rafmacalaba/data-use-mentions-extended huggingface dataset:
in the coefficient (see Tables B8 and B9). This result points to destinationspecific variation as the main driver of the pattern. Controlling for home-country concentration (see Tables B10 and B11) and disaggregating industry-level concentration (see Table B12) confirms the robustness of our findings.

# **5 Conclusions**

Using comprehensive data on cross-border investment projects from a wide range of countries, this study provides evidence that FDI has become more concentrated in the past decade. This trend is especially marked in developing countries and East Asian economies in particular (including China and India); it is also sharper when it comes to greenfield investment relative to M&A and has accelerated during the COVID-19 pandemic. Our analysis provides suggestive evidence that concentration is associated with both an increase in idiosyncratic macroeconomic uncertainty and, relatedly, a dramatic reduction in the number of foreign investors. Although the structural shift of FDI into services as well as rising market concentration in some major source economies 12
measures in the attorney general’s office, out of 60 employees dismissed almost 60 percent were women, a number of whom had been in senior positions for several years. The justification put forward for that act was that ‘married women were often absent from work’.” (Ali 2016, 12). Ali goes on to say that it was not just women in the formal economy who were targeted but women in the informal economy too, many of whom had been displaced to the capital, Khartoum, because of war or drought.

Based on the KIIs and FGDs that were conducted in Khartoum, Madani (Al-Gezira State), and Nyala (South Darfur State), the way women’s employment opportunities are shaped is aligned with these legal provisions. A woman from Madani who was not working reported that, “My brother’s wife is an engineer. She is graduated from Khartoum University and they offer her many jobs and she succeeded in all interviews, but my brother refuses to [let] her work. He believes that her job is to take care of her home.” According to a human resources manager of a medium-size commercial services operation in Khartoum, “Overall when I hire a female I check if she have kids and if she could work and leave their kids and her house because it’s hard for any female to leave her kids alone, and I ask them specific questions when I make any interview, for example I have two candidates male and female, and the female have kids so I ask her what will you do in some situations, and if she have solution for these cases she could be hired.”

# **_Protections and restrictions in the Labor Code_**

Overall, the 1997 Labor Code includes more restrictions on women’s ability to work than it does protections. That said, the 1997 Labor Code does include a few provisions that serve to protect women.

> 12 While women and younger cohorts seem to be slightly more open to equal
PASS-THROUGH OF COMPETITORS’ EXCHANGE RATES 7

# 3. Main Empirical Results

**Box 1:** Summary of four testable predictions from the simple model (Section 2) and associated empirical stylized facts (see Section 5 for a quantitative comparison of the full model and data). **Testable Prediction 1** : An appreciation of competitors’ exchange rates should increase US import prices.

Evidence: Significant pass-through of competitors’ exchange rates to US import prices, with a 1% increase in competitors’ exchange rates associated with a 0.35% increase in import prices in the preferred specification — larger than estimated bilateral pass-through.

**Testable Prediction 2** : The coefficient on the bilateral RER will be “biased upwards” in univariate regressions, with bilateral pass-through falling when competitors’ RERs are added as a control. Evidence: When competitors’ RERs are added as a control, bilateral pass-through falls by around a third (7ppts).

**Testable Prediction 3:** An appreciation of competitors’ exchange rates should increase US producer prices.

Evidence: Significant pass-through of competitors’ RERs to US producer prices: a 1% rise in competitors’ RERs increases US producer prices by about 0.11% in the preferred specification. **Testable Prediction 4:** An increase in foreign competitors’ prices should increase (a) US import prices and (b) US producer prices.

Evidence: A 1% increase in foreign competitors’ prices (in the same sector) raises import prices by 0.6% and US producer prices by 0.2% and lowers estimates of pass-through of competitors’ RERs.

3.1. **Data.** I use confidential monthly US import and producer price data from the Bureau of Labor Statistics (BLS), which are measured at the individual good level for manufacturing sectors (NAICS31-33). I calculate changes in log prices over the life of the good in the sample (conditional on a price change) which Gopinath and
pandemic on electricity consumption patterns among households and firms in Sub-Saharan Africa using administrative data on billing records from Ghana and Rwanda. We also explore the effects of SAH orders (lockdown) and pandemic relief measures such as electricity subsidies on household consumption.

Six main results emerge from the paper. First, residential electricity consumption during the pandemic increased in both Ghana and Rwanda, albeit the rate of increase was high in Ghana than in Rwanda. Specifically, average household consumption increased by about 23% in Ghana compared to 4% in Rwanda. Second, consumption in the nonresidential sector declined by about 11% in Rwanda, while rising marginally by about 6% in Ghana. Third, industrial consumption declined in both countries, highlighting the ef-

> 26Based on an exchange rate of USD 1: 5.6 GHS

16
# **Industrial Districts**

# **International Hubs**

|--in diverse places|--in the world's major cities|
|---|---|
|--small firms|--vertically integrated companies|
|--production networks|--market ties|
|--spatial co-location|--spatial separation|
|--localization economies|--urbanization economies|
|--spatial proximity to production|--time proximity to demand|
|--manufacturing|--services|

Cluster theory finds innovative firms aggregating in a diverse range of places: from small towns, like Modena in the Third Italy, [<sup>4</sup> ] to clusters in major city-regions, like Silicon Valley in the San Francisco Bay Area. International hub theory emphasizes the world's major cities, like London and New York. Cluster theory roots innovation in small firms networked in local industrial districts. International hub theory stresses large, vertically integrated corporations interacting with clients and customers via international markets. Cluster theory stresses the flexibility and transaction cost advantages of spatial co-location among firms performing specialized functions in a production system. International hub theory stresses the advantage of time proximity to world demand enabled by the spatial separation of functions in a transnational division of labor. Cluster theorists emphasize localization economies. International hub theorists stress urbanization economies. Cluster theorists focus primarily on innovative manufacturing firms. International hub theorists emphasize traded services.

Just as there is no one generally accepted model of urban innovation, empirical research suggests there is no one set of factors distinguishing highly innovative cities in the OECD countries. Each of the two models stresses a subset of factors found to be stimulants to innovation in particular places, but neither one covers the entire spectrum. The five city European study, by James Simmie and his colleagues (2001), is perhaps the most comprehensive recent attempt to specify place based sources of innovation. Part of an ESRC Research Program investigating urban and
Domestic water consumption|liter/capita/day|115<br>75|56|154|
|Revenue collection|% sales|60<br>85|44|100|
|Distribution losses|% production|40<br>62|61|27|
|Cost recovery [2]|% total costs|67<br>81|72|81|
|Operating cost recovery|% operating costs|94<br>114|100|145|
|Labor costs|connections per employee|96<br>25|34|369|
|Total hidden costs as % of revenue|%|194||140|
|||Angola|Low-income,<br>water-scarce<br>resources|Other<br>developing<br>regions|
|||2005<br>2009|||
|Average effective tariff|U.S. cents per cubic meter|120<br>230|60–120|3–60|

_Source:_ Demographic and Health Survey 2006/7 and AICD water and sanitation utilities database <u>(www.infrastructureafrica.org/aicd/tools/data).</u> _Note:_ Access figures from the 2001 Multiple Indicator Survey and 2007 Malaria Indicator Survey.

[1] Surface water includes rivers, lakes, and ponds in the vicinity as the main source of water supply, as well as other nonimproved sources of water such as water from vendors and rain water.

[2] Cost recovery is estimated based on the assumption of a capital cost of 40 cents per cubic meter.

The reduction in open defecation has been achieved by the expansion of higher-end forms of sanitation in urban areas and traditional latrines in rural areas. Access to septic tanks almost doubled between 2001 and 2007, from 17 to 31 percent. In urban areas about 4.8 percent of the population has been gaining access to septic tanks each year, an exceptionally high rate of expansion that mirrors the country
# V – Conclusion

Access to reliable electricity is a key determinant of both economic growth and individual wellbeing. However, when aggregated to summary statistics, traditional survey-based methods of collecting service reliability data are of questionable validity due to several potential sources of bias. A low-cost national electricity outage monitoring network of Smart Survey Boxes (SSBs) was built for this study, alongside the rollout of a high-frequency phone-based household survey called Listening to Tajikistan. The integration of the two allows benchmarking the survey responses against the unbiased automated system.

The results show that although the two measures were correlated, the survey data suffered from significant and systematic bias. Survey respondents i) systematically underreported the incidence and duration of electricity outages on average, but ii) systematically overreported the incidence and severity of outages during periods of abnormally widespread outages of long duration. These findings indicate that, where feasible, automated monitoring can provide more accurate measurement of the quality of electricity service provision. For survey settings, the results also suggest that estimates are much more accurate when short (daily) reference periods are used, but that care should be taken to account for i) time trends and ii) the salience of electricity outages for respondent subgroups.

16
_ANDR EE_<sup>_ ́_</sup> _AND PAPE_ _SEPTEMBER 5, 2023_ _20_ The graph in figure 2 shows the processed exchange rate data constructed from FAO FMPA parallel market quotes and daily rates from the ECB. The result can be used to dollarize the food price estimates. It is important to note that in many countries with strongly depreciating currencies, the exchange rate may correlate very strongly with food prices. In PNG, the data largely follows a different pattern. From the graph we can infer that the dollar appreciated roughly 40% against the Kina between 2009 and 2022 while food prices more than doubled. Over the long run, food has thus increased not only in local currency but also in dollar terms.

When price monitoring focuses on a single food item, however, it becomes more important to ensure that the individual price series have some degree of data support throughout the entire monitoring period. For instance, the results in figure 3 show an example of cabbage prices in Mt. Hagen, the third largest city and capital of the Western Highlands Province that is located in the large fertile Wahgi Valley in central mainland. The result highlights that in the PNG survey data, there are considerable data gaps during which the trust score dips severely. The chart also shows that there was a major price shock that resulted in triple digit product-specific inflation rates in the area. Such large price shocks are less likely to occur in countries where markets are more integrated, and highlight the importance of local price monitoring in places such as PNG where they are not.

# **IV. Discussion and conclusion**

While many countries globally are currently experiencing some of the highest inflation rates in decades, the Pacific Islands lack good capabilities to track food price developments in near-real-time. However, without such capabilities, it is difficult to monitor the situation and inform policies in a timely manner, which is particularly crucial in such a volatile environment.

To gain near-real-time insights, this paper applied a recently developed machinelearning approach to impute
Figure A.9. Share of Surveyed Individuals by Province <!-- Start of picture text --> Uzbekistan<br>Tajikistan<br>Turkmenistan<br>Badakhshan<br>Jawzjan Balkh Kunduz Takhar<br>Faryab<br>Samangan<br>Baghlan<br>Sar-e-Pul<br>Panjsher<br>Nooristan<br>Badghis Parwan Kapisa Kunarha<br>Bamyan Laghman<br>Kabul<br>Wardak Nangarhar<br>Herat Ghor Logar<br>Daykundi<br>Paktya<br>Ghazni Khost<br>Urozgan<br>Pakistan<br>Farah Paktika<br>Zabul<br>Helmand<br>Iran Nimroz Kandahar<br>Individuals Surveyed (% Tot. Sample)<br>0.000 - 0.100<br>0.101 - 0.300<br>0.301 - 1.500<br>1.501 - 3.500<br>3.501 - 51.788<br><!-- End of picture text --> _Notes:_ Source: Afghanistan Welfare Monitoring Survey conducted for this study in the fall of 2021. The Figure includes the 5,592 individuals using in the main regressions.
1994; Mortensen and Pissarides, 1994; Campbell, 1997) that emphasize the potential role of entry and exit: if new technology can be better harnessed by new firms, productivity growth will be dependent upon the entry of new units of production that displace outpaced establishments. Moreover, the existence of sunk costs implies that new firms using the “state-of-the-art” production technology coexist with older and less productive firms generating the observed heterogeneity.

In this paper, we look at harmonized firm-level data for several industrial, transition and developing countries to seek confirmation of the statistical regularities highlighted in previous studies and to assess the possible sources of firm heterogeneity exploiting cross sectoral and well as cross-country variations. As such, this is the first paper, to our knowledge, to exploit a crosscountry sample beyond industrialized countries.

# **_The Role of Market Structure and Institutions_**

It is tempting at first glance to hypothesize that countries -- and/or sectors -- where the creative destructive process is distorted in some manner will have less churning and lower productivity levels and productivity growth rates. Indeed, it is not hard to take extreme versions of the models discussed in the prior section and generate just this prediction. That is, making entry and exit (and adjustment more generally) prohibitively costly via distorted market structure and institutions will lead to a reduced pace of churning and lower productivity (see, e.g., Davis and Haltiwanger, 1999 for the illustration of this prediction in a calibration exercise using an extreme example where all reallocation is shutdown). Taken literally, this prediction can be tested by examining the variation by country, sector and year in our harmonized data and relating

> 4. Various empirical papers have attempted to identify passive and active learning processes. For example, using US data, Pakes and Ericson (1998) claim that manufacturing firms are more consistent with the active learning model whilst retailing firms are more consistent with the passive learning model.

5
# **1. Introduction**

Structural reforms can raise incomes, but the costs of reforms might fall disproportionately on some population groups, increasing inequality and fueling arguments opposing change. The risk of losses for large segments of society could be a powerful shaping force of economic and social policies. Identifying who will likely lose out from reforms and managing people’s perceptions about their future welfare is crucial for consolidating political support for costly measures and moving countries to higher growth trajectories. It might be particularly important to understand the factors that shape support for structural reforms for the middle-income countries in Europe, Central Asia, and elsewhere that are facing challenges in accelerating economic growth and raising their populations’ standards of living.

This paper investigates whether support for education reforms is affected by expectations about future economic mobility. The focus on the subset of structural reforms related to education stems from the evidence that investments in human capital play a crucial role in driving economic growth (Hanushek and Woessmann 2008) and that education reforms are a powerful policy channel through which countries can improve the inclusiveness of growth by leveling the playing field and enhancing the upward economic mobility of disadvantaged groups (IMF 2019).

The analysis is based on data on 39 countries in Europe, Central Asia, and the Middle East and North Africa from the 2010, 2016, and 2023 rounds of the Life in Transition Survey (EBRD 2023). This sample of countries is analytically interesting because, despite progress made over the last several decades, these countries lag the high-income countries in educational achievement (World Bank 2018). Reforms aimed at improving the quality of education are also more costly and politically challenging than efforts to expand access to education, which helped countries move from low-income to middle-income status (Aiyar and others 2013). Profound education reforms that take years to achieve lasting effects require a strong, broad-based, durable coalition at all levels of society.

The paper finds that individuals who expect themselves or their children to be upwardly mobile are more likely to support tax-financed education reforms
and come from information provided by the household interviews in the preceding wave of the panel. Our final (unbalanced) panel dataset includes 5,461 observations of 2,736 agricultural households from waves 2, 3, and 4 of the Nigeria GHS Uniform Panel Data that were also interviewed in the preceding wave. Descriptive statistics for the key variables of interest are reported in Table A1 in the Appendix. Note that the average value of the reversed GS-SPEI is 0.23, suggesting that in our sample drier-than-normal conditions represent the prevalent type of weather anomaly experienced by farming households, and that growing season weather throughout the survey period has been overall less favorable compared to the historical (1900-2020) long-run sample mean over which the SPEI is computed.

# **4. Methods**

Our identification strategy is based on the use of causal forests. Causal forests were specifically developed for the estimation of Conditional Average Treatment Effects (CATEs), which constitute non-parametric estimates of treatment effect heterogeneity based on a set of chosen covariates (Athey & Imbens, 2016; Athey et al., 2019; Wager & Athey, 2018). Causal forests are a causal inference adaptation of random forests—a powerful supervised machine learning method aimed at predicting a given outcome on the basis of a set of inputs (Breiman, 2001; Hastie et al., 2009)— to the task of predicting heterogeneity in causal effects.

The causal forest algorithm is an ensemble of causal trees. Each of these trees is defined by datadriven sample splits that generate leaves, which are followed by a prediction of the causal effect over a set of conditioning characteristics _X_ . The aim of a causal forest is to split the data so as to maximize treatment effect heterogeneity across leaves. To prevent the risk of overfitting, the algorithm is based on the so-called "honest approach" (Athey & Imbens, 2016), which randomly splits the sample in two equal parts: half of the sample (the _prediction_ sample) is employed to define the sample splits (
the proportion of occurrences in which both variables (policies) take a value of one in the panel dataset, over the occurrence of all other combinations, except both variables taking a value of zero.

Jaccard coefficient=a/(a+b+c)Jaccard coefficient=a/(a+b+c) _Eq. B.1_ Table Appendix B.1. Variables in the Jaccard coefficient.

|Var1, 1 Var1, 0|
|---|
|Var2, 1<br>a<br>b|
|Var2, 0<br>c<br>d|

_Source:_ (Jaccard, 1908) _._ We then systematically merge similar policies into groups, creating an agglomerative hierarchical clustered visualization. In the resulting dendrogram, each policy is placed along the y-axis and is connected to other policies via a horizontal line that ends at their corresponding similarity value. The shorter the lines, the more similar the policies.

The shape of dendrograms changes according to the method of linking groups. The methods pertinent to binary data are single, complete, and average linkages. Each method has its limitations. The single linkage may produce “chaining”, in which several clusters are joined because one of their cases is within proximity of a case from a separate cluster. However, in complete linkage, outlying cases prevent close clusters from merging. We choose the third method, average linkages, as, in theory, it provides a compromise between single and complete linkage (Greenacre & Primicerio, 2013; Yim & Ramdeen, 2015).

In Figure B.1, the three vertical dashed red lines allow the reader to compare dissimilarities visually. In our case, the dendrogram helps further support the idea that policies within Headings are most similar to each other and not most similar to those in other Headings.

67
% of children live in monetary poverty, with poverty rates a little higher for girls than boys (36% versus 31%). Children in rural areas are significantly more likely to be poor than those in urban areas, with poverty headcounts of 39% and 14%, respectively. Monetary inequality (as measured by the Theil index) is 0.32 for all children. Inequality is higher for girls and children in urban locations.

Second, disposable income is derived by adding direct transfers (PSNP and non-PSNP) and subtracting direct taxes (e.g., income tax, agriculture income tax and land use fee, property tax) from market income. Poverty headcounts remain broadly constant to those at market income, increasing by one percentage point for all children and those living in urban areas. This is partly due to the lack of progressivity in direct transfers: while direct taxes are progressive overall (i.e., the tax burden increases as households are getting richer), direct transfers are lowest for the poorest 20% of children and highest in the middle quintile. Monetary inequality decreases slightly between market and disposable income.

Third, consumable income adds indirect subsidies (kerosene and wheat subsidies) to disposable income and subtracts indirect taxes (VAT and excise). With indirect taxes significantly higher than direct taxes (although broadly progressive), and indirect subsidies being both small as well as benefiting mostly richer households, the fiscal system up to this point leads to an increase in poverty 18
observable characteristics measured in a survey.<sup>10</sup> Third, sibling correlation captures broader neighborhood eects (including school and peers) that are not correlated with parental education but shared by the siblings growing up together in the same neighborhood.

An important advantage of sibling correlation as a measure is that it is less susceptible to coresidency bias because we want to capture the factors shared by siblings when growing

> 8For a comparative analysis of these alternatives measures of intergenerational educational mobility with a focus on possible conicts among them, please see Ahsan et al. (2022). They use data from China, India, and Indonesia.

> 9For discussions on this point, see Bjorklund and Salvanes (2011), Bjorklund et al. (2010), Deutscher and Mazumder (2022).

> 10IOP can include variables that represent factors not shared by the siblings. See the discussion by Bjorklund and Jantti (2020).

3