rafmacalaba/datause-encoder-data huggingface dataset:
Lofgren, Hans, Martin Cicowiez, and Carolina Diaz-Bonilla, 2013. “MAMS – A Computable General Equilibrium Model for Developing Country Strategy Analysis”. pp. 159–276 in Dixon, Peter B. and Dale W. Jorgenson, D.W. (Eds.), Handbook of Computable General Equilibrium <u>Modeling. North Holland, Elsevier B.V.</u> Ministry of Finance. 2009. Fiscal Outturn Fiscal year 2008/2009. October. Monrovia, Liberia.

Ministry of Finance. 2010. Annual Donor Fiscal Outturn Report Fiscal year 2009/2010. October. Monrovia, Liberia.

Psacharopoulos, George, and Harry Anthony Patrinos. 2004. “Returns to Investment in Education: A Further Update,” pp. 111-134 in Education Economics, Vol. 12, No. 2.

World Bank. 2012. World Development Indicators. December 21.

15
Figure B.1: Innovation Proles and Productivity Distributions <!-- Start of picture text --> Idiosyncratic Distortions Entry Distortions<br>SST<br>SS0 t=1 t=10 SST SS0 t=1<br>t=10<br>1 2 3 4 1 2 3 4<br>Log Z Log Z<br>Idiosyncratic Distortions Entry Distortions<br>SST SST<br>SS0 SS0<br>t=1 t=1<br>t=10 t=10<br>1 2 3 4 1 2 3 4<br>Log Z Log Z<br>0.5 0.5<br>0.4 0.4<br>0.3 0.3<br>0.2 0.2<br>Innovation Probability 0.1 Innovation Probability 0.1<br>0.0 0.0<br>0.20 0.20<br>0.15 0.15<br>Pdf 0.10 Pdf 0.10<br>0.05 0.05<br>0.00 0.00<br><!-- End of picture text --> The top panel plots the innovation proles in the initial and terminal steady states, and periods 1 and 10 along the transition following reforms that reverse idiosyncratic (top left) and entry (top right) distortions. The bottom panels illustrate the _pdf_ of the distribution of physical productivity ( _TFP_ ) at the same points of the transition.

pact. Thereafter, the properties of the productivity distribution at the onset of the reforms discussed above allows for a quicker recovery than in the event of removing idiosyncratic distortions.

# B.2 Benchmark Reforms: The Role of Innovation and Reallocation

How do endogenous innovation and resource reallocation contribute to shaping the dynamics of development? We address this question in this section.

We evaluate reforms that dismantle idiosyncratic and entry distortions considering separately cases where innovation is endogenous, as in the previous section, or exogenous, in which case resource reallocation is the sole force driving the transition. To represent an economy with exogenous innovation, we endow rms with the same innovation prole as in the undistorted stationary allocation with endogenous innovation. Firms do not have to invest in achieving this innovation prole and hence, do not have a technology to innovate more or less as a result of distortions, so rm dynamics are exogenous. Recall that all experiments calibrate distortions so as to achieve the same _TFP_ growth (see table 2) Consider rst the role of innovation and reallocation for the behavior of macroeconomic variables in gure B.2. There, we plot _TFP_ and the investment rates for 43
# _Estimation_

To measure the average impact of the training program, we first estimate intention-to-treat (ITT) effects by regressing employment outcomes on an indicator for treatment offer and all stratifying variables, which include gender, whether the respondent was in National Youth Service Corp (NYSC) or final year of education at the time of application, whether the respondent was above or below the median baseline skills assessment score, and indicators for each initial assessment site, **δ** : 0 1t t 2 t 3N N 4f 5 ii ii ii ii ii ii ii yy = ββ + ββ tt + ββ ggttg + ββ + ββ ggttff yyt tt + ββ tta aatt mmttggffttgg + δδ<sup>**�**</sup> + εε This specification only makes use of the end line data, since most of the sample was in school or in national service at baseline and hence questions such as sector of employment or salary were not applicable. For a sub-set of variables that are measured at baseline, we can also estimate an ANCOVA specification that controls for baseline realizations of the employment outcomes.

To explore the role of potential labor market frictions, we also estimate heterogeneous treatment effects for each stratifying variable. Interpreting the main effects together with the interactions of each of these variables with an indicator for treatment offer can tell us the extent to which the program either reduces or exacerbates any labor market premium associated with gender, skills, internship, and location.

Given that we have non-compliance (not all individuals in the treatment group took up the training), we also estimate treatment-on-the-treated effects, in which treatment assignment serves as an instrument for program participation: 0 1 2 t 3N N 4f 5 ii ii ii ii ii ii ii yy = γγ + γγ ppttaaggt mm + γγ ggttg + γγ + γγ ggttff yyt tt + γγ tta aatt mmttggffttgg + δδ<sup>**�**</sup> + uu 0 1t t 2 t 3N N 4f 5 ii ii ii ii ii ii ii ppttaaggt mm = θθ + θθ tt + θθ ggttg + θθ + θθ ggttff yyt tt + θθ tta aatt mmttggffttgg + δδ<sup>**�**</sup> + ωω We define program participation in two different ways, although they are highly correlated. The first uses respondents’ self-reports of participation, while the second treats a respondent as a participant only if he or she appears in the attendance records of training providers.

Finally, we also use the data from the IATs to measure an additional set of heterogeneous impacts related to biases exhibited at baseline, 12
**Table 7. Controlling for Real Per Capita GDP**

|Dependent variable:|Fiscal balance/GDP|Total revenues/GDP|Total|
|---|---|---|---|
||(%)|(%)|expenditures/GDP<br>(%)|
|Constant|-6.44***<br>(-7.46)|-11.23***<br>(-7.42)|-5.01***<br>(-2.98)|
|Dummy fuel > 25%|-8.81**|-1.92|7.15*|
|exports|(-2.31)|(-0.38)|(1.74)|
|Dummy ores and|0.447|1.35***|1.24**|
|metals > 10%<br>exports|(1.50)|(2.75)|(2.42)|
|Hard peg dummy|1.36***<br>(2.89)|-2.11***<br>(-3.71)|-3.52***<br>(-7.19)|
|Political stability|0.0540***|0.282***|0.234***|
|(0 to 100)|(2.67)|(10.29)|(8.36)|
|Ln (real per capita|-0.0454|2.19***|2.21***|
|GDP)|(-0.29)|(9.19)|(8.86)|
|Fuel exporter|1.74***|1.22*|-0.515|
|dummy * ln (real per<br>capita GDP)|(3.43)|(1.83)|(-0.96)|
|Time trend|0.0177|0.223***|0.189***|
|(=0 in 2000)|(0.65)|(4.93)|(3.93)|
|n|1,352|1,366|1,352|
|R-squared|0.151|0.345|0.270|
|RMSE|4.71|7.81|7.82|

_Note_ : Figures in parentheses are robust t-statistics. *, **, ***: significant at 10, 5, and 1 percent respectively. Annual data from 1996–2013 for low- and middle-income countries. RMSE = root mean square error, or sample standard deviation.

15
**Table 7. Percentage of firms in each quartile of predicted probability of formality by group, ordered by quartile one, EcC 2012/13**

|**Profile**<br>**Group**|<br> <br>**Firm**<br>**Size**|<br>**Firm Age**<br>**Capita**|**l Wage**<br>**Worker**|<br>**Quartile of Ln(Labor**<br>**Value Added)**|**N (Obs.)**|**% of**<br>**Informal**<br>**Firms in**<br>**Group**|**% of Firm**<br>**Quart**<br>**Predi**<br>**Proba**|**s in Each**<br>**ile of**<br>**cted**<br>**bility**|
|---|---|---|---|---|---|---|---|---|
||||||||**Q1 Q2**|**Q3 Q4**|
|1|1|Less than 5 years 5K+|No|1|677|3.7|96.3 3.7|0.0 0.0|
|2|1|Less than 5 years <5K|No|1|963|5.1|96.2 3.8|0.0 0.0|
|3|1|Less than 5 years 5K+|No|2|721|3.9|94.9 5.1|0.0 0.0|
|4|1|Less than 5 years <5K|No|2|722|3.3|94.7 5.3|0.0 0.0|
|5|1|Less than 5 years 5K+|No|3|702|3.3|93.8 6.2|0.0 0.0|
|6|1|Less than 5 years <5K|No|3|523|2.3|88.9 11.1|0.0 0.0|
|7|1|Less than 5 years <5K|No|4|231|1.0|84.1 15.9|0.0 0.0|
|8|2|Less than 5 years 5K+|No|1|544|3.5|82.1 17.9|0.0 0.0|
|9|1|Less than 5 years 5K+|No|4|548|2.7|81.2 18.8|0.0 0.0|
|10|2|Less than 5 years <5K|No|1|442|2.6|74.7 25.3|0.0 0.0|
|11|2|Less than 5 years <5K|No|2|135|0.8|73.6 26.4|0.0 0.0|
|12|3-24|Less than 5 years <5K|No|All quartile<br>(Aggregate)|82|0.5|73.5 26.5|0.0 0.0|
|13|3-24|Less than 5 years 5K+|No|3|29|0.1|70.4 29.6|0.0 0.0|
|14|2|Less than 5 years 5K+|No|2|300|1.9|68.5 31.5|0.0 0.0|
|15|3-24|Less than 5 years <5K|Yes|1|180|0.5|68.4 30.9|0.6 0.0|
|16|1|5 years or above 5K+|No|1|335|1.9|67.8 31.6|0.6 0.0|
|17|2|Less than 5 years <5K|No|4|15|0.1|64.1 35.9|0.0 0.0|
|18|3-24|Less than 5 years 5K+|No|1|126|0.6|63.5 36.5|0.0 0.0|
|19|3-24|Less than 5 years 5K+|No|2|40|0.2|62.8 36.9|0.3 0.0|
|20|2|Less than 5 years 5K+|Yes|1|451|1.6|61.7 38.1|0.2 0.0|
|21|1|5 years or above <5K|No|1|666|2.7|61.0 37.6|1.4 0.0|
|22|2|Less than 5 years 5K+|No|3|146|0.7|58.4 41.6|0.0 0.0|
|23|2|Less than 5 years <5K|Yes|1|244|0.9|57.4 42.5|0.1 0.0|
|24|3-24|Less than 5 years <5K|Yes|3|64|0.1|57.3 29.5|13.2 0.0|

45
research generalizes our findings to other settings with extreme historical inequalities, it would suggest a new mechanism by which elites can sustain their superiority after the legal basis for it is removed. The historical legacy of the denial of basic social and economic rights may be to diminish the repressed groups’ capability to organize collective action and enforce contributions to public goods, which perpetuates the vulnerability and exploitability of these groups.

In the next section of our paper (Section I), we present the experimental design. In Section II, we discuss our hypotheses regarding the role of caste affiliation in punishment behavior and in Section III, we report our results. Section IV reports the follow-up experiment to investigate the effect of caste status on in-group affiliation. Section V summarizes our findings and relates them to the question: How does an elite resist reforms after it loses control over the political institutions? Whereas earlier work looked for an explanation in a framework of purely selfish agents, our findings suggest that the affiliation to different social positions in an extreme hierarchy affects people’s _social_ preferences: members of the repressed group are made more self-interested than members of the elite. One consequence may be a difference between the elite and the historically repressed group in the ability to sustain cooperative behavior among _themselves_ and thereby to coerce _others_ .

# **I. The Experimental Design**

We are interested in how the affiliation to castes with different social status affects the willingness to punish violations of a conditional cooperation norm. For this purpose, we developed a simple experimental game in which certain behaviors are likely to constitute a normative obligation that, if violated, may be punished by an “impartial” observer. In the following, we describe first the game and then the different treatment conditions.

# **_I.A. The Experimental Game_**

Figure 1 depicts the game between three individuals, A, B, and C. Each individual plays the game in his home village with anonymous players from other villages. Players A and B interact in a sequential exchange game. Each player has an endowment of 50 rupees, which is a initiatives do not operate in a social vacuum”; they require social pressures to sustain them. However, the required social pressures from the low castes were absent during the fifty-year period spanned by the five surveys of Palanpur.

8
**<u>Table 3: Infant and neonatal mortality and drinking water salinity, Continuous</u>**

||(1)<br>_model 1_|(2)<br>**Infant**<br>_model 2_|(3)<br>_model 3_|(4)<br>_model 1_|(5)<br>**Neonatal**<br>_model 2_|(6)<br>_model 3_|
|---|---|---|---|---|---|---|
|gwec|-0.00122*|0.000521|0.000460|-0.00165***|-0.00000312|-0.0000210|
||[0.000684]|[0.000887]|[0.000913]|[0.000638]|[0.000814]|[0.000831]|
|child male||0.00468|0.00471||0.00539|0.00537|
|||[0.00395]|[0.00394]||[0.00362]|[0.00361]|
|child birth order||0.00219|-0.000782||0.0000774|-0.00202|
|||[0.00185]|[0.00197]||[0.00167]|[0.00178]|
|maternal age||-0.00147***|-0.00104*||-0.00122**|-0.000928*|
|||[0.000545]|[0.000553]||[0.000508]|[0.000518]|
|mother's years of education|||-0.00133**|||-0.00121*|
||||[0.000673]|||[0.000624]|
|partner's years of education|||-0.000779|||-0.000315|
||||[0.000542]|||[0.000515]|
|mother currently married or living|||-0.00828|||-0.0123|
|in union|||[0.0151]|||[0.0148]|
|urban residence|||-0.00155|||-0.00315|
||||[0.00444]|||[0.00405]|
|standardized asset index|||-0.0136|||-0.0101|
||||[0.0130]|||[0.0122]|
|arsenic|||-0.0000303|||-0.0000193|
||||[0.0000238]|||[0.0000225]|
|groundwater depth|||-0.0000719|||0.000322|
||||[0.000655]|||[0.000633]|
|improved household sanitation|||-0.00121|||-0.00112|
||||[0.00448]|||[0.00426]|
|basin: Ganges-Padma|||-0.00631|||-0.00197|
||||[0.00908]|||[0.00843]|
|basin: Meghna|||-0.0152*|||-0.0151**|
||||[0.00781]|||[0.00683]|
|Constant|0.0548***|0.0945***|0.121***|0.0474***|0.0853***|0.108***|
||[0.00237]|[0.0148]|[0.0235]|[0.00223]|[0.0138]|[0.0223]|
|Birth Year Dummies|No|Yes|Yes|No|Yes|Yes|
|Division Fixed Effects|No|Yes|Yes|No|Yes|Yes|
|R-squared|0.000166|0.00937|0.0111|0.000355|0.00922|0.0105|
|Obs.|22158|22158|22158|22158|22158|22158|

Notes: omitted group of basins is basin: Brahmaputra-Jamuna; model 1: unadjusted (no controls); model 2: adjusted for maternal age, birth order and sex of the child; model 3: additionally adjusted for both parents years of education, rural or urban residence, household wealth score , improved sanitation, maternal marital status, groundwater arsenic level, depth of tubewell, riven basin children's birth year and geographical division fixed effects 25
WPS3277

**Does Greater Accountability Improve the Quality of Delivery of Public Services? Evidence from Uganda**

**Klaus Deininger**<sup>*****</sup> **Paul Mpuga**<sup>******</sup>

> *The World Bank, Washington D.C.

> ** Makerere University, Kampala-Uganda & Johannes Kepler University, Linz-Austria

# World Bank Policy Research Working Paper 3277, April 2004

_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._ We thank the Uganda Bureau of Statistics, especially James Muwonge and Tom Emwanu, for making the data available. Support from the African Statistical Capacity Building Initiative and the Norwegian ESSD Trust Fund is gratefully acknowledged.
# Table Al.lla - Volume Movements in the Unit2d States, Grain Mill Produ.ts, 1975-1977

|---------------------------------------------------------<br>Gross V<br>of Out<br>(million 1<br>accordi<br>Industrial<br>1977<br>----------------------------------<br>_------------------------------__----------__-|__--------<br>alue<br>put<br>972 US$)<br>ng to<br>Outlook<br>1975<br>-----------------------|----------__---------------------<br>Ratio<br>of 1975<br>to 1977<br>Quantity<br>for<br>Gross<br>Output|--_<br>Gross V<br>of Out<br>(million 1<br>accordi<br>Census<br>Valuation<br>1977|alue<br>put<br>977 US$)<br>ng to<br>Our<br>Estimate<br>1975|Ratio<br>of 1975<br>to 1977<br>Quantity<br>for<br> <br>Gross<br>Output|
|---|---|---|---|---|---|
|2041 Flour and Other Grain Mill Products 2,968.0|2,749.4|92.6348|3,683.3|<br>3,412.0||
|2046 Wet Corn Milling<br>1,052.7|809.1|<br>76.8595|2,014.8|1,548.6||
|TOTAL|||5,698.1|<br>4,960.6|87.0568|

Source: Gross Value of Output at 1972 US$ from US Dept. of Commerce, 1982 US Industrial Outlook, Washington D.C; Census Valuation of Output at 1977 US$ from 1977 US Census of Manufactures, see table A.l1; Table A1.llb - Price Movements in the United States, Grain Mill Products. 1975-1977

|=________________________.._____________________________________________-<br>Gross Value<br>of Output<br>in 1975<br>according to<br>ASM<br>Our<br>(1975 US$) Estimate<br>(1977 US$)|Ratio<br>of 1975<br>to 1977<br>Prices<br>for<br>Gross<br>Output|
|---|---|
|2041 Flour and Other Grain Mill Products 4,327.6<br>3,412.0<br>2046 Wet Corn Milling<br>2,141.7<br>1,548.6|126.8341<br>138.3022|
|TOTAL<br>6,469.3<br>4,960.6|130.4141|

Source: Gross Value of Output at 1975 US$ from US Dept. of Commerce. Annual Survey of Manufactures.
- [15] Romer, Christina, and David Romer, 2010. “The macroeconomic e¤ects of tax changes: Estimates based on a new measure of ...scal shocks,” American Economic Review 100, 763-801.

- [16] Stock, James, and Mark Watson, 2007. “Why has U.S. in‡ation become harder to forecast?” Journal of Money, Banking and Credit 39, 1-33.

23
Policy Research Working Paper 6814

# **Abstract**

This paper offers the first evidence on the prevalence of a central actor in modern growth theory—the engineer. Using newly collected sub-national, and international data as well as historical case studies, it then argues that differences in innovative capacity, captured by the density of engineers and patents at the dawn of the Second Industrial Revolution, in fact, are important to explaining present income differences across US counties, states within countries, and between the US and Latin America. This remains the case after controlling for literacy, other higher order human capital, and demand side elements that might be confounded with engineering or patenting. Instrumenting engineering using the Land Grant Colleges program further limits remaining endogeneity. A 1 SD increase in engineers at the turn of the 20th century accounts for a 16 percent increase in US county income today, and patenting capacity contributes another 10 percent. This can partly explain why countries with similar levels of income in 1900, but ten fold differences in engineering density diverged in their growth trajectories over the next century.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group. 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 authors may be contacted at wmaloney@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 those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Social Health Insurance vs. Tax-Financed Health Systems—Evidence from the OECD

## by

## Adam Wagstaff<sup>a</sup>

a Development Research Group, The World Bank, Washington DC, USA

**Contact author:** Adam Wagstaff, World Bank, 1818 H Street NW, Washington, D.C. 20433, USA. Tel. (202) 473-0566. Fax (202)-522 1153. Email: <u>awagstaff@worldbank.org.</u>

**Keywords:** Social health insurance; labor markets; health finance; health sector reform.

**Acknowledgements:** I am heavily indebted to Rodrigo Moreno-Serra: this paper employs methods and utilizes computer code that he and I jointly developed for two papers on the impacts of social health insurance in central and eastern Europe and central Asia. He also provided me with some helpful comments on the paper; due to timing, not all of them have been incorporated in this version. The findings, interpretations and conclusions expressed in this paper are entirely mine, and do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent.
# **3.1 Household size and composition**

Large households are strongly associated with lower per capita expenditure in both urban and rural areas, as shown in Table 2. The negative sign of the coefficient on household size implies that, other factors being equal, each additional household member is associated with a 7-8 percent reduction in per capita expenditure<sup>5</sup> . The stratum-level regressions show similar results (see Annex 2).

In rural areas, a household with a large number of elderly members, of children, and of females is likely to have low per capita expenditure. In urban areas, however, only the number of children is statistically significant (see Table 2). Household composition appears to matter less in urban areas than rural ones. It may be that the number of children, women, and elderly people have less effect on household welfare in urban areas because income-earning capacity in the cities and town is less dependent on physical strength.

Ethnicity<sup>6</sup> is a predictor of per capita expenditure, but a surprisingly weak one. In rural areas, the coefficient on ethnicity was significant only at the 10 percent level while in urban areas, it was not statistically significant (see Table 2). The urban coefficient is not surprising given the very small sample of ethnic minority households in urban areas (just 19 households). The weakly significant, although appropriately signed, coefficient for rural areas is more surprising given the strong correlation between poverty and ethnicity in Vietnam. Other research (Van de Walle and Gundewardana, 2000, Baulch _et al.,_ forthcoming) suggests that ethnic minorities have both lower levels of endowments and lower returns to those endowments. Our results are consistent with these findings, 5 A coefficient of -0.772 implies that a one-unit increase in the explanatory variable is associated with 7.4 percent reduction in per capita expenditure, since exp(-0.772)=0.926=l-7.4%. We must be careful before inferring that larger households are worse off than smaller ones, however, for two reasons. First, there may be economies of scale in household size, so that larger households do not "need" the same per capita expenditures as smaller households to reach an equivalent level of welfare. Second, our measure of welfare does not take into account household composition, so if larger households have more children than smaller households they might still have equivalent levels of expenditure _per adult_ equivalent. 6 In common with other studies of ethnic minority issues using the VLSS, we group _Hoa_ (Chinese) households along with the _Kin/h_ (ethnic Vietnamese).

13