rafmacalaba/datause-encoder-data huggingface dataset:
richest to the poorest with their estimated population (family or household head) shares and average incomes. Social tables are particularly useful in evaluating ancient societies where classes were clearly delineated and the differences in mean incomes between them were substantial. Theoretically, if class alone determined one’s income, and if differences in income within classes were small, then all inequality would be explained by the between-class inequality. One of the best examples of social tables is offered by Gregory King’s famous estimates for England and Wales in 1688 (Barnett 1936; Lindert and Williamson 1982). King’s list of classes summarized in Table 1 is fairly detailed (31 social classes). King (and others listed in Table 1) did not report inequalities within each social class so we cannot identify within-class inequality for 1688 England and Wales or for any other of the Table 1 observations. However, within-class inequalities can be roughly gauged by calculating two Gini values: a lower bound Gini1 which estimates only the between-group inequality and assumes within-group or within-social class inequality to be zero; an upper bound Gini2 which estimates the maximum inequality that is compatible with the grouped data from social tables assuming that all individuals from a higher social group are richer than any individual from a lower social group. In other words, where class mean incomes are such that yj>yi, it also holds true that ykj>ymi for all members of group _j_ , where _k_ and _m_ are subscripts that denote individuals. Thus, in addition to the between-class inequality component, Gini2 includes some within-class inequality (see equation 2), but under the strong assumption that mean incomes for all members of a given social class are poorer or richer than those respectively above or below them. This strong assumption is unlikely to be fulfilled in any actual social table, but it allows us to move beyond an accounting limited only to between-class inequality.<sup>9</sup>

> 9 Gini2 is routinely calculated when published income distribution data are only reported as fractiles of the

12
Figure 9. Contributions of Factors to GDP Growth (short-run method) <!-- Start of picture text --> 2.1% 2.0%<br>2.00% 0.10%<br>0.08%<br>1.70% 1.6%<br>0.07% 1.4%<br>1.40% 0.96% 0.99% 0.06% Copper reserves<br>Copper investment<br>Copper TFP<br>1.10% 0.97% Non-res investment<br>0.92% Labor force growth<br>0.80% 0.29% 0.29% Human Capital growth<br>Non-res TFP<br>0.50% 0.14% 0.10% 0.07%0.05% 0.03% Total GDP growth<br>0.20% 0.56% 0.53% 0.49% 0.47%<br>-0.02%<br>-0.10%<br>2025 2035 2045 2050<br>Source: LTGM simulation, World Bank staff.<br><!-- End of picture text -->

# **Why is the baseline growth rate declining?**

To understand why baseline growth falls, we run counterfactual simulations where the growth rate of each growth driver is kept constant at current rates. Table 2 depicts the impact of each driver on the fall in GDP growth between 2023-2050 (figures are in Appendix A2). These are normalized contributions, which means that the individual contributions are scaled so that they add up to the total fall in growth. The normalization is necessary because over the long term, the model is non-linear.

Demographics are the most important factor and explain almost two-thirds of the 0.78 ppts fall in GDP growth over 2023-50. Population growth is expected to fall 0.3 ppts over 2023-2050. Declining population growth reduces the growth of the labor force, which directly reduces GDP growth. This also reduces the marginal product of capital (which is proportional to the capital-to-output ratio), which reduces the effect of investment on growth.<sup>11</sup> The falling working age-to-total population ratio (WATP ratio) reduces GDP and per capita GDP growth in the late 2030s. The growth rate of the WATP falls by 0.3 ppts by the end of the simulation period, resulting in a normalized contribution of 29% to the overall fall in GDP growth over 2023-50.

The combined fall in TFP growth in the resource and non-resource sectors and HC growth over 2023-50 account for around 0.3 ppts of the fall in the GDP growth over 2023-50. The baseline TFP growth rate in the non-resource sector is expected to decrease from 0.61 percent (the value in the counterfactual) to 0.53 percent over the next three decades (Figure A2-e, appendix), a 0.1 ppts decline. The TFP growth rate in the resource sector is assumed to be constant at -0.5 percent in the baseline scenario (in the counterfactual, resource TFP growth is kept constant). Human capital growth falls from 0.31 percent (the value in the counterfactual) to 0.09 percent in the baseline, a 0.2 ppts decline (Figure A2-f, appendix). While this decline in human capital growth is larger than that of TFP non-resource growth, GDP growth rates are also less sensitive to human capital, resulting in a similar contribution.

> 11 The declining population growth increases GDP per capita growth (in contrast, other growth drivers have the same effect on GDPPC and GDP growth). In the baseline, GDPPC growth decreases by 0.29 ppts, and declines 0.46ppts with constant population growth.

12
variation (Column 2), and remain uncorrelated to treatment assignment (as shown under various specifications in Columns 3 to 6). The overall disruption dummy _Evd_ is then equal to one for 25% of villages, and again remains orthogonal to treatment assignment.

In the robustness check Table A6, the last row in each panel then shows the main results under this alternative construction for _Evd_ . We see that of the 12 significant coefficients in the baseline specifications, 9 remain statistically significant, and with similar point estimates.

# **References**

- [1] acevedo.p, g.cruces, p.gertler and s.martinez (2017) Living Up to Expectations: How Job Training Made Women Better Off and Men Worse Off, NBER WP23264.

- [2] adda.j (2016) “Economic Activity and the Spread of Viral Diseases: Evidence from High Frequency Data,” _Quarterly Journal of Economics_ 131: 891-941.

- [3] alfonsi.l., o.bandiera, v.bassi, r.burgess, i.rasul, m.sulaiman and a.vitali (2018) Tackling Youth Unemployment: Evidence from a Labor Market Experiment in Uganda, mimeo UCL.

- [4] allen.h (2004) “Prostitution,” in R.S.McElvaine (ed.) _Encyclopedia of the Great Depression_ , Vol.2., Macmillan Reference USA, New York.

- [5] amnesty international (2015) Shamed and Blamed: Pregnant Girls’ Rights at Risk in Sierra Leone,Amnesty International Publications, London.

- [6] anderson.m.l. (2008) “Multiple Inference and Gender Differences in the Effects of Early Intervention: A Reevaluation of the Abecedarian, Perry Preschool, and Early Training Projects,” _Journal of the American Statistical Association_ 103: 1481-95.

- [7] aral.s.o, j.s.st.lawrence, l.tikhonova, e.safarova, k.a.parker, a.shakarishvili and c.a.ryan (2003) “The Social Organization of Commercial Sex Work in Moscow, Russia,” _Sexually Transmitted Diseases_ 30: 39-45

- [8] arunachalam.r and m.shah (2018) “Prostitutes and Brides?,” _American Economic Review Papers & Proceedings_ 98: 516-22.

- [9] ashraf.n, e.field and j.lee (2014) “Household Bargaining and Excess Fertility: An Experimental Study in Zambia,” _American Economic Review_ 104: 2210-37.

40
1 1 To answer the last question about distortive effects of ALMPs solidly one would need a general equilibrium model of transition economies which goes beyond the task of this paper. However, some of the distortive effects (substitution and dead-weight) can in principal be analysed using an augmented matching model..

Public employment (public works) measures might serve as an example to demonstrate the distinction between general and partial equilibrium analysis of ALMPs in transition economies. When the stock of public employment participants is large relative to the unemployed this measure reduces the overall unemployment rate by a noticeable amount as we have with de-registration of public employment schemes participants a one-to-one reduction of the unemployment register as long as there are no substitution effects. The interesting question to ask is then how costly is this reduction of the unemployment stock. To this purpose one would need a cost-benefit-analysis which would also look at possible fiscal substitution effects. The analysis would become more complex if scheme participants "crowded out" other job searchers after the end of the scheme but it would still be of a general equilibrium type.

Public works participants are normally hired for a rather short period (in Poland e.g. for up to 6 months). In a partial equilibrium framework one would ask the following question when evaluating public works schemes: was the unemployed enabled to maintain or increase his/her human capital through his/her perticipation resulting in a higher re-employment probability in a regular, non-subsidised job?'° Given the data limitations, distortive effects of ALMPs in transition economies are also empirically difficult to analyse. Nevertheless, a general comment about policy design by the governments of CEE countries might be in order. These governments have undergone a learning process resulting in the formulation of ALMPs which clearly try to minimise distortive effects. While it is always virtually impossible to eliminate dead weight losses, the revised legislation in CEE countries shows the great efforts that have been made to reduce substitution and fiscal substitution effects.

The non-experimental evaluation of ALMPs taking place in a partial equilibrium framework is dominated by two approaches

> 10A less ambitious aim of public works participation could be the strengthening of labour market attachment by some of the unemployed. To analyse this question one would need to look at LFS data which include statements about participation in ALMPs. Thus far such data are no! available.
21 Figure 10 shows that the effect on the financial protection index of changing from one definition of catastrophic spending to the other varies across countries; in most it makes a relatively small difference (less than 10 percent in a sizable fraction of countries); but in some (Ethiopia and Lao PDR especially) it makes a big difference.

Figure 11 shows the breakdown for the BRICS countries: Brazil, China, India, Russia and South Africa. Also shown are the values of the financial protection index. In the filled-circle markers the categories are weighted 1, 2, 3, 4 and 5, and there is no adjustment for differences across countries in the share of nonspenders who are nonusers. In the unfilled-circle markers we adjust for such differences: nonusers are excluded and a score of 5 is given only to nonspenders who have received care.<sup>13</sup> The triangle-shaped markers make a different adjustment to the financial protection index: here we weight households in the immiserized and impoverished categories equally at 1.5; the other categories are weighted 3, 4 and 5 as before. In Figure 11 catastrophic spending is defined as spending in excess of 40 percent of discretionary consumption.

Seven points are worth highlighting. First, we see appreciable fractions of the population in some countries – notably Brazil, China and India – falling into the impoverishing and catastrophic spending categories; indeed at the $1.25-a-day poverty line, these categories dominate the immiserizing spending category in

> 13 The numbers are computed using the out-of-pocket spending data from the household questionnaire, except that we have used the individual-level share of users among nonspenders to estimate the fraction of nonspending households who received care.
largest FCS in the developing world. FCS in India increased from $1.6 to almost $40 billion during the same period.

The sources of foreign capital in developing countries differ from one region to another. A study by the Inter-American Development Bank (IDB 2005) reveals that FDI sources are different for China than for LAC. For this reason, it is perhaps more interesting to understand the evolution of FCS from OECD countries, which comprise about 68 percent of total stocks into Latin America. China and particularly India are still far from the levels of FCS found in the major LAC economies from these sources. Mexico had almost twice as much capital from the OECD than China by 2003, and almost seven times more than India. OECD stocks in China and India grew faster than in LAC during 1990-2003. This trend is less clear for the 1997-2003 period, especially for China. In fact, relative to GDP growth, FCS grew more in LAC countries than in China since 1997 (Cravino, Lederman, and Olarreaga 2006).

The effect of FCS in China and India on other economies is theoretically ambiguous, however. For China and India to have a negative effect on others, global FDI supply would have to be inelastic, as well as globally integrated so that competition for FDI becomes a zero-sum game. Furthermore, as noted by Eichengreen and Tong (2005), if production processes are vertically integrated across countries, increasing production in China or India would require increasing production in other locations. Therefore, empirical work is needed to ascertain whether the growth of China and India have been complements or substitutes for foreign capital in alternative locations.

We estimate these effects drawing on the “Knowledge-Capital Model” (KCM). We augment this model with Chinese and Indian Foreign Capital Stocks (FCS) and 2
the provider dispenses an unclassifiable medicine is decreasing in other measures of provider quality from the SP study, therefore we are reasonably confident that such medicines are more likely unnecessary treatments than not.

# **C Theoretical Appendix**

We provide a simple theoretical framework which demonstrates doctors’ choice of effort and treatment with and without market incentives as well as the effects of their choices on patient’s health outcome. This framework incorporates three possible channels which can generate excessive unnecessary treatments. The first channel is ignorance. Doctors want to treat patients, but they do not know the cause of patient’s symptoms and give out a cocktail of medicines hoping that one of the medicines would work. Second, there is pecuniary incentive to sell more medicines. Third, excessive treatment can be driven by patients. Patients have their own expectation about proper treatment, and doctors satisfy the patients by complying their expectation.

One key intuition of the framework is that unnecessary treatments are not only driven by market incentives but also can arise due to low effort of doctors. When doctors lack motivation to exert enough effort to substantially reduce their ignorance about the patients condition, mixing a variety of treatment is even necessary to maximize the health outcome. Market incentives induce higher effort but also lead to more unnecessary treatment at any given level of effort, thus, health outcome produced under market system does not necessarily dominate that of public system, and vice versa.

Our purpose is to provide a framework which helps to interpret the empirical findings related to the choice of effort and treatment by doctors facing different incentives. We abstract from any market equilibrium component such as pricing, entry and exit decisions of doctors, or any strategic interaction among doctors in the market or across sectors (public and private). Patients’ expectation also enters exogenously rather than endogenously formed in the market.

This appendix is comprised of two parts. In section 1, which is the main part of the appendix, we introduce doctors’ utility maximization problems and discuss whether market incentives induce higher effort and better health outcome. We first omit the patients’ expectation channel from the problem to focus on the effects of market incentives and introduce it again at the end of the section. In section 2, we provide one potential way of endogenizing the market incentives for interested readers.

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|Other|5,618*|-3,325|5,083|-3,291|
|---|---|---|---|---|
|Main source of lighting|||||
|(baseline=Electricity/Gas)|||||
|Kerosene|460.7|-2,876|1,533|-2,835|
|Candles/Batteries|2,693|-3,471|2,451|-3,373|
|Other|-1,926|-2,882|-1,960|-2,692|
|Main heat source for cooking|||||
|(baseline=Firewood)|||||
|Charcoal|-3,786|-6,211|-1,354|-5,766|
|Kerosene/Oil|7,966***|-2,853|6,802**|-2,796|
|Electricity/Gas|32,053**|-12,680|32,040***|-12,194|
|Other|13,071*|-6,961|12,845**|-5,687|
|Type of garbage disposal|||||
|(baseline=Bins)|||||
|Compound|-6,878|-4,986|-10,325**|-5,095|
|Empty Lots/streets/rivers|-6,616|-5,013|-9,727*|-5,054|
|**Assets**|||||
|TV|8,951***|-2,368|10,813***|-2,282|
|Fridge|3,858|-3,024|2,408|-3,106|
|Stove|6,867***|-2,470|6,178***|-2,366|
|Bicycle|1,321|-2,063|1,776|-2,046|
|Car|19,695***|-4,281|19,015***|-4,147|
|Iron|6,645***|-1,888|7,761***|-1,945|
|Constant|120,075***|-14,477|124,696***|-15,740|
|Observations|4,545||4,925||
|R-squared|0.453||0.462||

Source: Authors’ estimations based on GHS Data 32
**Table 16.  Village Financing by Province and Poor Villages in 2004**

|_RMBper capita_|**Total**|**Jiangsu**|**Sichuan**|**Shanxi**|**Jilin**|**Hebei**|**Poor**|
|---|---|---|---|---|---|---|---|
|**Revenues**|82|110|23|48|156|41|134|
|Transfers from above|_18_|_20_|_9_|_7_|_42_|_3_|_46_|
|**Total Expenditure, incl.**<br>**debt-financing**|95|168|56|52|104|50|85|
|Recurrent Expenditures|_40_|_62_|_20_|_27_|_54_|_22_|_38_|
|Repayment of Principal|_13_|_9_|_8_|_8_|_34_|_4_|_29_|
|Total Capital Expenditures|_42_|_97_|_28_|_18_|_17_|_24_|_20_|
|Deficit/Surplus|-13|-58|-33|-4|52|-9|50|
|**Total Public Goods**<br>**Investment**|_191_|_353_|_215_|_156_|_78_|_63_|_171_|
|**Total Village-level**<br>**Liabilities**|_253_|_177_|_91_|_542_|_275_|_355_|_664_|

Note: “Poor” is defined as the 20 percent poorest villages. Source: 2005 village survey.

# **3.4. Beyond the Budget: Financing Rural Infrastructure**

Infrastructure investments are the driving force behind village finance; this is especially true in recent years. Fieldwork studies and survey research consistently find investment in infrastructure such as roads, water supply, irrigation, etc., to be considered the most important public functions played by village authorities (e.g. Zhao et al 2006, Zhang et al 2007, Yi et al. 2008). This was reflected in Table 16, where public goods investments far outstripped recurrent expenditures in the 100 villages surveyed. It is further highlighted in Table 17, where, on average, villages spent RMB 42.5 per capita for capital investments in 2004, compared to recurrent spending of RMB 40.1.

31
and diversity is associated with a decrease of 0.16 standard deviation on trust among young women and this difference is statistically different at 10 percent.

Table 3.A. Subgroup Analysis: Trust Toward Outgroups (by income and sex)

|VARIABLES<br>Index Trust Outgroups(z-score)|(1)<br>Income<br>above<br>median|(2)<br>Income<br>below<br>median|(3)<br>F-test<br>(p-<br>value)<br>(1)-(2)|(4)<br>Male|(5)<br>Female|(6)<br>F-test<br>(p-<br>value)<br>(4)-(5)|
|---|---|---|---|---|---|---|
|[T1] Urban Design|0.08<br>(0.088)|-0.18**<br>(0.090)|0.04|-0.04<br>(0.084)|-0.05<br>(0.095)|0.92|
|[T2] Diversity|-0.01<br>(0.090)|-0.08<br>(0.090)|0.58|-0.03<br>(0.085)|-0.05<br>(0.093)|0.90|
|[T3] Urban Design + Diversity|0.06<br>(0.085)|-0.13<br>(0.085)|0.10|0.05<br>(0.079)|-0.16*<br>(0.091)|0.08|
|Observations|908|1,016||1,034|890||
|Control Mean|-0.03|0.03||0.15|-0.18||
|Selected Controls|X|X||X|X||

Notes: Robust standard errors are shown in parentheses. ***p<0.01 **p<0.05 *p<0.1 Second, Table 4 shows the effects of the intervention on the “perceptions toward outgroups index” and its components, measuring how happy and relaxed the respondents felt when thinking about outgroups. Again, none of the treatment arms has any effect on the index, but there is a statistically significant impact on perceptions toward the opposite sex at the indicator level. Specifically, exposure to diverse social groups in the VR experience is estimated to cause between 0.07 and a 0.08 percentage points reduction in the respondent’s perceptions toward the opposite sex (Columns 5-6), meaning that young women felt less relaxed or happy when thinking about the men they saw in the public space.

When looking at the subgroups, the only statistically significant results pertain to Urdu and nonUrdu speakers, taken as a proxy for Muhajir ethnic group versus other ethnicities. Table 4.A reports the effect of the intervention on “perceptions toward outgroups index” by ethnic affiliation. Exposure to urban design elements is associated with an increase of 0.31 standard deviation on the “perceptions toward outgroups index” among non-Urdu speakers. The difference between Urdu and non-Urdu is statistically different (p-value<0.01), suggesting that non-Urdu speakers report positive perceptions toward other ethnic groups, i.e. Urdu speakers, as well as toward the opposite sex, when they experience improved urban elements in the VR. It is interesting that the differences do not hold when the intervention introduces diversity in T2 and T3, suggesting that the positive impact of the urban design intervention 17
tailored to Bihar’s specific context and program guidelines. Professional actors performed in an entertaining and emotionally engaging story-based plot whose purpose was to provide information on how the scheme works, who can participate and how to go about participating. The main story line was centered on a temporary migrant worker returning to his village from the city to see his wife and young daughter. He learns that there is BREGS work available in the village, even though it is the lean season, so he can stay there with his family and friends rather than return to the city to find work. It was intended that the audience would identify strongly with the central characters. The film was disseminated between mid-February and mid-March 2010 in a randomly selected sub-sample of 40 out of the 150 villages that were surveyed in R1. (Section 2 discussed the timing of the intervention and follow-up surveys.) Compliance at the village level was complete. Since our 150 villages are drawn randomly from all villages in rural Bihar we can infer mean impacts of a village receiving a screening of the film for rural Bihar.

As a check on the randomization, we tested for differences in the sample means of the 75 village variables used in our analysis (including village means of household variables). The difference in sample means was only statistically significant at the 5% level for three out of 75 variables. (Some significant differences are to be expected by chance even when fully randomized.) The two samples are clearly well balanced.

In each village, the film was shown in two separate locations at different times over one or two days. At each location, the film was screened twice, followed by a question and answer session and distribution of one-page flyers that pictorially illustrated the main entitlements and processes under the scheme. On arriving in each village, efforts were made by the facilitators to announce and advertise the upcoming screenings in advance. Local officials such as the Mukhiya and Sarpanch, opposition leaders and local BREGS officials were invited to attend. The film was typically shown in common areas, such as an open ground, school building, or community hall. In 93% of the showings, the facilitators noted that the majority of people watched both screenings of the film. On average, about 365 people (38% women) attended either screening. In a third of the villages, the Mukhiya attended the show as did his assistant (the “mate”); the Panchayat Rozgar Sewak (PRS) attended in half the treatment villages and the local opposition leader did so in close to 60% of villages.<sup>14</sup> People in the majority (89%) of shows reported that

> 14  The PRS is hired on contract by the state government for implementing the scheme.

11