rafmacalaba/data-use-mentions-extended huggingface dataset:
movements have a small effect on employment and that job destruction is not substantially affected. Goldberg and Tracy (2001) offer an explanation for the finding that industry wages are significantly more responsive than industry employment to exchange rate changes. They find that the main mechanism for exchange rate effects on wages occurs through job turnover and the strong consequences this has for the wages of workers undergoing such job transitions. Workers who remain with the same employer experience little if any wage impacts from exchange rate shocks. In addition, they find that the least educated workers -who also have the most frequent job changes - shoulder the largest adjustments to exchange rates.

Insofar as appreciations affect the probability of job losses, whereas depreciation does not, differential effects may depend on whether industries (firms) are exporters or import-competing. Losses from appreciation are more likely to be concentrated in import-competing sectors. Revenga (1992) finds that in the US import competing industries reduce employment overall during currency appreciations. All these results suggest asymmetrical effects in the USA between appreciations and depreciations. This probably reflects a persistent pressure towards job reductions in tradables (due, perhaps, to technology or competition), with the exchange rate acting as a trigger for inevitable adjustments.

Using French firm-level data, Gourinchas (1999b) also finds that exchange rate appreciations reduce net employment growth, because of lower job creation and increased job destruction. Bentivogli and Pagano (1999) find for a number of European countries rather limited, but diverging effects of exchange rates changes on job flows. The latter may reflect differences in labor market institutions. Thus, Burgess and Knetter (1998) find in that in countries with the most rigid labor institutions, such as Germany and Japan, employment is not sensitive to exchange rates, while in other countries appreciations are associated with reductions in employment.

Work on developing countries has tended to be much more explicitly motivated by trade reforms. An early discussion of trade and employment was Krueger (1983) who argued that developing-country trade liberalization should boost labor–intensive output
columns 2 and 4 (excl. outl.)). 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. Amounts are in Indian Rupees (1 USD = Rs. 67.5). Data source: household survey.

# **6.4.1 Ruling out alternative explanations**

Finally, we consider alternative explanations for the empirical findings. While we believe that the intervention would have improved sanitation investments through the provision of (labelled) credit, it is possible that the mere availability of a sanitation loan raised the salience of sanitation investments among the MFI’s clients. If this were the case, we would expect that clients in the treated communities would be more informed about the costs and benefits of safe sanitation; and may also have stronger beliefs about the benefits of safe sanitation. We consider whether this is the case by studying the effects of the intervention on the perceived costs and benefits of safe sanitation.

Specifically, each client was shown the picture of the same twin-pit toilet and asked the degree to which she agreed or disagreed with a series of statements capturing the costs and benefits – both monetary and non-monetary – of a typical household in their village using the toilet.<sup>54</sup><sup>_,_55</sup> We use polychoric principal components analysis to combine these variables into summary measures of the perceived costs and benefits, and consider intervention impacts on these variables. These are reported in Table 15. The table shows no significant impact of the intervention on perceived benefits or perceived costs of safe sanitation, indicating that our findings are not driven by the intervention raising the salience of sanitation investments only.

> 54A small number of clients, mainly in the control GPs, were shown the picture of another toilet. We drop these households from the analysis. Attanasio et al. [2018] shows that the sample is balanced between treatment and control
placement of infrastructure, calculating remoteness’s importance at the household level or analyzing accessibility to individual service facilities. The unitless nature of index measurements also hinders communication to policy makers or adaptation to additional analyses; analysts must explain to a policy maker what a 4 vs. a 7 means on Dempsey’s scale, and such numbers cannot be meaningfully incorporated into other models that require physical measurements.

Both cost time models and weighted indices are complex, and their inner mechanics must be explained to users. But by employing a verifiable, commonly used metrics, cost time models give users confidence in their results and the ability to field test outputs for themselves. Additionally, cost time models produce raster outputs that can be scaled to any unit of analysis at or above the resolution of a raster pixel. These advantages make them more generically useful tools for specialists and non‐specialists alike that still successfully manage multiple input factors.

# Data collection and preparation

## Service facilities

We collected geospatial data for various categories of services facility types to objectively calculate travel times and distances to them. Our original intent was to compare these data with the reported distances and times from the survey; we have now moved this analysis to a separate paper. We amassed destination data on 77 DHQs, 7,840 banks, and 4,858 medical facilities. Table 2 summarizes these data sets and their sources.

**_Table 2: Facility data sets_**

|**Service**|**Number of facilities**|**Source**|
|---|---|---|
|District headquarters|77|Survey Department|
|Banks|7,840|Nepal Rastra Bank (NRB)|
|Medical facility|4,858|Ministry of Health|

The financial and medical facility data sets contained additional information about the specific type of facility (e.g. hospital, health post, etc.). We took advantage of this to repeat our analysis for important sub
_j_ will affect  1 , which represents the economy-wide returns to females.

One problem with applying this approach is that how does one determine what timeframe constitutes “the long-run.” Robertson (2004) provides one of the very few estimates of what the relevant timeframe is for the long-run and suggests that the Stolper-Samuelson effects begin to emerge in three to five years.

Working conditions can fit into this model too. The most straightforward way is to redefine the variable _w_ as compensation that includes working conditions such as bonuses, no mandatory overtime or unpaid overtime, or no use of child labor. Working conditions can be either substitutes or complements for wages. A compensating differential approach even suggests that wages and working conditions are substitutes: in this case, firms with poor working conditions need to pay workers higher wages to compensate them for enduring the poor conditions (and, consequently, pay them less for better conditions).

We constructthe country-specific measures of working conditions using the information from the household and labor force surveys. Conditions are considered favorable to the worker if he or she (i) is older than 14 years, (ii) works no more than 40 hours a week (or less than six days a week), (iii) has a low ratio of in-kind compensation over cash work, and (iv) has a high 7
developing countries.<sup>5</sup> (iii) In order to be able to detect differences between a country’s core and its non-core regions, we considered countries with at least three regions.<sup>6</sup> As a second next step, we had to determine which regions to consider as core regions. We defined these as follows:

- We determined a region’s relative size, defined as the number of firms in a region divided by the total number of firms in the country. This exercise is representative for each country, since Enterprise Surveys represent a stratified random sample with regard to regions. Note that this procedure includes both manufacturing and services firms. We set the core dummy _CORE_ = 1 for the region with the largest relative size.

- In some cases, the Enterprise Surveys defined regions around the country’s capital very narrowly, resulting in a relatively small relative size of such regions. In such cases we bypass the first rule and define these as core regions.

We set _CORE_ = 0 for all other regions. The procedure above results in 79 countries covering 467 regions, 92 of which are core regions. One downside of database is that the definition of regions is not harmonized across countries. The list of countries, year of most recent survey, number of regions by country as well as the number of firms (total and for manufacturing and services firms separately) can be found in _Appendix 1_ . _Appendix 2_ shows the names of the core regions for each of these countries.

# **2.2 Manufacturing Firms**

In a next step, we calculate weighted averages of selected indicators per firm in the core and compare these averages with non-core regions. We use sampling weights as provided by the Enterprise Survey Indicator Database. We also perform a t-test which tests for the null hypothesis that there is no significant difference between the core and non-core regions for a given indicator.<sup>7</sup> We do this
Policy Research Working Paper 10044

# **Abstract**

Most refugee hosting communities are characterized by high levels of poverty with precarious livelihood conditions, low access to public services, and underdeveloped infrastructure. While the unexpected inflow of refugees might bring both constraints and opportunities for improving and maintaining local livelihoods in these communities, the understanding of these effects remains limited. Using a household level micro data set from a 2018 baseline survey of the Ethiopia Development Response to Displacement Impacts Project, this paper assesses the impact of refugee inflow on the livelihood strategies of host communities with respect to diversification and agricultural commercialization. The endogeneity of refugee inflow is addressed by exploiting differences in factors that influence refugee arrival in the host communities. Specifically, the analysis uses potential refugee inflow as an instrument, which is the product of population density and intensity of conflicts (number of fatalities per event) in the closest region of the origin country to the refugee camp weighted by the distance of the refugee camp to the closest region. The paper also constructs an aggregate index to proxy households’ livelihood diversification strategies. The findings show that refugee inflow brings substantial benefits to host communities by creating significant jobs, in which people engage as secondary occupations, and triggers an increasing demand for livestock products. Specifically, while no effect was found on diversification of activities such as a primary occupation and crop product sales, a 1 percent increase - in refugee inflow leads to a 2.7 percent rise in diversifica tion of livelihood activities as a secondary occupation and a 15.9 percent increase in the value of livestock product sales. These effects tend to be heterogeneous across refugee hosting regions and the gender of the household head: negative effects were mainly observed in Gambella region, which hosts the largest refugee population in the country, and male-headed households were more likely to benefit from the refugee presence for the whole sample. The paper identifies households’ increased engagement in different livelihood activities and access to markets as a potential mechanism for the observed effects. The findings add to the growing literature
Policy Research Working Paper 10392

# **Abstract**

Data on global trade as well as capital and labor flows indicate a slowdown, but not reversal, of globalization post the 2008–09 financial crisis. Yet profound changes in the policy environment and public sentiment in the largest economies over the past five years suggest the beginning of a new era. Increasing anxiety about the labor market effects of import competition from low-wage countries, especially China, laid the groundwork, but was not the catalyst for the reversal in attitudes towards globalization. Similarly, the COVID pandemic provided novel arguments against free trade based on global supply chain resilience, but neither the pandemic nor short run policy response had enduring effects on trade flows. Global trade was remarkably resilient during the pandemic and that supply shortages would likely have been more severe in the absence of international trade.

After a temporary decline in 2020, global trade in goods and services increased sharply in 2021. Russia’s invasion of Ukraine raised new concerns about national security and the exposure of supply chains to geopolitical risk. This was followed by demands to diversify away from “non-friendly” countries and to the employment of trade policy, export restrictions in particular, to halt China’s technological development. The future of globalization is highly uncertain at this point, but these new policies will likely slow global growth, innovation, and poverty reduction even if they benefit certain industries in certain countries. Regarding resilience, the main goal of recent trade policy changes, measures of trade volatility or concentration can be helpful, but resilience will be elusive as long as we lack benchmarks against which policy performance can be measured.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at
the F2F measure. Visually, there is significant variation in the intensity distribution of the index, or equivalently, significant geographical variation in business lockdown exposure.<sup>18</sup>

# **3 Estimation Strategy**

To identify the impact of COVID-19 on Vietnamese household businesses, we estimate the following equation:

> 17Following Oldenski (2012), the score from each of the four work activities is aggregated for each firm in the O*Net database. The arithmetic average of the scores of all firms in each occupation then denotes the F2F index for that occupation.

> 18In Figure A.1, we aggregate our F2F indices to the district level for visualization purposes. There are 670 districts in the country, which comprises of over 11,000 communes.

7
. non- project respondents, pooling together respondents that participated in both waves and replacements for those who could not be tracked. Our main estimates, in other words, treat the panel dataset as two separate cross-sectional surveys.<sup>14</sup>

# **3.3 Outcomes of interest and aggregate indices**

As discussed above, the survey includes indicators of institutional capacity among officials and citizens in several dimensions that can be directly related to the five core systems targeted by the project: the urban planning system, the fiduciary or financial management system; infrastructure management; accountability and oversight by citizens; and the local property tax system. Accordingly, we structured the outcomes of interest, in both the household and government official surveys, in those five areas.

> 11Wards were selected in a manner that permits a balanced distribution between areas that received infrastructure projects and area that did not. Specifically, the ward in which the LGA headquarters is based was always selected and then one ward with a recent infrastructure project and one without a recent infrastructure project were randomly sampled. No sampling weights were included in the analysis as our ward sampling strategy should produce a sample representative of the LGA level.

> 12Attrition of original households was higher in ULGSP LGAs: 20% of households were replaced in those regions vs. 15% in control LGAs.

> 13The share of officials still holding the same position at endline was not statistically different between ULGSP and control areas (67% in ULGSP vs. 62% in control LGAs)

> 14Restricting the sample to only those households/officials that were interviewed in both surveys and estimating a fixed-effects panel model does not significantly change the results.

15
|Wait for water connection (days)|Number of days between application for a<br>water connection and the receipt of the<br>service|World Bank Enterprise Surveys|
|---|---|---|
|Establishment has checking or savings<br>account Y/N|Dummy variable that takes the value of 1<br>if the firm has a checking or savings<br>account at this time|World Bank Enterprise Surveys|
|Female manager Y/N|Dummy variable that takes the value of 1<br>if the top manager of the firm is female|World Bank Enterprise Surveys|
|Use technology licensed from foreign<br>companies Y/N|Dummy variable that takes the value of 1 if<br>the firm uses technology licensed from<br>foreign companies|World Bank Enterprise Surveys|
|Average historical temperature 1900-2014|Gridded dataset that contains monthly<br>observations of precipitation and average<br>temperature at the 0.5 degree grid cell<br>level. Data transformed into average<br>monthly temperature, and total<br>precipitation (mm), per year, for each grid<br>cell.|Matsuura and Willmott, (2012a)|
|Average historical precipitation 1900-2014|Gridded dataset that contains monthly<br>observations of precipitation and average<br>temperature at the 0.5 degree grid cell<br>level. Data transformed into average<br>monthly temperature, and total<br>precipitation (mm), per year, for each grid<br>cell. Precipitation shocks defined based on<br>the long run mean and standard deviation<br>of annual precipitation for each grid cell<br>from 1900-2014.|Matsuura and Willmott, (2012b)|
|Standardized Precipitation Index (SPI)|Gamma distribution fitted to precipitation<br>data to correct for the fact that<br>precipitation data is not
Policy Research Working Paper 8983

# **Abstract**

Attributing a rental value to the dwellings of homeowners is essential in various contexts, including distributional analysis and the compilation of national accounts, consumer price indexes (CPIs), and purchasing power parity indexes. One of the methods for making the attribution is to use homeowner estimates of the market rental value they would pay (receive) for their dwellings if these were rented. This is known as homeowner self-assessed rent. However, homeowner estimates may not be accurate because of the way questions aimed at soliciting such information are phrased, the sentimental attachment of the homeowners to the properties, lack of information about rental markets, and other reasons. Yet, researchers and practitioners often neglect to ascertain the accuracy of homeowner assessments. This study argues that comparing unconditional or conditional means may be misleading if one has not ascertained whether the observable characteristics of homeowner and tenant dwellings are similar. Using Peruvian data from 2003 to 2017, the study tests the accuracy of self-assessed rental values with matching estimators. In Metropolitan Lima, homeowners typically provide accurate estimates of the rental market values of their dwellings. In rural areas, market rental values are underestimated by homeowners in more instances. The direction and magnitude of the inaccuracies in Metropolitan Lima and in rural areas are comparable and range between −25 percent and −20 percent.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mranzani@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the
.0193)|
|Under 35 years|0.0164|0.0304|0.0394*|0.0452*|0.0473*|0.0138|
||(0.0154)|(0.0159)|(0.0198)|(0.0198)|(0.0199)|(0.0148)|
|Rural|-0.00774|-0.0294|-0.0171|-0.0110|-0.0101|-0.0268|
||(0.0251)|(0.0259)|(0.0322)|(0.0321)|(0.0321)|(0.0239)|
|Complete Secondary School|-0.0275|-0.0407*|-0.0521*|-0.0527*|-0.0509*|-0.0283|
||(0.0161)|(0.0166)|(0.0206)|(0.0206)|(0.0207)|(0.0153)|
|Richest 20%|0.0343|0.0557**|0.0656**|0.0695**|0.0726**|0.0360|
||(0.0198)|(0.0205)|(0.0254)|(0.0254)|(0.0256)|(0.0190)|
|Constant|0.0858**|0.0711*|0.205***|0.172***|0.154***|0.123***|
||(0.0318)|(0.0329)|(0.0452)|(0.0420)|(0.0406)|(0.0341)|
|Observations|1843|1843|
concessions, and conflicts.<sup>4</sup> Potential vulnerability is compounded by low levels of human capital accumulation and weak governance. In such a context, characterized by repeated droughts and a high volatility in consumption, standard poverty measures such as the poverty headcount rate only assess the current poverty status of a household but are not necessarily an indicator of future poverty status. For example, the current consumption of a household may be above the poverty line and thus they are classified as non-poor, but due to the high likelihood of shocks between the current and next period, the same household may fall below the poverty line in the next period. Such a household is “vulnerable to poverty” even though their current consumption is above the poverty line. In general, the extent of vulnerability to poverty depends on the risk management strategies of households and communities, the abilities of households to cope after the incidence of the shock (e.g., assets owned, herd size, social capital), and access to safety nets.<sup>5</sup> Understanding the extent and the sources of vulnerability in the drought-prone lowlands has important policy implications.

While the profile of poverty and its evolution over time are relatively well documented in Ethiopia, there is very limited evidence on the extent and nature of vulnerability to poverty. The available studies either focus on a specific shock (such as drought or price rise) and/or do not provide any estimates for the drought-prone lowlands. Hill and Porter (2017), for example, study vulnerability to drought and food price shocks and its determinants using historical climatic and price data along a nationally representative household survey from 2005 and 2011. They find that drought and price increases lead to significant reduction in consumption and hence vulnerability to poverty. Demissie and Kassie (2017) study vulnerability in rural areas by cross-sectionally estimating the probability of households’ consumption falling below the poverty line in the future and defining a household as vulnerable if the probability is higher than a