rafmacalaba/data-use-mentions-extended huggingface dataset:
quality may require a substantial increase in costs, which would eventually be translated into higher fares. After considered these arguments, it remains important to reconcile the establishment and implementation of indicator reference standards. Table 5.4. gives some examples of indicators and their associated standards.

**Table 5.4. Examples of reference standard levels**

|Financial|Return on capital investment|> 1.0|
|---|---|---|
|LAbor prou-tvit|r<br>yZ|20P0<br>to|
|Service quality|[Numberof square meters per occupant at<br>jpeak hours|_25-35_(international)<br>1 6-20 (national)|

Source: Adapted form various studies.

Doganis and Graham (1995) have carried out evaluations of the economic and commercial aspects of 25 European airports through the application of a set of performance indicators.<sup>3</sup> ' The authors emphasize the existence of comparability problems due to differences in the activities developed at airports in the study. They try to lessen the problem through corrections aimed at allowing for a consideration of the whole group as operators of the same activities.<sup>32</sup> The sample includes private airports such as Glasgow, partially privatized ones such as Copenhagen, publicly owned but commercially oriented airports such as Geneva, and airports like Stockholm, which is part of the Swedish Civil Aviation Authority. The main objective of this study was to analyze the trends and development of industry performance, and identify the relationship between profitability and type of airport. Airports with different ownership structures and varying sizes were incorporated into the study. Table 5.5 provides a summary of the results of this study.

Finally, it should be pointed out that differences in the type of services developed in airports, such as in the degree of public intervention, accounting systems, financial sources, subsidies and standards, complicates the use of indicators for assessing airport performance. In addition
car, and some households will change categories, from having at most a motorcycle to acquiring a car for the first time. On average, the predicted probabilities of owning a car increase by 93% compared to a 55% increase in motorcycle ownership. These averages hide important discrepancies between countries. For instance, the increase in motorcycle ownership is larger in Burkina-Faso than in Mongolia, where car ownership is more affected by income increases. Finally, the effect of rising income on bicycle ownership is less clear as some households switch from having a bicycle only to acquiring a motorcycle or a car.

Figure 19: Income changes and vehicle ownership <!-- Start of picture text --> Afghanistan Afghanistan<br>Burkina Faso Burkina Faso<br>Cameroon Cameroon<br>Kyrgyz Republic Kyrgyz Republic<br>Malawi Malawi<br>Mali Mali<br>Mongolia Mongolia<br>Nepal Nepal<br>Nicaragua Nicaragua<br>Niger Niger<br>Nigeria Nigeria<br>Pakistan Pakistan<br>Sierra Leone Sierra Leone<br>Tanzania Tanzania<br>Uganda Uganda<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Predicted probabilities in percent Predicted probabilities in percent<br>No vehicle Bicycle No vehicle Bicycle<br>Motorcycle Car Motorcycle Car<br>(a) Income at current level (b) Income at 2035 level<br><!-- End of picture text -->

**The impact of increased vehicle ownership on carbon emissions** We use country-level time-series to quantify the relation between vehicle ownership and CO2 emissions. Table 7 shows the results of the regression using transport carbon emissions per capita and vehicle ownership variables from the IRF unbalanced panel of data ranging from 2000 to 2017. Column (1) shows that carbon emissions per capita from transport increase by about 35% when the average number of cars per household doubles.<sup>9</sup> Column (2) reports the results for
# **On the International Transmission of Shocks: Micro-Evidence from Mutual Fund Portfolios**

Claudio Raddatz Sergio L. Schmukler<sup>*</sup>

## **Abstract**

Using micro-level data on mutual funds from different financial centers investing in equity and bonds, this paper analyzes how investors and managers behave and transmit shocks across countries. The paper shows that the volatility of mutual fund investments is quantitatively driven by investors through injections of capital into, or redemptions out of, each fund, and by managers changing the country weights and cash in their portfolios. Both investors and managers respond to returns and crises, and substantially adjust their investments accordingly. These mechanisms generated large capital reallocations during the global financial crisis. Their behavior tends to be pro-cyclical, reducing their exposure to countries experiencing crises and increasing it when conditions improve. Managers actively change country weights over time, although there is significant short-run “pass-through,” meaning that price changes affect country weights. Consequently, capital flows from mutual funds do not seem to stabilize markets and instead expose countries to foreign shocks.

## **JEL Classification Codes** : F32, F36, G11, G15, G23

**Keywords** : contagion, crises, global financial crisis, international capital flows, mutual fund investors, mutual fund managers

> * This paper was part of the MIT Sloan School of Management-NBER project on the global financial crisis. For very useful comments, we thank Charles Engel (Editor), Kristin Forbes (Editor), Jeff Frankel (Editor), Tarun Ramadorai, Luis Servén, Ajay Shah, Jeremy Stein, two anonymous referees, and participants at the AEA Annual Meeting (Chicago, IL), the Bank of Spain-World Bank Conference (Madrid, Spain), the CREI-U. Torcuato Di Tella Workshop (Barcelona, Spain), the Darden International Finance Conference (Charlottesville, VA), the LACEA-LAMES Annual Meeting (Santiago, Chile), the MIT-NBER Global Financial Crisis Project (Bretton Woods, NH), and
100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Total Rural Urban<br><!-- End of picture text --> _Source: Life in Transition Survey, 2016_ When last measured, traditional survey measures indicated that Tajikistan was among the worst performers in the ECA region in terms of the reliability of electricity supply to households (figure 1). Tajikistan also suffered from a uniquely large divergence in satisfaction between urban and rural areas 3
the discontinuity. Furthermore, the correlation between the poverty index and our social capital outcomes, controlling for state effects, is 0.005-0.009 across the cohorts and is not statistically significant.

**Table 4a** shows summary statistics for participant and non-participant groups for a series of geographic characteristics for the sample of polygons submitted in each cohort.  The first two columns show means and the third column of the table shows normalized differences of means (Imbens and Wooldridge 2009) between treatment and control polygons. It is important to note that in a regression discontinuity design, differences in average covariates across groups above and below the cutoff threshold are to be expected if it is the case that the running variable used for prioritization is correlated with these characteristics. The normalized differences were above the 0.25 standard deviation threshold (Imbens and Rubin, 2015) for only two of the 36 indicator-cohort comparisons. Enrollees tend to be located in higher elevations and to be ejidos for the 2011-2012 cohorts (normal differences of 0.342 and 0.334 respectively), with differences persisting for the 2013-2014 cohorts (0.186-0.196). The higher prevalence of ejidos is a direct result of the targeting criterion, and elevation in Mexico tends to be correlated with forest cover, another targeting criteria of the program.

**Tables 4b** and **4c** present summary statistics of community and household level characteristics, as reported in the leader and household surveys respectively. Similarly, we find that enrollees and rejected applicants have generally small normalized differences in means.

> 14 For the polygon environmental data (Appendix Table A2a), we run OLS of the covariates on the treatment threshold, a quadratic of the running variable with slopes that vary on either side of the threshold, polygon size, an ejido dummy variable, ecosystem indicator variables, and state and year fixed effects. For the community and household-level survey data (Appendix Table A2b), we run regressions that include the threshold dummy variable, the first and
headcounts across all groups included in this analysis. This results in a 9% increase in poverty headcounts (and 8% increase in the poverty gap) between market and consumable income. This increase is slightly more pronounced for boys than for girls (10% vs. 8%). The highest increase in relative terms can be found in urban areas, where the poverty headcount increases by 21% and the poverty gap by 25% between market and consumable income. Also noteworthy is a significant increase of the poverty gap for girls (17% increase between market and consumable income). This contrasts with small decreases in monetary inequality for almost all groups, with an average decrease of 2.5% in the Theil index.

_Table 6: Monetary child poverty across income concepts, by gender and location_

||**_Income concept_**|**_All children_**|**_Girls_**|**_Boys _**|**_Rural_**|**_Urban_**|
|---|---|---|---|---|---|---|
|**_Poverty_**|Market income|0.33|0.36|0.31|0.39|0.14|
|**_headcount_**||(0.004)|(0.006)|(0.006)|(0.005)|(0.005)|
||Disposable income|0.34|0.36|0.31|0.39|0.15|
|||(0.004)|(0.006)|(0.006)|(0.005)|(0.005)|
||Consumable income|0.36|0.39|0.34|0.42|0.17|
|||(0.004)|(
expenditure—that are not available in the PG data—can improve the accuracy of the poverty predictions significantly, this provides a strong argument for collecting

> 9 We consider the following five levels of education achievement: 1) below six years of schooling, 2) 6-8 years of schooling, 3) 9-11 years of schooling, 4) 12-14 years of schooling, and 5) university education or higher.

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Source: authors’ calculations based on Business Pulse Survey data.

# 3. Methodology

To investigate the effect of Russia’s invasion of Ukraine on private businesses in the Central Asia region, this paper exploits the panel dimension of our data. Our empirical analysis is divided into two main parts. First, we provide a descriptive overview of our outcomes of interest, highlighting how the distribution of these firm-level outcomes (sales and employment) changed as Russia’s invasion of Ukraine affected the region. Second, we interact this time variation with the cross-sectional heterogeneity present in our sample of firms. Specifically, we use firm-level exports to Russia as a proxy to measure the exposure of individual businesses to the negative spillovers caused by the invasion. We then analyze the interaction between this proxy and the onset of the invasion to identify the intensity of the invasion shock. Next, we examine the relative performance of firms with higher or lower exposure to the invasion compared to matching peers. This allows us to assess how firms with varying levels of exposure to the invasion were affected by the invasion.

## **Dynamics of firm-level sales and employment**

First, we analyze the dynamics of outcome variables of interest. The analysis focuses on the differences observed between 2022 when Central Asian economies were affected by the shock triggered by Russia’s invasion of Ukraine, and previous years. We are interested in two outcome variables: firm-level employment and firm-level sales. For every firm _j_ and every year _t_ , the analysis uses the following specification: (1) where is the invasion year dummy, YYjj tt = bb WWtt + αα TTtt is a vector of dummies for non-invasion BPS waves – one dummy + γγ SSjjtt + ρρ CCjjtt + θθXXjjtt + eejjtt for each of the two pre-2022 waves, is a vector of sector dummies, is a vector of country dummies tt tt – one dummy for each of the three countries covered in the analysis, WW TT is a set of controls, and is the jjtt jjtt SS CC outcome variable
. The IES only includes three countries for which we have recent data. The worker and firm data are not strictly comparable due to differences in the number of countries included in the analysis, the national versus main cities representativeness, and the informality focus of the firm level data. The worker and firm data have some overlap – namely among LE-SE, HESE, and employers – though comparisons across surveys on this subset are limited due to the IES sample frame limitations by region and industrial sector.

The paper findings offer some general policy guidance. First, informal employment and informal firms dominate the urban space, suggesting an immediate need for greater policy attention to these segments of the urban labor force and enterprise landscape. Second, the rate of formalization does not seem to have increased systematically in the last decade, despite economic growth and poverty reduction, pointing to a need for policies to support the urban informal sector to be more productive and resilient rather than assuming that SSA will “grow out of” informality in the short term. Third, increasing the availability of data on informal firms should become a priority especially for African countries where most firms are in the informal sector; this would allow for better policy making. Fourth, policy will need to take into consideration both heterogeneity across the informal sector in terms of job types and the different characteristics of those working in each job type. Gender and age are particularly relevant variables for informal self-employed (former) and unpaid (latter) Page **46** of **60**
small 0<br>states Moderate High In distress<br><!-- End of picture text --> _Sources_ : International Debt Statistics (GDDS database); International Monetary Fund; WEO (database); World Bank.

_Note_ : EMDEs = emerging market and developing economies.

A. Average government debt to GDP ratio for 32 small states and 111 other EMDEs. Whiskers indicate interquartile range.

B. Bars show the percentage of small states and other EMDEs with debt to GDP ratios of at least 60 percent for 32 small states and 111 other EMDEs.

C. Average share of public and publicly guaranteed external debt by type of creditor for 22 small states and 100 other EMDEs.

D. Average debt as a percent of GDP in 2023 for countries classified at different levels of debt risk ratings, based on the published Joint World Bank-International Monetary Fund Debt Sustainability Framework for Low-Income Countries as of March 30, 2024. Sample includes up to 19 small states and 48 other EMDEs.

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When classifying countries by income group, the sample has high coverage of low and upper middle income countries (more than 82 percent), partial coverage of low income countries (76 percent) and low coverage of high income countries. The two types of countries most underrepresented in the data – those in the Middle East and North Africa and high income countries – tend to have low poverty rates. Therefore, the sample overstates developing world poverty rates in the aggregate, though it is less clear how omitting these countries affects the comparison between child and adult poverty rates; this will be explored in future work. All subsequent tables and figures in this report are based on the GMD sample presented in Table 2.

The GMD sample is lined up to represent estimates of extreme poverty in 2013. Lining up the welfare aggregates in surveys to a common year makes them more comparable across countries, since the underlying surveys in both PovcalNet and the GMD were conducted in various years. Between 2009 and 2014, population generally increased and poverty generally fell; therefore failing to line up poverty estimates to a common year would give older surveys less weight overall, but greater weight when generating profiles of the global poor. Lining up the survey data to 2013 entails adjusting both the population and the poverty estimates. To adjust the population, we utilize population projections for each gender and age group provided by the United Nations Population Division (UNDESA), which are available by gender and five-year age cohort. As a result of the line-up, the sum of the survey weights for each gender and age group within each country match UNDESA’s reported population of that group in 2013.<sup>9</sup> The poverty estimates are constructed to equal those published by PovcalNet for each country. Full details of this ‘lining up’ procedure are given in Castañeda et al 2016.

# **Poverty rates among children and adults**

How do the prevalence and depth of extreme poverty differ by age in the sample of 89 countries? Table
level, interpretation of the survey data is strongly guided by theory. As section 2 indicates, theory gives us every reason to believe that institutions affect performance and that rule credibility, policy credibility, and resource adequacy and predictability are strongly implicated. However, theory provides no guidance on which of these institutional arrangements matter most and the findings at this disaggregated level are purely empirical. Finally, the model that has been developed is plausible but only one from very many that could have been constructed. In the country studies that offer detailed results, the model is only used to develop one set of interpretations where dialogue with government and World Bank operational staff suggest that this is useful.

The key stages in making an informed speculation on performance pay-offs are as follows:

# **Stage 1 - Develop an appropriate model**

# Model I

Performance = f (Institutional Environment).

This model has already been estimated in the previous section. It showed that predicted correlations are high when simple averages of the elements of institutional environment is regressed against aggregate performance as well as its three main components.

However, this information by itself is not enough for decision-makers in governments. They already know that performance is sometimes low because of weaknesses in the institutional environment; and if they could improve the whole institutional environment, the overall performance would improve. They need more precision. The logical next step is to move from Model I towards one in which the institutional environment is disaggregated into its components.

# Model II

# Performance = g (Rule Credibility, Policy Credibility, Resource Adequacy and Predictability).

Estimating Model II will also not give enough detailed information. Decision-makers in government will agree that improving policy credibility can improve performnance, but they will still ask what specifically they need to do to improve performance. Accordingly, a third model is required to capture the relationship between performance and particular elements of the institutional environment. This third model needs to be disaggregated enough to provide input for reform interventions. Improving the institutional environment as a method for improving
Policy Research Working Paper 10766

# **Abstract**

This paper investigates the effects of severe drought shocks on Tunisia’s agriculture sector during 2000–19. Using labor force surveys aligned with granular weather data, it calculates the Standardized Potential Evapotranspiration Index to detect moderate-to-severe drought shocks at the governorate level and frames the analysis in a staggered difference-in-differences setting. The findings show that shocked areas experience a drop of 7.4 to 10.6 percentage points in agricultural employment with respect the untreated or not-yet-treated governorates. There is a contemporaneous opposite dynamic in the employment rate of low-skill and less climate-sensitive sectors, as well as a modest and transient increase in unemployment. The effects are largely heterogeneous across groups of workers, with very young individuals, women, and low-educated workers paying the highest toll.

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/prwp. The authors may be contacted at falfani@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
field training and extensive practice to guarantee fluent and correct implementation of the different skill measurements. The first round of the survey started January 20<sup>th</sup> 2014– and took approximately 3 weeks. The retest survey was conducted in the following 3 weeks. A small household and farm survey was implemented in parallel and provides the agricultural outcome variables. All survey activities, including tracking of harder to reach respondents were finished by the end of March. Almost all surveys were conducted before the start of the main agricultural season. Additional surveys were implemented at the end of the following four agricultural seasons with information on production outcomes and practices, and are used to investigate which skills best predict these economic outcomes.

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land prices and prices of other local goods may increase in the locations affected by the oil extraction activities. The local economy will not benefit from the resource if higher prices increase the cost of living and discourage businesses from operating in affected districts.

Another channel through which oil extraction affects the local economy is through oil windfalls. Some governments redistribute oil windfalls through income transfers and provision of public goods such as roads, health facilities, classroom blocks and public water supply. The provision of public goods benefits both households and businesses. On the other hand, the presence of oil revenues can create corruption (Ades and Di Tella, 1999), rent seeking behavior (Brollo et al., 2013), and even conflicts (Angrist and Kugler, 2008). Vicente (2010), for example, finds that offshore oil discovery in São Tomé and Príncipe led to perceived increases in corruption.

# **4. Research Design**

In this paper, we examine the impact of natural resources on the local economy by focusing on the effects of oil extraction on the employment and standard of living of households in affected districts. We use consumption and poverty status of the household as a measurement of the standard of living besides income.

# _4.1.  Data_

We use three rounds of the Ghana Living Standards Survey (GLSS) covering the period between 1998 and 2013 (1998/1999-GLSS4, 2005/2006-GLSS 5 and 2012/2013GLSS 6).<sup>7</sup> GLSS is a nationally representative household survey and is collected by the Ghana Statistical Service (GSS). It reports detailed information on demography, income, expenditure and other household and individual characteristics. With the support of the Ghana Statistical Service, we created a complete dataset of location information of enumeration areas of the household surveys. The GLSS 6 sample includes 818

> 7 We use GLSS 5 (2005/2006) and 6 (2012/2013) in the main analysis, and GLSS 4 (1998/1999) in a