rafmacalaba/datause-encoder-data huggingface dataset:
biased toward distant partners than existing grade. As a robustness test, we complement the results using aggregate bilateral data, which includes all trade between two partners. First, we estimate the levels regression to determine the average distance elasticity for each industry over the whole period. We use average trade for the three periods (1985-1989, 1990-1994, and 1995-2000) and regress the log of average bilateral trade in each industry on country-time and partner-time fixed effects and log distance (equation 9).<sup>9</sup> Country-time fixed effects capture all country-specific determinants of trade growth, including income and trade policy, as well as changes in multilateral resistance that might otherwise bias our results.<sup>10</sup> Figure 4 shows the distribution of the distance coefficients. They are centered around –1.3 and the distribution is surprisingly normal.

We calculate an average distance elasticity by weighting the average distance coefficient in each period by its share of trade. We then decompose change in the average distance elasticity into that due to the change in the industry distance coefficients (the distance sensitivity effect) and that due to the changes in trade shares (the compositional effect). The results are reported in Table 1. Panel (a) shows the decomposition. Overall, trade growth was biased toward shorter distances—the distance coefficient increased in magnitude by 0.135 (Column 3). The distance-sensitivity effect fully accounts for the increase in the importance of distance, while the composition effect

> 9 We also include dummies for border, language, and preferential trade agreements.  These were not significant in most industries, and did not alter the results on the distance coefficient or the tabulations.  It is not too surprising since while these are likely to have level effects, it is not clear they will have growth effects.  In the aggregate bilateral trade equation, the dummy for preferential trade agreements is positive and significant, but its magnitude is small, leaving the result on distance unchanged (see Berthelon and Freund 2004).

> 10 In a well known paper, Anderson and van Wincoop (2003) show that gravity results on national border effects are biased because they omit a theoretically important “multilateral resistance” term.  Theory implies that bilateral trade is decreasing in the size of bilateral trade barrier relative to the average trade barrier of the two parties to trade with all partners.  In terms of distance, this implies that neighboring countries may be more pushed to trade with each other if they are very far from the rest of the world

15
Policy Research Working Paper 8107

# **Abstract**

This paper uncovers the reason why social networks are used in a job market. The data are novel: a nationally representative matched employer-employee data set in Bangladesh with detailed information, including direct measures of the use of social networks. The empirical analysis shows that compared with those who used open channels to find jobs, the employees who used social networks found jobs more easily, have lower observable abilities, and achieved lower employment outcomes conditional on observable and unobservable abilities. These results are robust whether firm-occupation fixed effects are controlled for or not. By comparing these findings with theoretical predictions, the paper concludes that social networks play the role as fast and easy but narrow-spectrum matching. That is, social networks allow job seekers to find jobs quickly and easily and thereby reduce search costs, but the types of jobs available from social networks are narrower than those from open channels. As a consequence, those who choose to use social networks are more likely to end up having mismatched jobs, that is jobs in which they cannot take advantage of their specialties. In the context of developing countries, a considerable number of poor job seekers may use social networks out of necessity even if the returns to finding goodmatch jobs through open channels are sufficiently high.

This paper is a product of the Education Global Practice Group. 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://econ.worldbank.org. The authors may be contacted atsnomura@ worldbank.org.

_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
# **1 Introduction**

Empirical studies have documented a robust association between extreme weather and reductions in national economic growth (e.g., Burke, Solomon M Hsiang, and Miguel 2015, Somanathan et al. 2021). Extreme weather may reduce economic activity by affecting the performance of firms. Economic theory predicts that rational, forward-looking firms should make self-protective investments. However, firms operating in low- and middle-income countries face a host of market imperfections that may constrain their ability to adapt. Understanding which types of firms are best able to cope with weather shocks and which features of the business environment support firm-level adaptation is critical for designing well-targeted policies for private sector climate resilience.

To this end, we combine granular satellite weather data with global firm-level data to study the impact of rising temperatures on firms’ performance, ability to adapt, and the policy constraints hindering their adaptation capacity. We use standardized, repeated crosssectional microdata from the World Bank’s Enterprise Survey (WBES, _Enterprise Survey_ 2023) covering 134 countries and more than 160,000 firms—of which more than half operate in low and low-middle income countries—over a 15-year period. We assess the marginal effect of weather deviations from historical trends on corporate revenues and the associated transmission channels such as firmsâ investments, labor productivity, wages, energy intensity, and propensity to innovate. The data also enable us to estimate how firmsâ characteristics and the policy constraints they encounter influence their capacity to adapt to higher temperatures.

This study is among the first to use granular satellite weather data combined with microeconomic data to assess the economic impact of climate change at a global scale, particularly in low- and middle-income countries. The integration of granular climate and firm-level data is critical for identifying the causal economic effects of temperature shocks as it enables us to measure temperature deviations at firmsâ specific locations and account for firm-level heterogeneities, addressing key endogeneity concerns. Our empirical strategy relies on computing location-specific differences between temperatures within firms’ fiscal year and historical 2
village-level governance (McCubbins and Schwartz, 1984). The idea of fire alarm, which fits with the standard shirking model, presumes that the principal has difficulty observing the agent’s efforts due to costly monitoring. Compared to higher level authorities, local civil society leaders may be able to observe the behaviors of village heads more easily. In contexts in which civil society leaders can communicate transgressions to superordinate political leaders who will then sanction bad behavior, this may motivate local leaders to improve their actions.<sup>4</sup> In this way, monitoring by civil society leaders may feed into top-down pressures for reform.

Finally, civil society leaders can also coordinate sanctions from the community as a whole. In communities facing large collective action dilemmas with regard to both observing transgressions by local leaders and then mobilizing to hold them accountable, civil society leaders can play a critical facilitating role (Popkin, 1979). In this way, civil society leaders can activate bottom-up pressures.

It should be noted that most of these mechanisms depend on civil society leaders having incentives to implement reforms. Especially given our non-normative definition of civil society leaders, this is likely to vary among places and among different civil society leaders. In contexts where leaders do not have adequate incentives to push for reforms, they may collude with or be co-opted by government (Mattingly, 2016). However, in many places, the diversity of interests among village elites will create a constituency for reform within this group (Tendler, 1997).

Our primary goal in this paper is to test the effectiveness of mobilizing village-level civil society leaders to press for local government reform, assuming that any such effects could occur through either one (or a combination) of the afore-discussed channels. Although we present preliminary evidence on the mechanisms through which civil society leaders influence local governance, we cannot definitively adjudicate between mechanisms, and indeed, it is quite likely that more than one mechanism is at work.

4In this regard, community leaders may be playing the same theoretical role as the cheap mobile monitoring in the Niger experiment by Aker, Ksoll et al. (2015), which was shown to contribute to adult education outcomes. However, the Cilliers et al. (2016) study on locally monitored performance of teachers in Ugandan primary schools suggests that monitoring by itself may be inadequate to induce desired behaviors. In their study, effects on teacher attendance were the result of monitoring combined with financial incentives, rather than monitoring alone.

9
**-** **<u>Table 1. Final sample of firm year observations</u>**

|**Year**|**Manufacturing**|**Services**|**Mining**|**Total**|
|---|---|---|---|---|
|2008|15,056|31,346|320|46,722|
|2009|14,038|31,598|318|45,954|
|2010|12,997|29,759|317|43,073|
|2011|9,492|25,942|239|35,673|
|2012|10,151|26,983|245|37,379|
|2013|9,806|29,460|229|39,495|
|2014|10,381|29,872|253|40,506|
|2015|10,286|28,844|260|39,390|
|2016|10,199|28,449|249|38,897|
|2017|10,018|28,280|254|38,552|
|Total|112,424|290,533|2,684|405,641|

_Source:_ World Bank staff calculations based on SBS data set.

_Note:_ Total number of observations by year and sector with production function variables.

**<u>Table 2. Summary statistics</u>**

|**Year**|**Revenu**|**e (ln)**|**Employm**|**ent (ln)**|**Intermediate m**|**aterials (ln)**|**Capital st**|**ock (ln)**|
|---|---|---|---|---|---|---|---|---|
||**Mean**|**SD**|**Mean**|**SD**|**Mean**|**SD**|**Mean**|**SD**|
|2008|14.73|2.29|2.89|1.36|12.07|2.67|11.68|2.71|
|2009|14.70|2.20|2.82|1.32|11.87|2.62|11.82|2.69|
|2010|14.70|2.28|2.76|1.35|12.02|2.68|11.84|2.68|
|2011|15.33|2.00|3.06|1.33|12.52|2.60|12.28|2.58|
|2012|15.23|2.08|3.04|1.32|12.53|2.61|12.22|2.59|
|2013|15.03|2.12|2.92|1.33|12.32|2.64|12.09|2.63|
|2014|15.07|2.13|2.94|1.32|12.37|2.62|12.04|2.64|
|2015|15.32|1.98|3.07|1.28|12.55|2.55|12.11|2.64|
|2016|15.40|1.91|3.11|1.26|12.44|2.63|12.14|2.63|
|2017|15.46|1.89|3.13|1.25|12.41|2.70|12.20|2.62|
|Total|15.08|2.12|2.97|1.32|12.29|2.64|12.03|2.65|

_Source:_ World Bank staff calculations based on SBS data set. _Note:_ SD = standard deviation.
We consider that EU treatment begins with the signing of the EU Association Agreement since it represents the point at which countries commit to start implementing all the economic and political transformations required to meet EU membership criteria. This leads us to divide the 10 EU treatment post-socialist countries into two cohorts based on the year they signed such an agreement (see Annex 5). The first cohort – named Cohort 1995 – encompasses Bulgaria, Czech Republic, Hungary, Poland, and Romania as these countries signed their EU Association Agreement between 1994 and 1995. The second cohort – named Cohort 2000 – gathers Estonia, Latvia, Lithuania, Slovak Republic, and Slovenia –, which signed the Association Agreement between 1998 and 1999. The period for this analysis goes from 1990 to 2010 and it includes the 1990 data point to have at least one observation before Cohort 1995 signed the Association Agreement, which is a requirement to implement the DiD method.

The presence of two cohorts implies a staggered treatment that calls for a Staggered DiD method. The Staggered DiD approach allows the treatment effect to be different for each cohort, as well as to differ in different periods. Once all these treatment effects that vary over treatment groups and over time are estimated, they can be combined into a single effect. That is why we ran three sets of panel regressions: the first to estimate the treatment effect for each cohort, the second to compute the treatment effect by period, and the third to combine all cohort and time effects into a single effect.

Table 6 shows the results of the regression set used to estimate the treatment effect for each cohort. The first model of this set exhibits that only the treatment countries that started the EU adhesion process around 1995 experienced a positive and significant effect on their returns due to all the changes brought by EU membership. The second model adds the type of governance through the variable of polity, which also proves to be positively and significantly associated with the returns to education. The third model uses trade instead of polity, and in this case, the first cohort still reports a slightly higher positive and significant effect on their returns to education due to EU adhesion while trade also positively and significantly contributes to this variable. The fourth model adds polity and trade at the same time and exhibits that EU membership preserves its positive and significant effect on the returns to education of the first cohort while trade and polity also exert a positive and significant effect on the returns of all the countries included in the sample.

17
The fact that expenditure data are expensive and time-consuming to collect makes it plausible that there will always be occasions when a proxy indicator of economic status is needed. Data used to construct asset indices are simple to collect and are frequently available. This paper takes an empirical approach to investigating the attributes of asset indices both in terms of how they correlate with household expenditures, as well as how they affect our understanding of the link between economic status and human development outcomes. Despite the existing literature on the issue (discussed in more detail below) there remain outstanding questions. By systematically comparing results across 11 surveys we address three main questions: _Are_ alternative measures of economic status different? If so, _does it matter_ for the types of analysis that they are typically used for? And, _when_ are alternative approaches most likely to be different?

After an overview of some of the recent literature on methodological aspects of asset indices (Section 1) and a set of definitions for the different economic status measures and data used in our analysis (Section 2), we address each question in turn. First, we assess whether alternative approaches are different by analyzing how similarly they rank households—in so doing we not only compare expenditures against an asset index but also compare alternative asset indices (Section 3a). Second, we assess whether using alternative approaches matters for inferences about the economic gradients in education, health, fertility, mortality and labor force participation outcomes (Section 3b). We also assess whether the approach matters for inferences about the relative poverty status of urban versus rural households, or households of different demographic composition (Section 3c). Third, we explore the factors that lead to greater congruence and divergence in rankings, focusing on measurement error, transitory shocks, and household economies of scale (Section 4).

# **1)    Literature overview**

The motivation behind the use of asset indices is straightforward: in the absence of information on household expenditures (or income), can one use the information that is often collected in the context of surveys to characterize a household’s economic status? The approach that has become popular in the past decade uses an “asset index” constructed from variables 3
# **B. Migration**

Traditionally, linguistic, family, cultural, and economic ties between Russia and neighboring countries created strong migration flows to Russia, especially from Central Asia (World Bank 2017). An increase in migration flows will partially offset the adverse demographic implications on potential growth.

We simulated the impact of increased migration on potential growth.<sup>5</sup> We used the medium variant population projection from the Russian Federation Federal State Statistics Service, which assumes successful implementation of ongoing reforms to improve Russia’s demographic outlook (Federal State Statistics Service 2017). The most notable difference from our baseline assumption taken from the United Nations is the expected number of migrants.

In this scenario, the number of net migrants per year increases to 289,000 until 2028 (while the United Nations forecast is 100,000 in the same year). This results in a 0.2 percentage point higher potential growth. The effects are expected to continue after 2028. The cumulative impact of increased migration is around 2 percent of potential output during 2018-2028.

# **C. Investment**

Many countries have sizable investment needs. UNCTAD (2014) estimated that unfilled global investment needs amount to up to 3 percent of global GDP. A low capital to GDP ratio suggests that Russia has substantial room for expanding investment.

Russian investment needs are vast and estimated at 75 percent of its 2015 GDP (World Bank 2016). Underinvestment in physical infrastructure limits connectivity, which damages the profits of firms due to higher transport costs, and it reduces income opportunities and well-being by limiting labor mobility or access of the population to services. By mobilizing private capital through public-private partnerships (PPPs) and opening up infrastructure subsectors to direct private investment, infrastructure can be expanded without creating excessive pressure to the fiscal deficit.

> 5 Even though the government projects higher net migration, it is worth noting that recently, Russia saw less, not more net in-migration due to a weaker exchange rate and mediocre income growth, which has made Russia less attractive for migrants. Also, due to data limitations, we do not consider the quality of migrant labor. As of now, most migrant labor into Russia is cheap, unqualified labor, in low-productivity sectors of economy.

13
In this paper, we propose a new methodology to estimate the gains from international diversification in which the optimal asset allocation of pension funds is constrained by financial frictions. We apply our empirical strategy to the aggregate holdings of pension funds in a group of 49 countries (19 developed and 30 developing) to calculate the gains from increasing the current (or last reported) level of exposure to international securities.

The intuition of our strategy can be explained as follows: Using the asset allocation of pension funds between 2002 and 2019 for a group of 77 countries, we estimate as a benchmark the “natural” level of foreign exposure, that is, the expected level of foreign holdings for pension funds operating in a country with a particular level of financial market development, integration to international markets, and where the pension system characteristics, such as its total size, size of contributions among other observables, are considered. Notably, the benchmark rate is not a first best (optimal) allocation – specifically, it is not normative. Instead, it might be interpreted as a peer benchmark, providing a pragmatic view of what is possible elsewhere. We then use portfolio optimization techniques to calculate the maximum gains that a pension fund operating in a given country can achieve if it increases its level of foreign exposure to the benchmark rate. We refer to this analysis as the constrained optimization problem and compare the findings to a setting where financial frictions play no role. The latter case denotes the scenario where pension funds can achieve the maximum allocation suggested by standalone portfolio optimization.

To be more precise, we use the latest reported portfolio holdings in domestic equity, domestic debt, foreign equity and foreign debt, by all the pension funds operating in a specific country. Following the well-known approach of Black and Litterman (1992), we estimate the expected returns and risk (i.e. standard deviation of returns) of an investor domiciled in the country with unhedged foreign positions. We then estimate the maximum expected returns if the investor increases her foreign assets to the benchmark rate, maintaining the current level of risk. The increase in returns are precisely the gains from augmenting the foreign exposure to the natural level that is given by macro and financial variables. Overall, the analysis presented in this paper