rafmacalaba/data-use-mentions-extended huggingface dataset:
|252|
|No. of countries|120|112|111|113|106|105|
|R-squared|0.05|0.04|0.03|0.06|0.05|0.05|

_Source_ : Dependent variables are from the GWP (Gallup 2011 **)** . Economic growth data are from the IMF (2010), and food and total CPI data are from the ILO (ILO 2011). _Note_ : *, **, and *** indicate significance at the 10 percent, 5 percent, and 1 percent levels, respectively. Standard errors are reported in parentheses. The robust regressions are estimated using the _rreg_ command in stata, with default settings. For fixed effects regressions, standard errors are adjusted for country clusters. Outliers are identified based on _dfbetas_ greater than 0.20. _Economic growth_ is the percent change in GDP per capita between the two years in which the GWP surveys were conducted. Note that the robust regressor does calculate a pseudo R-squared, but it is generally regarded as inappropriate to report this value. Hence, the R-squared reported in this table is derived from an ordinary least squares regression that excludes outlying values.

In table 7, we run the same regressions with the addition of separate price change indicators to determine whether certain types of inflation have additional explanatory power over real economic growth rates. Specifically, we add inflation in the total CPI and food CPI relative to the nonfood CPI. The first represents an aggregate price effect, and the second represents a relative food price effect. Table 7 shows that overall inflation has a significant positive effect on 22
scanning and georeferencing 6,598 physical maps: 35 maps of states and union territories, 640 district maps, and 5,923 subdistrict maps.

The subdistrict maps present the administrative boundaries of towns and villages in their jurisdiction. The location, size, and shape of these towns and villages were digitized in the form of 649,818 boundary polygons. Attributions such as location codes, names, and administrative types were added to the polygons. This study takes advantage of these attributions. Information on the administrative types of the cities, towns and villages allows a refined stratification strategy for sampling.

Data on administrative boundaries are adjusted for this study in three ways. First, in the original database multiple polygons were retained for towns that extended over more than one subdistrict. In this study, those multiple polygons are merged into a single administrative boundary for each town. This results in 649,649 units whose boundaries have a one‐to‐one correspondence with administrative units.

Second, some villages were presented as points on the source maps and their boundaries were  unavailable, totally or partially they are mostly in the mountainous or forest areas of four states (Arunachal Pradesh, Chhattisgarh, Himachal Pradesh, and Meghalaya). These villages are excluded from the analysis. And, third, population information was not available in the 2011 Census of India for some villages, which raises concerns about the quality of the corresponding official maps. These villages are excluded as well.

As a result of these mergers and exclusions, 564,052 administrative units with well‐identified boundary polygons are retained as the universe for the analysis. However, for the purpose of extracting a sample of cities, towns and villages, the set of administrative units is further restricted to those belonging to the 21 largest states and union territories. These large states and union territories are covered by the high‐quality household surveys whose data are needed for a proper stratification. Focusing exclusively on them further reduces the number of administrative units considered to 561,624.

The
# **4. POVERTY AND VULNERABILITY IN RURAL HAITI**<sup>**27**</sup>

In order to fully characterize the determinants of poverty and vulnerability in rural Haiti, a unique survey can be used to assess the impact of idiosyncratic and covariate shocks on economic well-being (such as household consumption or income). This household survey on Haitian food security and vulnerability has been conducted in 2007 in rural areas. The number of households is around 3,000 distributed in 228 communities. It contains quantitative information on household consumption, production, income and assets as well as a good deal of qualitative information on perceived shocks, coping strategies, response capacity and other risks.

# **4.1.Methodology**

# **_Vulnerability to Shocks_**

In this section, we explore the relationships between consumption or income, on the one hand, and various idiosyncratic and aggregate covariates on the other hand. We suppose that households are imperfectly insured against shocks and have limited access to credit. So, assuming uninsured exposure to risk, we can write: where _yij_ is the consumption of household _i_ in community _j_ , _X ij_ is a vector of household characteristics, _Sij_ is a vector of observable shocks,  _j_ is a community specific effect and  _ij_ is the error term.

In the above equation, two parameters are of particular interest. First, we should assess whether  is significantly different from zero that is whether observable shocks have significant impact on economic well-being. Second, in order to ascertain whether observable shocks have different impacts depending on household and community characteristics, we should also assess whether  is significantly different from zero.

Community specific effect  _j_ can be modelled either as a fixed effect or a random effect. In what follows, we will see how to model this unobservable component within a two-level linear random coefficient model. Finally, we should take into account the possibility that the error term  _ij_ can be correlated with observable household characteristics and shocks so
Effective obs.|161|3479|3427|241|161|

_Notes_ : This table reports RD estimates of _γ_ in equation (1). Units of observation are villages in columns 1, 4 and 5, and citizens in columns 2 and 3. In column 1, the dependent variable is a standardized index of local public service provision constructed using the 2021 Podes data. The index has the following components: drinking water, sewage, garbage collection, street lighting, paved roads, public transit, kindergartens, primary schools, village maternities ( _polindes_ ), and community health centers ( _puskesmas_ ). The index is standardized to have mean 0 and standard deviation 1. In column 2, the dependent variable is an index of access to local services constructed using our citizen survey data. The index ranges between 0 and 1 and is constructed as the average of binary variables denoting access to each type of service, where 1 denotes that the service is accessible in the village. In column 3, the dependent variable is a standardized index of service quality based on citizens’ ratings of service quality for each service on a 5-point Likert scale. In column 4, the dependent variable is an index of village governments taking action on a public service, constructed as the average of binary variables indicating that the village government took action or made investments to improve each type of service, where 1 denotes that such action was taken. The indices in columns 2–4 each have the following components: water, garbage collection, paved roads, electricity, kindergartens, primary schools, and community healthcare. In column 5, the dependent variable is a standardized index of local public service provision constructed using the 2014 Podes data, and serves as a balance check. The 2014 index has the same components as in column 1, with the exception of garbage collection, which was not collected in 2014, and a different coding for street lighting, which was categorized into two groups in 2014 ( _no lights_ , _any lights_ ), compared
We use two rounds of household panel surveys conducted in 2007 and 2010, which represent the main agro-climatic zones in Indonesia. Since the first round was prior to the 2007 food price crisis, followed after three years by the second round in 2010, we can examine the effect of the food price shock on investment decisions. Using (i) monthly provincial-level food price data and (ii) farm and plot-level data on farming activities, we construct a household-level price shock variable by weighting the commodity-specific price changes by the household’s production share. This measure captures farm-specific exposures to the food price crisis. First, we use the price shock variable to estimate the responsiveness of the producer’s investment to the price increase. Since the future price level is uncertain and is highly unpredictable to households, we also examine whether the expectation formation affects their forward-looking investment behavior, by decomposing the price shock into anticipated and unanticipated components. Second, we test whether farmers’ responses to the price shock were affected by household characteristics and their asset holdings including farm land. For example, if liquidity constraint is binding farmers, they cannot invest enough in productive assets even though they surely expect a higher price in the near future (thus, the expected returns to investments are high). Family labor endowment can also affect investment decisions if increased production responding to an increase of food price is well accommodated by available family labor, reducing necessity for additional physical investments.

In the empirical analysis, we found that (i) farmers benefited from the positive price shock in general, but the anticipated components of the shock created an incentive to invest in productive assets. Their investment decision was, however, influenced by the initial wealth endowment, showing a clear contrast between wealthier and poorer farmers in the presence of food price increase. That is, poorer farmers tend to increase investments in productive assets, probably 4
and Employment Partnership, a multi-institutional collaboration

> 1 In this paper, paid work entails time spent on an income earning activity (even if explicit wages or salary are not given to the person); this also includes work in non-subsistence farming, nonfarm household enterprises, and as an employee.

> 2 Specifically, SDG Target 2.3 looks at doubling agricultural productivity and incomes of small-scale farmers, with a particular focus on women and poorer/marginalized groups.  The 50 x 2030 initiative is a global partnership to conduct regular surveys of small-scale farmers across 50 countries, and make these data available to countries for policy making.

2
Table 6: Effect of MNREGA on emission rates of pollutants from biomass burning and months exceeding ambient PM2 _._ 5 standard

||E|missions Rates||Ambient PM2_._5 Standard|
|---|---|---|---|---|
||Black Carbon|Organic Carbon|SO2|Share of months _>_ standard|
|Post-MNREGA|0.384<sup>_∗∗∗_</sup>|0.378<sup>_∗∗_</sup>|0.491<sup>_∗∗∗_</sup>|0.014<sup>_∗∗∗_</sup>|
||(0.143)|(0.163)|(0.176)|(0.003)|
|Districts|558|558|558|560|
|Months|144|144|144|144|
|N|80,352|80,352|73,656|80,640|
|Pre-MNREGA Mean|45.81|536.09|52.09|.12|
|District FE|X|X|X|X|
|Year _×_ Month FE|X|X|X|X|
|Weather Controls|X|X|X|X|

Notes: Each column represents separate regressions. In columns 1-3 the outcome is the monthly emissions rate of the pollutant named at the top of the column measured in ng/m<sup>2</sup> s. In column 4 the outcome is the share of months in which the measured PM2 _._ 5 concentration exceeds the annual ambient air quality standard for India set by the Air Prevention and Control of Pollution Act (1981). Concentrations are measured in _μg/m_<sup>3</sup> . All data comes from the MERRA-2 satellite system. In columns 1-3 the
0.063|830|0.045|913|0.058|802|0.031|859|0.035|690|0.028|
|**Gmeaning**|878|0.067|830|0.076|913|0.060|802|0.055|859|0.045|690|0.058|
|**Gselfest**|878|0.156|830|0.129|913|0.127|802|0.096|859|0.116|690|0.075|
|**Rural**|878|0.369|830|0.384|913|0.426|802|0.353|859|0.469|690|0.432|
|**poors_Az1**|878|0.228|830|0.224|913|0.174|802|0.196|859|0.164|690|0.191|

Source: own elaboration on the ILO School-to-work transition survey of Azerbaijan.

# **Table A4. Descriptive statistics by** **<u>gender, the employed</u>**

|||**All**<br>||||**Wome**<br>**n**<br>|||||**Men**<br>|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Variable**|Obs|Mean|Std.<br>Dev.|Min|Max|Obs|
Policy Research Working Paper 5590

# **Abstract**

A long empirical literature has examined the idea that, in the absence of hedging mechanisms, currency risk should have an adverse effect on the export volumes of risk averse exporters. But there are no clear conclusions from this literature, and the current consensus seems to be that there is at most a weak negative effect of exchange rate volatility on aggregate trade flows. However, most of this literature examines the impact of exchange rate volatility on aggregate trade flows, implicitly assuming a uniform impact of this volatility on exporters across sectors. This paper explots the fact that, if exchange rate volatility is detrimental for trade, firms exporting goods that offer a natural hedge against exchange rate fluctuations—i.e.

those whose international price is negatively correlated with the nominal exchange rate of the country where they operate—should be relatively benefited in environments of high exchange rate volatility, and capture a larger share of the country’s export basket. This hypothesis is tested using detailed data on the composition of trade of 132 countries at 4-digit SITC level. The results show that the commodities that offer natural hedge capture a larger share of a country’s export basket when the exchange rate is volatile, but there is only weak evidence that the availability of financial derivatives to hedge currency risk reduces the importance of a sector’s natural hedge.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group. 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://econ.worldbank.org. The author may be contacted at craddatz@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
Policy Research Working Paper 9612

# **Abstract**

Official ending is much larger than commonly known, often surpassing total private cross-border capital flows, especially during wars, financial crises and natural catastrophes. This paper assembles the first comprehensive long-run dataset of official international loans, covering 230,000 loans, grants and guarantees extended by governments, central banks, and multilateral institutions in the period 1790–2015. Historically, wars have been the main catalyst of government-to-government lending. The scale of official credits granted in and around WW1 and WW2 was particularly large, easily surpassing the scale of total international bailout lending after the 2008 crash. During peacetime, development finance and financial crises are the main drivers of official cross-border finance, with official flows often stepping in when private flows retrench. In line with predictions of recent theoretical contributions, this paper finds that official lending increases with the degree of economic integration. In financial crises, governments help those countries to which they have greater trade and banking exposure, hoping to reduce the collateral damage to their own economies. Since the 2000s, official finance has made a sharp comeback, largely due to the rise of China as an international creditor and the return of central bank cross-border lending in times of stress, this time through swap lines.

This paper is a product of the Office of the Chief Economist, Development Economics Vice Presidency. 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 sebastian.horn@econ.lmu.de; carmen_reinhart@harvard.edu or creinhart@worldbank.org; and christoph.trebesch@ifw-kiel.de.

_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
15 months of implementation ( _UC_<sup>1</sup> ); the fifth equalling one when the month is between 24 and 36 months of implementation ( _UC_<sup>2</sup> ); and the sixth equalling one when the month is 36 months or more after implementation ( _UC_<sup>3</sup> ). If UC has a causal effect on _y_ , we would expect  and  to be zero, and at least some of  ,  ,  and  to be nonzero, depending on the time profile of UC impacts.

# V. **DATA**

Our data are from Thailand’s Labor Force Survey (LFS) conducted by the National Statistical Office of Thailand (NSO). The data were collected quarterly from 1985 to 2000, and then monthly from January 2001 (UC was launched in the first provinces on April 2001) until the present. Each round of the survey covers all provinces and all industries in the entire country. The sample size is between 300,000 and 460,000 persons per year, with some rotation across surveys. The LFS gives a weight variable which allows us to gross up to the population. In this paper, we use the data from the start of 1997 to the end of 2005. This time frame covers the four years and three months prior to the earliest implementation of UC, the nine months of staggered implementation across the remaining provinces, and four years after the last province acquired UC. This gives us a sample size of 4.7 million individuals. Our effective sample size in terms of UC impacts is, of course, much smaller because the program operates at the province level, and there are only 76 provinces. Offsetting this is the fact we have data for 74 periods: 12 quarters covering the
sources of FDI, which are nearly evenly divided between mostly OECD countries and Hong Kong (SAR of China), Taiwan (China) and Macau (SAR of China) (henceforth, Hong Kong-Taiwan-Macau for short); China‘s extraordinarily heterogeneous ownership structure, involving state, foreign, and domestic ownership, and tax incentives such as income tax holidays and tariffs. Many foreign investors in China over the last ten years have faced much lower corporate tax rates; before 2008, foreign investors received a 15 percent corporate tax rate while domestic enterprises faced a regular 33 percent corporate tax rate<sup>4</sup> . This policy of promoting foreign investors and other favored firms in China was only discontinued in 2008.

In addition to exploring the differential effects of foreign investment linkages across special characteristics in explaining productivity performance, we also examine how globalization has affected Chinese firm performance. Until 1990, average tariffs on manufacturing in China were as high as 50 percent. There is a rich literature which examines the impact of trade liberalization on productivity, although there are fewer studies that disentangle the effects of input and output tariffs. One example is Amiti and Konings (2007), who use Indonesian manufacturing census data to show that the effect of reducing input tariffs significantly increases productivity, and that this effect is much higher than reducing output tariffs. For China, Brandt, Biesebroeck and Zhang (2008) focus specifically on the impact of trade liberalization on productivity. Using Chinese firm-level data (1998-2005), they suggest that a ten percentage point reduction in final good output tariffs results in an increase in TFP of 0.42 percent.

Our results suggest varied outcomes from promoting domestic productivity growth through these different instruments. The benefits via vertical linkages from foreign investment have been significant and positive, but the impact of tariffs on total factor productivity growth has been negative. We find some horizontal 4 However, the government adjusted this preferential policy in 2008. Starting from Jan 1, 2008, the new corporate tax policy for foreign-invested firms
based on the grid square in which it is located, as well as local neighborhood measures from the grid squares surrounding each firm’s own-cell. Specifically, we define three rings around the own-cell to capture local neighborhood effects and the extent of spatial decay; as illustrated in Figure B4a of Appendix B, ring 1 captures the 8 cells in the immediate queen neighborhood of the reference cell, expanding incrementally by one cell in every direction. Ring 2 captures the 16 cells around ring 1, and finally ring 3 expands to the 24 cells around ring 2. For each ring, we calculate the mean density of 2002 population, 2002 employment and 2012 Landscan<sup>20</sup> For the 2002 COBE we also start with that scale for total employment and own industry employment. However a second spatial scale is possible for employment, since we know point locations. For each survey firm we can form precise circular rings of all firms within any distance. We then count employment and plants within each of those rings. We experimented with fine rings in 500 meter increments; but, for the own industry, firm counts in those distances were sparse so we settled on 1km increments with 4 rings: 0-1km , 1-2km, 2-3km and 3-4km.

Table 10 pools all types of industries and uses the coarser spatial scale. It presents the results of regressions of the log of value added per worker for each firm in our sample, on measures of density in the firm’s own-cell and its respective rings. For each regression, we include industry fixed effects and controls for distance to the city center, Lake Victoria, and the Kampala Northern Bypass. Column 1 presents results using population as the measure of density and shows that there are no significant effects on firm value added, with no hint at a strong explicable pattern. Similarly, in column 2, we detect no significant effects on value added from Landscan ambient population. Our results for employment are more interesting. First, they suggest that it is employment density that
provinceXmonth FE|Yes|Yes|Yes|Yes|
|Control Groups|All Other|Restricted|All Other|Restricted|

Standard errors in parentheses, *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1 Note: this table shows the quarterly impacts of Formosa for the rest of 2016, on fishing activity in the fishingrestricted region (i.e. 20nm near-shore of Formosa-affected provinces), using VIIRS’ monthly-aggregate boat detection data. Each observation is a grid-month. Sample includes all monthly observations between 2013 and 2016. The grid size is 10-mile-square. A grid is considered near-shore when it is located less than 20 nautical miles away from the coast line. The reported outcome variables include log-transformed monthlyaggregate boat counts in each grid (columns 1-2), and 0/1 probability that the grid was detected with at least a boat in that month (columns 3-4). The control group ”All Other” refers to all coastal provinces in the country except for the four Formosa-affected central provinces (Ha Tinh, Quang Binh, Quang Tri, and Hue). The control group ”Restricted” refers to all coastal provinces located south of Phu Yen (i.e. distant from the Formosa-affected region).

36