rafmacalaba/datause-encoder-data huggingface dataset:
Conversely, the role of intra-...rm ...nancial linkages in helping MNC subsidiaries attain greater resilience should depend on the incidence of the crisis in MNCs’home-country ...nancial markets. A worse credit condition at home will weaken the capital supply from parent ...rms, adversely a¤ecting MNCs’performance overseas.

Following Rose and Spiegel (2009a), the incidence of the crisis in ...nancial markets is measured here by countries’drop in credit ratings during 2007-2008. Countries that experienced a bigger drop in credit ratings are considered to have a greater incidence of the crisis in ...- nancial markets. As in Rose and Spiegel (2009a), we adopt country credit ratings from the Institutional Investor magazine, which ranks 177 countries on a scale from 0 to 100 with 100 representing the least likelihood of default (as of March 2009, Switzerland was the most highly ranked country with a score of 94.0, while Zimbabwe rated at the rear with a score of 4.6). According to this rating, countries such as New Zealand, Ireland, Greece and Singapore were among the group with the largest decline in credit conditions while countries such as Brazil, Peru and Ecuador showed a lesser incidence of the ...nancial crisis.

[Table 7 about here] Table 7 reports the results. We ...nd that the e¤ect of ...nancial linkages on establishment resilience to the crisis increases in host countries’incidence of ...nancial crisis. MNC subsidiaries exhibited a greater advantage over local competitors in host countries that experienced a larger decline in credit ratings. In contrast, the positive e¤ect of ...nancial linkages is smaller when MNC home countries experienced large negative ...nancial shocks. MNC subsidiaries performed less satisfactorily when their home countries’credit conditions worsened.

# 6 Additional Analysis

## 6.1 Foreign v.s. Domestic Linkages

So far our analysis has emphasized on the e¤ect of cross-country production and ...nancial linkages between foreign owned subsidiaries and their foreign parents. But would the same e¤ect exist for the production and ...nancial linkages between domestically owned subsidiaries and their domestic parents?

Similar to foreign parent ...rms, domestic parents can engage in intra-...rm trade with subsidiaries and provide the latter internal capital supply. These linkages can help subsidiaries sustain resilience to local demand and ...nancial shocks. However, when the shocks occur at the country level, the e¤ect of these linkages will be much weaker than the linkages of foreign owned subsidiaries because the former are located within the same country and exposed to the same shocks. The linkages between subsidiaries and foreign parents, in contrast, can provide more stability in this case, because the parent ...rms are not directly a¤ected by the host-country crisis.

19
# CHAP1T:R I. INTRODUCTIION: PURPOSE AND SCOPE OF REVIEW

1. The purpose of this PHN Annual Sector Review is **to** examine and evaluate lending and sector work undertaken during FY90, and the degree to which it contributeA 10 the fulfillment of Bank objectives overall. The report: (a) provides relevant data on the volume and mix of lending and sector work accomplished during the year, (b) undertakes a qualitative assessment of strengths and weaknesses of the work; (c) identifies special features, trends and issues pertaining to such work which may have implication for future strategies and choices in the PHN sectors; and (d) makes suggestions for possible improvements and refinements, which -'ould contribute to the relevance and effectiveness of Bank interventions in the PHN sectors lThe report also describes research and policy work undertaken by PHRHN and its relevance to issues and information gaps identified.

2. The scope of the report has its limits, which should be made explicit in order to %ualify the observations and suggestions made. The report reviews and evaluates lending and sector work; its coverage of projects under implementation is limited, however, to an integration of the major conclusions of ARIS reports, prepared thus far by PHR technical divisions, into this report's analysis of PHN work, contained in Chapter IIL Furthermore, this report's analysis of lending work draws heavily from the 12 PHN projects and six social development projects with important PHIN components approved in FY90. While recognizing that lending work undertaken during FY90 actually encompasses a larger sampling of projects (i.e., those at the preparation, appraisal and negotiation stages at the end of FY90), time and staff available did not permit a complete and rigorous review of all projects being developed during the FY.
Policy Research Working Paper 9268

# **Abstract**

As COVID-19 continues to wreak havoc across the world, researchers are attempting to quantify the economic fallout from the pandemic as it continues to unfold. Estimating the economic impacts of a prevailing pandemic is fraught with uncertainties about the epidemiology of the disease and the breadth of disruption of economic activities. This paper employs historical and near real-time Google search data to estimate the immediate impacts of COVID-19 on demand for selected services across 182 countries. The analysis exploits the temporal and spatial variations in the spread of the virus and finds that demand for services that require face-to-face interaction, such as hotels, restaurants and retail trade, has substantially contracted. In contrast, demand for services that can be performed remotely or provide solutions to the challenges of reduced personal interactions, such as information and communications technology (ICT), and deliveries, has increased significantly. In a span of three months, the pandemic has resulted in a 63 percent reduction in demand for hotels, while increasing demand for ICT by a comparable rate. The impacts appear to be driven by supply contractions, due to social distancing and lockdown measures, and demand shocks as consumers shelter in place, with the latter dominating for most services. The magnitude of the changes in demand varies considerably with government responses to the pandemic..

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 ktafere@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 names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Figure 4. ILO’s Employment Legislation (EPlex) and Level of Development**

<!-- Start of picture text --> VEN<br>PAN<br>HND CHL<br>PER<br>MEX ATG<br>SLV<br>LCA ARG<br>BRA<br>6 8 10 12<br>Log of GDP PC PPP<br>Log ILO measure of regulation (eplex) Log ILO measure of regulation (eplex)<br>Fitted values Fitted values<br>0<br>-.5<br>-1<br>-1.5<br>Log ILO measure of regulation (eplex)<br>-2<br><!-- End of picture text --> Source: Authors using ILO (2016) Finally, Figure 5 shows the close relationship between our new PCA index of employment protection constructed with the Doing Business data, and that of IADB-OECD and ILO. Figure 6 presents our composite PCA index of employment protection and how the new measure relates with level of development. Interestingly, and in contrast to IADB-OECD’s EPL, our Doing Business PCA measure of employment protection shows a uniformly inverse relationship with countries’ income per capita (Figure 6).

14
# What Explains Global Inflation

Jongrim Ha, M. Ayhan Kose, Franziska Ohnsorge and Hakan Yilmazkuday<sup>1</sup> December 2023

# <u>Supplemental Appendix</u>

# <u>Not for Publication</u>

This appendix contains: Appendix A. A Dynamic General Equilibrium Model Appendix B. Additional Figures and Tables

> ∗ Ha: World Bank, Prospects Group; jongrimha@worldbank.org. Kose: World Bank, Prospects Group; Brookings Institution, CEPR, and CAMA; akose@worldbank.org. Ohnsorge: World Bank, South Asia Region, CEPR, and CAMA; fohnsorge@worldbank.org. Yilmazkuday: Florida International University; hyilmazk@fiu.edu. The findings, interpretations and conclusions expressed in this paper are those of the authors and should not be attributed to the World Bank, its Executive Directors, or the countries they represent.

36
a reduction of about 440,000 jobs in 2025 relative to the baseline employment of about 12 million.<sup>7</sup> This approach is flawed for (at least) two reasons. First, it assumes that all changes in labor supply are voluntary: employment decreases because the household chooses to work less. Second, the representative labor supply represents aggregate hours worked, not the total number of employed workers, and the calculation therefore assumes no change in the number of hours worked per worker, which directly contradicts the labor-leisure decision of the representative household in the model. This approach also ignores climate policy impacts on carbon-intensive leisure decisions.<sup>8</sup>

# **C. Wage Rigidities**

Models with perfect labor markets assume that wages adjust such that total labor supply (either fixed or endogenous) equals total labor demand. Given the evidence that labor markets do not perfectly clear in the real world, the CGE literature began to explore how wage-setting assumptions may introduce unemployment into these equilibrium models. Specifically, if wages were rigid and inflexible in response to climate policy, then markets may not clear. The literature has explored many options for introducing wage rigidities: economywide minimum wages, urban-rural divides with minimum wages in urban areas, and wage-unemployment curves. Here we explore each of these in greater detail.

# _i. Economywide Minimum Wage_

One way to introduce unemployment into a general equilibrium model is to assume an economywide minimum wage, which could represent a minimum wage law or the effect of strong labor unions. If this wage is set above the market-clearing level, then it will lead to unemployment. One example of this type of model is Bovenberg and van der Ploeg (1996). This approach is very simple and tractable, but more recent work has

> 7 These numbers were cited by President Trump on June 3, 2017 as a key reason for pulling the United States out of the Paris Agreement. 8 Carbon Trust (2006) estimates recreation and leisure as the most carbon‐intensive consumption, largely due to large amount of time spent on travel.

29
Table 2: Decennial Changes in Distortions

|||Ind|ustry|||Ser|vices||
|---|---|---|---|---|---|---|---|---|
|||P|ercentile|||P|ercentil|e|
|Period|N|10|50|90|N|10|50|90|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|1980s – 1990s|22|-0.54|-0.09|2.17|23|-0.66|-0.02|1.94|
|1990s – 2000s|58|-0.55|-0.20|1.91|60|-0.66|-0.07|1.22|
|2000s – 2010s|66|-0.87|-0.10|1.34|65|-0.80|-0.18|0.83|

Includes only those countries in which distortions have the same sign across the decades.

Table 3: Productivity Impact of Changes in Distortions Over Time (%)

|||Implie|d Grow|th (%)|Percen<br>Grow|tage of<br>th Acc|Actual<br>ounted||Perc<br>Gro<br>(Actua|entage o<br>wth Acc<br>l Growt|f Actual<br>ounted<br>h≥10%)|
|---|---|---|---|---|---|---|---|---|---|---|---|
|||P|ercenti|le|P|ercenti|le|||Percent|ile|
|Period|N|10|50|90|10|50|90|N|10|50|90|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
|1980s – 1990s|24|-8.5|0.6|5.7|-54.5|2.0|62.1|16|-34.4|1.2|7.4|
|1980s – 2000s|25|-6.2|0.4|6.3|-27.5|0.5|44.6|22|-20.5|0.4|13.3|
|1980s – 2010s|22|-12.5|0.9|5.7|-13.1|1.7|81.6|20|-14.0|1.4|9.2|
|1990s – 2000s|64|-5.6|-0.1|3.4|-31.9|-0.2|16.6|57|-22.5|0.1|12.0|
|1990s – 2010s|55|-6.9|0.4|5.2|-20.1|1.5|23.3|50|-19.4|1.2|11.5|
|2000s – 2010s|71|-2.1|0.2|2.6|-33.2|2.2|42.6|33|-22.3|1.3|22.2|

42
# **4.2 Yield regression**

Results from the household-level yield regression for the full sample of 6,626 households are in table 4 with number of parcels (cols. 1-3) or the Simpson index (col. 4-6) as key indicators for fragmentation of land holdings. Beyond a basic specification with the DEM/GIS-based distance measure (col. 1 and 4), we introduce the Shannon crop diversity measure (col. 2 and 5) and shares for key crop categories (col. 3 and 6). Coefficients on both the number of parcels and the Simpson index are positive and significant throughout, suggesting that fragmentation improves yields per cultivated area; according to the point estimate, adding an additional parcel would increase yield by between 7 and 9 percentage points, similar in magnitude to half a standard deviation (the standard deviation is 0.246) increase in the Simpson index. A one standard deviation (0.465) increase in the Shannon diversity measure would, according to the estimates, further enhance yields by about 25 percentage points. Interestingly, while total cost distance and its square are insignificant in the specification where the number of parcels is used as the proxy for fragmentation, estimates point towards a positive effect of distance on yield (cols 4-6), up to a distance of 4.7 hours, well beyond the mean observed in the data. A dummy for whether the household has parcels outside the cell is insignificant.<sup>14</sup> Coefficients on other variables in the regression are in line with expectations. The negative relationship between farm size and gross output that has also been found elsewhere (Ali and Deininger 2014) is clearly visible with a doubling of cultivated area estimated to reduce yield by between 48% and 56%. Having experienced a crop shock is estimated to reduce output by between 21 and 26 percentage points. Soil quality has a clear output-increasing effect with good quality parcels having yield that is higher by some 16% and medium quality ones with output that is higher by 7%, though the latter is only marginally significant. Location in a wetland, a proxy for better access to water, also increases output by between 14% and 16% while irrigation is too rare to have a measurable impact. Output is higher on land that has been held for a longer time; each additional year is estimated to augment yield by 0.4%. Reliance on extension messages is estimated to have a very positive effect increasing gross output by between 17-19%. However, as unobservable attributes such as farmers’ ability may affect whether or not extension advice is sought in the first place, this should not be interpreted in a causal sense.

At the household level, higher levels of education clearly enhance crop output, with primary and secondary education increasing yield by 8-9 and 13-15 percentage points, respectively. Access to young family labor augments output; an additional member below the age of 35 is estimated to be associated with an increase

> 14 Considering only households who have all of their parcels in the cell significantly reduces the sample size to 3,957. Results for these are reported in the appendix as a robustness check

12
about the direction of causation between the income and individual participation? We address this issue by instrumenting individual (as well as program) participation (column 3). We instrument individual participation using information in the survey on whether or not the household already has a "boy heir" and whether the woman is a cadre, both being regarded as correlated with participation decision but not with household income. Both these variables were insignificant when included in the income equation (column 4) yet highly significant in determining individual participation (column 1), with the probability of individual participation being lower for a woman with a boy heir and higher if she is a cadre. The IV estimates (column 3) suggest a somewhat different pattern of influences: the impact of individual participation on income is still higher for those living in treatment villages (at 13%) but is now negative for those living in control villages (at 273%). Similarly, the effect of program treatment is positive for participants (at 169%) and negative for non-participants (at -114%).

Although the sign pattern of our IV estimates of program impact is plausible, the same cannot be said of the magnitudes. This could be due to model mis-specification, i.e. not taking account of self-selection by individuals. We use the Heckman two-step estimation procedure, which allows for scif-selection, to deal with the model mis-specification issue. The results, reported in columns 5 and 6, show that both the sign and the magnitudes of the program effects seem more plausible than that from the OLS. Our estimates suggest that the program increases the income of participants by 62% but decreases non-participants income by around 20% (but this latter effect is only significant at the 10% level). This finding indicates that the program may generate some form of negative externalities for non-participants. In other words, in the presence of the program, the benefit to participants comes partly at the expense of non-participants. If this is indeed the case, it raises the important question of how to design such programs so as to avoid such adverse effects.

comparison of unconditional mean incomes acToss control and treatment villages in Table Ia.

20
Policy Research Working Paper 5214

# **Abstract**

The “distance effect” measuring the elasticity of trade flows to distance has been rising since the early 1970s in a host of studies based on the gravity model, leading observers to call it the “distance puzzle”. This paper reviews the evidence and explanations. Using an extensive data set of 124 countries over the period 1970-2005, the authors confirm the existence of this puzzle and identify that it only applies to poor countries (the bottom third in per capita income terms in the sample—i.e., the low-income countries according to the World Bank classification, 2006). The analysis shows that this group has intensified trade with closer partners and has chosen new partners that are closer than existing partners, leading to a regionalization of their trade at both extensive and intensive margins (regionalization of trade is absent for the other countries). Combining several methods on cross-section and panel estimates of the gravity equation, the authors estimate that lowincome countries exhibit a significant rising distance effect on their trade, around 18 percent between 1970 and 2006. There is no more distance “puzzle” for trade within richer countries (the top third in per capita income terms in the sample). The paper disposes of several previous explanations of the puzzle, and notes that this regionalization could well be a reflection of increased integration of this group of countries in the world economy or greater marginalization.

This paper—a product of the Trade and Integration Team, Development Research Group—is part of a larger effort in the department to explore the linkages between trade costs, facilitation, and economic development with support through the Multidonor Trust Fund for Trade and Development. The project website is accessible at http://econ.worldbank.org/ projects/trade_costs. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at celine.carrere@u-clermont1.fr, jaime.demelo@unige.ch, and jswilson@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 names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
data and details on the construction of the main variables. Thereafter, we outline our econometric specifications, report our findings, and test for the robustness of our main results. We conclude the paper with a brief discussion on the policy implications of our study.

# **2 Data**

We use data from two primary sources to conduct our analysis. The essential data come from the Afghanistan Living Conditions Survey (ALCS) (Central Statistics Organization (2016)) household survey, which we use to construct measures of labor income uncertainty and household wealth. The ALCS is a large sample survey conducted by the Central Statistics Organization (CSO) and is representative at the province level. The detailed nature of the ALCS data allows us to control for many household-level characteristics which are relevant to the saving decisions. We use conflict data from the Global Terrorism Database (GTD) (START (2017)) to measure the conflict intensity at the province level. We discuss some details of these data in this section.

## **2.1 Wealth**

The ALCS has been conducted sporadically for more than a decade. Yet, the 2013-14 crosssection is of particular importance because the survey instrument for that year was designed to capture household wealth holistically. This survey round contains information on household ownership of gold and silver as well as livestock, which are essential components of household wealth as evidenced by the previous literature.

We begin by constructing a narrow measure of household wealth that corresponds to the total value of gold, silver, and livestock owned by the household. The survey contains detailed information in regards to the livestock ownership. Households are asked to report the ownership of livestock across nine categories ranging from cattle and camels to chicken and small birds. The price data for this diverse set of livestock category are not available for Afghanistan and we rely on price data from different sources to aggregate the total value of livestock.8 A first observation from the data is the widespread prevalence of livestock ownership across the Afghan households. Around two-thirds of the households in our sample report owning any livestock. Nonetheless, there exists a marked heterogeneity across the provinces. The share of households with any livestock ownership varies from a high of 90 percent in Paktika and Khost to 25 percent in the more urban Kabul province. The nature of ownership is diverse as well with households not only keeping animals

> 8See appendix section on livestock for more details on this aggregation.

5