rafmacalaba/data-use-mentions-extended huggingface dataset:
Where i is the labor variable of interest, and is the impact of climate change – often temperature – ii in area YY _j_ at time _t_ , to which worker _i_ (in micro studies) or sector or country ββ _i_ (in meso or macro studies) is exposed ( i ). Some papers look beyond temperature, at precipitation, or other climate change events. ee ii i reflects other climate change events that may be included or not in the regression. TT i are individual ee ii ii CCworker (or sector or country) characteristics that affect i . and are unobserved individual and XX ii ii ii location fixed effects respectively (which are combined in some studies), and YY μμ μμ i is a time trend.<sup>iii</sup> This estimation typically combines longitudinal data on economic outcomes<sup>iv</sup> with climate data from weather γγ station, gridded, satellite, or other secondary sources and exploits short-run, _plausibly_ exogeneous variation in climate to estimate impact on labor outcomes of interest.

Studies differ in the variables they include on the right-hand side. Research focusing on temperature typically control for other climate events like precipitation. The observed subject characteristics that are included as control variables also vary. Including fixed effects and time trends yields a more robust estimate.

The next sections zoom in on specific left hand side labor variables of interest, one at a time: demand, supply, productivity, income, allocation, and migration. The studies on allocation compare estimates from across different sectors. Throughout the estimations, challenges remain in obtaining estimates of that reflect causal impact, given the non-experimental nature of the data. We see five issues of concern. ββ First, the temperature (or other climate event) to which workers are actually exposed remains unobserved. Locally measured temperature (or climate change event) serves as a proxy.<sup>v</sup> This will affect precision, and can lead to biased estimation, if the exposed temperature is very
The role of structural reforms in raising economic growth in Central America.” _IMF Working Papers_ (2011): 1-20.

**Williamson, John.** “What Washington means by policy reform.” In John Williamson, ed., _Latin American Adjustment: How Much Has Happened?_ , Washington DC: Institute for International Economics (1990)

**Williamson, John.** “What should the World Bank think about the Washington Consensus?.” _World Bank Research Observer 15.2_ (2000): 251-264.

**Winter, Carolyn, and T. H. Gindling.** “Women's labor force participation and earnings in Honduras.” Case Studies on Women’s Employment and Pay in Latin America. Washington, DC, United States: World Bank (1992).

**World Bank.** “World development indicators.” The World Bank, Washington DC (2019).

**World Bank.** “World Bank country and lending groups.” The World Bank, Washington DC (2020).

35
reported life satisfaction than do life circumstances, but empirical research on this issue is very scant.

In this paper we look at life-satisfaction outcomes within transition countries using Life in Transition Survey (LiTs) data for 27 transition countries<sup>2</sup> . We measure comparison

> 1 See Kimball and Willis (2006) for an excellent review of the psychology of adaptation

> 2 Countries included from European region: Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, and Slovenia; Balkan region: Albania, Bosnia and Herzegovina, Croatia, Macedonia, Montenegro, and Serbia; low-income CIS region: Armenia, Azerbaijan, Georgia,

3
Policy Research Working Paper 5830

# **Abstract**

When deciding who should receive welfare benefits with the aim to increase household well-being, it is necessary to understand the effects of the distribution of power within the households at which the aid is directed. Two primary household models have been used to study intrahousehold bargaining and decision making: the unitary model and the collective model. The unitary model seems to fit Pakistan’s context because the prevailing traditional culture positions the male head as the household decision maker. However, using a set of direct measures of decision-making power from the Pakistan Social and Living Standard Measurement Survey, this study finds that even in a country where men seem to have more power than women, the collective household bargaining model applies. This study also finds that, in Pakistan, when women have more decision-making power at home, households tend to spend more on women’s preferred goods (such as clothing and education), family members eat more non-grain food items, and children, particularly girls, are more likely to be enrolled in school.

This paper is a product of the Human Development Social Protection Unit , South Asia Region. 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 xhou@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
children aged 5 is also a potential problem, with 20 percent of children engaged in such work. This may make the transition to school more difficult and could be potentially hazardous to children’s well-being. Here too, available data do not allow for comparisons over time, given that the outcomes are from a single survey.

> 23 In the 2006/07 data, the question was asked about all children under the age of five. In the 2011 data, the question was asked about the youngest child only. Additional analyses showed that at every age children were substantially less likely to be engaged in development activities in 2011 as compared to 2006/07. Thus, differences over time are not driven by differences in who the question covers.

10
|**01/02**<br>17|
|NAE-AE|28|30|23|14|
|F-AE|25|24|22|16|
|SE-AE|38|19|19|20|
|AE-NAE|41|43|43|42|
|NAE-NAE|49|39|47|48|
|F-NAE|50|49|41|39|
|SE-NAE|42|47|39|40|
|AE-F|23|26|25|37|
|NAE-F|17|25|26|31|
|F-F|18|23|32|39|
|SE-F|16|26|39|30|
|AE-SE|5|6|8|4|
|NAE-SE<br>|6<br>|6<br>|5|7<br>|
|F-SE|6|4|5|6|
|SE-SE|4|8|3|10|
|W-W|75|68|68|61|
|NW-W|77|71|62|56|
|W-NW|25|32|32|39|
|NW-NW|23|29|38|44|

Source: Moldova HBS 1998-2002. Legenda: F= Farmer; AE=Agricultural Employee; NAE=Non-Agricultural Employee; SE=Self-Employed (non-farmer); W=Wage workers (AE+NAE); NW=Non Wage workers (F+SE) So, why have people continued to move into private farming and into the non-wage sector during the recent growth period? The two possible answers can be classified as a 'pull' factor, i.e. private farming offers better income opportunities or as a 'push' factor, i.e. private farming is only an escape from unemployment and poverty. To test these two hypotheses we use the Moldova HBS panel 1998-2002 and split the sample of workers into wage and non-wage workers and into 'stayers' and 'movers'
is found to be a fairly rare event in Tanzania – only about 10 percent of female HE operators and 18 percent of male HE operators had any training at all. Those who received training tended to be concentrated in a few sectors. To clarify the effect of training on earnings, interaction terms between training and the sector of activity were included in the regression. The results show that indeed in some sectors training can increase earnings, but not for women HE operators. This is most likely because in Tanzania, the training sector is still quite underdeveloped. In Ghana, where the apprenticeship sector is more developed, similar analysis showed strong positive results for informal training for both male- and female HE operators.

35. What do these findings mean for policy efforts to increase the productivity and earnings of HE operators? The picture is less clear since the information available from the ILFS can only explain about 30 percent of the variation in HE earnings (compared with over 60 percent for wage-earners in Fox and Novella, 2011). Obviously, other variables not measured in the data set matter a lot for HEs‟ success. What is clear is that for now, wage and salary employment in Tanzania is mostly not available for those without at least secondary school qualifications. Those with these qualifications tend to find a wage and salary job, and thus for them staying in the HE sector does not pay off relative to the costs of school. For primary school leavers, however, operating an HE is likely to be the only alternative to agriculture. Since they make up the majority of new entrants to the labor force, and given the shortage of placements in secondary schools, the crucial question is how to make this economic activity pay off. The next sections will examine this issue using information mainly from other data sets, such as those collected from the qualitative FGD study.

18
*Individual labor market experiences have a role, but less than expected**

**Working for a public company and being self-employed are associated with lower perceived downward mobility.** Public workers are more likely to report improvements in the economic situation, though this effect is statistically weak. This result certainly contradicts previous findings in the literature (e.g. Tortia (2008)), although previous results have focused in developed countries and did not analyzed periods with dramatic changes comparable to the transition lived in the ECA region. Considering the generalized reduction in the size of the state across countries in the region and the strong preference for public jobs<sup>14</sup> (EBRD 2010), being currently employed for the public sector may indicate that these workers were able to retain these positions and associated benefits. Hence, it is intuitive for them to perceive an improvement in their situation since, for many of their peers, reforms involved considerable undesired changes. In the case of self-employment, this can be signaling entrepreneurial ability and willingness to take risks, values highly regarded in a market economy.

# **Surprisingly, labor history does not play any significant role on the perceptions of economic mobility.**

The share of time in the labor force spent working for the public sector is negatively correlated with upward mobility, although the degree of statistical significance is weak. The share of time working for the private sector does not have a significant effect over the perceptions. When the sample is restricted to household heads and older than 35 years old workers the share of time employed in the public sector shows a positive effect over the probabilities of perceiving downward mobility (Table A3.2). The variable is defined as shares of time spent 14 According to data collected in the LiTS 2010 (EBRD 2010), 45% of workers 18+ in the region would prefer to work for a state‐owned enterprise (30%) or the public administration (15%). Being self‐employed
10 Unsurprisingly some of the first applications of DEA and SFA in health were on the hospital sector (e.g., (Wagstaff 1989) and (Ray 1991)), and there has been a good deal of work undertaken since then: EconLit contains 56 publications with “hospital” and “frontier” in the abstract). According to Hollingsworth and Street (2006), however, this work has had a relatively modest impact on policymakers.

Our data are from Vietnam‟s official public hospital inventory, the same dataset used by Weaver and Deolalikar (2004) in their study of economies of scale and scope in Vietnamese hospitals. By the standards of low- and middle-income countries, this is an unusually good dataset. However, it does lack detailed information on patients treated, distinguishing only between inpatients, surgery cases and outpatients and not between different departments, let alone different diseases and treatments. In what follows we have included only district hospitals that have between 50 and 500 beds. We have excluded central hospitals run directly by the health ministry, and level-1 and level-2 hospitals (more complex hospitals). Our sample consists of 795 hospitals. Our data are for three years: 1998, 1999 and 2000. The expenditure data cover recurrent costs.

# III. **THE SINGLE OUTPUT CASE**

We start with the simplest case, where we have just one output, or one dimension of quality. (We allow for multiple outputs in the next section.)

# **Methods**

We assume labor and nonlabor inputs are combined to produce an output _y_ at a cost _C_ . Costs can exceed their feasible minimum because the input bundle used does not yield the maximum possible output (technical inefficiency), or because inputs are used in the wrong proportions given their prices and marginal products (allocative inefficiency), or both. We do not try to disentangle the two, instead presenting an estimate of overall inefficiency.

Suppose we have data from multiple service-delivery units. We can then generate a
# **2.2.Hurricane damage frequency**

Hurricanes cause the most devasting disasters in the Caribbean and they have shaped the region’s history and will continue to shape its future. The movement of every tropical cyclic storm is characterized by extreme winds, storm surges, and exceptional levels of rainfall, which may cause flooding (HendersonSellers et al. 1998). Although the official hurricane season in the Greater Caribbean region begins on June 1 and lasts through the end of November, atmospheric conditions in the Atlantic Basin mean that approximately 80 percent of tropical storms and hurricanes form in a 45-day window from mid-August to late September.<sup>11</sup> The number and strength of Caribbean storms vary greatly from year to year, which makes detecting trends in the frequency or intensity of hurricanes over time a challenge. In the last 20 years, the islands in the region have faced storms from low to high intensity, classified in four types by wind speed, as determined by the Saffir-Simpson Hurricane Wind Scale:

- Tropical depression: cyclone with maximum sustained winds of 61 kph or less (Category 0)<sup>12</sup>

- Tropical storm: winds of 62–118 kph (Category 0)

- Hurricane: winds between 119 kph (Category 1) and 177 kph (Category 2)

- Major hurricane: winds of 178–208 kph (Category 3), 209–251 kph (Category 4), and 252 kph or higher (Category 5).

During our sample period of 2000–12, 146 hurricanes (Categories 1–5) and 196 tropical cyclones were reported in the Caribbean region. Figure 2 shows that Cuba, The Bahamas, Haiti, and the Dominican Republic were most affected by hurricanes, with more than 40 episodes in that period. Cuba and The Bahamas were affected by major (Category 3) hurricanes.<sup>13</sup> By contrast, southern areas like the islands of Aruba, Martinique, and Dominica are
**Table 6:** The effects of AIA on the demand for data workers

|Regressions|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Variable|||Worker|Wages|||
|Occupation<br>Group|Non-|Data|Other|Data|Data|Analytics|
|Y|2007-|2007-|2007-|2007-|2007-|2007-|
|ear|2013|2019|2013|2019|2013|2019|
|AIA Dummy|-0.006|-0.008|0.008|-0.011|-0.015*|-0.016**|
||(0.005)|(0.006)|(0.012)|(0.009)|(0.009)|(0.007)|
|Observations|144,136|288,347|30,812|61,863|51,415|104,859|

Note: Worker-level regressions using annual data. Worker wages are in (logged) thousands of pounds. Treated firms are identified taking the average investment values for firms in 2005/06 and calculating if this lagged investment average is above or below the AIA threshold in any given year. Regressions use a binary AIA eligibility indicator. Data occupation groups are defined following Corrado et al. (2022). Specifically, Data Analytics reflects occupations with time used for data intelligence. The remaining occupations are decomposed into Other Data, those with time used for creating data stores, databases or software; and Non-Data, those that do not spend time on data activities. The regressions are estimated using the method outlined in Callaway and Sant’Anna (2021). All regressions
.” _Journal of Agricultural and Applied Economics_ 44, no. 4 (2012): 517-531.

**Funkhouser, Edward.** “Mobility and labor market segmentation: The urban labor market in El Salvador.” _Economic Development and Cultural Change_ 46, no. 1 (1997): 123-153.

**Gaddis, Isis, and Janneke Pieters.** “The gendered labor market impacts of trade liberalization evidence from Brazil.” _Journal of Human Resources_ 52, no. 2 (2017): 457-490.

**Gindling, Thomas H.** “Women's wages and economic crisis in Costa Rica.” _Economic Development and Cultural Change_ 41, no. 2 (1993): 277-297.

**Gollin, Douglas.** "Getting income shares right." _Journal of Political Economy_ 110, no. 2 (2002): 458-474.

**Guerriero, Marta.** “The labor share of income around the world: Evidence from a panel dataset.” _ADBI Working Paper 920_ , Asian Development Bank Institute, Tokyo (2019).

**Hsieh, Chang-Tai, Erik Hurst, Charles I. Jones, and Peter J. Klenow.** “The allocation of talent and US economic growth.” _Econometrica_ 87, no. 5 (2019): 1439-1474.

**International Labor Organization.** “International standard classification of occupations: Structure, group definitions and correspondence tables.” _International Labor Office, Geneva_ (2012).

**Juhn, Chinhui, Gergely Ujhelyi, and Carolina Villegas-Sanchez.** “Men, women, and machines: How trade impacts gender inequality.” _Journal of Development Economics_ 106 (2014): 179-193.

**Kalsi, Priti.** “The impact of US deportation of criminals on gang development and education in El Salvador.” _Journal of Development Economics_ 135 (2018): 433-448.

**Kasy, Maximilian, and Alvaro Ramos-Chaves.** “The Impact of Changing Family Structures on
. 96/83 of August 1996 and relate to 1995.

Vanuatu Data on paid employment in non-agricultural activities are taken from the United Nation's Statistical Yearbook for Asia and the Pacific 1995 and refer to 1989. Unemployment data are taken from the United Nation's Statistical Yearbook for Asia and the Pacific 1995 and refer to 1989.

Population estimate is taken from Social Indicators of Development and relates to 1994. Labor force estimate is taken from IMF Staff Country Report No. 06/75 of August 1996.

Vietnam Data on paid employment in non-agricultural activities are taken from the United Nation's Statistical Yearbook for Asia and the Pacific 1995 and refer to 1993.

Data on Employed Labor Force, Education and Public Health employment are taken from Vietnam: Economic Report on Industrialization and Industrial Policy of October 17, 1995.

Consolidated Central Government wages and salaries are for 1994 and are taken from IMF Research Assistant for Vietnam.

# Eastern Europe and Former USSR

Albania Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and are for 1991.

Central Government, Non Central Government, Education and Health employment is taken from Country Economic Memorandum (draft) of September 3, 1996 and relates to June 1996. (See Annex 5). Military employment data include 22,800 conscripts, but do not include personnel in paramilitary units, e.g., the Militia (estimated at more than 20,000), under the authority of the Ministry of Interior.

Data on GDP at market prices, as well as Consolidated Central Government wages and salaries, are for 1995 and are taken from IMF memo: _Albania: notes on fiscal situation,_ dated April 3, 1996.

# **Bulgaria**

Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and are for 1994.

Central Government employment is estimated, on the basis of the information on IMF Report No. SM/95/306 of December 11, 1994
20 minutes is used as the maximum walking time _per leg_ of the trip when public transport is being used. Second, we use the public transit network that has been collected. The collected frequency of departure, and speeds by which the public transport vehicles travel on the route are used for the analysis (see 2.1. for details and maps about this data). Third, we use the distribution of employment in each city. We base it on Barzin et al. (2022), which estimates the relative density of employment across the metropolitan area using a machine learning method and remote sensing data.

> 21 A given destination will be considered to be reachable from a residential location if it can be reached in under 60 minutes during at least 50% of the departures within the 8:00 to 9:00 am window.

14
**Figure 2.5: More expensive imports are driving a negative trade balance.** Trade balance on goods and services (% of GDP), 2000-2021

<!-- Start of picture text --> 0<br>-2<br>-4<br>-6<br>-8<br>-10<br>External balance on goods and services (% of GDP) -12<br>-14<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020<br><!-- End of picture text -->

**Source:** Authors’ elaboration using data from the World Development Indicators.

Trade liberalization coincided with some export diversification, with new products reshaping Cambodia’s export basket. Today, moderate and low-complexity products— including apparel, footwear, leather goods, precious metals and stones, and tourism—constitute most of Cambodia’s exports (Figure 2.6). Up to 2008, apparel represented more than half of Cambodian exports, and even though its export value has continued to grow in the last 20 years—from about US$1 billion in 2000 to US$8 billion in 2020—its export share has decreased over time due to increasing participation of other products. Thus, while garments remain Cambodia's main export product, their share has decreased 20 percentage points from about 60 percent to 40 percent between 2000 and 2020.

Growth in the mining sector represents the other side of this coin. The discovery of new mines in recent years stimulated exports of precious metals and stones. Since issuing its first industrial mining license in 2016, Cambodia has significantly increased its export share of precious metals from 0.4 percent in 2000 to 14.2 percent in 2020. Moreover, during the Covid-19 years, the sector reported growth, unlike other industries. Between 2000 and 2020, mining activities grew from US$7 million in exports to US$3 billion.

4 |