rafmacalaba/datause-encoder-data huggingface dataset:
Table N13: Effect of SNAP-benefit changes on shopping behavior of households

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Specifcation|OLS|IV|OLS|IV|OLS|IV|OLS|IV|
|VARIABLES|Coupon|share|Deal s|hare|Store bra|nd share|Log|trips|
|Both ineligible|0.00394|0.0272**|-0.00605|0.00485|-0.0301**|-0.107**|-0.0384|-0.00719|
||(0.00323)|(0.0107)|(0.0122)|(0.0555)|(0.0112)|(0.0469)|(0.0325)|(0.165)|
|Household eligible only|-0.00732*|0.0161|-0.0687***|-0.0589|-0.0461***|-0.123**|-0.0360|-0.00918|
||(0.00374)|(0.00995)|(0.0184)|(0.0557)|(0.0156)|(0.0493)|(0.0514)|(0.179)|
|Product eligible only|0.00314|0.0269**|0.0210|0.0376|-0.0290**|-0.108**|-0.0449|-0.0157|
||(0.00235)|(0.0102)|(0.0133)|(0.0557)|(0.0121)|(0.0461)|(0.0319)|(0.163)|
|Both eligible|-0.00569**|0.0177|0.00192|0.0195|-0.0542***|-0.132***|-0.0441|-0.0185|
||(0.00266)|(0.0106)|(0.0194)|(0.0588)|(0.0143)|(0.0462)|(0.0520)|(0.183)|
|Observations|2044662|2044662|2044662|2044662|2044662|2044662|2044666|2044666|
|R-squared|0.624|0.623|0.734|0.734|0.454|0.454|0.770|0.770|
|Prob _>_ F|0.000|0.000|0.000|0.000|0.000|0.000|0.011|0.000|
|Number of units|23911|23911|23911|23911|23911|23911|23911|23911|
|Number of clusters|49|49|49|49|49|49|49|49|
|First stage F-stat||12.258||12.258||12.258||12.258|

Notes: Robust standard errors are in parentheses, clustered by state. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Each coefficient comes from a regression of the outcome on log SNAP benefits per recipient, interacted with four group indicators. Both ineligible refers to outcomes by SNAP-ineligible households on SNAP-ineligible products, household eligible only refers to outcomes by SNAP-eligible households on SNAP-ineligible products, product eligible only refers to outcomes by SNAP-ineligible households on SNAP-eligible products, and both eligible refers to outcomes by SNAP-eligible households on SNAP-eligible products. Control variables as well as household-month and period fixed effects are also included. Observations are weighted by sampling weights.

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# **Table 5: Welfare Impacts of a 15% reduction in CO2 emission by tax policy:**

|Equiv<br>**Household**<br>**Income**<br>**Deciles**|alent Varia<br>**Base**<br>**value**|tion,Percent<br>**tax on**<br>**carbon**<br>**emission**<br>**by**<br>**activities**|Changef<br>**sales**<br>**tax on**<br>**energy**|rom Baselin<br>**sales tax**<br>**on**<br>**energy-**<br>**intensive**<br>**sectors**|eHousehold <br>**tax on**<br>**carbon**<br>**emission**<br>**by**<br>**activities**|Expenditu<br>**sales**<br>**tax on**<br>**energy**|re<br>**sales tax**<br>**on**<br>**energy-**<br>**intensive**<br>**sectors**|
|---|---|---|---|---|---|---|---|
|||**I. Referen**|**ce case**||**II. Rigid c**|**ase**||
|1st (poorest)<br>|11.22|-1.38|0.23|0.80|-1.98|2.23|1.21|
|2<sup>nd</sup><br>|15.76|-0.77|1.82|3.37|-1.03|4.79|4.41|
|3<sup>rd</sup>|21.79|-0.62|1.23|-1.17|-0.66|3.95|-1.15|
|4<sup>th</sup>|28.41|-0.28|1.92|-0.33|-0.16|4.91|-0.14|
|5<sup>th</sup>|36.81|-0.41|0.92|-3.9|-0.31|3.29|-4.51|
|6<sup>th</sup>|47.1|-0.39|0.54|-4.77|-0.28|2.6|-5.59|
|7<sup>th</sup>|65.6|-0.37|-0.13|-5.29|-0.27|1.33|-6.28|
|8<sup>th</sup>|92.22|-0.44|-0.95|-5.15|-0.44|-0.29|-6.17|
|9<sup>th</sup>|135.95|-0.45|-1.84|-4.24|-0.43|-1.8|-5.07|
|10th - lower<br>5%|105.23|-0.35|-1.51|-3.36|-0.27|-1.26|-4.05|
|10th - next<br>1.25%|36.12|-0.14|-2.22|-4.39|0.23|-2.08|-5.21|
|10th - next<br>1.25%|42.62|-0.16|-1.76|-2.63|-0.06|-2.1|-3.25|
|10th - next<br>1.25%|48.22|-0.12|-1.31|-1.77|-0.11|-1.66|-2.32|
|10th – top<br>1.25%|99.27|0.02|-0.15|2.78|-0.45|-1.55|2.76|
|TOTAL|786.32|-0.33|-0.72|-2.76|-0.35|-0.19|-3.35|

_Source:_ CGE model simulations. _Notes_ : Energy = coal, electricity & gas, and petroleum; energy intensive sectors = basic iron and steel, transportation, basic non-ferrous metals, and metal products excluding machinery. In all scenarios, capital is activity specific in coal, gold, and other mining.

Table 5 shows the welfare impact (measured as standard equivalent variation in monetary equivalent of the change in utility) associated with the policy change. Although these welfare measures refer only to the cost side of the cost-benefit equation and disregard the social benefits associated with the changes in environment quality brought about by a reduction of carbon emissions, they are made comparable by keeping the target CO2 emissions constant across experiments. We further express the welfare results as percent change from the base values for easy comparison. Moreover, equity implications are derived for household groups by income deciles. The total welfare change is a simple aggregation of the welfare results for the households in all income groups; although possible, no additional weighting scheme is attempted. Because of the inequality in South Africa, the highest income group is further divided into 5 income 15
# **_4.2 Limited empirical research uses LSMS-ISA data sets to explore the climate-migration nexus_**

Our literature search on the number of works on migration and climate change which used LSMS-ISA data returned only five papers, four of which published in peer-reviewed journals: Ocello et al. (2015); Kubik and Morel (2016), Mueller at al. (2020), Becerra-Valbuena and Millock (2021), and the recent working paper by Di Falco et al. (2022).<sup>13</sup> Ocello et al. (2015) and Kubik and Morel (2016) both focus on Tanzania, and employ the LSMS-ISA National Panel Surveys. While the first looks at both the 2008-2009 and the 2010-2011 waves (although the latter is used only to identify migrants), Ocello et al. (2015) only employ the first. Both also share similar identification strategies (a two-stage setting with an IV probit model for the former, logit regression the latter) based on a cross-sectional setting which is not invulnerable to identification threats. Despite the similarities, they arrive at somewhat contrasting results: Kubik and Morel (2016) find that a reduction in agricultural income caused by a weather shock _increases_ the probability of internal migration. However, this effect is significant only for middle-income households, whereas it is insignificant for the poorest and the richest households, confirming that the decision to migrate as an adaptation strategy depends on liquidity constraints and initial endowments, with the poorest households that cannot afford migration costs, while the richest ones can afford _in-situ_ adaptation strategies, such as irrigation or drought-resistant crops. Ocello et al. (2015) document that being exposed to droughts or floods or crop diseases is associated with an overall decrease in the likelihood of inter-district mobility, with the exception of low-educated individuals.

Mueller et al. (2020) employ the LSMS-ISA data from four countries, namely Ethiopia, Malawi, Tanzania and Uganda. They combine these household panel data with climatic data from NASA’s MERRA to investigate temporary migration responses to weather anomalies in the East African context. Using a linear probability model, they find that climate impacts tend to _decrease_ outmigration and, perhaps surprisingly, are most pronounced in urban areas.

Becerra-Valbuena and Millock (2021) combine LSMS-ISA Malawi surveys with satellite weather data covering the timespan 2000–2016 to estimate the probability of migration for reasons related to work and marriage separately for men and women. They find that overall droughts inhibit marriage-related

> 13 We here refer only to studies exclusively focusing on the causal links between climate change and migration, and exclude works devoted to other research questions that incidentally find climatic impacts on migration decisions.

26
25 adoption from abroad. Data for both royalty and license fee payments and receipts are obtained from the World Bank World Development Indicators and are in current U.S. dollars.

# **Gross Foreign Direct Investment**

Gross foreign direct investment is the sum of the absolute values of inflows and outflows of foreign direct investment recorded in the balance of payments financial account. It includes equity capital, reinvestment of earnings, other long-term capital, and short-term capital. This indicator differs from the standard measure of foreign direct investment, which captures only inward investment. The indicator is calculated as a ratio to GDP in U.S. dollars and was obtained from the World Bank World Development Indicators.

# **Manufacturing Trade Share**

Two papers, namely Lee (1995) and Mazumdar (2001), show that imported machinery or capital goods tend to have a statistically significant positive effect on growth of per capita income across countries. They both interpreted their results as evidence of the positive effects of technological adoption from abroad on long-term economic growth. In light of the above, we attempt to capture the effects of imported capital goods by using the manufacturing trade share, which is defined as the ratio of the sum of manufacturing imports and exports to GDP. Note that manufacturing trade share also contains the amount of manufacturing exports, which we consider to be an indicator for international competitiveness.

Annual data for manufactures imports (as a share of merchandise imports), manufactures<sup>39</sup> exports (as a share of merchandise exports), merchandise imports40, merchandise exports41 and GDP were obtained from the World Bank World Development Indicators. The manufacturing trade share was computed as the ratio of the sum of manufactures imports and exports in current U.S. dollars to GDP in current U.S. dollars.

> 39 Manufactures comprise the commodities in SITC sections 5 (chemicals), 6 (basic manufactures), 7 (machinery and transport equipment), and 8 (miscellaneous manufactured goods), excluding division 68 (nonferrous metals).

> 40 Merchandise imports show the c.i.f. value of goods received from the rest of the world valued in U.S. dollars.  Data are in current U.S. dollars.

> 41 Merchandise exports show the f.o.b. value of goods provided to the rest of the world valued in U.S. dollars. Data are in current U.S. dollars.
i

# FOREWORD

This paper was commissioned in 1985, by the former Population, Health and Nutrition Department (PHND). The objective was to review the historical experience of the Bank's lending and sector activities in the population field, and to evaluate the Bank's strengths and weaknesses in this field. It was carried out primarily by two consultants, George Simmons and Rushikesh Maru of the University of Michigan, under the general direction of Dr. Fred Sai, Senior Population Adviser to the World Bank.

The review of population activities is the last in a series of reviews of the sectors in PHND. It is also the fourth in a series of reviews of population activities carried out since the Bank began lend'ng to population in 1970. The present review was begun and completed prior to the recent reorganization of the Bank and was written in the context of the "Old Bank". It was not intended to be a review of the organizational structure of the PHND. The review was also not mandated to analyze staff composition in terms of specialized skills and experience. Nor was it intended to be a descriptive review; detailed description of what comprises a Bank population project is therefore, limited.

Specifically, the review was expected to focus on PHND's experience in developing, designing, implementing and evaluating population projects. It tried to assess the impact of projects in terms of attainment of project objectives, establishment of institutional capacities necessary to further project work, development of coherent and effective national population policies and a capacity for furcher policy development. It examines the extent to which sector work has been effective in identifying and designing projects and fostering policy dialogue. The review attempts to determine the strengths and weaknesses of the Bank's general approach to development as they relate to the population sector: for example, does the fact that the Bank works through credits and loans limit its effectiveness in this sector?

Based on analysis of Bank documents, interviews and field visits, the review concludes that the Bank's overall performance in the population field has been commendable and its efforts to differentiate its activities in the various parts of the world laudable. PHN's work in establishing policy dialogue with governments on population is identified as the area in which the Bank's impact has been great.cst. The Bank has been particularly effective with development of policy in countries with new programs rather than in those with established programs. The Bank's ability to deal with high level policy issues and to establish inter-sectoral linkages is seen as strong and effective.

The greatest weaknesses in Bank project work are identified as the lack of diversity in project format (projects tend to have a standard format using standard components and show little innovation), the organizational structure through which projects are implemented and the manner in which the projects are supervised. A notable feature of Bank-funded projects is the
simulation exercise. Using the Ethiopia Rural Household Survey, Porter (2012) found that more extreme shocks impact consumption to a far greater extent than lesser shocks. Rainfall in the bottom quintile of the 30 year village distribution caused up to 20% drop in household consumption, whereas for the next quintile of the rainfall distribution (i.e. less than average rainfall, but to a lesser extent), the impact was around 2% (and nonsignificant). Therefore in the simulations we also allow crop-loss to have a non-linear impact on consumption, increasing ten-fold for shocks that are more than two standard deviations above the long-run mean.

The baseline consumption from the “fixed effects” or household and community characteristics is given by _Xhjktβ_<sup>ˆ</sup> . Together, with the estimates of shock-induced consumption losses this allows us to calculate consumption for different states of the world for each household.

# **3.3 Estimating vulnerability**

We create various vulnerability measures using the estimated CDF of _ch,t_ +1 for each household, noting that these capture vulnerability to the shocks that are captured in our data.<sup>12</sup> We first define all those with a predicted consumption lower than the national poverty line as poor. The national absolute poverty numbers that are used by the Government of Ethiopia (Woldehanna and Porter, 2013) are based on the Foster-Greer-Thorbecke (FGT) family of poverty indices (Foster et al., 1984). A household is defined as poor if their total expenditure on all items is less than the national absolute poverty line of 3,781 birr in December 2010 prices. This is the amount of money needed to purchase food of 2,200 kilocalories for every adult-equivalent in the household, and other (extremely basic, necessary) items for everyone in the household.

We next define as “vulnerable”, anyone with a probability of being poor higher than 0.5 across the 6,000 states of the world simulated (Pritchett et al., 2000; Chaudhuri, 2003). We also calculate the expected poverty gap and squared expected poverty gap (equation 3, with _α_ = 0 _,_ 1 _,_ 2; Christiaensen and Subbarao (2005)) to show vulnerability to extreme poverty. Finally the Calvo-Dercon “no-compensation” vulnerability measure (Calvo and Dercon, 2013), with the _θ_ risk exposure reaction parameter set to 2, is also calculated.<sup>13</sup> Finally, we note that we include only idiosyncratic shocks that negatively affect consumption, though a positive draw from the rainfall distribution would decrease poverty. To the extent that there are any other positive shocks experienced by households, our vulnerability estimates could be overestimates.

> 12Though noting that the HICES 2011 contained questions about 20 different shocks, including “other, specify” and there were no others with a frequency higher than 3 or 4 percent.

> 13In robustness checks, we vary the value of _θ_ parameter.

12
# Appendix

Table A.1: Emissions and cost-effectiveness accounting for emissions from electricity generation

|**Variable**|**No**<br>**Policy**<br>**(level)**|**Current**<br>**Policy**<br>**(diff)**|**Sales tax**<br>**reduction**<br>**only (diff)**|**Tariff**<br>**reduction**<br>**only (diff)**|**Carbon**<br>**tax**<br>**(diff)**|
|---|---|---|---|---|---|
|Lifetime CO2emissions<br>(thousand tons)|**Panel A:**<br>3416.8|**Year 2019** <br>-11.4|<br>-2.1|-6.1|-52.6|
|Fiscal cost (USD) per ton of CO2|0.0|562.7|188.1|357.9|-1107.8|
|Welfare cost (USD) per ton of<br>CO2|0.0|61.5|-47.0|113.9|-564.3|
||**Panel B:**|**Year 2020**||||
|Lifetime CO2emissions<br>(thousand tons)|2126.0|-13.9|-2.7|-7.1|-36.1|
|Fiscal cost (USD) per ton of CO2|0.0|671.3|374.4|580.6|-833.6|
|Welfare cost (USD) per ton of<br>CO2|0.0|65.0|149.7|339.8|-545.6|

Note: This table shows the counterfactual simulations of emissions and cost-effectiveness for the five scenarios in 2019 and 2020, including emissions coming from electricity generation. The "No Policy" scenario’s calculations are in levels. All other scenarios’ calculations are increments from the "No Policy" scenario. The Lifetime CO2 emissions are computed by multiplying the emissions produced per vehicle times the average mileage driven per year in Colombia times the expected lifetime of a vehicle. We assume that the average mileage driven in Colombia is 12 thousand miles, while a vehicle's lifetime is ten years. The "Fiscal cost per ton of CO2" is computed by dividing the fiscal cost by the difference in lifetime emissions in each scenario. The "Welfare cost per ton of CO2" results from dividing the difference in total welfare by the difference in lifetime emissions in each scenario. Emissions from electricity generation are calculated based on International Energy Agency data for Colombia’s electricity generation mix in 2019 as 211.09 t/GWh.

38
characteristic. For example, one of the dummy variables created from “gender” is the share of households only having male members.

We use nine variables for the individual-level data: Hindu and age, religion (e.g., Muslim), caste (e.g., upper caste, scheduled castes, and scheduled tribes), discipline (e.g., law and medicine), marital status (e.g., married, unmarried, and divorced), literacy, and education level (e.g., primary school and middle school). Except for age, all other variables are categorical variables. Similarly, we generate the corresponding dummy variables before aggregating them to the state level. Again, we also use the average new confirmed daily cases as a share of the state population.

Using this approach, we compare the outcomes of treated states with that of a weighted combination of control states, the “synthetic” treated states without the free bus ride scheme, which has similar pre-treatment trends as the treated group. The weights applied in our regression for the household-level analysis and individual-level analysis are defined as follows: Household-Level: Weight _ijt_ = State Weight _j ×_ Time _t ×_ Household Weight _it_ , (4) Individual-Level: Weight _ijt_ = State Weight _j ×_ Time _t ×_ Individual Weight _it_ . (5) In equations (9) and (10), “State Weight _j_ ” and “Time _t_ ” are the state unit weights and time weights derived from SDID, respectively. “Household Weight _it_ ” is the sampling weight of a household in the CPHS data. “Individual Weight _it_ ” is the sampling weight of a member of a sample household in the CPHS data.

# **A.2 Delhi Primary Survey**

**Travel Purpose & Distance** The survey inquires about respondents’ primary travel the a selection of work purpose during post-scheme period, offering options including (including work and job search), education, healthcare, shopping, religion, leisure, pick/drop off, and others. Additionally, the survey collects information regarding users’ travel distances and travel distances buses. It should be average average specifically by noted that the survey only asked new users but not continuous users about their average travel distance prior to the implementation of the scheme.

33
Table 6a: Analysis of mean input counts by district-industry

||Organize|d sector|Unorgani|zed sector|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Log of district population|-0.003<br>(0.024)|0.094+<br>(0.053)|-0.043+<br>(0.024)|0.018<br>(0.033)|
|Log of district population density|0.046<br>(0.030)|0.103++<br>(0.045)|-0.116+++<br>(0.020)|-0.037<br>(0.030)|
|Age profile (demographic dividend)|-0.107+++<br>(0.037)|-0.080<br>(0.062)|0.026<br>(0.025)|-0.067+<br>(0.039)|
|Educated worker share (% pop graduate)|0.017<br>(0.040)|0.078<br>(0.057)|0.019<br>(0.027)|0.064+<br>(0.034)|
|Literacy rate|0.100+++<br>(0.039)|0.133++<br>(0.052)|0.061++<br>(0.025)|0.092+++<br>(0.033)|
|Infrastructure: electricity access|0.038<br>(0.025)|0.109+++<br>(0.038)|0.049+++<br>(0.019)|0.044<br>(0.031)|
|Infrastructure: paved roads|-0.037<br>(0.028)|0.000<br>(0.047)|0.080+++<br>(0.020)|-0.025<br>(0.032)|
|Log travel time to closest large city|0.030<br>(0.019)|0.069+++<br>(0.025)|-0.001<br>(0.013)|0.003<br>(0.017)|
|Strength of household banking environment|0.079+++<br>(0.026)|-0.056<br>(0.047)|0.002<br>(0.019)|-0.013<br>(0.030)|
|Urbanization rate|-0.009<br>(0.047)|-0.140++<br>(0.064)|0.087+++<br>(0.029)|-0.006<br>(0.040)|
|Log per capita consumption|0.218+|0.271|0.013|0.138|
||(0.124)|(0.170)|(0.075)|(0.098)|
|Log organized employment for district-industry|0.013<br>(0.012)|0.012<br>(0.012)|-0.005<br>(0.012)|-0.008<br>(0.012)|
|Log unorganized employment for district-industry|0.036+++<br>(0.013)|0.033++<br>(0.013)|-0.011++<br>(0.005)|-0.018+++<br>(0.005)|
|HHI of local employment across mfg industries|-0.026<br>(0.019)|-0.031<br>(0.022)|0.039<br>(0.025)|0.053++<br>(0.026)|
|Industry fixed effects|Yes|Yes|Yes|Yes|
|State fixed effects||Yes||Yes|
|Observations|3,589|3,589|10,674|10,674|
|Adjusted R-squared|0.307|0.330|0.270|0.289|

Notes: Estimations quantify the relationship between average district-industry input counts and local conditions. District-level traits are taken from the 2001 Census. Estimations weight observations by an interaction of district size and industry size, include industry fixed effects, and cluster standard errors by district. Non-logarithm variables are transformed to have unit standard deviation for interpretation. Appendix Tables 4a and 4b provide additional specifications.