rafmacalaba/datause-encoder-data huggingface dataset:
# **<u>Executive Summary</u>**

This paper examines the nexus between development and culture. We try to understand the process by which a large-scale anti-poverty intervention, in a very poor and patriarchal region of India, induced a cascading set of changes that led to the empowerment of women. Through three years of qualitative fieldwork in rural Bihar, we examine this in the context of _Jeevika_ , a poverty alleviation project assisted by the World Bank and implemented by the Government of Bihar. It provides a particularly interesting venue for examining the relationship between development and culture for three reasons: First, primarily targeted towards women and structured to induce rapid economic and cultural change, _Jeevika_ began operations in six districts in 2006 and is projected to cover all 38 districts of Bihar and 12.5 million households by 2022. A project of this scale, gives us the opportunity to understand how cultural change can be brought about, not in one or two communities, but at a significantly large scale. Second, _Jeevika_ operates in rural Bihar, a particularly ‘hard context’ with respect to existing inequalities – the state is one of the poorest in India, the population is almost 50% illiterate, and gender and caste hierarchies are oppressive – making cultural change very hard. Third, our quantitative analysis of Phase1 of _Jeevika_ suggests that it had considerable impact on women’s empowerment both within the household, and in the public sphere. In this paper we dig further and ask – what are the processes and mechanisms of change that remain invisible in the quantitative study, but result in the social impact captured by it.

By relying on qualitative data (semi-structured interviews, focus group discussions with members, non-members and key stakeholders, as well as participant observation of group activities), collected in four villages (i.e. two matched pairs of treatment and control villages) in the first phase of _Jeevika_ over a three-year period i.e. from 2011 to 2015, we find support for an integrative view of culture. Comparing two pairs of treatment and control villages, we find that, by giving women privileged access to a) _symbolic resources_ (that facilitate the formation of a new identity anchored in the SHG, rather than caste or kinship), b) _physical resources_ (such as group money, access to credit and passbooks), and c) an associated _institutional environment_
<!-- Start of picture text --> 7<br>Old-age pensions as transfers Old-age pensions as deferred income<br>6<br>5.8<br>5.3<br>5<br>4<br>3.5<br>3<br>2.7<br>2<br>1<br>0<br>Market Income (M.I.) Disposable Income Consumable Income<br>Poverty gap (%)<br><!-- End of picture text --> Source: Own estimates based on Turkey 2016 HBS. Note: The figure shows the poverty rate and the poverty gap for income concepts described in Figure 1.

The combined impact of direct taxes and direct transfers reduces the incidence of poverty. The poverty headcount ratio decreases from 11.1 to 10.3 percent, when pensions are treated as deferred income. Similarly, the poverty gap is strongly reduced by the combined action of direct taxes and transfers.

Indirect taxes, however, lead to substantial increases in poverty. While all individuals are affected by indirect taxes along the income distribution, the less well-off suffer a relatively higher impact (as shown in the inequality results of Figure 3). As such, poverty rises by more than 5 percentage points from Disposable to Consumable income. The poverty gap increases by almost 50 percent, from 2.7 to 5.3 percent.

In comparative terms, the overall impact of taxes and transfers on poverty in Turkey and peer countries is mixed (Figure 5). Poverty decreases in 4 countries, increases in 4 others, and stays constant in one of them. A common pattern across countries is that indirect taxes are poverty increasing, since low-income households allocate all their income to consumption. Turkey’s relative performance is below-median, driven by the relatively stronger poverty-increasing impact of indirect taxes, and the comparatively smaller poverty-decreasing impact of transfers and direct taxes. While the poverty rate for Consumable income is higher than for Disposable income in all upper-middle-income countries with available data, the magnitude of the increase in Turkey is among the highest of the lot.

21
Raina, V.K., 2009. Himalayan Glaciers: A State-of-art Review of Glacial Studies, Glacial Retreat and Climate Change. MOEF Discussion Paper. Science & Public Policy Institute Reprint Series.

- Rajendran, K. and A. Kitoh, 2008. Indian Summer Monsoon in Future Climate Projection by a Super High-resolution Global Model. Current Science 95: 1560-1569.

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2007. Warming Trends in Asia Amplified by Brown Cloud Solar Absorption. Nature 448: 575-578.

Rathore, B.P., A.V. Kulkarni, and N.K. Sherasia, 2009. Understanding Future Changes in Snow and Glacier Melt Runoff Due to Global Warming in Wangar Gad Basin, India. Current Science 97: 1077-1081.

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<u>http://www.fao.org/fileadmin/user_upload/agp/AgBD_NAPA_report_NEPAL.pdf.</u>

- Regmi, B.R., A. Pandit, B. Pradhan, S. Kovats, and P. Lama, 2008. Climate Change and Health in Nepal. CLACC Working Paper 3. LI-BIRD, Nepal: The RING alliance of policy research organizations and International Institute for Environment and Development. pp. 30.

- Ren, J., D. Qin, S. Kang, S. Hou, J. Pu, and Z. Jing, 2004. Glacier variations and climate warming and drying in the central Himalayas. Chinese Science Bulletin 49:65-69.

- Revadekar, J.V., S.K. Patwardhan, and K. Rupa Kumar, 2011. Characteristic Features of Precipitation Extremes over India in the Warming Scenarios. Advances in Meteorology Article ID 138425, doi:10.1155/2011/138425.

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- Richardson, S.D. and J.M. Reynolds, 2000. An Overview of Glacial Hazards in the Himalayas. Quaternary International 65: 31-47.

41
# **Table 2. Share of Domestic Listed Companies**

|||Full sample|||Full sample||Low- and|Middle-Incom|e Countries|Low- and|Middle-Incom|e Countries|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Stock Market Capitalization<br>(% of GDP)|-0.138|||-1.630|||-0.678|||-1.286|||
||(0.749)|||(1.103)|||(0.573)|||(0.930)|||
|Stock Market Total Value||-0.289|||-0.492|||-0.417|||-0.491||
|Traded (% of GDP)||(0.624)|||(0.734)|||(0.608)|||(0.654)||
|Stock Market Turnover|||0.948*|||1.138*|||0.925|||0.897|
|Ratio|||(0.553)|||(0.658)|||(0.643)|||(0.753)|
|Foreign Direct Investment,|0.0259**|0.0111|0.0120|0.0239**|0.0155|0.0144|0.121|0.414|0.143|0.0174|0.453|0.198|
|<br>net inflows (% of GDP)|(0.0124)|(0.0177)|(0.0166)|(0.0113)|(0.0163)|(0.0149)|(0.265)|(0.317)|(0.218)|(0.287)|(0.331)|(0.204)|
|Political Stability and<br>Absence of|6.290|6.678|7.219*||||0.960|1.318|2.685||||
|Violence/Terrorism|(4.156)|(4.576)|(3.892)||||(1.698)|(2.112)|(1.749)||||
|Investment Profile||||-1.180**|-0.416|-0.517*||||-1.193*|-0.0588|-0.368|
|||||(0.463)|(0.271)|(0.278)||||(0.660)|(0.291)|(0.398)|
|Constant|90.77***|90.83***|85.97***|110.6***|96.05***|91.54***|92.53***|93.12***|88.66***|106.3***|93.06***|90.87***|
||(5.566)|(1.600)|(1.476)|(9.573)|(3.694)|(3.004)|(5.140)|(1.597)|(1.668)|(10.42)|(2.531)|(2.685)|
|Country Fixed Effects|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Time Fixed Effects|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Observations|607|577|636|567|557|603|293|256|284|278|253|275|
|R-squared|0.086|0.05|0.016|0.054|0.06|0.029|0.076|0.148|0.104|0.099|0.157|0.113|

_Note:_ The table reports fixed effects panel regressions using 5 years of data. The outcome variable is total share of listed domestic firms (as percentage of total listed firms in domestic stock market). FDI net flows as percentage of GDP, political stability and absence of violence/terrorism, as well as investment profile are used as controls. This analysis includes both country and year fixed effects. One-year lags (t-1) are applied to all the explanatory variables and controls. Markings *, ** and *** denote significance at 10%, 5% and 1%, respectively. Robust standard errors are presented in parentheses. All variables are described in Table A-7 and Table A-9 in the annex.

13
Bensch and Peters (2013) conduct an _ex post_ evaluation of the Jambar charcoal stove in Senegal. Using OLS and propensity score matching, they find that the Jambar stove reduces charcoal to cook typical meals by 25% compared with traditional stoves. In a randomized control trial also in Senegal, the same authors find that fuel saving per meal cooked on the Jambar stove compared with a control group is even higher at 48%. They also find co-benefits associated with the virtually 100% usage of the stoves, including fewer eye infections and less cooking time.

Thakuri (2009) evaluates a customized improved wood burning stove and vent hood in a sample of 400 Nepali households. Though the data are observational and firewood consumption is based on 24-hour respondent recall, he estimates that improved cookstoves use 42% less wood than traditional stoves. Also based on reported data is Nepal et al. (2010), who use the nationally representative 2003/2004 Nepal Living Standards Survey to estimate the effect of improved stove use on fuelwood consumption. Using OLS, 2SLS and village level fixed effects, they conclude that user-identified ICS do not reduce fuelwood use. Evaluations of other stoves find fuelwood reductions (e.g. Johnson et al, 2009; Smith et al, 2007; Masera et al, 2007), but mass-produced brands are found to use only about 1/3 less than traditional stoves (Adkins et al., 2010).

In much of the above literature ICS fuelwood savings are estimated by measuring fuelwood consumption in traditional and improved stoves over an interval and comparing the two results, with the difference being how much fuelwood is saved. An important issue is leakage, because we know that households often incompletely adopt commercial fuels (e.g. Heltberg, 2004; Brouwer and Falcao, 2004). It therefore makes sense they may not completely substitute ICS for their traditional technologies. Careful and complete measurement of all cooking technologies in the home is therefore ideal.

The Mirt stove is designed primarily for cooking _injera_ , which is the main staple in the study area and much of Ethiopia. We therefore see the Mirt stove as an ideal application of the CCT, because we know that on a day-to-day basis households will cook _injera_ , which is exactly what is cooked in the CCT. We conduct CCTs applied to both traditional three-stone stoves and Mirt stoves in the same households on the same day three times over a period of a year. Our CCT sample consists of 108 households in 36 sites in rural Ethiopia, which allows us to capture systematic spatial and temporal differences in per-minute fuel savings.

As we analyze the same household’s preparation of _injera_ holding all things constant except the technology, we believe that we have utilized a particularly effective control in our estimates of

**6 |** P a g e
Table 26. Indonesian Exports, ICT use, and Log of Skills Measures Excluding Industries Involved in GVCs. All workers. 2005-2010

||||||N|on-routine<br>|skill measur<br>|es|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Dependent variable:||Anal|ytical|||Interp|ersonal|||Ma|nual||
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|
|IDN exports per worker|-0.000976||-0.000571|-0.0018|-0.00101||-0.000578|-0.00188|0.00103||0.000547|0.00194|
||(0.000897)||(0.000891)|(0.00109)*|(0.000868)||(0.000861)|(0.00103)*|(0.000867)||(0.000861)|(0.00102)*|
|Share of hhlds with computer||0.109|0.0881|0.0868||0.0902|0.0681|0.0665||-0.0794|-0.0544|-0.0543|
|||(0.0554)**|(0.0579)|(0.0590)||(0.0477)*|(0.0512)|(0.0516)||(0.0456)*|(0.0492)|(0.0503)|
|IDN exports per worker *||||0.00851||||0.00906||||-0.00948|
|Share of hhlds with computer||||(0.00318)***||||(0.00303)***||||(0.00299)***|
|Observations|2,933|2,933|2,933|2,933|2,933|2,933|2,933|2,933|2,933|2,933|2,933|2,933|
|R-squared|0.882|0.883|0.883|0.882|0.510|0.515|0.512|0.510|0.876|0.877|0.876|0.876|
|||Ma|nual|Routine ski|ll measures|Cog|nitive||||||
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|||||
|IDN exports per worker|-0.000557||-0.00104|0.0000582|0.00119||0.000828|0.00225|||||
||(0.00118)||(0.00119)|(0.00138)|(0.000916)||(0.000915)|(0.00106)**|||||
|Share of hhlds with computer||-0.0968|-0.0657|-0.0732||-0.0381|-0.0212|-0.0107|||||
|||(0.0730)|(0.0718)|(0.0756)||(0.0381)|(0.0476)|(0.0393)|||||
|IDN exports per worker *||||-0.0065||||-0.0111|||||
|Share of hhlds with computer||||(0.00332)*||||(0.00321)***|||||
|Observations|2,933|2,933|2,933|2,933|2,933|2,933|2,933|2,933|||||
|R-squared|0.977|0.977|0.977|0.977|0.870|0.871|0.871|0.870|||||

Source: Authors’ calculations based on Sakernas 2005-2010 and 2011-2015, Susenas 2005-2010 and 2011-2015, 2015-2016 Philippines STEP survey, and UN Comtrade database 2005-2010 and 2011-2015.

Notes: IV regressions. IV: IV: Global demand for foreign products excluding Indonesian products. Indonesian exports per worker in thousands of USD at 2010 prices and global demand for foreign products in millions of USD at 2010 prices. ICT variable at the province level is the share of households with computer. Province level controls include the age structure of the population, the share of urban population, real GDP per capita, and the minimum wage level. Industry level controls include the gender and educational composition of employment. All models include province, industry, year fixed effects, and linear province time trends. Robust standard errors between parentheses clustered by province-industry. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively.

43
- 50 -

such activities and the division of responsibilities of the central and departmental governments. The answer is not in the arbitrary earmarking<sup>of</sup> such taxes which apparently has not been sufficient to prevent the virtually complete substitution of national for departmental resources, but in the reform of the sharing formula for the tax allowance to take account of departmental needs and to induce greater local fiscal efforts.<sup>4 3</sup>

# D. MuniciDal **Level Earmarking**

53. The major sources of revenue for Colombia's municipalities are the property tax, the industry and commerce tax, and transfers from higher levels of government (esp. the central government). Other taxes (e.g. on billboards, vehicles, gambling, and public sFectacles) generate relatively minor amounts of revenues. Of these sources, the major contribution to municipal level earmarking is the valorization tax, a betterment tax levied on real propertv that has benefitted from the construction of local public works.<sup>4 4</sup> 54. Valorization represents one of the purest forms of earmarking. This purity results from several characteristics:

- the fact that taxpayers and beneficiaries overlap to a substantial degree;

- the use of benefit-cost type considerations in making

- investment decisions ("appraisal" or "analysis" might be too

- 43/ See our discussion of the "tax allowance" in para. 29.

- 44/ For an evaluation of the practice of valorization in Colombia see

- W.G. Rhoads and R.M. Bird. "Financing Urbanization by Benefit Taxation" Land Economics 43 (November 1967), pp. 403-12; IBRD Valorization CharQes as a Method for Financing Urban Public Works: The Example of Bogota. Colombia Staff Working Paper No. 254 (March 1977); and International Tax Program, Intergovernmental Finance in Colombia:<sup>Final</sup> Report of the Mission on Intergovernmental Finance (The Law School of Harvard University; Cambridge, 1984), pp. 280-282.
# **Figure 7 Yield growth**

<!-- Start of picture text --> KCal/hectare, year-on-year difference<br>750<br>600<br>450<br>300<br>150<br>0<br>-150<br>-300<br>1961 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011 2016 2021<br><!-- End of picture text --> _— 28 —_
7 Inserting (11) and (12) into (8) and taking logs gives the following expression: lnVij = γ1infrastructurei + γ2incentivesi + γ3institutionsi + γ4landlockedi + (1 −β1 −β2)Dij + β2TCj + lnYi + lnYj + πlnΠi + πlnPj (13) Estimation of equation 13 can be used to construct a trade- capacity index. Using the estimated coefficients that proxy for the impact of infrastructure, incentives, and institutions on exports, we can indeed derive the following empirical trade-related capacity index: TCio = (γ�infrastructure1 i + γ�incentives2 i + γ�institutions3 i)/(γ�+1 γ�+2 γ�)3 (14) This indicator represents a weighted average of infrastructure, institutions, and incentives, where the weights are based on the importance of each standardized dimension in explaining exports.<sup>3</sup>

# **4. Estimation strategy and data**

We translate empirically the aid for trade allocation rule (equation 7) using as dependent variable the logarithm of the total aid for trade commitment amounts as a share of the country GDP. We proxy the quality of the environment, using the Kaufman-Kraay “rule of law” indicator. Moreover, we control for the log of the real per capita GDP as a measure of economic development. The aid for trade data used in the analysis cover the period 20072010.<sup>4</sup> Data for constructing aid for trade commitments are taken from the OECD CRS database and are constructed using the OECD-WTO Aid for Trade definition. Data for GDP and GDP per capita are taken from the World Bank, WDI database. (See the annex for data sources and for the preliminary statistics, including the correlation table).

We construct an initial trade-related capacity index, TC, using the average values of the past three years for the variables that proxy the quality of infrastructure, incentives, and

> 3 We standardize each variable such that they have mean equals to zero and a standard deviation that equals one. 4 As of 2007, the OECD CRS database includes also data on trade related budget support. Moreover, the OCED CRS database provides data on trade related budget support starting in 2007.
their investments tend to be less irreversible. We call this variable the capital intensity (CapIntensity).

Weather variables In our study, we calculate the weather variables for each year and grid using daily temperatures.<sup>13</sup> Denoting the daily temperature for each grid by _Ti_ , the mean and maximum are _N_<sup><u>1</u></sup> � _Ni_ =1<sup>_Ti_and max(</sup><sup>_Ti_), respectively, for each grid</sup><sup>_i_, with</sup><sup>_N_= 365.</sup> To construct temperature uncertainty, we aggregate all temperature observations for each unique index (spatial grid) across multiple years and fit a smoothing spline<sup>14</sup> to capture the overall seasonal temperature pattern for each index. This spline represents the assumed constant expectation of firms regarding temperature levels and seasonality over time. For each year _t_ within a given index, we calculate the residuals _Rdt_ by comparing the actual daily temperature _Tdt_ to the predicted values from the common spline _T_<sup>ˆ</sup> _t_ for day _d_ in year _t_ : Figure 1 presents an example spline estimate for grid index 52259 (longitude: -1.5, latitude: 63.5) covering the period from January 1, 2000, to December 31, 2019. The smoothing spline is estimated over the full 365 days, using 20 years of daily observations to predict the temperature for each day.

Temperature uncertainty is defined as the standard deviation of these residuals, calculated annually to quantify the variability in temperature: This standard deviation represents the degree to which the actual temperature deviates from firms’ constant expectations, capturing yearly variations in weather conditions. This assumption of constant expectations is reasonable for several reasons as firms typically base their planning and operational strategies on long-term climate averages and historical weather patterns, which tend to remain relatively stable over short time horizons.

# 3.4 Grid-level aggregation

In our study, the primary unit of observation is the grid, aligning with how weather data is measured and recorded. For the firm-level data, we aggregate this data at the grid level.

We implement two distinct levels of aggregation in our analysis:

> 13For the daily temperature measurement, three variables are available from the BERKEARTH dataset at the Climate Data Store: minimum, mean, and maximum daily temperatures. We do not consider the minimum temperature in this study. Between the mean and maximum daily temperatures, there is a difference in data availability. The mean temperature data is accessible only up to June 2018, whereas the maximum temperature data extends through 2019. Due to this disparity in data coverage, we have chosen the maximum temperature variable as our primary variable for analysis. Both the mean and maximum daily temperature variables are highly correlated.

> 14The smoothing spline is fitted using the `smooth.spline()` function from the base R package. The default method for estimating the tuning parameter ( _λ_ ) uses generalized cross-validation (GCV), which balances the trade-off between smoothness and fidelity to the data.

9
50 Table 8: Topical distribution of the most-cited 50 publications of the United States‘ top-5 institutions and Europe‘s top institution

||Harvard U|World Bank|MIT|U CA,Berkeley|Chicago U|U York|
|---|---|---|---|---|---|---|
|Health and its value|2||1|1|1|5|
|Efficiency and equity|4|6|1|1|2|6|
|Determinants of health and ill-health|6|14|5|11|14|3|
|Public health|5|5|5|6|5||
|Health and the economy|5|9|8|10|2|5|
|Health statistics and econometrics|3|2|1|1|5|3|
|Demand for health and health care||3|3|3||2|
|Medical insurance|5||11|2|4||
|Supply of health services|7|1|4|1|7|3|
|Human resources|6|3|7||2||
|Markets in health care|1||1|||4|
|Economic evaluation|2||1|4|1|19|
|Total|46|43|48|40|43|50|
|Normalized Herfindahl index|0.089|0.176|0.118|0.160|0.156|0.181|

Note: The starting point was our database of 33,000 publications in EconLit with a health JEL code. Google Scholar citation data were obtained for as many as possible (80 percent) in June 2011. Each listed institution was given full credit for a publication in the case of a co-authored publication, but only the first institution was credited where an author listed multiple institutions. Institutional affiliations below the institution level were aggregated up to the level of the institution, so that for example publications originating from the Harvard School of Public Health were allocated to Harvard University along with publications originating from the Department of Economics at Harvard University. As explained in the text, only addresses with five or more articles to their name were retained for cleaning and aggregating. The 50 most-cited publications for each of the listed institutions were then assigned either one of the 12 topics listed in the appendix or a ―topic not assigned‖ code. The latter included publications that were too general (some were actually textbooks) some had no economic content despite being in EconLit, and some had no health content despite the JEL code.