, we argue that if bias exists in our empirical strategy, then equation (1) generates estimates of the lower bound effect of the pandemic on food security. We perform a number of robustness and sensitivity checks on our results. First, in each table reporting regression results we show two variations of our core specification: one that does not include household fixed effects and one that does include household fixed effects. The household fixed effects allow us to account for time-invariant omitted heterogeneity between households which may bias our results. Second, in the Supplemental Appendix, we show results that exclude all households in Bamako from the regression specification and therefore estimate differences between urban and rural areas outside Bamako. Although these estimates suffer from limited statistical power, we find results that are qualitatively similar to our core results
# **2. Data**

# **2.1. Data sources**

Since we are interested in the phenomenon of firm informality, for this study we focus on a universe of private non-agricultural firms with fewer than 25 workers, but situate this universe within employment in Egypt. This focus is because public enterprises and government establishments are not at risk of being informal.<sup>7</sup> Formality is typically not relevant for most agricultural activities, and informality among firms of 25 workers or more is rare (Assaad, AlSharawy, & Salemi, 2019). Given this universe, we use data sets from household surveys with a household enterprise module and establishment surveys or censuses that represent both formal and informal firms of all sizes.

The first data source we use is the Egypt Labor Market Panel Survey (ELMPS) carried out by the Economic Research Forum in collaboration with Egypt’s Central Agency for Public Mobilization and Statistics (CAPMAS). Four waves of this survey were carried out in 1998, 2006, 2012, and 2018.<sup>8</sup> We primarily use the 2012 and 2018 waves for contemporaneity with our other data sources, but use the 1998 and 2006 waves when exploring firm dynamics. The second data source is the 2012/13 Economic Census (EcC), which is an in-depth survey of establishments carried out by CAPMAS every five to six years. The third is the Egypt 2017 Establishment Census (EsC), which is a full census of establishments, also carried out by CAPMAS in conjunction with the decennial population and housing census. These data sources have different coverage and depth of detail, as we discuss below.

# **2.1.1. Egypt Labor Market Panel Survey**

The ELMPS is a nationally-representative household survey that has tracked a panel of households (continually adding refresher samples) since 1998.<sup>9</sup> The ELMPS has, since its inception, included a nonagricultural household enterprise module
those using Wooldridge’s method display

> 19 We use the unweighted HBS data because population weights are not available. Official estimates by the Russian National Statistical Agency “Rosstat” also suggest that the Gini coefficient remains stable between 1994-2015 around 0.41 (Rosstat, 2016). See also Yemtsov (2008) for discussion on other issues related to reweighting and non-response with the HBS data.

26
and growth of the economy, as informal firms are generally less productive than formal firms because they prefer to remain small and ―invisible‖ and because informal workers tend to receive less training than formal workers. The incidence of informal activities should therefore be considered when making economic analysis and designing policies.

5. This paper examines the incentives and distortions created by tax policy and administration structures that motivate individuals to un- (or under-) declare work in the new EU member countries and Croatia (to be referred to as NM-EU even if Croatia is not yet a member). As a multi-country, regional paper, certain countries among this group will be selected as archetypes for in-depth analysis and distilling of lessons learned. While crosscountry benchmarking of taxation and tax policy indicators is critical, not all countries will be covered comprehensively or uniformly in this analysis. This paper seeks to provide answers to the following questions:

- To what extent is the level and ―mix‖ of tax instruments deployed by governments - and the relative reliance of certain instruments over others, such as taxes on earnings versus other taxes – responsible for the size of undeclared work?

- Are most of these countries making the same ―mistakes‖ with respect to tax policy and administration and the disincentives to formalize?

- What has been the experience with special taxation regimes set up to attract workers and entrepreneurs back into the formal economy? What are the failures, successes, and lessons for policymakers?

- How has the introduction of a ―flat tax‖ on income from labor and capital impacted workers and entrepreneurs in terms of formalizing work?

- Are there successful experiences (inside or outside the region) with shifting away from an ―over reliance‖ on labor taxes to a more efficient tax mix?  Has such a shift led to an increase of the formal economies?

6. The following sections first look at the extent of undeclared work in the NM-EU countries (Section 2) and how – according to opinion polls – people in these countries perceive the role
labor costs, capital stock, firm size, and a somewhat noisy measure of value added. I compute these figures for the whole manufacturing sector and for the garment sector. Further, I use data from the World Management Survey to study the quality of management.

My main finding is that Ethiopia’s labor cost advantage is more than offset by low productivity with respect to all but one of the comparison countries (Bangladesh). Ethiopia’s industrialization is thus on a knife’s edge. Its fundamentals make it competitive with respect to the countries at the bottom of the productivity distribution. In recent years, the increase in labour costs has also been modest compared to these countries. However, given the likely pressure on wages that will result from further expansion of the manufacturing sector, additional gains in productivity will be crucial in order to secure Ethiopia’s position as an attractive investment destination.

The low productivity of Ethiopian firms is not explained by differences in size, cap2
lower inequality levels). When we consider initial income and its square among the explanatory variables, the last column in table 6 would indicate that there appears to be an inverted-U relationship between the Gini value and income. In fact, this relationship would indicate that the Gini coefficient rises with GDP per capita values of less than US$2940 (1985 dollars) and declines thereafter. We also find that including the initial level of GDP among the regressors does not significantly affect the estimates of the coefficients of the policy variables under consideration. We next consider these.

The results for education would indicate that countries with better education (as measured by secondary enrollment rates) would be less unequal. The estimated coefficients are very similar in the three empirical models for inequality and of a similar order of magnitude to those in the growth model. These results are in line with those of Datt and Ravallion (2002) and Lundberg and Squire (2003). Using 20 household surveys for India’s 15 major states, Datt and Ravallion (2002) also conclude that poor basic education is an impediment to the ability of the poor to participate in opportunities for economic growth. Lundberg and Squire (2003) also find that education is likely to be correlated with both faster growth and lower income inequality.

The estimated coefficient for financial development is always significant and suggests that progress on this front would be associated with increases in inequality. Possible explanations for this finding are: (i) the more educated (and likely richer) are able to exploit the new opportunities better; (ii) adoption of capital intensive technologies is likely to substitute for unskilled labor. Thus to some extent, our results would support the findings of Behrman, Birdsall and Szekely (2001) who used household data over more than two decades for 17 Latin American countries to estimate that the financial sector liberalization reforms that took place during the 1990s negatively affected income distribution. Within a cross-country framework, Dollar and Kraay (2002) also find that financial development negatively affects inequality.
skills in job descriptions. As mentioned earlier, we substantially improve the granularity of the skill taxonomy by adding a layer of language-specific key words, key phrases and text patterns.<sup>4</sup> Moreover, our paper relates to a broader literature building on job portal data in various contexts. Postings on platforms provide detailed data for general labor market analysis. For example, Azar et al. (2017) use US job vacancies collected by Burning Glass Technologies at the county level to study market concentration. Job platforms allow to study employers and job seekers’ behavior. Text analysis of the requirements posted by employers on job ads can inform us on existing gender bias (as shown in China by Kuhn and Shen, 2013). Applications can be sent through platforms to assess the extent of discrimination from employers, in the same spirit as resume audit-studies (Kuhn and Shen, 2015). Using US data from the portal CareerBuilder.com, Marinescu (2017) tracks how job seekers’ search behavior is impacted by changes in unemployment benefits duration. The same portal also allows Marinescu and Rathelot (2018) to study the importance of distance-tohome in job search strategies, and the geographical mismatches it can induce.

The remainder of this paper is structured as follows. In Section II, we present the labor market in Kosovo and highlight its challenges. Section III describes the data, followed by the methodology in Section IV. We present our key findings on vacancy characteristics in Section V, and our results on skill needs in Section VI. We conclude in Section VII. An online appendix is available for additional details.<sup>5</sup>

# **II. The Kosovo labor market context**

Kosovo is the youngest and one of the poorest countries in Europe in terms of statehood and demographics, and GDP per capita. Despite stable economic growth in the last 10 years, job creation in Kosovo is at a low level. Between 2012 and 2018 about 44,000 new jobs were
the other two, we use the cooperative-level price and catch data to demonstrate empirical support for the model’s three implications. The difference in price and catch between the cooperative with strong property rights and the other two cooperatives is large, and the cooperative with property rights disincentivizes effort more aggressively than the other two when growth rates are likely to be small. But the magnitudes of the estimates also indicate that the difference in management aggressiveness across the cooperatives shrinks in economically meaningful ways when resource scale is large and growth rates are high.

Given the small number of cooperatives in the analysis, one should be cautious in extrapolating these findings to other settings. Instead, we view our results as improving the general understanding of when and how cooperative-based property rights can be effective. Our work complements the rich literature on community-based resource management institutions. Ostrom (1990) reviews case studies of institutions and derives a set of principles that differentiate those that are successful. We examine the role of some key principles from the Ostrom framework, such as clearly defined boundaries and effective rule enforcement. However, rather than making binary assessments of “success,” we empirically quantify the influence of property rights on economic outcomes. Gutiérrez et al. (2011) consider case studies of fishing cooperatives in particular and find predictors of success, including the existence of quotas, enforcement institutions, long-term planning, and resource mobility. Recent economics literature examines the decision-making of villages or other local user groups regarding other resources (e.g., Edmonds [2002] and Foster and Rosenzweig [2003] on forests). In contrast to these studies, we examine the short-term dynamics of resource management, illuminating the mechanisms that institutions may use to achieve management goals. We do so in the context of fisheries, which are characterized by important spatial externalities and environmental fluctuations not relevant to some other natural resources.

The theoretical literature on optimal fisheries management strategies considers these challenges. For instance, Costello and Kaffine (2010) and White and Costello (2011) examine the
Policy Research Working Paper 8962

# **Abstract**

In response to rising inequality following decades of trade liberalization, many countries are adopting trade restrictions. Can temporary trade restrictions have long-lasting effects on the spatial distribution of employment and resource allocation? To analyze this, this paper exploits the civil war in Côte d’Ivoire (2002–07), which disrupted access to the world market for two neighboring landlocked countries: Mali and Burkina Faso. The Ivorian war forced rerouting of trade from the Abidjan route to non-Abidjan routes. This paper builds a general equilibrium model where a subsistence-based autarkic hinterland coexists with an integrated segment, and there are two alternative routes to international markets. A trade shock to one route affects resource allocation in both routes by shifting the spatial margins of market integration and sectoral specialization. The effects are heterogeneous, depending on the pre-war market access of a location. The empirical analysis takes advantage of panel data and estimates the effects on structural change in employment on the non-Abidjan route using a triple difference design with location fixed effects. The areas that remain in autarkic equilibrium before and after the trade shock provide plausible estimates of the changes arising from long-term factors unrelated to the trade shock. The estimates show that the temporary trade shock created divergence between the Abidjan and non-Abidjan routes, with accelerated structural change in favor of manufacturing and services employment in the non-Abidjan route. This paper finds evidence of persistence in the effects through higher sunk investment in built-up density, agglomeration through concentration of skilled labor and greater public investment in complementary inputs such as electricity infrastructure (measured by nightlights density).

This paper is a product of the Development Research Group, Development Economics. 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 fshilpi@worldbank.org.

_The Policy Research Working