rafmacalaba/datause-encoder-data huggingface dataset:
Figure 4: Fixed- and within-effects estimation <!-- Start of picture text --> Employment (2-digit) and GDP per capita (PPP) for all countries<br>Pooled (N=4,932) Between (N=140) Within (N=4,929)<br>oses Ss ° s. s = ss ose Ssss<br>GDP per capita, PPP (constant 1995) ‘GDP per capita, PPP (constant 1985) Change in GDP per capita, PPP (constant 192<br><!-- End of picture text --> Source: Authors’ calculations The first panel of Figure 4 displays the same lowess curve as Figure 2: the non-parametric lowess curve for the Gini index on GDP per capita, on pooled data. The smoothed values of the Gini index are obtained from local weighted regressions of the Gini index on GDP per capita according to the following equation: y,, = a+ $x, + €,,. The lowess curve has a U-shaped pattern showing two stages of diversification.

<!-- Start of picture text --> curve has<br><!-- End of picture text --> <!-- Start of picture text --> pattern showing<br><!-- End of picture text --> <!-- Start of picture text --> stages of diversification.<br><!-- End of picture text --> The second panel displays the non-parametric lowess curve of the average Gini index on average GDP per capita. The smoothed values of the Gini index are obtained from local weighted regressions of the country-average Gini index on the country-average GDP per capita according to the following equation: <u>7,</u> = aPE 4 BBE <u>TZ</u> + <u>=.</u> The lowess curve displays again a U-shaped pattern, this characterizing the between-country relationship between Gini and GDP per capita.

<!-- Start of picture text --> relationship<br><!-- End of picture text --> <!-- Start of picture text --> between Gini and GDP per capita.<br><!-- End of picture text --> The third panel displays the non-parametric lowess curve of the de-meaned Gini index on the de-meaned GDP per capita. The smoothed values of the Gini index are obtained from local weighted regressions of deviation from the country-average of the Gini index on deviation from the country-average of the GDP per capita according to the following equation: <!-- Start of picture text --> ation from the<br><!-- End of picture text --> <!-- Start of picture text --> of the<br><!-- End of picture text --> <!-- Start of picture text --> GDP per capita<br><!-- End of picture text --> <!-- Start of picture text --> according<br><!-- End of picture text --> <!-- Start of picture text --> to the following equation:<br><!-- End of picture text --> 15
Fisher, M and G. Shively. 2005. Can income programs reduce tropical forest pressure? Income shocks and forest use in Malawi. _World Development_ , vol 33, No. 7: 1115-1128/ Fisher, M, G. Shively, and S. Buccola. 2005. Activity Choice, Labor Allocation and Forest Use in Malawi. _Land Economics_ , vol 81, no. 4: 503-517.

Food and Agriculture Organization (FAO 2005). Forestry 2005 Country Tables, Malawi. www.fao.org/forestry/site/32085/en Franklin, J. 1986. Thematic mapper analysis of coniferous forest structure and composition. _International Journal of Remote Sensing_ , Volume 7, 1287-1301 Friedl, M. A. and Brodley, C. E. 1997. Decision Tree classification of land cover from remotely sensed data. _Remote Sensing of Environment_ , Volume 61, 399-409 Government of Malawi, Ministry of Forestry and Natural Resources, Department of Forestry, 1993, _Forest Resources Mapping and Biomass Assessment for Malawi._ Lilongwe, Malawi.

Heckman, J.J. 1978. Sample selection bias as a specification error. _Econometrica_ , Volume 47, 153-161.

Ilahi, N and F. Grimard (2000). Public infrastructure and private costs: Water supply and time allocation of Women in rural Pakistan. _Economic Development and Cultural Change_ , 49 (1): 4575.

IFPRI, 2000. Poverty Monitoring Service Policy Brief No. 2. Malawi State of the Environment Report 1998 Jodha, N. 1986. Common property resources and rural poor in dry regions of India. _Economic Political Weekly_ , vol 21, No. 27.

Khandker, S. R. 1988. Determinants of women’s time allocation in rural Bangladesh. _Economic Development and Cultural Change_ , 37 (1): 111-126.

27
The coefficient on the Female _×_ Gender language interaction is _−_ 0 _._ 11 (p-value 0.025) after including controls for country-round fixed effects, plus age and religion categories and interactions between those and the female dummy. Thus, grammatical gender is associated with both lower female labor force participation and larger gender gaps in labor force participation at both the cross-country and the individual level.

# **5.3 Educational Attainment**

Next we consider the within-country association between grammatical gender and women’s educational attainment. In our cross-country analysis, we did not find a statistically significant relationship between the country-level prevalence of gender languages and either women’s educational attainment or gender differences in educational attainment.<sup>22</sup> However, rates of primary and secondary school completion are quite high (for both men and women) in many countries, limiting the statistical power of cross-country analysis. Moreover, many countries have compulsory schooling laws in place, and these may attenuate the impacts of both cultural values and beliefs about labor market returns (which will differ by gender if women are less likely to participate in the labor force) in decisions about girls’ enrollment in school.

Average levels of education are still quite low in many African countries. In the 37 African countries included in the Barro-Lee data set, the average level of educational attainment among adult males is only 5.6 years. Moreover, gender differences in educational attainment persist throughout Africa. Among African countries in the Barro-Lee data set, the average level of education among women is only 4.3 years, and women obtain less schooling than men in all but five African countries. Primary school has only recently been made free in many African countries, and compulsory schooling laws are still relatively rare.

We estimate the association between having a gender native language and the likelihood of completing primary school (Table 10, Columns 1 through 3) and secondary school (Table 10, Columns 4 through 6) in Kenya, Niger, Nigeria, and Uganda using the Afrobaromater

> 22As noted above, we did find a marginally significant association between grammatical gender and the gender gap in primary school completion after controlling for continent fixed effects.

29
2<sup>K</sup> potential combinations of regressors, but the vastly more possible combinations of interactions between them.

In concluding, we want to be clear that we do not think that the information painstakingly gathered in the many individual indicators that comprise the GII and DB datasets is irrelevant. To the contrary, most if not all of them measure things that plausibly are intrinsically good on their own (it is hard to imagine why it might be a _bad_ idea to simplify business entry regulation from current levels in most countries, for example), and it also seems intuitive that they matter for outcomes. Rather, we simply note that it seems extremely difficult to quantify the partial effects of these many indicators on relevant outcomes. And so it is very difficult to use these indicators as a recipe or a roadmap to reforms in the real world where policymakers must choose where to spend their political capital.

29
We begin by controlling for the quality of democracy. Amin and Djankov (2009) show the quality of democracy to be highly correlated with the reform measures discussed above and also known to be inversely correlated with natural resource abundance (see, for example, Ross 2001). We use the “democracy” variable for year 2003, the beginning of the reform period, from the Polity IV data (Democracy). The variable ranges between 0 and 10 with higher values implying a better quality of democracy.

Next, we control for differences in income levels across countries using (log of) GDP per capita (constant 2005 USD, PPP adjusted) and the average annual growth rate in GDP per capita. Both these variables are taken from World Development Indicators, World Bank and averaged over the period 1998-2002. Of the three resource abundance measures, the share of primary exports in total merchandise exports shows a substantial negative correlation with GDP per capita (correlation of -0.37). As discussed above, a number of studies have shown that natural resource abundant countries show lower growth rates although it is unclear whether this is a causal effect of the former on the latter. We may also expect higher income and faster growing countries to reform more because these countries have greater capacity for reform, and more checks and balances on the government that prevent the use of business regulations by politicians for generating rents.

Third, we use two sets of controls for geography. The first is the absolute distance of a country from the equator divided by 90 (Latitude). The second is region fixed effects where regions include East Asia and Pacific, Latin America and Caribbean (LAC), Middle East and North Africa (MENA), North America, South Asia, Sub-Saharan Africa 8
**Table 5. Total food expenditures as proportion of total household expenditures**

|**Welfare**<br>**decile**|**1**|**2**|**3**|**4**|**5**|**6**|**7**|**8**|**9**|**10**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Share of**|||||||||||
|**food in total**<br>**expenditures**|24.9%|23.9%|22.9%|22.3%|22.0%|22.2%|22.2%|21.5%|20.7%|17.4%|

_Source:_ Own calculations using POF 2017/18.

Table 6 shows the upper and lower poverty lines yielded by this parametric approach, based on the food poverty line of R$258, coming from our main specification. Being able to cover very essential nonfood needs (as represented by the lower poverty line) almost doubles the amount of the poverty threshold.

**Table 6. Total poverty lines – Parametric approach, in 2018 urban Southeast prices**

|**Lower**<br>|**Upper**|
|---|---|
|**Totalpoverty line**<br>R$455<br>|R$1,061|
|s<sup>F</sup><br>0.235|0.243|
|_Source:_Own calculations using POF 2017/18 data.<br>_Note:_ These total poverty lines are based on the food poverty line of R$258, coming from our|main specification, which is based|

_Note:_ These total poverty lines are based on the food poverty line of R$258, coming from our main specification, which is based on the minimum calorie requirement of 2,100 calories per person per day and average cost per calorie of the bottom 40 percent of the welfare distribution, with calories having been assigned in a two-step procedure and deflated by household-specific foodprice Paasche indices, as described earlier in this section.

_Nonparametric approach._ To complement this result, we apply a nonparametric approach to estimating the food expenditure shares for the lower and upper poverty line. The approach taken here was to obtain the average food share of households with either total expenditures (for the lower poverty line) or food expenditures (for the upper poverty line) in the range of +/– 10 percent of the food poverty line. These estimated shares are used to obtain the lower and upper poverty lines following equations (8) and (10), respectively. Table 7 shows the results. The poverty lines remain generally robust to changes in the approach to the nonfood adjustment, with values being similar across the two approaches, especially for the lower poverty line.

**Table 7. Total poverty lines – Nonparametric approach, in 2018 urban Southeast prices**

||**Lower**|**Upper**|
|---|---|---|
|**Totalpoverty line**|R$452|R$973|
|**Average food share**|0.2457|0.2649|

_Source:_ Own calculations using POF 2017/18 data.

_Note:_ These total poverty lines are based on the food poverty line of R$258, coming from our main specification, which is based on the minimum calorie requirement of 2,100 calories per person per day and average cost per calorie of the bottom 40 percent of the welfare distribution, with calories having been assigned in a two-step procedure and deflated by household-specific foodprice Paasche indices.

16
Table S.3: Descriptive Statistics

|Variable|Mean|Std. Dev.|Min.|Max.|N|
|---|---|---|---|---|---|
|_Road activity controls_||||||
|<br>Cost per km (2000 US$)|261741.12|558093.15|50.57|7810495.02|3322|
|Estimate|0.44|0.5|0|1|3322|
|Contract|0.27|0.44|0|1|3322|
|Actual|0.28|0.45|0|1|3322|
|World Bank|0.6|0.49|0|1|3090|
|Bilateral Donor|0.22|0.41|0|1|3090|
|Funded by government|0.11|0.31|0|1|3090|
|Funded by other donor|0.08|0.26|0|1|3090|
|Missing: funding source|0.07|0.25|0|1|3322|
|Local|0.84|0.37|0|1|708|
|Foreign frm or JV|0.16|0.37|0|1|708|
|Missing: Contractor|0.79|0.41|0|1|3322|
|Length_>_than 50 km|0.37|0.48|0|1|2452|
|Missing: Length|0.26|0.44|0|1|3322|
|Flat|0.38|0.49|0|1|1587|
|Hilly|0.09|0.29|0|1|1587|
|Mountainous|0.27|0.44|0|1|1587|
|Rolling|0.26|0.44|0|1|1587|
|Missing: Terrain|0.52|0.5|0|1|3322|
|_Geographic controls_||||||
|Log Ruggedness|4.46|0.92|2.44|6.51|3322|
|Log of Rainfall|2.43|0.5|0.36|3.62|3319|
|Missing: Rainfall|0|0.03|0|1|3322|
|Log dist to coast|-1.45|1.21|-5.85|0.79|3322|
|<br>Log of Surface Area|6.17|1.65|0.62|9.75|3322|
|Population Density|1.43|2|0.02|9.76|3322|
|Log of GDP per capita|6.72|1.14|4.53|9.07|3310|
|Missing: Log of GPD per capita|0|0.06|0|1|3322|
|Log of GDP pc (1985)|6.39|1.17|4.76|8.72|2985|
|Missing: Log of GPD pc (1985)|0.1|0.3|0|1|3322|
|East Asia & Pacifc|0.21|0.41|0|1|3322|
|Europe & Central Asia|0.09|0.28|0|1|3322|
|Latin America & Caribbean|0.24|0.42|0|1|3322|
|Middle East & North Africa|0.03|0.17|0|1|3322|
|South Asia|0.14|0.35|0|1|3322|
|Sub-Saharan Africa|0.29|0.46|0|1|3322|
|_Confict and corruption controls_||||||
|ACD Confict|0.25|0.43|0|1|3322|
|ACD Post-Confict|0.12|0.32|0|1|3322|
|<br>WGI Instability|0.56|0.63|-1.26|2.21|3322|
|TI Corruption|3.91|1|0.1|5.60|3318|
|<br>Missing: TI Corruption|0|0.03|0|1|3322|
|WGI Corruption|0.48|0.46|-1.45|1.6|3322|
||||Con|tinued on nex|tpage|

5
Biogas – $86; Gas Combined Cycle – $94; Geothermal – $102; Hydro – $105; Concentrating Solar Thermal (CSP) – $127; Nuclear – $153; and Biomass – $165. This study used fixed 2008 price for fuels instead of projected fuel prices and prices of fuels and materials were at their peak in that year. The costs do not include external costs such as carbon tax.<sup>3</sup> Lazard (2008) also estimates that wind power is one of the cheapest options for generating electricity in the United States with levelized cost ranging from $44 per MWh to $91/MWh in 2008 dollars. For other generating technologies, Lazard (2008) estimates levelized cost ranges of (in 2008 US$ per MWh) $221-$334 for peak (open-cycle) gas, $104$134 for IGCC, $98-$126 for nuclear, $74-$135 for advanced supercritical coal,<sup>4</sup> $73-$100 for Gas Combined Cycle, $109-$154 for solar PV (crystalline), $96-$124 for solar PV (thin film), $90-$145 for solar thermal, $115-$125 for fuel cell, $50-$94 for biomass, $50-$81 for landfill gas, and $42-$69 for geothermal. These estimates do not represent the true cost to society, however, because they include various government incentives, such as investment and production tax credits, accelerated asset depreciation and reduced tax rates. Further assumptions are 60% debt financing at 7% interest rate and 40% equity financing at 12% cost. Fuel prices used in the estimates are current fixed prices instead of projected prices. Since the study included taxes and subsidies, it deviates from cost-benefit analysis (CBA) methods.

All of the above cost calculations are for the United States. Moreover, those costs do not include transmission costs to connect electricity grids, which could be very high for wind power, especially where wind farms are remotely sited (e.g., offshore or mountainous regions). Also ignored are the increased costs of managing an electric grid when variable wind power enters into an existing generation mix.

The cost of wind power relative to other technologies varies significantly across countries and locations. ESMAP/World Bank (2008) estimates of the costs of electricity generation equipment for three countries, the United States, India and Romania, indicate that there are large variations in overnight construction costs across size of generation capacity

> _3 Please refer to CPUC (2008) for detailed information on data and methodology used and assumptions made while estimating these costs._

> _4 Upper range includes 90% carbon capture and storage._

9
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36
# **Intrinsic motivation, effort and the call to public service**

Sheheryar Banuri & Philip Keefer<sup>1</sup> Development Economics Research Group, World Bank 1818 H St NW, MC 3-356, Washington, DC, 20433 <u>sbanuri@gmail.com pkeefer@worldbank.org</u>

**JEL Codes** : C91; H83; J45

**Keywords** :  public sector reform, civil service, intrinsic motivation, extrinsic motivation, performance

**Sector Board** :  Public Sector Governance (PSM)

1 Banuri: Development Economics Research Group, World Bank, 1818 H St NW, MC 3-356, Washington, DC, 20433 (e-mail: sbanuri@gmail.com); Keefer: Development Economics Research Group, World Bank, 1818 H St NW, MC 3- 363, Washington, DC, 20433 (e-mail: pkeefer@worldbank.org). The authors have no relevant or material financial interests that relate to the research described in this paper. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent. The authors are grateful for financial support from the World Bank, and acknowledge helpful conversations with Ghazala Mansuri. In addition, the authors are grateful to Riatu Qibthiyyah at the University of Indonesia, Dr. Muhammad Taufiq at STIA, Mr. Ridwan Galela at STAN, and Maria Tambunan at the World Bank, for arranging for access to the three institutions where we conducted the experiments; and to Eric McLester for his invaluable help in running the experiments.
TRANSmON ECONOMICS DIVISION POLICY REsEARCH DEPARTMENT WORLD BANK RESEARCH PRoEC INCOME DISnhEBUTION DURiNG THE TRANSITON

# INCOME NEQUALITY, WELFARE AND POVERTY: AN ILLUSTRATION USING UKRAIIAN DATA

Nnak Kakwani **The** University of New South Wales Kensington, New South Wales Austulia This paper is a product of a research project on social expenditus in Eastan Europe undertaken by the Socialist Economs Reform Unit under the direction of Bnkmo Milanovic. These papers carry the name of ffie autos mnd are prminay versions for discussion only. The findings, ineprtations, and conclusions are the author's own. They should not be attributed to the World Bank. its Boad of Directors, its managemt. or any of its member countries. Comments and suggsdons are welcome Phone (202) 4736968, Fax (202) 522-1151
# **11. Annex IV**

In this annex we present the results obtained by following the same methodology but using the lower bound health costs as indicated in table 5 and referenced in page 11

|||**Table A**|**IV-1 – N**|**et effect**|**on house**|**hold expe**|**nditures**|**(%)**|||
|---|---|---|---|---|---|---|---|---|---|---|
|_Price shock under_|_Decile 1_|_Decile 2_|_Decile 3_|_Decile 4_|_Decile 5_|_Decile 6_|_Decile 7_|_Decile 8_|_Decile 9_|_Decile 10_|
|Lower-bound elasticity|-0.05|-0.08|-0.10|-0.12|-0.14|-0.16|-0.12|-0.16|-0.16|-0.12|
|Medium elasticity|0.04|-0.03|-0.04|-0.06|-0.09|-0.10|-0.07|-0.11|-0.11|-0.08|
|Upper-bound elasticity|0.12|0.03|0.02|-0.01|-0.03|-0.04|-0.03|-0.06|-0.06|-0.04|

_Source:_ Based on data from the 2015 Household Budget Survey. _Note:_ The table shows the share of total consumption for each decile.

## **Figure IV-1 - Net Effects of Tobacco Taxes**

23