rafmacalaba/data-use-mentions-extended huggingface dataset:
budget surveys. Nonetheless, it is useful to check that the strong performance of the household-level model holds when estimated in simulation settings, as is commonly done in the literature.<sup>36</sup>

# a. Model-based simulation

We begin by conducting a Monte Carlo simulation study, following the basic structure outlined in Das and Chambers (2017). We perform 100 simulations, drawing a new simulated population each time, in 80 areas. Each of these areas contains between 15 and 29 subareas, with the exact number chosen randomly along a uniform distribution. Each subarea contains 15 and 29 households, also chosen randomly from a uniform distribution. Each household is assumed to contain one member.

At the sub-area level, we draw two X variables, X1sa and X2sa, from a joint normal distribution. All households in the same sub-area are assigned the same random values of X1sa and X2sa. X1sa is distributed with mean 0 and variance 2. The mean of X2sa is set equal to 0.05 times the area index, which varies from 1 to 80, with a variance of 2. The covariance of X1sa and X2sa is set to 0.5. We draw area effects (ηa), subarea effects ( sa), and an idiosyncratic error term for each household (εi). These error terms are drawn from normal distributions with mean zero and variances of 1 for the area effect, uu 0.5 for the sub-area effect, and 2 for the household idiosyncratic error. We then generate the population welfare measure as (6) The variance structure of the two X variables and variance components was selected to achieve an yyii = 6 + 0.5 ∗XX1ssaa −0.6 ∗XX2ssaa + ηηaa + uussaa + εεii The variance structure of the two X variables and variance components was selected to achieve an approximate R<sup>2</sup> of 0.25 when regressing household welfare on X1sa and X2sa.

From this population, we set the poverty line at the
sum of employment created by the public and by the private sector. Countries with a large public sector have, in general, a large total employment rate, which can be compatible with a bad business environment. That is because public employment does not respond to any of the business environment institutions we are interested on in this paper.

7 The service employment level comes from the ILO database. It is the sum of the employment in the following sectors: wholesale and retail trade; repair of motor vehicles, motorcycles and personal household goods; hotels and restaurants; transport, storage and communications; financial intermediation; real state, renting and business activities. Hence we do not include employment in public administration and defence, employment in education, health and social work and other community, social and personal services. The reason is that employment in the latter sectors does not come generally from private initiative, which is the focus of the paper. In most cases data are from Labour Force Surveys, which ensures comparability. However, for the following set of countries the data source is official estimates: Azerbaijan, Bulgaria, Kyrgyzstan, and Uzbekistan. The service employment rate is calculated as percentage of working age population (15-64).

8 To control for the economic development we run a regression of the service employment rate against the GDP per capita. The residuals from that regression represent the service employment rate NOT explained by the development of the economy. The residual is what we call the “service employment rate controlled for GDP per capita.” 10
# **Data Appendix**

Detailed data on antidumping, countervailing duties, global safeguards and China‐specific safeguards are available from the World Bank’s _Temporary Trade Barriers Database_ (Bown, 2010a). For antidumping and countervailing duty policies, the data in Bown (2010a) are derived from original government source documents. Each government reports tariff‐line product codes that are subject to the investigations, the dates and countries from whom imports are being investigated, and the decisions of whether to impose preliminary and final trade barriers, as well as when they are removed. The data on use of global safeguards and China‐specific safeguards are derived from both original government source documents and what they report to the WTO’s Committee on Safeguards. Bown (2010a) provides a complete discussion of the data sources, as well as the other information contained in the database that is not utilized in the analysis here.

The tariff line product codes from Bown (2010a) are then matched to bilateral import data at the 6‐digit Harmonized System (HS) product‐level is taken from Comtrade via WITS. The following countries had missing years of import data at the HS‐06 level: Argentina (1990‐1992), China (1990‐1991), Japan (2009), South Korea (2009), USA (1990) and South Africa (1990‐1991). The “value” share measures derived throughout the paper are based on non‐oil import data only.

Because the composition of the European Union changes over the 1990‐2009 period and I am especially interested in recent changes to EU change policy against non‐members, I define the European Union as being made up of the EU‐27 member countries throughout the entire sample. Given that definition, I focus on extra‐EU imports and I drop all EU trade policy actions against other (even eventual) EU member states during the time period. For example, an EU antidumping case against Romania in 2002 would be dropped
the urban labor force by early 2008. They note that this upward trend in formality took place with a simultaneous credit expansion. The ratio of credit to rms rose from 15 percent of GDP in 2003 to 22 percent in 2008. They investigate this credit channel for Brazil using the Rajan-Zingales measure of nancial dependence and a dierence-in-dierences approach applied to household survey data. They nd that formalization rates increased with nancial deepening, especially in sectors where rms are typically more dependent on external nance. They also highlight that the main channel through which formalization took place was through shifts in the formality rate within each rm size category. To a lesser extent, they also nd some evidence that nancial deepening shifted the composition away from self-employment and towards larger rms. Regarding the interpretation of their results, they note that one important implication is that nancial deepening may lead to better resource allocation and hence to higher productivity through 28
revealed the limits of using standardized data sets in seeking to understand the heterogeneity of drought impacts across household types.

We find that consumption is reduced by 10-20 percent in the worst weather observations in our surveys (corresponding to about a 1 in 10-year drought event). We use the estimated vulnerability functions and the full hazard distribution in each household location to calculate the cost drought poses to welfare outcomes. The results indicate that risk is large. Poverty is 1–12 percent higher under the worst weather conditions relative to the best conditions observed in the last 13 years. This amounts to an increase in the total national poverty gap that ranges from US$4 million to US$2.4 billion (2011 PPP) across countries. Estimates are more precise for countries with a large share of production in maize, highland and pastoral areas. Potential biases in estimating impacts mean this is likely an underestimate of the true cost.

The cost of weather shocks on welfare provides an indication of the size of the increased need that would need to be met by adaptive social protection systems or access to financial services. The cost of weather shocks on consumption could be reduced by investments in adaptation policies such as soil management and tree cover or by other adaptive farming techniques. Investments in development that enable households to switch into less rainfall-dependent economic activities, either when droughts occur or as part of broader structural transformation, would also reduce the welfare cost of drought. Further work is needed to estimate how different investments would reduce the welfare cost of drought. This type of work is needed to help prioritize investments.

Statistical analyses that rely only on observed historical data are generally limited by their short observation periods. In the context of drought risk, patterns and severity of future drought events might differ from what has been observed historically. Stochastic data sets provide a synthetic view of all events that could occur in a given year, together with their likelihood of occurring. These data sets typically allow for a more reliable analysis of drought risk, especially for events
<mark>developing and maintaining content as well as delivering this content on a timely basis at a large scale. These transaction costs are especially high in regions where agricultural productivity is low because low productivity is more likely to be associated with low overall economic development. In less developed regions with small-scale farm structures, poor public infrastructure, and insufficient human capital investments, there are higher transaction costs of providing relevant advice and services. As a result, low supply of agricultural advice and warning leads to a vicious cycle of inefficient production. In a more general framework, Lio and Liu (2006) investigate the impacts of information and communication technology on agricultural productivity using panel data for 81 countries during the period 1995-2000. They show gains in using low cost tools for improving the efficiency of delivering agricultural production advice, but caution that the returns from ICT in agricultural production of the richer countries are about two times higher than those of the poorer countries.</mark>

# **5. Enabling efficient logistics**

<mark>Digital technologies also improve agricultural supply chain management. With globalized food systems, ensuring food safety has become more complex. These trends have catalyzed innovations to trace the food supply from producer to the consumer, important for developing countries that want to reach new export markets. Smallholder farms turn to cooperatives and aggregators who use digital tools to improve collection, transportation, and quality control. By opening up new specialized market opportunities, the internet improved consumer protection and farmers’ livelihoods. Cunden and van Heck (2004) have argued that digital technologies have not only extended traditional market mechanisms into the electronic realm, but have also done so in areas where there are limited physical markets for buyers and sellers to gather in one place to begin with. Similarly, Site and Salucci (2006) argue that such technologies are especially useful in deep rural areas because of poor public infrastructure to facilitate rural farmers’ access to local and global markets.</mark> <mark>Transporting produce often requires coordination between
ratio is less volatile than the debt to GDP ratio. While both denominator and numerator are measured in the US dollar for the debt to exports ratio, GDP data are basically on a local currency basis and then converted to the US dollar for the debt to GDP ratio. In this sense, the debt to GDP ratio may fluctuate more than the debt to exports ratio when exchange rates are unstable. In order to verify whether the debt to exports ratio is less volatile than the debt to GDP ratio, the standard deviation of two indicators of fifteen countries<sup>(8)</sup> are calculated between Q1 1980 and Q4 2002 depending on data availability. In eleven countries out of fifteen, the debt to GDP ratio is more volatile than the debt to exports ratio. Although arrears are one of the key indicators of debt burdens (Non-Aligned Movement Ad Hoc Advisory Group of Experts on Debt (1994), Detragiache and Spilimbergo (2001)), data on arrears are difficult to obtain on a quarterly basis. **2.3.5 Growth Rate Crises** Conceptual and operational definitions of growth rate crises are rarely found in various studies, although as a rule of thumb NBER (National Bureau of Economic Research) defines a US recession when seasonally adjusted quarterly growth rates are negative for two straight quarters. Bruno (1996, p.1) defines growth crises as “at least three consecutive years of negative growth (cumulating in a drop in growth of at least nine percent).” Here, conceptually growth rate crises can be defined as a sharp decline in real outputs. Operationally real economic growth rates on a year-on-year basis can be used as an indicator, since seasonally adjusted figures are not often available in emerging economies.

**2.3.6 Financial Crises**

There is no consensus on conceptual and operational definitions of financial crises. However from various studies, the essence of financial crises is ‘an abrupt loss of confidence in a country’s financial sector’
of fighting, which in turn can affect societal, demographic and household-level norms around women’s decision-making.

Our preliminary results find an increase in intrahousehold women’s decision-making since the conflict started in areas under the control of the International Recognized Government in Yemen. Using a difference-in-difference approach that controls for individual and household characteristics, we find that this result is driven by households living in districts with medium intensity conflict as compared to low intensity conflict. This result holds up to a series of robustness checks, and could be explained by changes in household composition, whereby men are more likely to leave the household in conflict affected districts, leaving women in charge of household decisions.

# II. Context

Yemen is the poorest country in the Middle East and has been facing systematic deprivations in key dimensions of human wellbeing over decades. According to the World Bank’s Human Capital Index (2018), a person born today in Yemen can expect to achieve only 37% of their full productive potential by the time they reach the age of 18, underlining the large scale of unfulfilled potential due to poverty, and low investments in good quality nutrition, health and education services. The vulnerabilities in terms of economic wellbeing and development have been driven and exacerbated by political instability. After the unification of South and North Yemen in 1990, the country was on a path to relatively stable economic and political consolidation for a few decades.

In the 2010s resentment over corruption, unjust representation and rising food prices built up in the Middle East, culminating in the Arab Spring. Yemen was no exception, as years of corruption and the dwindling oil reserves were not enough to maintain the peace in such a fragmented society. Protests began in Sana’a in 2011 and reached a turning point in 2014 when the government removed a series of subsidies. Houthi rebels had been engaged in military confrontation in the North since 2004, but seized the opportunity to make unprecedented territorial gains, forcing the government from Sana’a to Aden. This was followed by Saudi
--|---|
|Gini index|21433|0.256|0.200|0.0|0.940|
|Mean logarithmic deviation|21433|0.271|0.334|0.0|4.353|
|Theil index|21433|0.247|0.331|0.0|7.289|
|Sen index|21433|0.072|0.778|0.0|43.545|
|Std dev. rainfall (mm)|26014|4.538|4.617|0.0|88.428|
|Std dev. NDVI|26014|0.826|1.532|0.0|7.838|
|Log(Population)|26014|11.173|1.425|3.651|15.931|
|Log(Constant prices)|23745|1.051|3.239|0.0|17.424|
|Log(Average prices)|23745|1.078|3.301|0.0|17.767|

Notes: (1) The table shows descriptive statistics across a sample of 2,247 districts across 39 countries in Africa during 2001– 2012. (2) Inequality indicators are at the district level and measured using night-time light intensity. (3) The number of observations varies because some districts have observations with zero light intensity. (4) Constant (measured at constant 1998 US dollars) and average (measured as annual averages) prices come from USGS. (5) NDVI stands for Normalized Difference Vegetation Index, a proxy for agricultural productivity.

Source: authors’ calculations.

Table 5: The effects of mineral deposits’ and mining sites’ presence on lights Gini index Dependent variable: district lights Gini index

||No|prices|Constant|mineral prices|Average|mineral prices|
|---|---|--
data are shown to capture macroeconomic performance (GDP), even in low- and middle-income countries (Narita and Yin, 2018). Google search data and relevant queries are shown to realistically capture actual unemployment trends (e.g., Pavlicek and Kristoufek, 2015). Others have demonstrated the potential of Google search indices to measure actual consumption spending, sometimes outperforming common survey-based indicators (e.g., Vosen et al., 2011). Some recent studies have already validated some of the search queries we are employing in our study, those related to air transport and tourism services (e.g., Rivera, 2016; Havranek and Zeynalov, 2019). Table B.1 in Appendix B provides a list of selected studies that use Google search data to investigate various topics.

Nonetheless, we conduct alternative validation exercises to show that Google search data are indeed strong predictor of consumer demand for the services that we study in this paper. Because high-frequency data are not available for all services and countries covered in this study, we focus on countries and services for which relevant data are publicly available. The first dataset is the daily number of passengers screened at the Transportation Security Administration (TSA) checkpoints in the U.S. from March to May 2019 and the same period in 2020.<sup>8</sup> Figure 3 compares the number of passengers arriving in the U.S. with the Google search index for the term “airport” for these months. The top panel presents the daily volume of passengers and Google search intensity, and the bottom panel shows the growth rate of the number of passengers and Google search intensity. From visual inspection, the Google search index strongly traces out both the actual number of passengers and the variability in the volume of passengers for both years. We also run some linear regressions (Table B.2 in Appendix B) and find that the Google search indices associated with our search terms, “flight” and “airport,”
majority of the variation is between district, not within districts (unlike, for example, Breza and Kinnan (2021)). This method has also been applied in the Indian context by Mukherjee (2020) who uses REDS district-level data to look at the link between credit markets and technology adoption, and includes a host of demographic, economic and soil controls, and Viswanathan and Kumar (2015) who connects weather with migration using district-level data, and includes controls for the agro-ecological zones.

The descriptive statistics of all variables are presented in Appendix Table A3.

# **IV specification**

While the number of control variables is extensive, we might have missed some remaining time-variant connections between land and labor markets. Unobserved shocks to local agro-industries, for example, labor strikes, accidents, and road construction, can both affect emigration (through wages and lack of jobs which might push migrants out) and land tenure (through changes in input markets and available agricultural technologies).

To make further headway in the identification strategy, we distinguish between migrant push factors versus migrant pull factors. Push factors relate to the conditions in the source district and “push” migrants out. Pull factors relate to conditions in the destination district and “pull” migrants in. Most concerns regarding identification of the effects of out-migration relate to push factors. Hence, we instrument for emigration with measures of pull factors that influence emigration but are plausibly exogenous to local land and labor market conditions.

This requires a measure of a pull force which varies across districts and time. We arrive at this measure using a shift-share approach which exploits differences in migrant networks across districts as measured in our bilateral migration data. This is an adaptation of an approach used in studies of the impact of international immigration on local labor markets (Card, 2001; Peri, Shih and Sparber, 2015). Our instrument for migration is composed of two 25
*Company 3**||||
|**2008**|Yes|Yes|No|Yes|Active|Data|
|**2009**|Yes|Yes|Yes|No|Active|issues|
|**2010**|Yes|No|Yes|No|Active||
|**2011**|No|No|No|No|Inactive||
|**2012**|Yes|No|No|No|Active||

_<u>Note:</u>_ OPEX = operational expenditure; PAYE = pay as you earn taxes; VAT = value‐ added tax.

**Third** , the status of economic activity derived in step 2 is then merged with the RDB registration database. Of the 29,180 companies registered in the period to 2012, 58 percent can be matched with tax files, thereby indicating an activity status. The remaining 42 percent are classified as “inactive.” To confirm that the lack of activity is the result of poor communication between the RDB and the RRA, the RDB registration database is matched with the RRA’s master list of all taxpayers. The match is excellent.  Of all registered companies with the RDB, only 43 are not found on the RRA’s master list.

37
for each of the surveys k=1,2,:3.9 Inserting this into Equation (3), the variance of the aggregate governance indicator based on this hypothetical data will be the same for each country and is equal to V[g(j)l y ( = 1 +2 . To give an idea of the magnitude of the corresponding 90% confidence intervals, we superimpose them on the hypothetical distribution of governance in the upper panel of Figure 3, for various values of p. As p increases, the confidence intervals become shorter. However, for the correlations of around 0.75 typically observed in our governance data, this confidence interval remains large relative to the units in which govemance is measured.

In the lower panel of Figure 3, we relate this to the significance of cross-country comparisons. A simple summary statistic is the proportion of countries whose true level of governance lies within the 90% confidence interval of a particular country. This proportion will depend on the location of the reference country, and on the correlations in the observed data. We plot tliis proportion for the median country and the country at the first quartile of the distribution of govemance, for various values of p. For the observed correlation of indicators of around 0.75, the 90% confidence interval around the point estimate of govemance for the median country encompasses the true level of governance in about half of all other countries in the world, and somewhat less for a country at the first quartile. Only if the observed correlations are very large is it possible to distinguish the median country from most other countries with a high degree of confidence.

> 9 To see this, it is only necessary to solve the system of nine equations relating the three sample means and the six unique elements of the sample covariance matrix of the indicators to their population counterparts and solve for the unknown parameters.

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