rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Online Appendix**

## **OA.1 Construction of the Market Access Index**

Our Market Access measure is calculated as follows: where, MAit is market access of District _i_ at time _t_

- ttijt is the travel time (in hours) between Districts _i_ and _j_ at time _t_

Pjt _=_ population in destination District _j_ at time _t_ θ= trade elasticity To construct the Market Access measures, population and travel times were aligned as follows:

|MA|Travel Time|Population|
|---|---|---|
|MA1988|1988|**1991**|
|**MA1996**|**1996**||
|MA2004|2004|2001|

Note that for the main analysis, we focus on Market Access calculated using travel time from 1996 and population from 1991. We explore alternative travel times (1988, and 2004) as robustness checks. Following Allen and Atkin (2016) we use trade elasticity equal to 1.5, and report results using trade elasticity equal to 3.8 as well.

## **OA.2 Calculation of the GQ-based instruments**

Our identification strategy relies on two main sets of instrumental variables: one inspired by India’s Golden Quadrilateral (GQ) highways and the other by its colonial railways in the 1880s. Both are calculated using geospatial software. Here, we describe the calculation of the first instrumental variable.

For the first GQ IV, we construct a hypothetical linear network connecting the main cities targeted by the GQ project (see Figure 2). The target cities of the GQ include: New Delhi, Kolkata, Chennai, Mumbai and Bangalore. We construct two networks: one based on Euclidean distance and another “least cost path” derived from elevation and slope data. Our main results rely on the linear, Euclidean distance instrument but are robust to using the least-cost path instrument instead. It is also important to note that though the
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# **Table A1:**

|**Variable**|**Defnition**|
|---|---|
|_Household characteristics_||
|Household size|Number of family members living in the household as reported by respondent.|
|Household head is female (1=Yes)|Dummy equal to 1 if the gender of the household head is female.|
|Education of head of household (years)|Number of years of complete education by the household head.|
|Satisfed with ... (1=Yes)|Measures based on the response to the following question: “How satisfed are<br>you with... (i) the quality of your foor; (ii) the quality of your walls; (iii) the<br>quality of your roof; and (iv) the quality of your family life. Would you say you<br>are “Very Unsatisfed”, “Unsatisfed”, “Neither satisfed nor unsatisfed”, “Satis-<br>fed”, or “Very satisfed?" Each measure is a dummy variable that equals 1 if the<br>respondent reports that being “Satisfed” or “Very satisfed” and 0 otherwise.|
|Quality of Life Index (ICV)|Index computed by SIUBEN to assess the poverty status of a given household<br>based on the following characteristics: availability of electricity and water, waste<br>management, fuel used, hosueholds goods, type of house, household head edu-<br>cation, schooling of children, type of foor, walls and roof, type of toilet used and<br>household size divided by house size. The ICV ranges from 1 (lowest quality of<br>life) to 4 (highest).|
|Hours at home per day|<br>Number of hours that the head of the household reported staying at home on a<br>
# **Appendix S1. Alternative price indices**

Food prices can be measured in different ways. The analysis in this paper uses the consumer food price index relative to the total price index to measure changes in real food prices. Alternative aggregated price indices include the FAO producer price index for all agricultural commodities or all cereals, or the agricultural GDP deflator. All three indices uses production data to weight the indices, and all are deflated using the total GDP deflator to capture real price changes. All are also available for a reasonably large array of countries, although the FAO producer price indices are only available after 1990, and are not available for all countries. These series are also widely regarded as being prone to substantial measurement error, and the same is likely true of the agricultural GDP deflator, since national accounting systems are quite poor in many African countries in particular. Finally, international price indices were also available to us. We constructed country-specific indices of international prices based on weighting prices for nine food staples by domestic consumption patterns, as measured by the share of calories obtained from each of these food items. Since international prices are exogenous, we use this price series as an instrument in most of our regressions.

Table A1 shows a correlation matrix of these various price indices, all measured in real terms using either the total CPI or the total GDP deflator to convert the nominal indices into real price indices. Strikingly, the correlation between the domestic price indicators is often quite weak. The correlation between the real consumer food price index and the agricultural GDP deflator is moderately strong (0.47), but the FAO agricultural and cereal price indices are quite weakly correlated with both consumer prices and the agricultural GDP deflator, suggesting these FAO series may include substantial measurement error. Perhaps most pertinent – at least in terms of our instrumentation strategy – is that it is the consumer food price index which shares the strongest correlation with the two international prices indices (0.31-0.34). This suggests that consumer
|[-6.65]|[-0.59]|
|Observations|28,017|28,152|27,987|30,057|30,057|
|R-squared|0.46|0.42|0.41|0.42|0.45|

Notes: *** p<0.01, ** p<0.05, * p<0.1. Robust standard errors are clustered at the DHS cluster level. All regresssions employ cluster x month fixed effects. The weights used are person weights adjusted for the national populations. All regressions use all available data on rural households from 5 Southern African countries (Mozambique, Malawi, Tanzania, Zambia and Zimbabwe). Additional controls are for roof, floor and wall quality, cooking fuel, employment in agriculture, source of water, type of toilet, ownership of radio, tv, bicyles, refridgerators, cars, trucks, birth order, birth interval, gender of child and the gender of the household head. All metrics are based on children being measured below -2 standard deviation thresholds. 41.0 percent of the children are stunted, 15.1 percent are underweight, 5.1 percent are wasted, 12.6 percent are stunted and underweight, and 45.2 percent are malnourished based on any of the three metrics.

25
Income is estimated directly from the data sets for Bangladesh, Colombia and Costa Rica; it is proxied using consumption in all others. When deriving the income proxy the team paid special attention to how gifts are recorded. In certain surveys, this was in the consumption module itself; in others, it was collected through a special “assistance” module. Another area of attention was the value attached to shelter (a gift for many refugees and as such deducted from income).

The value of essential needs is reflected by the global poverty lines for low-income countries (LICs), lowermiddle-income countries (LMICs) and upper-middle-income countries UMICs. These are respectively PPP$ 2.15, PPP$ 3.65 and PPP$ 6.85. To convert 2017 PPP dollars into current US dollars, an exchange rate of 1.23 current US dollars for every PPP dollar is used (January 2024). Each country’s income classification is based on World Bank data as per the end of 2022.

**<mark>Table 1: Microdata sets used to estmate refugee income poverty gap</mark>**

|**Economy**|**Survey Name**|**Income**<br>**measured as**|**Poverty**<br>**line**<br>**used**|**Survey**<br>**Year**|
|---|---|---|---|---|
|Bangladesh (*)|Cox’s Bazar Panel Survey|Income|3.65|2023|
|Chad|4<sup>th</sup>Natonal Harmonized Survey on Households’<br>Consumpton and Informal Sector|Consumpton|2.15|2018|
|Colombia|Gran Encuesta Integrada de Hogares (GEIH) Survey ;<br>Encuesta Pulso de la Migracion|Income|6.85|2021
|793|0.31|0.31|0.31|765|0.32|0.32|0.32|
|_Other Variables_|||||||||
|Monthly profits in October/November 2008|729|124|133|104|704|93|129|99|
|Monthly sales in January 2009|790|724|463|630|762|412|402|595|
|Number of hours worked in last week|785|58.82|60.55|57.13|757|59.03|60.64|56.64|
|Total Capital Stock in January 2009|784|468|454|418|757|446|438|410|
|Inventories at end of January 2009|791|258|213|201|763|239|203|198|
|Uses a Susu Collector|791|0.49|0.46|0.49|763|0.49|0.46|0.51|
|Business operated out of home|793|0.76|0.78|0.82|765|0.77|0.78|0.83|
|Age of Firm|788|7.87|7.13|7.22|761|7.88|7.11|7.14|
|Ever had bank or microfinance loan|793|0.11|0.10|0.07|765|0.10|0.09|0.07|
|Business has a tax number|786|0.15|0.14|0.13|758|0.14|0.14|0.13|
|Owner is Married|791|0.65|0.64|0.67|763|0.65|0.63|0.68|
|Owner's
Finally, the Area under the Curve (AUC) is a measure of the efficacy of a targeting method in identifying the poor population at different targeting thresholds (Wodon, 1997, Olken and Hanna 2018). The curve in question is a receiver operator characteristic (ROC) curve, which indicates the trade-off between true and false positives at different poverty lines. We plot the ROC curve using each percentile of the benchmark village welfare predicted from the census. In other words, for poverty lines defined at each percentile of the benchmark distribution, we plot the true positive rates (TPR) on the Y axis and the false positive rate (FPR) on the X axis. The former is defined as the proportion of poor villages that are correctly predicted to be poor when using predicted village welfare from each candidate prediction method, while the latter is defined as the proportion of non-poor that are incorrectly predicted to be non-poor. Because the true positive rate is on the Y axis, a higher AUC score represents an improvement in true positives for a given level of false positives, or a better targeting method. The 45-degree line, which is what one would expect if villages were ranked randomly, corresponds to an AUC score of 0.5, while a perfectly accurate ranking that correctly identifies poor households under all poverty lines would receive an AUC score of 1.

# 3.3 Candidate Targeting Methods for Identifying Poor Villages

Proxy Mean Test scores in the UBR The administrative data from Malawi’s UBR provides information on households’ characteristics to assess their prospective eligibility for social programs. The data set contains an extensive range of variables such as geographic location, households’ assets, food security questions, and economic characteristics.

This information was used by the Malawian government and the World Bank to calculate ProxyMeans Test (PMT) scores to identify poor and vulnerable households. It is used to create a proxy score of weighted variables that are highly correlated with household consumption. The PMT, like our benchmark welfare measure, is a measure of chronic
# **I. Introduction**

Between 1983 and 2002, the United States economy experienced a boom in offshoring and a doubling of imports of manufactured goods from low-income countries. Over this same period, roughly 6 million jobs were lost in manufacturing and income inequality increased sharply. These parallel developments led some critics of globalization to conclude that “good” manufacturing jobs had been shipped overseas at the expense of the domestic labor force, putting downward pressure on wages of American workers. Concern over these developments motivated the US Congress to pass the American Jobs Creation Act of 2004. Yet the degree to which changes in the US labor market are related to international trade and offshoring is still the subject of a heated debate.

The standard approach to identifying effects of import competition on wages is to use variation in the prices (or quantities) of imported goods across industries over time. This approach has been used to measure the impact of globalization on industry wage differentials as well as to measure the effects of sector-specific import competition on wages and employment. For example, Feenstra and Hanson (1999) use a two-step procedure, first identifying the impact of outsourcing and high technology investments on productivity and prices and then tracing through the impact of induced productivity and price changes on production and non-production wages. Using data for the US manufacturing sector between 1979 and 1990, they find that the real wages of production workers were probably unaffected by offshoring activities, while the real wages of non-production workers increased by 1 to 2 percentage points.

However, recently scholars have suggested that wage pressure from developing countries is becoming increasingly important. Feenstra (2008) singles out expanded competition from China as having exerted pressure on US wages, and he is not alone in this view. A similar sentiment is captured by Freeman’s (1995) provocative title for his _Journal of Economic Perspectives_ article “Are Your Wages Set in Beijing?” Empirical evidence for this conjecture is lacking, however.
In particular, electricity consumption refers to electricity purchased from PLN, which accounts for the majority of electricity expenditures and that purchased from other providers. Fuel consumption includes diesel, gasoline and lubricants, which together to electricity account for over 80% of all energy consumption.<sup>21</sup> Since the focus of this paper is on the distinction between fossil fuels and electricity, we categorize each energy type used by the plants into one of the two groups. To do so, we convert consumption of fuel in kWh equivalents and compute plant-level quantity shares in order to capture the relative importance of each source.<sup>22</sup> The shares sum to one within each category. To avoid potential endogeneity of timevarying proportions, for each plant we fix shares in the first year of observation and then drop that year from the sample.<sup>23</sup> One of the key challenges of the Statistik Industri data is the lack of complete series of capital stock. Earlier studies tried to re-construct capital stock series applying the perpetual inventory method (PIM) to the first year of capital stock data reported by the plant (Amiti and Konings, 2007; Javorcik and Poelhekke, 2017). However this imputation method crucially relies on the capital value selfreported by the plant the first year the data are available, which is not necessarily accurate.<sup>24</sup> One potential advantage of using PIM is that purchase and sales data might be more accurate relative to the self-reported value of the stock, requiring an appropriate calculation of market values and depreciation. However, PIM needs to rely on measures of capital depreciation, which are difficult to accurately estimate. To mitigate such trade-off, we have adopted a hybrid strategy. We first clean the self-reported data, adopting an algorithm which keeps only observations that fulfill a battery of tests.<sup>25</sup> Then, we apply the PIM only to fill the gaps between the

> 21The Indonesian survey asks manufacturers
# I. INTRODUCTION

Children living in poverty are exposed to a wide range of negative influences, such as poor housing, inadequate nutrition, parental stress, and lack of cognitive stimulation (Walker et al. 2011). Additionally, these children are often exposed to multiple stressors simultaneously and repeatedly over time (Najman et al. 2009). There is consistent evidence of socioeconomic gradients for child health outcomes in both high-income countries (Case et al. 2002; Currie 2009; Shonkoff et al. 2009) and low- to middle-income countries (Currie and Vogl 2013; Petrou and Kupek 2010).

Low socioeconomic status (SES) is also a risk factor for poor child development outcomes (e.g., language, cognition, and behavior), and children of parents with less income or education perform worse than children whose parents have more income or education (Cunha and Heckman 2008; Kelly et al. 2011), according to studies from high-income countries. There is evidence of socioeconomic gradients in child development outcomes from lower and middle-income countries as well, but existing data are primarily cross-sectional. For example, wealth gradients in child development scores are evident as early as 3–23 months in India, Indonesia, Peru, and Senegal (Fernald et al. 2012). Preschool children (3–6 years old) exhibit significant gaps in receptive language in Ecuador (Paxson and Schady 2007), several other Latin American countries (Schady et al. 2015), and the four countries of the Young Lives Study (Engle et al. 2011). Unfortunately, these studies are limited by the lack of panel data with repeated measures of child development outcomes across critical and sensitive periods. Cross-sectional studies cannot untangle issues of timing where age effects are confounded with cohort effects and where the role of early skills on later development cannot be modeled. With few exceptions (e.g., [Kobrosly et al. 2011]) and the Young Lives cohorts, longitudinal panels have primarily been focused in the US
results for industry exposure to globalization. These results are consistent with recent empirical work demonstrating the importance of occupational tenure and downplaying the importance of tenure within a particular industry for a worker’s wages (Kambourov and Manovskii 2009).

The downward pressure on wages due to import competition and offshoring has been overlooked because it operates between rather than within sectors. Our results indicate that a ten percent increase in occupation-specific import competition is associated with a 2.9 percent decline in real wages for workers who perform routine tasks. While some occupations have experienced no increase in import competition (such as teachers), import competition in other occupations (such as shoe manufacturing) has increased by as much as 40 percentage points.<sup>2</sup> We also find substantial wage effects of offshoring: a ten percentage point increase in occupation-specific exposure to overseas employment in low wage countries is associated with a 0.73 percent decline in real wages for workers performing routine tasks. Our results suggest that domestic workers performing routine tasks have suffered from overseas competition, though the effect can only be observed by considering workers across sectors.

We then examine the mechanisms behind the contrast between the small _positive_ wage effects of globalization within manufacturing and the relatively large _negative_ wage effects we observe at the occupational level. Using a large panel of CPS workers who are matched across surveys, we show that workers who switch industries within manufacturing experience almost no decline in wages. However, when workers relocate to the service sector, they experience a significant wage loss, and the negative wage impact is particularly large among displaced workers who also switch occupations. We estimate wage losses of 2 to 4 percent among workers leaving manufacturing and an additional 4 to 11 percent wage loss among workers who also switch occupations.

> 2 See the supplementary online appendix tables for further information on imports by occupation.
# **_4. Conclusions_**

Empirical estimates of the size of the economic shocks triggered by the pandemic and lockdown evolution have been scarce since official economic indicators are only made available with a significant lag. In this paper, we provide an illustration by tracking the evolution of high-frequency variables, related to the trends in public transit data as well as traffic congestion.

The availability of such high-frequency variables allows us to investigate the economic impact of lockdowns, which have been the main public health policy implemented by governments around the world to contain the pandemic. It has been argued that these measures, which are useful in “flattening the curve” of health costs, may come at high economic costs, but also that a silver lining of reduction in congestion is only a temporary result, reverted by the increase in motorization, The analysis in this paper also confirms the significant cost of such measures in terms of reduction in urban mobility in terms of public transport and traffic congestion. Interestingly, it shows that beyond the impact of the lockdowns, also the spread of the disease itself has an economic impact distinct from that of lockdown measures, as the incidence of death per population increases the drop in activity associated with the spread of the disease, and is statistically highly significant, both in terms of reduction in urban mobility by public transport and a reduction in traffic congestion.

We also find different results on the magnitude of impact of cross sectoral vis-à-vis sectoral restrictions on urban mobility and congestion. Whereas the magnitude of the spread of the disease is higher than the overall stringency of the lockdown, we turn to introduce sector specific restrictions and, as expected, results are significantly different. The impact of restrictions on public transit is much higher than the spread of the disease and acts indirectly as a disincentive also to move on the road. More effective safety measures, such as those related to the use of facial covering are associated with higher use of public transport and an increase in the likelihood of low congestion.

We find no evidence of