rafmacalaba/datause-encoder-data huggingface dataset:
predominantly in Europe and Central Asia.<sup>21</sup> These insights correspond with several studies that find negative effects of cold weather on aspects such as productivity, health, and economic growth (Cook and Heyes, 2020; Dell _et al._ , 2012; Deschênes and Moretti, 2009).

# _Transient Poverty_

Unlike chronic poverty, transient poverty is characterized by short-term fluctuations in poverty status, often due to temporary shocks or income volatility. Understanding transient poverty is therefore crucial for assessing how individuals and communities cope with short-term disruptions caused by climate change and weather shocks, which could have long-term implications if not effectively managed. For better comparison with earlier results, we employ three related measures of poverty: (1) chronically poor, (2) transiently poor, and (3) generally poor. Specifically, we classify a subnational unit as “chronically poor” if it falls below the poverty threshold in both periods within a 5-year window, conditional on having at least two data points during that period. Conversely, a subnational unit is considered “transiently poor” if it falls below the poverty threshold in either (but not both) of the two periods within the same 5-year window. Finally, a subnational unit is classified as “generally poor” if it is either chronically poor or transiently poor—that is, it falls below the poverty threshold in either of the two periods. While this new definition of chronic poverty is somewhat simpler than those employed for Table 2 (and it is most similar to the measure for the share of time that a subnational unit is in poverty during a specified period), it allows for better comparison with transient poverty and general poverty.

> 21 We provide additional heterogeneity analyses in Appendix A. For Figure A2, we use the World Bank’s income classifications and divide our sample into two groups: firstly, the low income and lower-middle income countries, and secondly, the upper-middle income and high-income nations. The figure consistently reveals the negative effects of hotter temperatures on chronic poverty. In addition, the effects of colder temperatures are also discernible, but predominantly within the high-income bracket. In Figure A3, we present the estimates of hotter temperature on chronic poverty, adjusted for real GDP per capita on a country-by-country basis. This underscores poor countries’ strong exposure to warming temperatures, with African countries such as Togo, Malawi, and Uganda being particularly vulnerable.

31
WPS3735

# **Global Impacts of the Doha Scenarios on Poverty**

## by

Kym Anderson, William J. Martin, and Dominique van der Mensbrugghe

## **Abstract**

This paper illustrates some of the potential consequences of the WTO’s Doha Round of multilateral trade negotiations on incomes and poverty globally. Using the global LINKAGE model to generate changes in domestic and international prices that have a direct impact on factor incomes and consumer prices, we estimate the change in real income at the poverty line that would accompany various reform scenarios. When accompanied by additional information about the elasticity of poverty with respect to income, this provides an estimate of the change in poverty by country. Under most liberalization scenarios considered, unskilled wages rise more than average incomes, but the estimated impact on global poverty is modest, especially if developing countries are unwilling to undertake much reform.

## **JEL codes: C68, D58, F17, Q17**

**Key words:** Trade policy reform, computable general equilibrium modeling, poverty alleviation

## World Bank Policy Research Working Paper 3735, October 2005

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._
9

# **IV. Estimates** of Price **Elasticities**

The price elasticity estimates for various passenger and freight demands for traneport are presented in Tables 2 and 3. Appendix 2 provides a more detailed listing of price elasticity estimates, and Annex A is an annotated bibliography of the studies reviewed in connection with this study.

The first sub-section below comments briefly on our summary of elasticity results. Subse,quent sub-sections comment on the variability of estimates of transport demand elasticities across different studies, and how we arrived at a 'most likely' range of price e asticities of demand for various transport markets.

# A. Simary of elasticity results

Tables 2 and 3 report both own-price as well as some mode choice elasticities. As noted earlier, mode choice elasticities can be linked to ordinary demand elasticities providing sufficient information is available. Unfortunately, virtually no mode choice study reports the required information. As a result, we were unable to convert mode split elasticities to ordinary elasticities. We present mode choice elasticities in brackets in Tables 2 and 3 (and in separate columns in Appendix 2). It is important to recognize that the mode split elasticities are not directly comparable to the ordinary ownprice elasticities (the mode choice own-price elasticities underestimate corresponding ordinary own-price elasticities).

Some elasticity estimates for a relevant but mode-specific market, the demand for gasoline, are presented in Table 4 below. Most of the estimates in this table are taken from the survey paper by Blum, Foos and Gaudry (1988). They surveyed a total of 21 studies.

The focus of this survey is on the own-price elasticity of demand. We did not emphasize cross-price elasticities in our review. Unlike own-price elasticities, we find almost no ability to generalize about cross-price elasticities. They are very sensitive to specific market situations and to the
**Table 12 Panel Data Analysis**

|VARIABLES|I|II|III|IV|
|---|---|---|---|---|
|JobCr/Firm|-0.109|-0.200|-0.027|-0.049|
||(0.056)*|(0.058)***|(0.033)|(0.027)*|
|Log(labor)|0.072||0.023|0.014|
||(0.011)***||(0.006)***|(0.005)***|
|Foreign|0.233|0.305|0.033|0.061|
||(0.040)***|(0.038)***|(0.033)|(0.032)*|
|Age|0.000|0.003|-0.000|-0.000|
||(0.001)|(0.001)***|(0.001)|(0.000)|
|Growth||0.109<br>(0.033)***|||
|Observations|1501|1496|962|1006|
|R2(p)|0.267|0.237|0.135|0.141|

Robust standard errors clustered by country and sector are in parentheses. In the regressions, I also control for sector, survey year, and country fixed effects. (I) uses firm level net job creation as dependent variable, (II) same as (I) but controls for employment growth, (III) same as (I) but restricts the set to firms that didn't export three years ago, (IV) sama as (III) but uses conservative definition of exporting. Coefficients for the probit regressions show the marginal effects at their mean values. *** p<0.01, ** p<0.05, * p<0.1.

29
# **Appendix A: Summary Statistics and Description**

||**Variables**|**Definition**|**N**|**Mean**|<br>**SD**|**Min**|**Max**|**Primary Data Source**|
|---|---|---|---|---|---|---|---|---|
||Living Standards|Government Handling of: Improving<br>living standards of the poor|109019|0.30|0.46|0|1|Afrobarometer - Round 4, 5, 6|
|||Government Handling of: Water and|131280|044|050|0|1||
||Water & Sanitation|sanitation to households||.|.|||Afrobarometer - Round 3, 4, 5, 6|
||Jobs|Government Handling of: Job creation|130277|0.27|0.44|0|1|Afrobarometer - Round 3, 4, 5, 6|
|_Dependent variables_|Health|Government Handling of: Improving<br>educational needs|132161|0.58|0.49|0|1|Afrobarometer - Round 3, 4, 5, 6|
||Education|Government Handling of: Basic health<br>services|131661|0.60|0.49|0|1|Afrobarometer - Round 3, 4, 5, 6|
||Public Services|Composite measure of how government is<br>handling public services listed|104699|0.50|0.39|0|1.14|Afrobarometer - Round 3, 4, 5, 6|
|||Expected living standards in 12-month|||||||
||Optimism|<br>time|79300|0.79|0.41|0|1|Afrobarometer - Round 3,4,5,6|
||Active 50 km|Distance 50 km or less to an active mine|136727|0.07|0.25|0|1|SNL Metal & Mining by S&P|
||Inactive 50 km|Distance 50 km or less to an inactive mine|136727|0.22|0.42|0|1|SNL Metal & Mining by S&P|
||Local corruption|Average perception of corruption at the<br>local level|134327|0.85|0.13|0.12|1|Afrobarometer - Round 3, 4, 5, 6|
|||Incidence of bribe ament - averaed at|||||||
|_Variables of interest_|Bribe|py g<br>the local level|136727|0.21|0.15|0|0.94|Afrobarometer - Round 3, 4, 5, 6|
||Distrust in local governments|Average distrust level in local government<br>councillors|136727|0.48|0.19|0|1|Afrobarometer - Round 3, 4, 5, 6|
|||Measure of the discretionary power of|||||||
|||subnational governments over tax and|123539|0.13|0.11|0|0.61||
||Decentralization|revenue instruments||||||Vincent(2020)|
||Age (ln)|Age (natural logarithm)|135473|3.53|0.38|2.89|4.87|Afrobarometer - Round 3, 4, 5, 6|
||Age<sup>2</sup>|Age squared|135473|12.63|2.70|8.35|23.69|Afrobarometer - Round 3, 4, 5, 6|
||Gender|Gender of the respondent|136727|1.50|0.50|1|2|Afrobarometer - Round 3, 4, 5, 6|
||Education|Education (primary and above)|136350|0.80|0.40|0|1|Afrobarometer - Round 3, 4, 5, 6|
||Urban|Urban area|136727|0.39|0.49|0|1|Afrobarometer - Round 3, 4, 5, 6|
|_Control variables_|Employed|Employed at the time of the survey|136161|0.36|0.48|0|1|Afrobarometer - Round 3, 4, 5, 6|
||Interest in Public Affairs|Interest in public affairs|135361|0.61|0.49|0|1|Afrobarometer - Round 3, 4, 5, 7|
|||Resondents lack basic necessities -|||||||
||Deprived|p<br>higher/worse|134991|1.45|1.09|0|4.59|Afrobarometer - Round 3, 4, 5, 8|
|||Natural resources rents (%GDP) (natural|||||||
||Natural Resources Rents (ln)|logarithm)|136727|2.21|0.71|0.89|3.92|World Development Indicators|
||Natural Resources Rents|Natural resources rents (%GDP)|136727|11.62|8.81|2.43|50.25|World Development Indicators|

34
number of deaths for workers and the total population are as follows: (a) low case: 11 and 18 million, respectively; (b) middle case: 74 and 117 million, respectively; and (c) high case: 137 and 214 million, respectively. The number of deaths is influenced by the fact that the global population is growing. For the high case, the yearly total number of deaths corresponds to 2.3% of the UN-projected population in 2050. To provide some historical context, the influenza pandemics of 1918-1919 (also known as the Spanish Flu or La Grippe) killed between 20 and 40 million people, which was more than those killed in World War I (Barry 2005); the bubonic plague of the 14<sup>th</sup> century (commonly called the Black Death) killed between 50 to 200 million, wiping out 40-60% of Europe’s population at the time (Benedictow 2007).

# **_Fall in labor productivity_**

In Smith et al. (2005), the influenza case in Keogh-Brown et al. (2010), and the HIV/AIDS case in Kambou et al. (1992), labor productivity is expected to fall by about 1.0-1.5%. In Burns et al. (2006), the fall in labor productivity in the case of the avian flu ranges from 1.4% for high-income countries to 1.9% for Sub-Saharan Africa. In World Bank (2006) and McKibbin and Sidorenko (2006), the potential output losses imply a much higher (or more than double) decline in labor productivity. Hence, we use a figure of 1.5% for our low case and scale it up to 3% and 4.5% for the middle and high cases, respectively. Because of the potential snowballing effect of AMR, these numbers could be very conservative.

# **_Rise in health care cost_**

KPMG (2014) projects that prolonged and secondary health care costs, such as the extra number of hospital days, could rise significantly. According to our assessment, these costs may double the shares of total health expenditure in GDP, which in 2010 were as follows for our regions: low-income 5.4%, lower middle-income 4.2%, higher middle-income 6.1%, and high-income 12.7%. In dollar terms, these increases are amplified over time given GDP growth. To capture the global- and economy-wide repercussions of rising health cost, these cost increases are translated into additional government spending on services, paid for by a direct tax on households, thus maintaining an unchanged fiscal balance. The resulting decline in household post-tax income leads to reductions in consumption, savings, and investment, reducing GDP and trade.

# **_Reduction in livestock production_**

Following the meta-analysis of various studies by Laxminarayan et al. (2015) (particularly for cattle in the more recent of the two periods under investigation), we impose the following declines in total productivity in the livestock sector: low case 3%, middle case 5%, and high case 7%.

# **_Global restrictions on livestock trade_**

Not unlike the impact of the 2014 Ebola epidemic (World Bank 2014), increased AMR is likely to have a disproportionately strong negative impact on trade in livestock products due to border 10
diversity distribution – often very poor households – experience large declines in dietary diversity as a result of the wheat flour price increases. Since households living at subsistence levels cannot make major cuts to caloric intake, they must adjust the compositions of their diet in order to maintain energy levels.

To the best of our knowledge, this paper is the first to utilize the UQR estimator to examine the impact of food price shocks on household food security. It represents one of a handful of empirical analyses that use nationally-representative household data collected prior to and during a significant price shock. Since such data are extremely rare, most empirical analyses rely on data collected during earlier periods and utilize simulation models to look at the potential impact of the price shock on household welfare. In this analysis, we seek to inform local and national policymakers and development agents working in Afghanistan by providing systematic quantitative analyses of household responses to food price shocks.

# **Data**

We use consumption and price data from the National Risk and Vulnerability Assessment (NRVA) 2007/08, conducted by the Government of Afghanistan Central Statistics Organization and the Ministry of Rural Rehabilitation and Development. The survey was administered between August 2007 and September 2008 and covered over 20,500 households (over 150,000 individuals) in 2,572 communities in all 34 provinces of Afghanistan. The long time frame made it possible to obtain seasonally-adjusted estimates of household food security and allowed for coverage of conflict-affected areas.

The sample was selected based on a stratified, multi-stage design. The survey was stratified explicitly geographically and implicitly over time.<sup>1</sup> The 11 provinces with the most populous provincial centers were each stratified into urban and rural areas. The remaining provinces were treated as separate rural strata, and the nomadic Kuchi population was treated as a separate stratum. The stratification resulted in 46 domains or strata. In the first stage of selection, 2,441 primary sampling units (PSU) from urban and rural settled

> 1 The population frame is based on a 2003-05 national household listing.

4
study are based on the same approach. The possible reasons for Kravis estimates to be higher than the esimates developed here are: a) Kravis study calculated the PPPs by a sample of prices with limited coverage.

b) In the present study, the several adjustments in housing and service sectors have been made which lead to PPPs for these categories which are nuch higher than those shown by the acta prices.

62. To calcuate the dollar per capita GDP of China n 1991 in curmen pnces, the growth rates computed from national curncy GDP data in constant prices were used and then the estimates were adjusted by the U.S. iation rate. First, China's per capita GDP for 1991 in 1986 constant pnces was calculed by the following equation: _percapita GDP',"'_ = _percapita GDPmxgrnwthratebya*ionaIcurrency (O0)_ So China's 1991 per capita GDP in 1986 intemational doDlars is: I$1,044 x 132.07/o = I$1,379, where 132.07% is the growth factor for GDP for China from 1986 to 1991, calclated from GDP in 1987 piices (see the World Tables 1994). Second, the 1991 China's per capita GDP in current prices was calculated by the folowing equation: M _~~~~USGDPDe&ltOr,.9, percacpita GDP'I9V =percpilta GDP, "x USGDPDeflator, (1)_ = Thus the 1991 China's per capita GDP in 1991 international dollars is: I$1,379 x 116.9% / 96.92% I$1,663 and C:hinas aggrgte GDP in 1991 is I$1,909.1 bilion.

27
Since consumption increases with ε, Cov(U’(c), ε)<0. Since U’(c) <0 this implies that _r_ < _f_ ' ( _K_ , θ) . The size of the gap between the market interest rate and the marginal return to capital will be increasing in the level of risk in business profits, and in the level of risk aversion displayed by the household. We test this by interacting the treatment effect with measures of the risk aversion of the entrepreneur, and the perceived uncertainty they have in their profits.

# **8.2. Estimation of Heterogeneous Treatment Effects and Measurement of Factors Determining Heterogeneity.**

The above theory shows that the pattern of heterogeneity of treatment effects can inform us about the reasons why returns are so high and exceed market interest rates. We allow for heterogeneity in treatment effects through estimation of variants of the following fixed effects regression: The parameter γs then shows how the effect of the treatment amount varies with characteristic _s_ .<sup>16</sup> Since the evolution of profits over time may vary with _Xs,i,t_ even in the absence of treatment, we allow the wave effects δt to also differ with individual characteristics.

The theoretical model suggests that the heterogeneity of returns could vary with the number of workers in the household, household wealth, entrepreneurial ability, risk aversion, and uncertainty. We measure each of these factors as follows:

- _i)_ The _number of workers in the household_ is the number of paid wage workers in the household in the pre-treatment baseline survey.

- _ii) Household wealth_ is difficult to measure accurately, especially since liquid wealth should be more important for funding the household budget than

> 16 The upper limit of 100,000 rupees of capital stock may result in an interaction between ability and some of the characteristics we are interacting with the treatment. For example,an entrepreneur of a given ability level may stay within the sample criteria if there are no additional workers in the household, but grow beyond that limit if there are additional workers. The presence of additional workers would then be negatively correlated with entrepreneurial ability in the sample. This should be kept in mind when interpreting the coefficients on the interaction effects.

25
Policy Research Working Paper 10823

# **Abstract**

Vulnerability to climate change and water scarcity is increasing globally. How this affects individual employment outcomes is still not well understood. Using survey data collected from approximately half a million individuals across Sub-Saharan Africa over from 2005 to 2018, this paper examines the causal relationship between water availability and labor market outcomes. It combines georeferenced household survey data with a drought index that captures the exogenous effects of both rainfall and temperature on water availability. The findings suggest that extremely dry periods decrease employment by 2.5 percentage points on average, and wet periods with an abundance of soil moisture (not flooding) increase employment by 4 percentage points. The negative effects of dry shocks are larger in rural, poorer, and agriculture-dependent areas and for individuals who hold low-skilled jobs or work as farmers. Moreover, the paper finds that the burden of dry shocks disproportionately falls on women, while the benefits of wet shocks accrue more to men. The presence of irrigation infrastructure and the historical evolution of local livelihood strategies—historical mode of subsistence—partly mediate the impacts of water shocks.

This paper is a product of the Water 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 akhan31@worldbank.org, fzhang1@worldbank.org, lkuat097@uottawa.ca, and rpongou@uottawa.ca.

_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
|P071039|2004|2007|11/10/2006|6 Closed|Bosnia and Herzegovin|
|---|---|---|---|---|---|
|P071040|2001|2001|6/28/2001|1 Closed|West Bank and Gaza|
|P071040|2001|2002|5/15/2002|3 Closed|West Bank and Gaza|
|P071040|2001|2003|5/15/2003|5 Closed|West Bank and Gaza|
|P071040|2001|2004|4/29/2004|7 Closed|West Bank and Gaza|
|P071040|2001|2005|4/27/2005|9 Closed|West Bank and Gaza|
|P071040|2001|2006|12/28/2005|10 Closed|West Bank and Gaza|
|P071040|2001|2006|5/31/2006|11 Closed|West Bank and Gaza|
|P071052|2006|2006||0 Closed|Turkey|
|P071052|2006|2007|6/29/2007|1 Closed|Turkey|
|P071052|2006|2019|5/9/2019|2 Closed|Turkey|
|P071061|2003|2003|6/25/2003|1 Closed|Kyrgyz Republic|
|P071061|2003|2004|5/24/2004|3 Closed|Kyrgyz Republic|
|P071061|2003|2005|6/23/2005|6 Closed|Kyrgyz Republic|
|P071061|2003|2006|6/29/2006|7 Closed|Kyrgyz Republic|
|P071061|2003|2007|6/29/2007|8 Closed|Kyrgyz Republic|
|P071061|2003|2008|6/23/2008|9 Closed|Kyrgyz Republic|
|P071062|2002|2002||0 Closed|Djibouti|
|P071062|2002|2003|6/26/2003|4 Closed|Djibouti|
|P071062|2002|2004|6/29/2004|7 Closed|Djibouti|
|P071062|2002|2005|4/22/2005|9 Closed|Djibouti|
|P071062|2002|2006|12/1/2005|10 Closed|Djibouti|
|P071062|2002|2006|5/11/2006|11 Closed|Djibouti|
|P071062|2002|2007|12/28/2006|12 Closed|Djibouti|
|P071062|2002|2007|2/18/2007|13 Closed|Djibouti|
|P071062|2002|2008|9/27/2007|14 Closed|Djibouti|
|P071062|2002|2008|4/28/2008|15 Closed|Djibouti|
|P071062|2002|2009|9/8/2008|16 Closed|Djibouti|
|P071062|2002|2009|12/31/2008|17 Closed|Djibouti|
|P071062|2002|2009|5/29/2009|18 Closed|Djibouti|
|P071062|2002|2010|8/11/2009|19 Closed|Djibouti|
|P071062|2002|2010|2/21/2010|20 Closed|Djibouti|
|P071062|2002|2011|9/6/2010|21 Closed|Djibouti|
|P071062|2002|2011|4/3/2011|22 Closed|Djibouti|
|P071062|2002|2012|10/16/2011|23 Closed|Djibouti|
|P071062|2002|2012|4/24/2012|24 Closed|Djibouti|
|P071063|2003|2003|6/25/2003|1 Closed|Kyrgyz Republic|
|P071063|2003|2004|6/29/2004|4 Closed|Kyrgyz Republic|
|P071063|2003|2005|6/23/2005|7 Closed|Kyrgyz Republic|
|P071063|2003|2006|6/28/2006|8 Closed|Kyrgyz Republic|
|P071063|2003|2007|6/29/2007|9 Closed|Kyrgyz Republic|
|P071063|2003|2008|6/24/2008|10 Closed|Kyrgyz Republic|
|P071063|2003|2009|3/17/2009|11 Closed|Kyrgyz Republic|
|P071063|2003|2010|12/30/2009|12 Closed|Kyrgyz Republic|
|P071063|2003|2010|5/21/2010|13 Closed|Kyrgyz Republic|
|P071063|2003|2011|12/15/2010|14 Closed|Kyrgyz Republic|
|P071063|2003|2011|3/12/2011|15 Closed|Kyrgyz Republic|