rafmacalaba/data-use-mentions-extended huggingface dataset:
# Mij = β0 + β1ln(Pi) + β2ln(Pj) + β3ln(Dij) + β4ln(Xi) + β5ln(Xj) + εij       (4)

**This approach solves two fundamental problems of estimating gravity models using OLS.** First, the loglinearization of the dependent variable truncates the sample in the absence of migrants for certain specific origin‐destination combinations (which is common in large and sparse gross migration matrices or when dividing the sample by subpopulation groups). Second, under heteroskedasticity, the parameters of loglinearized models estimated by OLS lead to biased estimates of the true elasticities.

**The model specification includes the basic gravity variables: population sizes and distance.** The population sizes in origin and host states are expected to have a positive effect. On the one hand, the more people in the source state, the more people are likely to migrate. On the other hand, the larger the population in the host state, the larger the labor market for immigrants. The distance is considered a fundamental explanatory variable that proxies the migration costs. The longer the distance, the lower the incentive to migrate due to higher migration costs. In this study, the distance between a state pair is calculated as the approximate distance in kilometers between the most populated cities of each state. The data come from the “Catalog of Geographic Metadata" of the National Commission for the Knowledge and Use of Biodiversity (CONABIO),<sup>8</sup> which provides information on the latitude and longitude coordinates of each municipality in Mexico.

**Following the literature on spatial determinants of migration flows, economic variables, labor market variables, and environmental variables are used as explanatory variables for describing different features of the origin and destination (push and pull factors).** The most representative and common economic variable used is the per capita gross domestic product (GDP).<sup>9</sup> In terms of labor market variables, both employment rate and
an area benefited from RSDP. Yet, many areas benefited from RSDP road improvements in multiple years. Long-difference approaches alleviate this concern by examining aggregate changes in the road network, but they require stronger assumptions for causal interpretations to be valid. Consequently, we emphasize findings that appear consistent across the multiple approaches.

Ethiopia’s RSDP is now in its fifth phase, with each phase focusing on different priorities. The first three phases (1997–2010) focused on improving trunk roads and regional roads, while the fourth and fifth phases (2010–2020) focused on rural roads to support the development of commercial agriculture. The vast majority of upgrades took place in the fourth and fifth phases (Figure 1). Using data on road upgrades from 1997 to 2016, our analysis focuses on RSDP phases I-IV. Aggregating across phases, we find that road upgrades contributed to increases in local economic activity (proxied by nighttime lights) and urbanization. However, the aggregate results mask heterogeneity in results across baseline levels of economic activity. Specifically, while areas with moderate to high initial economic activity benefited the most from the upgrades, we have mixed evidence for areas with low initial economic activity. The differences-in-differences and long-difference models show that these areas either did not benefit or only saw small gains from upgrades, and market access results show these areas either did not benefit or even saw a reduction in outcomes due to upgrades. Lastly, we find that road upgrades contributed to a reduction in cropland in areas with moderate to high initial economic activity; these cropland areas largely transitioned to urban land.

We contribute to multiple strands of the literature. First, we contribute to a young literature on the effects of transportation infrastructure on economic development in Ethiopia. Several recent papers have examined the effect of Ethiopia’s RSDP on firm-level outcomes. For example, Fiorini et al. (2021) focus on the role of road infrastructure on the effect of trade. Using the Ethiopia manufacturing census from 1998 to 2009 to construct measures of firm productivity,
|2821|2821|2821|

_Notes_ : SL equals sanitation loan arm. Standard errors clustered at the village level are shown in round parentheses. *, **, *** indicate significance at the 10, 5 and 1 percent level, referring to Romano-Wolf p-values. These are obtained from a step-down procedure to adjust for multiple hypothesis testing performing 1000 bootstrap replications as proposed by Romano and Wolf (2005). Covariates: Toilet ownership at baseline, indicator for presence of a child aged 0 - 2 at baseline, ratio of number of sampled clients to village size. HH stands for household. Functioning toilet is defined as toilet that is either in use (household report), or is not in use because of household preferences or a lack of water. Dependent variable in Column 4 is defined on household reports related to functioning of toilets observed by interviewers. Data source: household survey.

correlated variables into a set of linearly uncorrelated variables in a manner such that the largest principal components explain the largest possible variance among the original variables. The analysis yields one component for underground quality and two for overground quality. A detailed description of the approach we used for the construction of these composite measures is provided in Online AppendixD.<sup>44</sup> Table 6 displays the findings on these dimensions. The upper panel shows impacts on the overall sample and the lower panel shows impacts separately by whether or not the household had a toilet at baseline. The latter would reveal whether households with toilets at baseline upgraded them, or whether households building new toilets after baseline were building higher quality toilets as a result of the sanitation loan program. When we conduct inference using cluster-robust standard errors, the results suggest that the provision of sanitation loans led to an improvement in overground quality, potentially affecting different overground components for both new and existing toilets. However, these effects are not robust to adjustments for multiple hypothesis testing, as can be seen from the p-values calculated using the step-down procedure of Romano and Wolf [2005
# Malawi’s Infrastructure: A Continental Perspective

VIVIEN FOSTER AND MARIA SHKARATAN

## **Acknowledgments**

This paper draws upon a wide range of contributions from sector specialists from the Africa Infrastructure Country Diagnostic Team; notably, Dick Bullock on railways, Mike Mundy on ports, Heinrich Bofinger on air transport, Maria Shkaratan on power, Elvira Morella on water and sanitation, Michael Minges on information and communication technologies, Nataliya Pushak on public expenditure, and Alvaro Federico Barra on spatial analysis.

The paper is based on data collected by local consultants and benefited greatly from feedback provided by colleagues in the relevant World Bank country teams; notably, Tim Gilbo (country manager), Supee Teravaninthorn (transport), Rob Mills (power), Midori Makino (water and sanitation), and Mark Williams (ICT).
Our final test explores the variation in bank credit growth across years and countries in our sample.<sup>14</sup> To test the effect of bank credit growth on trade credit behavior before, during and after the crisis, we use the following model: Where CREDITGR is the country-year growth rate in the private credit to GDP ratio (obtained from the IFS). The coefficients β 3, β 4 and β 5 show the reaction of trade credit to bank credit growth during the pre-crisis, crisis and post-crisis respectively. As before, we allow for the same set of control variables for robustness checks. Finding positive coefficients on β 3, β 4 and β 5 would suggest that increase in bank credit leads to more trade credit provided and/or received by firms in our sample, consistent with the redistribution story.

# **IV. Results**

# **IV.1. Aggregate patterns**

We present our main results using the unbalanced sample.<sup>15</sup> All tables show the first three regressions without control variables and the following three including the set of time varying control variables.

Table 2 presents our basic results. The coefficients on the crisis and post-crisis dummies show the difference in trade credit during these stages relative to the pre-crisis

> 14 Since we only have 6 countries and at most 7 years, we are concerned about the degrees of freedom. Therefore these results should be interpreted with caution.

> 15 Results for the “balanced 5” and “balanced 7” samples are very similar and are available on request.

13
Within the T3 hybrid set of economic and care subcategories, the following disaggregations were created and located within the post-fiscal income and geographic distributions (Table 4). Two of the most common subcategories in the general population are households where male earnings account for more than 60 percent of all earnings, and households in these two subcategories are more often urban and in the top three quintiles of the income distribution (rather than in the bottom two quintiles). The most common subcategory of households where females account for greater than 60 percent of all earnings are those households with no young- or old-age dependents; this subcategory is also far more likely to be urban-based and far less likely to belong to the bottom two quintiles of the income distribution.

Table 4. Household Types Based on Armenia ILCS 2020 Demographics + Income (proxy for intrahousehold roles and bargaining power) + Presence of children and elderly (proxy for care needs and domestic responsibilities)

|Subcategory T4||Population<br>Share (%)|Bottom 40<br>(%)|Top 60<br>(%)|Rural<br>(%)|
|---|---|---|---|---|---|
|Dependents|Female (>60% earnings)|1.2|37|63|46|
|+ Elderly|Male (> 60% earnings)|3.4|51|49|44|
||No labor earnings|1.5|61|39|54|
|Dependents,|Female (>60% earnings)|3.9|46|54|43|
|No elderly|Male (> 60% earnings)|19.7|45|55|46|
||No labor earnings|5.6|59|55|46|
|No dependents,|Female (>60% earnings)|4.4|35|65|29|
|
- Table 3.1: Profit elements covered by alternative regulatory regimes

- Table 3.2: An example of sliding-scale regulation

- Table 4.1: Abolition of government controls in UK utility sectors

- Table 6.1: Daily asset beta estimates for AT&T against alternative indices

- Table 6.2: Average beta values by country and sector

- Table 6.3: Summary of regulatory regimes

- Table 6.4: Average asset beta values by regulatory regime and sector

- Table 6.5: Average asset beta values across regulatory regimes

- Table Al .1: Bias in beta estimation due to non-synchronous trading

- Table A 1.2: Bias in beta estimation due to price adjustment lags

- Table A1.3: Bias in beta estimation due to investment horizon length

- Table A1.4: Data on debt betas

- Table A1.5: Asset beta estimates

- Table A1.6: Impact on asset beta estimate

- Table A3.1: US gas companies

- Table A3.2: US gas companies' average beta

- Table A3.3: US water companies

- Table A3.4: UK RECs

- Table A3.5: UK RECs' average beta

- Table A3.6: UK gas and telecommunications companies

- Table A4. 1: Confidence intervals for five-year beta estimates calculated from data with

- different interval lengths

- Table A4.2: Confidence intervals for daily beta estimates for BT calculated over periods of different lengths
)|(0.0866)|(0.106)||(0.0507)**|(0.0511)**|(0.0603)***|
|IDN exports per worker *||||0.00916||||0.109||||-0.197|
|Share of hhlds with computer||||(0.181)||||(0.177)||||(0.120)*|
|Observations|195|195|195|195|195|195|195|195|195|195|195|195|
|R-squared|0.941|0.942|0.942|0.942|0.962|0.962|0.962|0.962|0.934|0.935|0.936|0.936|

Source: Authors’ calculations based on Sakernas 2005-2010 and 2011-2015, Susenas 2005-2010, 2015-2016 Philippines STEP survey, and 2011-2015, and UN Comtrade database 2005-2010.

Notes: IV regressions. IV: Global demand for foreign products excluding Indonesian products. Indonesian exports per worker in thousands of USD at 2010 prices and global demand for foreign products in millions of USD at 2010 prices. ICT variable at the province level is the share of households with computer. Province controls include the age structure of the population, share of urban population, real GDP per capita, minimum wage level, gender and educational composition of employment. All models control for province and year fixed effects. Robust standard errors between parentheses clustered by province. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively.

28
decreases at around 7% between the 3<sup>_rd_</sup> and 6<sup>_th_</sup> deciles before markedly declining to approximately 2.5% at the top of the distribution. This is broadly consistent with the notion that, in relative terms, the poorest are more vulnerable to climate variability. We replicate the analysis on the specification including a regional fixed-effects interaction and leave results in appendix C.4 for the sake of conciseness. It is worth noting that, for some regions, we find that the OLS estimate differs quite significantly from marginal effects estimated in the percentile interval of interest. In our final prediction model calibration, we partially address this issue by allowing for distinct vulnerability functions across household groups within regions, defined based on characteristics that also capture differences in food consumption ceteris paribus. Also, we estimate our final model with quantile regressions at different percentile values and choose the value that minimizes prediction errors at the regional level.

> 35We estimate moderate food insecurity rates at the national-level from FSMS for the period 20112015 and we find a minimum of 28.8% in 2013 and a maximum of 41.9% in 2012 as a result of the 2011 drought. The 25<sup>_th_</sup> -45<sup>_th_</sup> percentile interval considered is thus a conservative interval.

27
2009. “A Concordance Between Ten-Digit U.S. Harmonized System Codes and SIC/NAICS Product Classes and Industries”, NBER Working Paper No. 15548. Portugal-Perez, Alberto, Jos ́e-Daniel Reyes and John S. Wilson. 2010. “Beyond the Information Technology Agreement: Harmonization of Standards and Trade in Electronics.”, The World Economy, Vol. 33, Issue 12, pp. 1870-1897, December.

Reyes, Jos ́e-Daniel. 2011. “The Pro-Competitive Effect of International Harmonization of Product Standards.”, mimeo. The World Bank.

Swann, G. P. (2010), International Standards and Trade: A Review of the Empirical Literature, OECD Trade Policy Working Papers, No. 97, OECD Publishing.

Shepherd, Ben. 2006. “The EU Standards Database: Overview and User Guide.”, mimeo, available at http://go.worldbank.org/6OEYNCYSD0 Shepherd, Ben. 2007. “Product Standards, Harmonization, and Trade: Evidence from the Extensive Margin”, Policy Research Working Paper No. 4390, The World Bank.

Swann, G. P. 2010. “International Standards and Trade: A Review of the Empirical Literature”, OECD Trade Policy. Working Papers, No. 97, OECD Publishing.

20
unsurprising to the extent that the news literature uses utilization-adjusted technology rather than labor productivity, which aims to remove some business cycle variation from the data. In addition, the maximized shock is considered orthogonal from period 1 technology innovations.<sup>17</sup> A second key difference is that our shock is not orthogonalized to contemporaneous innovations in productivity.

# **6 Applications to Emerging Markets**

In the United States, data is of relatively good quality, and volatility is relatively low. In emerging markets and developing economies (EMDEs), macroeconomic variables of interest are more volatile, and also often considered to be subject to larger business cycle fluctuations than advanced economies (Neumeyer and Perri (2005), Garc ́ıa-Cicco et al. (2010)). EMDEs are therefore ideal candidates to demonstrate differences across the proposed SVAR identifications.

EMDE data availability is limited. Few EMDEs publish quarterly data on hoursworked or employment before 2000. Instead, we prioritize data _span_ over data frequency, given our identifications focus on the persistence of shocks. Using annual data from the World Bank’s World Development Indicators (WDI), The Conference Board, and the Penn World Table 9.1, we estimate four-variable VARs consisting of productivity (output per employee), employment, the share of consumption in output, and the share of investment in output.

For illustrative purposes, we show results for three major EMDEs below, with time horizons restricted by data availability.

- Brazil - 1988-2017

- Indonesia - 1961-2017

- South Africa - 1961-2017

The first key finding is that the technology shock in the Max-Share identification always captures the largest proportion of forecast error variance in these EMDEs, particularly in the early stages of the estimation (Figure 13). This is consistent with previous simulation results showing that volatile but less persistent shocks can bias the results of the Max-Share identification. Both the Spectral and NAMS approaches are less influenced by these shocks and demonstrate a lower share of explained forecast-error variance initially (note that ’periods’ are
# **1. Introduction**

Gender inequality has been at the core of the policy debate concerning development for the past few decades. This policy concern has been matched by an equal level of scholarly interest, which has produced a large body of research intended to show that reducing gender inequality leads to development for individual women and for women in general. This evidence has been used to provide support for inequality-reducing policies as a valid and effective tool to directly and indirectly promote development.

In this paper, we review the existing evidence from cross-country studies of inequalityreducing policies to assess whether and how this evidence can be used to inform policy. Our analysis shows that, although it is helpful to identify and understand aggregate patterns, evidence from cross-country studies is of limited use for policy design.

Cross-country research designs have three features that limit their value and use for policy implications. First, the direction of causality has not been identified. For example, the fact that the gender gap in education is lower in richer countries is consistent with both equality fostering development and development fostering equality. Distinguishing between these alternative interpretations is central to effective policy because policies that promote gender equality do not foster development unless causality runs from equality to development rather than vice versa.

Second, cross-country studies are typically silent on the mechanisms that drive or affect the relationship between gender inequality and development, and identifying these mechanisms is critical for the design of more effective policies. For instance, whether a policy that promotes girls’ schooling (e.g., with conditional cash transfers) directly fosters development through an increase in women’s labor force participation depends on the reason girls’ education was at a low level in the first place. If this situation is driven by social norms that limit women’s participation in the labor market and hence also limit or reduce girls’ return to schooling, exogenously increasing girls’ return to schooling will not increase labor market participation and development or economic growth. An understanding of the values, social norms, and other
it does not imply that the impacts will be equally distributed among the population. To analyze how climate change will affect specific sectors of the population one needs to use household-level data and explicitly model the channels through which future warming will affect economic activity.

# **3. INTRODUCING HETEROGENEITY: MICROECONOMIC APPROACH**

The discussion so far has relied on the evidence emerging from the relationship between climate (temperature and precipitation) and growth or the level of GDP in a cross section or a panel of countries or municipalities within selected countries. While informative, these studies do not shed any light on the channels through which climate change can impact household welfare. For example, climate change may have a negative effect on agricultural productivity and also affect poor people’s livelihood through its effects on health, access to water and natural resources, and infrastructure. Considering the complexities involved in modeling some of these channels, the literature has largely focused on the impacts on agricultural output. This section 10
Unfortunately, the input-output data for the Central American economies are not usually available. Barring Costa Rica, for which the input-output tables are available in the OECD database (OECD, 2017), I couldn’t find such data for the other countries. To overcome this challenge, I use the average intermediate shares from eight regional countries (including Costa Rica). In the data appendix, I show that these shares of regional economies lie within close range. Therefore, their average provides a reasonable benchmark for the Central American countries.

Table 1 reports the country-wise coverage of cleaned data obtained from various sources. Column (6) shows the effective period of analysis for each country, which corresponds to the overlapping years in columns (2)–(5).

||**T**|**able 1: Countr**<br>**Value**|**y-wise time co**<br>**Trade**|**verage **|**Effective**|
|---|---|---|---|---|---|
|**Series**|**Employment**<br>**shares**|**added**<br>**shares**|**shares**<br>**(aggregate)**|**Sectoral trade**<br>**shares**|**period**<br>**of coverage **|
|||||**Atlas of**||
|**Source**|**ILO**|**UNSD**|**UNSD**|**economic**<br>**complexity**||
|(1)|(2)|(3)|(4)|(5)|(6)|
|Costa
for different setups in terms of the number of food items, markets and the temporal dimension of the survey data. Section IV concludes on the viability of the methods and the potential use of machine-learning augmented high frequency surveys in the Pacific Islands.

# **II. Methods**

# _A. Imputation strategy at a high level_

Table 1 visualizes the general missing data problem when gathering price data for the use of tracking inflation. In particular, the example considers tracking the basket-price in a simple three item setting, showing that even when there is a reasonable amount of price data, it may not be possible to observe the change in basket price at any given moment. The overall idea behind the suggested solution is to fill price gaps by leveraging prices of the same item in different markets or of other items in the same market.

Completing the missing entries is challenging using standard imputation tools. For instance, carrying the last observation forward suggests zero change and so results in a major bias in an application that aims to monitor inflation (price change), particularly in high inflationary environments. This is an issue when