rafmacalaba/data-use-mentions-extended huggingface dataset:
_ 0.05; _∗∗∗_ p _<_ 0.01 Note: Regression with the scaled employment response to technology shocks as the dependent variable, and a range of variables reflecting labor market frictions as the regressors. WEF variable refers to the World Economic Forum’s survey of firm’s ability to easily hire and fire workers, ranked from 1 (difficult) to 7 (easy). OECD indicators are the OECD’s ranking of the strictness of employment legislation to protect against individual and collective dismissal, with a higher index value indicating increased strictness

**_Labor market frictions and ease of hiring and firing_** Cross-country variations in the degree of labor market flexibility and employment protections provide an alternative explanation for the variation in the persistence of employment changes following a technology shock (Rujin, 2019). The OECD’s Indicators of Employment Protection cover 73 economies, 55 of which are included in the dataset of SVAR estimations. This database includes relative measures across economies of the strictness of protections against individual and collective dismissal. A separate database produced by the World Economic Forum produces a measure of labor market flexibility on a comparative scale across all economies where SVAR estimates of the employment impact are available. While labor market structures may play an important role in governing the persistence of the employment response in theory, these variables are found to be uncorrelated with the employment impact at all horizons (Table 3).

19
**Figure 4. Inequality in account ownership remains high in Turkey**

<!-- Start of picture text --> 90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Men Women Richest 60% Poorest 40% People 15‐ People 25<br>24 and older<br>Percent of adults with account<br><!-- End of picture text --> _Source_ : Global Findex database. The height of the bars shows the percentage of adults with an account at a financial institution or through a mobile money provider.

# Who Remains Unbanked in Europe and Central Asia?

Globally, about 1.7 billion adults remain unbanked. Account ownership is almost universal in high‐ income economies; nearly all unbanked adults live in the developing world. Nearly half of them live in populous developing countries such as Bangladesh, China, India, Indonesia, Mexico, Nigeria, and Pakistan.

In 2017 there were 116 million unbanked adults in ECA. The majority live in Romania, the Russian Federation, Turkey, Uzbekistan, and Ukraine.

These people share various characteristics. First, women represent 58 percent of all unbanked adults in ECA. In some economies, such as Armenia, Kosovo, and Turkey, their share is even higher.

Second, poor people account for a disproportionate share of the unbanked in ECA (figure 5). Half of all unbanked adults are from the poorest 40 percent of households in the region. In Romania, for instance, nearly 60 percent of the unbanked are from the poorest 40 percent of households. Seventy‐one percent of the richest 60 percent of adults in Romania have an account, while only 38 percent of the bottom 40 percent do. Such income gaps in account ownership are prevalent in ECA countries, with a handful of exceptions,
grant well-being because of two main threats to identification. First, as Table 3 highlights, RAMV migrants migrated earlier (by seven months, on average), meaning some differences could be due to longer assimilation time. Second, although PEP was introduced unexpectedly and RAMV was introduced earlier without any announcement or expectation that it would be used to provide benefits, registration in both was still voluntary. Hence, it is possible that RAMV and non-RAMV migrants were already different or that the decision to register was driven by unobservable characteristics correlated with refugee well-being.

The data in Table 3 to ease the concern that RAMV and non- helps first by demonstrating RAMV migrants were largely comparable across a range of pre-RAMV sociodemographic characteristics, including retrospective socioeconomic characteristics in Venezuela (prior to migrating) and factors that might be correlated with migrating to Colombia. However, the data on reasons for not registering in RAMV and PEP is suggestive of potential confounding factors (Tables E.1–E.2). For instance, the decision to not register in RAMV stemmed from lack of information, lack of proof of Venezuelan nationality, and inability to take time off from income-generating activities. The decision to not register in PEP also included lack of information and loss of proof of RAMV registration, which was sent to registered email accounts. To the extent that these characteristics might be correlated with the outcomes of interest and with unobserved factors such as connections to relevant networks and entrepreneurship, the simple Intent-to-Treat comparisons between RAMV and non-RAMV migrants would result in biased estimates of PEP’s true effects.

# **IV. B Identification strategy: Fuzzy regression discontinuity design**

To address these challenges, we base our empirical strategy to identify PEP’s causal effects on a fuzzy regression discontinuity design (RDD).<sup>12</sup> The fuzzy RDD exploits the discontinuity in the likelihood of receiving PEP based on the RAMV cutoff date. As described earlier, only migrants who had registered in RAMV were eligible to apply for PEP. The RAMV
# Microinsurance: A Case Study of the Indian Rainfall Index Insurance

# Market

Xavier Giné, World Bank Lev Menand, Yale Law School Robert Townsend, Massachusetts Institute of Technology James Vickery, Federal Reserve Bank of New York

# 1. Introduction

Households in India and other developing nations are often critically exposed to extreme weather-related events, including drought, flood, tidal waves, and hurricanes. For example, in a household survey conducted by us in Andhra Pradesh, 89% of surveyed rural landowners cited drought as the most important single risk faced by the household (Giné, Townsend, and Vickery, 2008). Weather shocks often affect all households in a local geographic area, making some forms of risk coping, such as seeking help from nearby family, friends, and neighbors, relatively less effective. Globally, household exposure to extreme weather events is likely to increase over future decades due to climate change as well as population growth in risk-sensitive areas.<sup>1</sup> Efforts have been made in India and other countries in recent years to develop formal insurance markets to improve diversification of weather-related income shocks. The goal of this chapter is to survey the features of one of these markets, the Indian rainfall index insurance market. “Index insurance” refers to a contract whose payouts are linked to a publicly observable index; in this case, the index is cumulative rainfall recorded on a local rain gauge during different phases of the monsoon season.<sup>2</sup> This form of insurance is now available at a retail level
he compares the values for the corresponding corridors with the trucking costs from Teravaninthorn and Raballand (2009). His trade cost estimates are 50 to 100% higher than survey-based freight costs, which, per the discussion above, is an expected outcome since price-differentials capture more than transport costs. The fraction accounted for freight is informative about what policy makers can hope to achieve in terms of boosting trade and economic activity with policies and investments that can affect transport costs in the short run.

Despite its shortcomings listed above, it is the structural gravity model that enables predicting the GDP impact of particular transportation projects. A data challenge in linking gravity-based and survey-based measures in a developing country context is the fact that data on within-country trade flows is generally lacking in such environments. It is precisely for this reason that notable attempts to estimate trade costs in Africa cited above (e.g., Atkin and Donaldson, 2015; Porteous, 2019) use price data. Prices,

> 9To estimate transport and insurance costs, they use the difference between reported cost-insurancefreight (CIF) and free-on-board (FOB) prices in customs records.

18
# **Russia’s Invasion of Ukraine and Firm Performance in Central Asia:**

# **_The Role of Export Links and Digital Gains_**<sup>**_*_**</sup>

Nicolo Dalvit, Mariana Iootty, Martin Melecky, Nithya Srinivasan _World Bank_

**Keywords:** Ukraine Invasion, Firm Performance, Sales, Employment, Central Asia, Trade link with Russia, Business Pulse Survey, Difference in Differences. **JEL Classification:** D22, F51, L23, L25, O14.

> * We thank Arti Grover and Leonardo Iacovone for their helpful comments on the earlier draft of the paper.
operational for our purposes. For the counterfactual analysis we use as baseline the latest available year in our data set, which is 2011 and was determined by the availability of capital stock data. In order to perform the counterfactual analysis, we need to set values for the parameters. Some parameters are borrowed from the literature: i) the elasticity of substitution is set equal to _σ_ = 6 , which is standard in the trade literature, ii) the consumer discount factor is set equal to _β_ = 0 _._ 98 (Yao et al., 2012), and iii) the country-specic capital shares of production _αj_ and the country-specic adjustment costs of capital _δj_ are calculated using the Penn World Tables and reported in columns (3) and (4) of Table 5, respectively.

We calibrate other parameters in order to match the observed data. The share of technology capital of a country to all destinations as a share from total world technology capital ( _ηi_ ) is calculated using _FDIij_<sup>_value_</sup> : _φj_ is calculated using the relationship between inward FDI ( _FDIj_<sup>_in_</sup> =<sup>�</sup> _i_<sup>_FDI_</sup> _ij_<sup>_value_</sup> ) and physical capital in the production function along with FDI and physical capital data and data on the capital shares: The exact values for _η_ and _φ_ are given in columns (5) and (6) of Table 5.

Table 5: Calibrated Parameters

|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|
|ISO3|Country|_α_|_δ_|_η_|_φ_|
|AGO|Angola|0.47|0.0528|0.00078|0.056|
|ARG|Argentina|
# **Table A.6. Differences in Poverty Rates for Households with and without Children for Different Equivalence Scales, RLMS 1994-2017**

**Panel A. Headcount poverty in households with children (percent)**

|**Child scale**<br>|||||**Adult s**|**cale par**|**ameter e**||||
|---|---|---|---|---|---|---|---|---|---|---|
|**parameter**|||||||||||
|<br>**b**|**0.1**|**0.2**|**0.3**|**0.4**|**0.5**|**0.6**|**0.7**|**0.8**|**0.9**|**1**|
|**0.01**|0.4|0.8|1.2|1.7|2.8|5.0|7.5|10.9|15.9|23.1|
|**0.02**|0.3|0.7|1.1|1.5|2.5|4.8|6.6|9.9|15.1|21.5|
|**0.03**|0.2|0.6|1.0|1.4|2.2|4.1|6.1|9.4|13.9|19.7|
|**0.
# **_Global Value Chains in the Electronics Industry                       Sturgeon and Kawakami_**

“intangible” aspects of GVCs with any degree of specificity, the scale and rapid growth of intermediate goods trade in the electronics industry is certainly indicative of its importance and dynamism in GVC formation.

## **Effects of the Economic Crisis on Electronics Industry GVCs**

As with almost all other sectors, the electronics industry was deeply affected by the economic crisis of 2008–09. The scale of the crisis in trade is reflected in figures on overall ocean transport traffic, which carried all but the most lightweight and expensive electronics shipped over long distances. The combined results of the 16 largest ocean container carriers publishing quarterly figures—including Maersk Line, Hapag-Lloyd, China Shipping, “K” Line, and NYK Line—showed revenue declines of 40 percent for the first nine months of 2009, $56 billion, in comparison to figures from a year earlier, $94 billion (Barnard 2009).

Aggregate international trade statistics for 2008 and 2009 are still being finalized at this time, and preliminary estimates are unreliable. Nevertheless, past patterns are a reasonable indicator of recent and future patterns. Figure 1 shows world export growth from 1962 to 2006 in terms of intermediate, capital, and consumption goods, as well as capital and consumption goods combined into a “final goods” category. As the figure indicates, trade in intermediate goods appears to be much more volatile than trade in capital or consumption goods. This supports the notion of “bullwhip” effects of recessions and business cycles, where slowdowns and downturns affect part and component shipments more than final goods shipments because final goods producers tend to draw down parts inventories and delay reordering during periods of uncertainty (Escaith, Lindenberg, and Miroudot 2010). In addition, intermediate goods trade usually grows notably after recessions, especially U.S. recessions—U.S. company outsourcing has been one of the most important drivers of GVC expansion—but also following sectoral bubbles (for
after the SSP program ended.<sup>15</sup> These stark differences in treatment effects by baseline overdraft behavior are also apparent in the survey data. We examine heterogeneous treatment effects by augmenting equation 1 to include interactions between the different treatment effects and either a “heavy overdrafter” (above median overdraft score at baseline) or “light overdrafter” (below median overdraft score) dummy.<sup>16</sup> Table 4 shows that during the commitment period, light overdrafters saved more both with NVRB _and_ with other banks and credit unions – commitment crowded additional savings into the banking sector. While there is some crowd out of informal savings during the commitment period, the point estimate is small compared to treatment effects on formal savings. Consequently, we reject the null of no impact on total

> 15In contrast, treatment effects on overdrafts are similar for the two groups – see Appendix Table A6 for detail.

> 16We also include interactions between overdrafter status and time period.

10
as compared to the labor module. The discrepancy, however, is much greater under the business-as-usual approach, and also relatively greater for women (an increase from 37 to 51 percent in women’s ownership/management moving from the labor module to the nonfarm enterprise module, or a 14-percentage point increase) as compared to men (a 12-percentage point increase).

In addition, there were cases of proxy respondents in the enterprise module (those that neither owned/managed or worked in the enterprise), although for the sample of individuals used in the analysis, the share of proxy response was lower in the IHPS (6.7 percent, or 64 individuals) compared to the IHS4 (10.4 percent).<sup>17</sup> The last two columns of Table 5 show that proxy response in the non-farm enterprise module has a greater effect on reporting of ownership/management in the IHS4, particularly for men — where own-reporting tended to be lower than the overall (proxy plus own-reporting) sample. There was, otherwise, no effect of proxy response in the IHPS non-

> 17 The enterprise module provides a closer look at the types of enterprises that had a proxy respondent — nearly all (99 percent) of these enterprises had only one owner/manager, with most (about 80 percent) having no other supporting workers. These tended to be enterprises owned by the husband, with the spouse reporting. Among those enterprises with the owner/manager not reporting and which had workers, however nearly all (20 out of 25) of IHPS respondents were working in that enterprise, compared to 69 percent in the IHS4 (66 out of 96 enterprises).

18
j (some of which might be imported). X includes a time trend, the life-length of the good and the growth rate of US GDP as controls.

In the baseline specification (column 5), a 1% increase in competitors’ exchange rates in a sector is associated with a statistically significant 0.11% increase in US producer prices (verifying Testable Prediction 3; βC<sup>P P I</sup> = 0.11). A 1% increase in materials prices (a control variable) is associated with a 0.5% increase in US producer prices (δM<sup>P P I</sup> = 0.5). The average share of materials costs across sectors in the US Producer Price Index (PPI) data is about 50%, which is broadly consistent with the estimated coefficient.<sup>10</sup> Column 6 shows that when materials prices are removed as a control, βc<sup>P P I</sup> = 0.34, which is very large. Goldberg and Campa (2010) argue that the aggregate CPI in the US is insensitive to exchange rates, which puts an upper bound on the degree of pass-through to producer prices. The higher estimate might be due to other variables: movements in materials prices could be correlated with competitors’ RERs due to (i) imported intermediates or (ii) other shocks (e.g. oil prices) that jointly affect materials prices and RERs.<sup>11</sup> 3.4. **Pass-through of foreign competitors’ prices.** Consistent with the simple model, importing firms respond strongly to the prices of their foreign competitors, and this reduces the explanatory power of competitors’ exchange rates. In columns 3-4 and 7-8 of Table 1, I add foreign competitors’ prices to import and producer price regressions (respectively) as an additional control. A 1% increase in foreign competitors’ prices increases import
Lastly, our results indicate that women living in communities where the traditional division of roles persists are more likely to report being employed or working when they are exposed to the LOA module compared to other women living in similar communities who have not seen this list. In certain cultural or societal contexts like El Salvador, women might not explicitly acknowledge unpaid care as work, which leads to a lack of recognition for the time and effort that they invest in care-related activities. The list of activities can help women (and other members of the community) to recognize unpaid work as a form of work. On a broader level, this new perception can have implications for how unpaid—or domestic—work is valued, understood, addressed, and integrated into discussions on labor, gender roles, and societal contributions.

This paper’s findings offer lessons for researchers, survey practitioners, and policy makers responsible for producing national statistics. Specifically, they shed light on the impact that various survey methods have on labor market bias contingent on the sex and age of the respondent. Moreover, given the differences in the budget implications between using the LOA module or enforcint self-reporting survey methods, it is crucial to analyze the trade-offs between collecting data from the actual respondents and the additional cost to do so. In the case of this experiment, enforced self-reporting has a clear impact on the labor indicators of young males that are not captured via traditional household surveys that permit proxy reporting. Yet, this statistical benefit comes at a 30% higher implementation cost. Conversely, the LOA module has a significant impact on capturing higher levels of work and employment, and its implementation costs are negligible.

24
shown to be substantial (Carrère & Grigoriou, 2015), with 77% of all bilateral trade flows reported by both partners at the aggregate level and only 47% of bilateral trade flows reported by both partners at the six-digit level. Larger discrepancies in disaggregated products have long been observed (Rozanski & Yeats, 1994) and are often attributed to human error (misclassification) or deliberate misreporting.

An important question then is how to infer whether the observed discrepancy is “expected,” i.e., implied by structural reasons, as opposed to deliberate, i.e., caused by factors that are not purely logistic in nature. If some discrepancies are structurally expected, a dependable reporter is not one without any discrepancies, but one that exhibits discrepancies that are most likely to 6
beginning of our panel, for the school year 2007-2008 to construct the following school level indicators: student/teacher ratio, whether school is “multi-grado” meaning a teacher has more than 1 grade in his/her classroom, teacher education levels (measured by proportion of teachers who obtained their bachelor’s degree of all teachers) and the availability of a computer.

Finally, we merge data at the community level from components of the margination index produced by Conapo based on the 2000 Mexican Population Census. The index is the normalized first principal component of nine locality-level population shares: the share illiterate, the share with less than primary school education, the share without a toilet, the share without electricity, the share without running water, the share without drainage, the share with crowding (few rooms per capita), the share with a dirt floor, and the share without a refrigerator. These indicators are also useful as control variables.

We now turn to a discussion of our methodology. Our methodology is non-experimental in that we derive a comparison group not though randomization but rather by choosing a comparison group that potentially represents a counterfactual for the beneficiary group in the absence of the program. In this study, we compare PROSPERA recipients and non-recipients to estimate program impacts. As we will see below, even when PROSPERA and non-PROSPERA students study in the same school, PROSPERA students typically have a lower socioeconomic status than 11
The table indicates that the typical study household is Hindu (68 percent), has on average five household members, and is headed by a male household member (90 percent). Household heads are almost always married (91 percent), are 45 years old on average and have 6 years of education on average. 41 percent of household heads belong to Scheduled castes/tribes, while General Caste and Other Backward Castes represent 24 and 34 percent of the sample respectively. The vast majority of households (96 percent) live in a dwelling they own. Dwellings are of moderate to high quality, with 66 percent of households living in a dwelling constructed with a mix of high-quality and low-quality materials (semi-pucca) and for 18 percent living in a dwelling constructed from high quality materials (pucca). Around 59 percent of the MF client sample holds a Below Poverty Line (BPL) card, while 28 percent has an Above Poverty Line (APL) card. The typical household’s earnings come to a very large extent from agriculture-related activities, with 52 percent of the sample reporting receiving wages from agricultural labour and/or from cultivation or allied agricultural activities. Another important source of income (28 percent) are wages from employment outside agriculture.

Using toilet construction date, we create an indicator of toilet ownership before the start of the intervention.<sup>24</sup> We observe that only 25 percent of control group households owned a toilet at baseline. Remarkably, this matches closely with the 2012 baseline survey conducted by the Indian Ministry of Drinking Water and Sanitation, which shows that on average 27.4 percent of households in the study GPs reported to have a toilet in 2012 (SBM and Sanitation, 2014). Sanitation interventions are ultimately interested in safe toilet uptake, not just any type of latrine ownership. Following the guidelines provided in WHO/UNICEF [2017] to define a safe toilet, we find that 99.6 percent of all reported toilets in our study area belong to any
. The underlying mechanism they suggest is the lower incentives to invest in human capital among those with a larger initial network.

Another aspect related to initial conditions, which has been understudied, is the role of local attitudes towards refugees. The two existing studies, both focusing on Germany, have used self-reported measures of attitudes extracted from existing surveys, and they have examined the role of general attitudes towards migrants, and used it as a proxy for attitudes towards refugees. Aksoy et al. (2020) measure attitudes with cross-sectional state-level data from the European Social Survey. They find that positive attitudes towards migrants at the time of arrival, have a positive effect on refugees’ economic 8