rafmacalaba/data-use-mentions-extended huggingface dataset:
covers housing and asset ownership, risks and coping mechanisms, and coverage of social protection programs on the household level. Interviews were done in December 2014 and January 2015. For the first time for a large-scale household survey in Bhutan, tablet-based computer-assisted personal interviews (CAPI) were conducted. The objective was to improve data quality and streamline data analysis. As another measure to assure high data quality, all respondents were asked for their cellphone numbers and during a meticulous data cleaning exercise these numbers were used to reach out to respondents in case any inconsistencies or missing values had been discovered in the raw BLFS data.<sup>**1**</sup> The BLFS contains information on household members’ monthly earnings and weekly hours. Both variables are available for dependent as well as own-account workers. Under the assumption that each month consists of 4.35 weeks this makes it possible to construct a measure for hourly wages.<sup>2</sup> The survey also ascertains whether a worker is employed in the civil service, other government agencies, the armed forces or state-owned enterprises (which following ILO, 2009, together constitute the public sector), in agriculture or private enterprises (defined henceforth as the private sector) or by a non-governmental organization, international non-governmental organization or community service organization (a very small group of workers that is henceforth disregarded).

Additionally, the 2014 edition of the BLFS questions respondents about a wide range of household members’ individual characteristics useful for an analysis of wages. These include information on labor market characteristics such as whether an individual is a regular paid employee, casually paid employee, contract worker, own-account worker or unpaid family worker. Henceforth, unpaid family workers and/or workers for whom an hourly wage cannot be discerned will not be considered. Comprehensive sectoral information is also available in the BLFS. For the purpose of this study, this information is aggregated to the level of 16 broad sectors according to the ISIC Rev. 3 classification (agriculture, hunting and
large share of firms are poorly managed. Nearly 3 percent of the surveyed firms adopt no structured management practices, 60 percent of them score below 0.5 but only 3.5 percent of these firms score over 0.75. By comparison, establishments in the US not only have a much larger average score (0.6), but 18 percent of them have a score over 0.75.

_Fourth_ , variations in management practices among Russian manufacturing firms are not driven by the differences in sectors, or in the federal districts covered by this survey but can be found in mostly idiosyncratic, firm-specific factors. Nonetheless, among the characteristics that partly explain the heterogeneity in adoption of structured management, it appears that firm size, foreign linkages and the education level of non-managers are the most relevant ones. Surprisingly firm age is not associated with better management practices, implying that unlike in the US and even in Mexico, firms do not learn to be better managed as they age. This indicates the possibility of misallocation in Russian manufacturing where learning and market selection are not operating perhaps due to the lack of pro-competitive forces.

_Finally,_ consistent with the findings in other countries and data sets, structured management, that is, more intensive monitoring and more aggressive performance incentives (e.g., hiring, firing, pay and promotions) is associated with superior firm performance. Firms with more structured management practices have higher productivity, and employment growth. This result is highly robust to numerous checks and controls. As is true in related studies, we cannot claim that this is a causal relationship, however, our results strongly suggest that management practices have a tight association with firm-level outcomes. Among the types of management practices, those related to data driven performance

> 8 During consultations it became clear that the authorities were more interested in learning about firms that are less exposed to structured management practices. Hence it was agreed to have the survey on management practices for SMEs located in the specified federal districts, and not including
|1.38***|
||(0.558)|(0.597)|(1.407)|(5.245)|(0.250)|
|Number of observations|405|619|295|872|2336|
|Adjusted R-squared|0.72|0.07|0.33|0.42|0.59|

Source: Anderson, Cockburn, and Martin 2010, Table 2.12, based on data in Anderson and Valenzuela 2008.

Note: Results are calculated using ordinary least squares. Numbers in parentheses are the standard errors; *** p<0.01, ** p<0.05, * p<0.10.
# Executive summary

This study explores the determinants of interstate migration in Mexico between 2015 and 2020 and the earnings gains from the migration decision. Internal migrants are identified using 2020 Census data, which provide information on individuals who resided in Mexico in 2015 and moved to another state. First, the study provides descriptive statistics on internal migration patterns and the socio-demographic profile of movers and stayers. Then, the study analyzes both spatial characteristics (push and pull factors in the origin and destination states) and individual factors that influence the decision to migrate and where to migrate. Push and pull factors are assessed using a gravity-type model. Individual factors are analyzed using a multinomial regression model that accounts for migration reasons. Subsequently, the study measures the impact of internal migration on labor income. Earnings gains are estimated using a double selection model that accounts simultaneously for the decisions to migrate and to work.

At the aggregate level, the results from the gravity model suggest that migration costs (proxied by the distance between the origin and destination) are more relevant for women than men. Employment is a key pull factor, while informality is a push factor. Besides, the perceived levels of corruption also appear to be a significant push factor. In addition, the availability of social networks in the destination is an important pull factor attracting migrants. At the individual level, the results from the multinomial regression indicate that younger individuals, especially women, are more likely to migrate for economic reasons. Women and men with higher levels of education are more likely to migrate than their lesseducated counterparts, regardless of the reasons for migration. Being single increases women's odds of migrating for economic reasons but reduces men's odds. Having young children reduces women's odds of migrating for economic reasons. Furthermore, the results also confirm that access to migration networks is fundamental for the individual migration choice.

Overall, the results also show that men who migrated benefit more than women from the migration decision. However, when migration happens for economic reasons, women
paper, arises. Thus, this paper offers an important contribution to the literature by documenting the extent to which the various microeconomic impacts of mobile connectivity translate into aggregate economic outcomes.

This paper is closest with Czernich et al. (2011) and Goldbeck and Lindlacher (2021) who analyze the impact of broadband Internet on economic growth in the OECD and Sub-Saharan Africa respectively. Czernich et al. (2011) for instance using cross-country panel data for OECD countries between 1996 and 2007 found that a 1 pp increase in broadband penetration is associated with a 0.09 – 0.15 pp increase in economic growth. The paper however differs from Czernich et al. (2011) by analyzing the impact of a broader spectrum of digital infrastructure (voice communication (2G) and mobile broadband (3G and 4G)) using granular and recent data from about 120 countries. Goldbeck and Lindlacher (2021) also exploit the staggered arrival of the submarine fiber-optic cables to Africa and estimate that access to internet connectivity via the fiber cables is associated with a 2 pp increase in local economic growth. The estimates from Goldbeck and Lindlacher (2021) are similar in order of magnitude to the findings in this paper

> 3Beuermann et al. (2012), also finds similar evidence in Peru

4
# _Increasing contribution of remittances_

Remittances increasingly contribute to the Tajik economy: from 5 percent in 2002, the GDP share of net remittance inflows skyrocketed to 45 percent in 2008, with an estimated value of nearly USD2.5 billion, before declining during the global crisis to reach 25 percent in 2010 (Figure 1). In 2010, the remittance inflows were coming come from an estimated 791 thousands migrants working in Russia, Ukraine, Uzbekistan, Kazakhstan and the Kyrgyz Republic (Migration Factbook, 2011).

**Figure 1. Tajikistan: GDP Expenditure Decomposition, 2002-2010**

(share of GDP) <!-- Start of picture text --> 2.5 0.50<br>0.45<br>2.0 0.40<br>Investment<br>0.35<br>Government<br>1.5 0.30<br>consumption<br>0.25 Net import<br>1.0 0.20<br>Household<br>0.15 consumption<br>0.5 0.10 Net remittance<br>inflows (RHS)<br>0.05<br>0.0 -<br>2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text --> Source: WDI 2010, Migration Factbook 2011 and authors’ calculation.

By stimulating domestic consumption through soaring imports, remittances have significantly contributed to Tajikistan’s growth since 2004 (Figure 1). However, there isn’t much evidence that remittances have fueled private investment. Indeed, Tajikistan statistics agency data indicate that the share of private investment in agriculture, trade and light industry (sectors reportedly receiving investment from migrants) has decreased from 2 to less than 1 percent between 2007 and 2008, when remittance inflows were reaching record levels. This situation makes an export-led growth strategy more difficult to achieve, particularly given that the country cannot rely on domestic demand from
- _55_ -

The elasticity estimates presented in Table 11 show how responsive consolidated budget per capita infrastructure expenditures are to each of the factors. The urbanization rate, labor force participation rate and level of development are the most important determinants of consolidated budget infrastructure expenditures. All have elasticities greater than 1 in absolute value at the sample mean. Population density, the sectoral balance, and commercial bank flows also have a substantial impact on consolidated budget per capita infrastructure expenditures with elasticities greater than 0.3, but less than 1.0 in absolute value, at the sample mean. The stock of infrastructure, the internal balance, and the size of the foreign sector only have a modest impact on consolidated budget infrastructures, with elasticities ranging between 0.1 and 0.3 in absolute value. Institutional development, the level of foreign savings flows, and the share of foreign investment in foreign savings flows only have a tiny impact on consolidated budget per capita infrastructure expenditures. The estimated elasticities are below 0.1 in absolute value at the sample mean. The relative importance of the different factors, especially those factors with nonlinear relationships, will differ for countries where the value of the factors concerned deviate substantially from the sample mean.

# D. A Comparison **of** Analyses **of Per Capita Infrastructure Expenditure Using Central Budget** vs. Consolidated **Budget Data**

Most of findings regarding the determinants of public infrastructure expenditures are broadly consistent regardless of whether central budget or consolidated budget expenditures are the focus of concern. The most important determinants of infrastructure expenditures are the level of development, the urbanization rate and the labor force participation rate. The size of the foreign sector, sectoral balance, and the stock of infrastructure are also important determinants of per capita infrastructure expenditures. The level and mix of foreign funding play a lesser role, but influence central and consolidated budget per capita infrastructure expenditures in a similar fashion. There is only weak evidence that terms of trade shocks and debt service obligations significantly influence either
Migrants”. The latter is used as a proxy for Syrian refugees for the purposes of this paper. National official surveys that are conducted under-report the refugee population. Yet, since about 10 percent of Syrian refugees are in camps and the remaining are residing throughout the country, it is not surprising that they are accessible to interviews by the LFS.

Despite data limitations, there are strong and significant trends in the poverty rates for the recent foreignborn, especially for those near the Syrian border. In 2013, recent migrants near the Syrian border were the poorest group<sup>4</sup> in Turkey. While this statistic in itself is not initially surprising, fluctuating welfare trends of recent migrants over time is noteworthy. In previous years, migrant households in Turkey tend to have much lower poverty on average than even the host community. Across comparison groups and time, the poverty rates of recent migrants is higher than among the host community in only one instance: in 2013 near the Syrian border. This sudden change in the historically stable pattern implies that the LFS is able to capture at least a part of the incoming SUTPs who have significantly different socioeconomic profiles in comparison the previous economic migrants.

Throughout history, immigration to Turkey has been relatively limited and consisted mostly of those of Turkish heritage. In the early 20<sup>th</sup> century, immigration was encouraged by the government as a method to increase the population. Since 1970, immigration has slowed down and has been even discouraged at times. Many immigrants to Turkey are of Muslim Turkish background, since the government prioritized preserving a national identity. This is likely why “migrants” had very similar or even lower poverty rates than the host community.

The sharp degradation of welfare among recent migrants in 2013 illustrates the severity of poverty that is arising very likely from a growing population of Syrian refugees. The Syrian refugee inflow to Turkey

> 4 Based on grouping by host community, established migrant, recent migrant.

3
||||w4 and w5, UGA<br>wave 4 and above|
|---|---|---|---|
|_twi_|Potential wetness<br>index||From the<br>geovariables files,<br>which are missing<br>in NER w2, TZA<br>w4 and w5, UGA<br>wave 4 and above|
|_nutrient_availability_<br>_nutrient_retention_<br>_rooting_conditions_<br>_oxygen_availability_<br>_excess_salts_<br>_toxicity_<br>_workability_|Nutrient<br>Availability<br>Nutrient Retention<br>Rooting<br>conditions<br>Oxygen<br>availability<br>Excess salts<br>Toxicity<br>Workability||From the<br>geovariables files,<br>which are missing<br>in NER w2, TZA<br>w4 and w5, UGA<br>wave 4 and above|
|_soil_fertility_index_|<br>Soil fertility index|PCA of soil variables above||
|_plot_slope_|Plot Slope<br>(percent)||From the<br>geovariables files,<br>which are missing<br>in NER w2, TZA<br>w4 and w5, UGA<br>wave 4 and above|
|_plot_dist_household_|Plot Distance in<br>(KMs) to HH||From the<br>geovariables files,<br>which are missing<br>in NER w2, TZA<br>w4 and w5, UGA<br>wave 4 and above<br>Only present in<br>ETH, MWI.|

Supplementary Table 4. Directory of variables in the household-level dataset

|**Variable name**|**Label**|**Data processing notes**|**Additional notes**|
|---|---|---|---|
|_hh_size_|
.007|0.006|0.004|-0.010|-0.016**|-0.013*|-0.006|
||[1.23]|[1.20]|[1.16]|[0.73]|[1.46]|[2.29]|[1.93]|[0.66]|
|Investment environment||-0.017||||0.102***|||
|||[0.59]||||[4.16]|||
|Chinn-Ito index of capital account openness|||-0.046||||0.100**||
||||[1.45]||||[2.45]||
|Bank assets, % of GDP||||0.001||||0.007*|
|||||[0.16]||||[1.76]|
|Country fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|2,197|2,175|2,093|1,945|1,945|1,923|1,853|1,702|
|R-squared|0.039|0.040|0.042|0.044|0.012|0.030|0.020|0.019|
|# of countries|29|29|29|29|27|27|27|27|

_Notes:_ The dependent variable is portfolio equity inflows as % of trend GDP in Columns 1-4 and portfolio equity outflows as % of trend GDP in Columns 5-8. The dependent variables are standardized by subtracting the country-specific mean and dividing by the country-specific standard deviation. The sample spans from 1990:Q1 to 2015
# **I. Introduction**

The transition process has opened a wide wedge in economic development among the transition economies of Central and Eastern Europe and Central Asia. While in 1992, GDP per capita in US dollars varied between 500 and 8,000 for the countries in this region, it varied between 400 and 12,000 in the year 2002.<sup>1</sup> During the same period, GDP per capita increased by 51 percent in Poland and shrank by 63 percent in neighboring Ukraine. While all transition economies faced the difficult task of building new market-compatible institutions, this process, and the speed and success of reform more generally, has varied greatly across countries. Generally, the Central and Eastern European countries proved to be more successful reformers and today score better in terms of institutional and economic development than the countries of the former Soviet Union, including the Baltic States. For example, while Poland and Turkmenistan started off with approximately the same level of GDP per capita in 1990, Poland’s GDP per capita increased to more than twice the level of that of Turkmenistan by 2002.<sup>2</sup> Why have some transition economies fared so much better than others? Why have some transition economies succeeded in building a new institutional framework after the fall of socialism, while others have not?

This paper proposes and tests a hypothesis based on political economy of why institution building has varied so much across countries in this part of the world and shows the importance of institution building in explaining the variation in economic performance across transition economies. The literature examining the growth experience of transition economies over the last decade has focused mostly on reform strategies – shock versus gradualism –, macroeconomic

> 1 The gap in GDP per capita as measured by PPP dollars opened in similar terms. While in 1992, income in PPP dollars ranged from 1,100 to 10,800, it varied between 900 and 16,400 in 2002.

> 2 This comparison is based on WDI data of
country and the richer consumers in the poorer country. These income groups gain from integration not only because imports provide a wider variety of choices, but also because trade shifts the composition of available products toward those goods that they are most likely to buy.

Our nested logit demand system is familiar from the empirical literature. For this reason, we believe that it would be possible to estimate key parameters of the model from data on household income and spending. Armed with such estimates and observable data on expenditure patterns, one could utilize our expressions for the average welfare changes in di¤erent income groups to calculate the gains from trade across the income distribution.<sup>41</sup> Finally, our framework is simple enough to allow for extensions and variations. For example, it is straightforward to introduce direct foreign investment as an alternative means for ...rms to serve foreign markets. Then the model could shed light on the spread of Chinese and Indian multinational corporations to other developing countries (see Boston Consulting Group, 2006). We are pursuing such an extension in our ongoing research.

> 41 See Porto (2006) for an ambitious attempt to estimate the distributional impacts of trade policy in a model that includes heterogeneous consumer gains as well as wage and income e¤ects. Porto does not allow for non-homothetic demands, but rather he takes the variation in budget shares for di¤erent households to be an exogenous re‡ection of heterogeneous tastes.

30
, using the weights proposed by Webb (2013) for samples with fewer than 10 clusters. Results tables report _p_ -values and significance levels based on these corrections. Because the bootstrap produces valid _p_ -values but not confidence intervals (Cameron and Miller 2015, 27), we also report standard errors clustered by region based on the usual asymptotic approximation (Labonne 2013) but caution that these are illustrative and not appropriate for inference.

# III. DATA

## _Sources_

Outcome data are the universe of student exam records from the West African Examinations Council (WAEC). Subject-level scores are available for each student registered for the exam between 1998 and 2012, allowing for several years of pretreatment outcomes for each region.<sup>8</sup> In addition to omitting region 1, we also omit private schools because they were ineligible for the scholarship program. However, all raw test results are converted to _z_ -scores based on the universe of results in a given year, including students from private schools and region 1. This standardization allows us to 8
Policy Research Working Paper 8698

# **Abstract**

Understanding the magnitude and importance of income shocks, such as drought or conflict, in causing and perpetuating poverty is critical to designing policies aimed at building resilience and contributing toward the goal of ending poverty. This paper uses micro-data from two waves of the Somali High Frequency Survey to assess the impact of the severe drought that Somalia experienced in 2016/17 on poverty, hunger, and consumption. The analysis uses a regression framework to quantify the effects of the drought, relying on spatial variation in drought exposure and the timing of data collection, which took place before and during the drought, for identification. The drought is found to have a sizable effect on poverty, consumption, and hunger in rural areas, where agricultural households and those lacking access to infrastructure and basic services are most severely affected. A renewed drought shock could lead to an increase in poverty of 9 percentage points. The findings underscore the importance of investing in rural resilience, especially among agricultural households.

This paper is a product of the Poverty and Equity Global Practice. 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/research. The authors may be contacted at upape@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