rafmacalaba/datause-encoder-data huggingface dataset:
-16-

**Table 5b: Gini Coefficient Estimates: Bulgaria and Eastern**<sup>**Europe**</sup>

|Country|Year|Gini coefficient(%)|
|---|---|---|
|Bulgaria|1992|25.8|
|Hungarya|1993|27.9|
|Polanda|1993|32.4|
|Romaniab|1992|25.0|

ource: W World Bank staff estimate (Policy Research Department). 'Provisional estimate provided by Mansoora Rashid (1994),<sup>'Household Welfare in a Transition Economy:</sup> Growth, Equity, and Poverty in Romania, 1989-1992',<sup>World Bank memo.</sup>

# **IV. Characteristics of Households by Income**<sup>**Groups**</sup>

Classifying households by socio-economic characteristics<sup>can provide</sup> important insights into why households are in poverty.<sup>It also provides important</sup> information for targeting social benefits more effectively.<sup>This section classifies household</sup> characteristics by income decile and discusses the most important<sup>features of low-income</sup> households.

Defining the bottom 20 percent of households as the<sup>"poor", several</sup> characteristics are immediately evident (see Table<sup>6). They are older--head of households</sup>
# **Figure 3 Historical and Projected Population Pyramids for Rural China**

# A. Historical Population Pyramids for Rural China

<!-- Start of picture text --> Rural Population Pyramid, 1982 Rural Population Pyramid, 2005<br>90-94 90-94<br>85-89 85-89<br>80-84 80 -84<br>75-79 75-7 9<br>70 -74 70-74<br>65-6 9 65-69<br>60-64 60-64<br>55-59 55-59<br>50-54 50-54<br>45-49 45-49<br>40-44 40-44<br>35-39 35-39<br>30-34 30-34<br>25-29 25-29<br>20-24 20-24<br>15-19 15-19<br>10-14 10-14<br>5-9 5-9<br>Under 5 Under 5<br>-6 -4 -2 0 2 4 6 -6 -4 -2 0 2 4 6<br>Males Females Males Females<br>1982 Population Census 2005 One Percent Population Sample<br>20 20<br>15 15<br>10 10<br>5 5<br>0 0<br><!-- End of picture text -->

- B. Projected Rural Population Pyramids in 2020 and 2030

<!-- Start of picture text --> Rural Population Pyramid, 2020 Rural Population Pyramid, 2030<br>90-94 90-94<br>85-89 85-89<br>80-84 80-84<br>75-79 75-79<br>70-74 70-74<br>65-69 65-69<br>60-64 60-64<br>55-59 55-59<br>50-54 50-54<br>45-49 45-49<br>40-44 40-44<br>35-39 35-39<br>30-34 30-34<br>25-29 25-29<br>20-24 20-24<br>15-19 15-19<br>10-14 10-14<br>5-9 5-9<br>Under 5 Under 5<br>-6 -4 -2 0 2 4 6 -6 -4 -2 0 2 4 6<br>Males Females Males Females<br>20 20<br>15 15<br>10 10<br>5 5<br>0 0<br><!-- End of picture text --> Source: Cai, Giles and Wang (2009). Historical population pyramids are calculated from the National Bureau of Statistics, 2005 One Percent Population Sample (2007), China Statistical Press. Yao, Xinwu and Yin Hua, China Normally Used Population Dataset, China Population Press (1994).

30
comparability purposes with other countries the analysis uses the total income definition, instead of the monetary income definition, to account for the imputed rent. In pursuance of a better understanding of the incidence of fiscal policy in Chile, the following income groups are defined: _poor_ , as those individuals with per capita income below the $4 a day poverty line —distinguishing within this group the “ultra-poor” (living with less than $1.25/day), the “extreme poor” (living on $1.252.5/day), and the “moderate poor” (living on $2.5-4/day)—; _vulnerable_ , as those with per capita income between $4 and $10 a day; _middle class_ , as those living on $10-50/day; and _wealthy_ , as those with per capita income above $50/day.<sup>18</sup> The analysis also considers the incidence on the extreme and moderate poor as defined using the official poverty lines in Chile, as well as on income deciles.

# **4. MAIN RESULTS**

# **4.1 Redistributive effects of Chile’s fiscal system**

Are fiscal interventions in Chile equalizing? Figure 2 shows that income inequality in Chile, as measured by the Gini coefficient, declines from 0.494 to 0.467 when moving from market income plus pensions to disposable income<sup>19</sup> —i.e., after the intervention of direct taxes, social contributions to health and unemployment insurance, and direct transfers—.

**Figure 2: Effects of fiscal interventions on income inequality** _(Gini coefficients)_

<!-- Start of picture text --> 0.55<br>0.50<br>0.494<br>0.487<br>0.45 0.467 0.464<br>0.420<br>0.40<br>0.35<br>0.30<br>Market income plus Net market income Disposable income Consumable income Final income<br>pensions<br><!-- End of picture text --> _Source_ : Author’s elaboration based on Martinez-Aguilar and Ortiz-Juarez (2016).

18 The poverty line of $1.25/day is the standard used by the World Bank to measure the incidence of poverty globally; its value corresponds to the average of the poverty lines of some of the poorest countries in the world. The $2.5/day and $4/day poverty lines are equivalent to the conditional mean of the national extreme and moderate poverty lines, respectively, across Latin American countries (conditional on GDP per capita). The thresholds to define the vulnerable, middle class, and upper class groups are those proposed by Lopez-Calva and Ortiz-Juarez (2014). All these figures are expressed in 2005 PPP prices.

19 The Gini coefficients shown in Figure 2 are different from the official estimates because the latter uses the monetary income definition which excludes the imputed rent, whereas this paper uses the total income definition to include it therefore allowing for cross-country comparisons. If the imputed rent is excluded from the analysis, for instance, the Gini coefficient for disposable income would be 0.490 instead of 0.467, which is virtually the same value as that reported by the Ministry of Social Development (2016): 0.491.

11
interest in the paper. The measurement of the quality of care is, however, still an unsettled issue in the literature. It is also likely that patients’ bypassing decisions are partly based on subjective perceptions of the quality offered by facilities in their choice set rather than the facilities’ objective quality. It is important for policy to determine to which extent, controlling for prices and other relevant variables, subjective quality perceptions dominate the providers’ “true” quality which is the one that matters for the health of the population. If ceteris paribus patients bypass a nearer high-quality facility to seek care to a more remote facility just because their perceptions are “wrong”, then there is room for policy intervention through, say, report cards to improve the health of the population.

# 8.2.1. Objective Quality Indices

The facility survey collected information on the health centers’ infrastructure, equipment, and personnel which we use to build three composite indices that attempt to capture the objective quality of care. The first index is the infrastructure index which is an average of providers’ access to water and electricity. Second, using staff data, the personnel index is simply the ratio of skilled to unskilled employees in the facility. This index captures the facility’s staff-mix. However, several recent studies have shown that counting the number of employees may not provide an accurate measure of health workers’ labor supply in the facility because of high absenteeism rates. We therefore include the rate of absenteeism in the facility to complement the personnel index. A staff is considered absent if she was not on the premises of the facility at the time of the survey. On average, about 21% of health workers were unaccounted for at the time of the survey. The absenteeism rate is even higher for doctors and technicians and reaches an average of 33%. Though, this seems low relative to absenteeism rates observed in other developing countries (see for instance Chaudhury et al., 2006), it could still negatively impact the quality of care offered.

In addition to these indices, we also constructed a price index using user fees the health centers charge for 10 main services such as acute respiratory infections or malaria. We also use the difference between each facility’s price index and the 22
# Table IV.4 Auto Parts Sample: Choice of Export Clianncls

||Early|Exporters (<br>_N=6_|Barore 1985)||||Late Ex|porters (1985 o<br>N=6|r Later)|
|---|---|---|---|---|---|---|---|---|---|
||Direct<br>Effort<br>of Firm|Foreign<br>Buyers|Non-Profit<br>Agency<br>Support|Sub.<br>Contracting||Direct<br>Effort<br>of Firm|Foreign<br>Buyers|Non-Profit<br>Agency<br>Support|Sub-<br>contracting|
|Relative Importance<br>At First Export||||||||||
|All|2.5|1.0|2.2|3.3|All|1.5|3.8|2.0|2.2|
|Small_(<_80 workers)'|3.3|1.0|1.3|3.0|c150|_1.5_|4.5|1.0|1.5|
|Large (-> 80 workers)'|1.7|1.0|3.3|3.7|_150_+|1.5|3.5|2.5|2.5|
|Relative Importance<br>Current"||||||||||
|All|3.2|2.7|1.2|2.8|Ali|2.8|4.5|13|3.2|
|Small Size'|4.3|1.7|1.3|3.0|<150|2.0|5.0|1.0|2.5|
|Large Size'|2.0|3.7|1.0|2.7|150 +|3.2|4.2|_1.5_|3.5|
|RelativeImportance<br>Current||All Expo|rters|||||||
|Sniall Exporter<br>t< 1500 million won)|3.3|3.3|1.2|2.8||||||
|Large Exporter<br>(> 1500 million won)|2,8|4.0|1.4|2.8||||||

* Size at first export.

" The lime elapsed since start-up averages 10 years for early exporters and 4.2 years for late exporters. _Note:_ 'Current" period if 1991.

_61_
indicators for 1980-8 and 1987-88. For example, in the case of investment he concludes that: capital accumulation slowed down in five-sixths of these fadjustingl countries. In 1987-88 the average (unweighted) gro investment/GDP ratio was 30 percent lower than in 1980-81.

(Cornia, 1991: 23) Jespersen's (1992) discussion of the same topic also gives before and after data on capital accumulation, manufacturing value added and growth performance. The terminological sleight of hand between the description of the data and ascribing causation is clear from the sentence: With few exceptions, stabilization was accompanied by sharp losses in GDP growth, investment and human capital development. Of the 18 countries which managed to stabilize their economies in the 1980s, only five recorded positive growth in GDP per capita. In all others macroeconomic stabilization was achieved at the expense of growth.

CJespersen, 1992: 14) The descriptive term 'accompanied" becomes the causal relationship "at the expense of". OScon the logical _non sequitur_ of the above quotations is left implicit, as in the following examples: .. he per capita income of non-cocoa farmers [in Ghana] has stagnated... In suommary, conventional adjustment policies.. often aggravated poverty.

(Cheru, 1992: 507) the [Tanzanian] reform program has seen a sharp improvement in export performance, increasing from an average of 9.6 per cent of GDP in the pre-reform period to an average of 26.9 per cent during the period of reform.

(Nord _et al,_ 1993: 17) The quote from Nord _a at._ is doubly misleading. Not only do they apply the before-after approacb but also base the comparison on ratios calculated using current prices, ignoring price effects from devaluation. The current price ratio may be written as: EXP _EP',, X_ (2.1) **_INC_** PJY where EXP and INC are nominal exports and income respectively, E is the exchange rate (local

**_6_**
combustion is associated with a range of other toxic pollutants, including sulfur dioxide and nitrogen oxides. Overall, our findings suggest that coal prices do not significantly and systematically alter urban PM2.5 concentrations in our sample of 133 countries. However, while this might hold globally, the relationship may be significant in different subsamples, such as regions or national income classifications.

# 5. Discussion

Exposure to air pollution is widely accepted as a leading health issue in many countries, regardless of income classification (WHO 2021). In urban environments, fossil fuel combustion is a significant contributor to airborne PM2.5, making fossil fuels an attractive target for regulation to improve health outcomes. While the relationship between fossil fuel prices and consumption has had ample attention in empirical economics, the cascading effect on air quality is less thoroughly studied. At a fundamental level, one would expect higher fuel prices to lead to lower consumption, which, in turn, would result in better air quality outcomes. Earlier works on the price-pollution channel have been at the city level and found mixed results regarding price impacts on air pollution; our contribution to the literature is in examining whether a relationship exists at the global level by using the CCE estimator with a novel panel dataset that covers 133 countries over 19 years.

Our results indicate a significant relationship between PM2.5 concentrations and the price of two transport fuels. For gasoline, a US$1 increase in prices yields 22.2 μg/ m<sup>3</sup> reduction in PM2.5 concentrations; for diesel, the reduction is 31.05 μg/ m<sup>3</sup> . This trend seems to mirror findings in the literature, which has found that diesel prices in particular are effective in mitigating emissions. Both fuels are frequently used in public transit and freight transportation, and diesel is a major source of urban air pollution. Their consumption tends to be variable, as consumers can switch to alternatives, such as public transit, or invest in more fuel-efficient vehicles when prices are high. In contrast, we do not find a significant relationship between coal prices and PM2.5 concentrations. One possible explanation lies in the nature of coal use. Unlike gasoline and diesel, coal is often used in baseload power generation, heavy industry, and residential heating, where there is limited flexibility to react to price changes. Coal combustion is also associated with a range of other pollutants apart from PM2.5. Thus, we find that the pollution-price channel is heavily contingent on the nature of a fuel’s application.

The analysis presented in this study is limited by the available data. Going forward, the analysis could be further improved by: 12
**<u>Table 7: Input share response to fragmentation by farm size class for paddy and wheat fragments</u>**

||<br>**Pa**|<br>**ddy fragments**|<br>|<br>**W**|<br>**heat fragmen**|**ts**|
|---|---|---|---|---|---|---|
||**Small**|**Medium**|**Large**|**Small**|**Medium**|**Large**|
|**Simpson Index**|||||||
|Seeds and Seedlings|-0.128|-0.126|0.009|0.039|0.306***|0.112|
||(-1.571)|(-1.282)|(0.065)|(0.784)|(3.593)|(1.310)|
|Fertilizer|<br>-0.034|<br>-0.010|<br>-0.138|<br>-0.022|<br>-0.148**|<br>-0.019|
||(-0.733)|(-0.168)|(-1.520)|(-0.441)|(-2.438)|(-0.241)|
|Pesticides and Fungicides|<br>-0.038|<br>0.022|<br>-0.071|<br>-0.032|<br>-0.032|<br>0.215|
||(-0.623)|(0.222)|(-0.373)|(-0.582)|(-0.275)|(1.451)|
|Bullocks|<br>0.024|<br>0.072|<br>0.021|<br>0.008|<br>0.072|<br>-0.036|
||(0.340)|(0.925)|(0.217)|(0.118)|(1.074)|(-0.571)|
|Machinery|<br>-0.256***|<br>-0.266***|<br>0.064|<br>-0.176***|<br>-0.306***|<br>-0.044|
||(-4.075)|(-3.237)|(0.437)|(-2.752)|(-3.800)|(-0.449)|
|Irrigation|<br>-0.080|<br>0.156*|<br>0.221|<br>0.027|<br>0.016|<br>0.104|
||(-1.067)|(1.783)|(1.632)|(0.350)|(0.204)|(0.922)|
|Hired labor|<br>0.179***|<br>-0.143|<br>-0.027|<br>0.062|<br>0.151|<br>0.245*|
||(2.752)|(-1.601)|(-0.234)|(0.622)|(1.422)|(1.957)|
|Family labor|<br>0.143***|<br>0.171***|<br>0.038|<br>0.167**|<br>0.156*|<br>-0.266***|
||(3.013)|(3.139)|(0.557)|(2.234)|(1.697)|(-3.259)|
|**Spatial fragmentation**|||||||
|<br>Seeds and Seedlings|-0.021|-0.028*|0.001|0.006|-0.001|0.062***|
||(-1.285)|(-1.944)|(0.050)|(0.549)|(-0.094)|(4.700)|
|Fertilizer|<br>0.013|<br>-0.007|<br>-0.013|<br>-0.018*|<br>0.017|<br>-0.026**|
||(1.322)|(-0.856)|(-1.087)|(-1.699)|(1.615)|(-1.984)|
|Pesticides and Fungicides|<br>0.009|<br>-0.004|<br>0.000|<br>-0.002|<br>0.042**|<br>0.008|
||(0.772)|(-0.252)|(0.012)|(-0.160)|(2.003)|(0.350)|
|Bullocks|<br>-0.013|<br>0.001|<br>0.013|<br>-0.010|<br>0.007|<br>-0.009|
||(-0.901)|(0.124)|(1.075)|(-0.741)|(0.590)|(-0.907)|
|Machinery|<br>-0.015|<br>-0.005|<br>0.035*|<br>-0.005|<br>-0.002|<br>0.001|
||(-1.150)|(-0.377)|(1.862)|(-0.341)|(-0.151)|(0.092)|
|Irrigation|<br>-0.031**|<br>-0.007|<br>-0.021|<br>0.028*|<br>-0.017|<br>0.000|
||(-2.021)|(-0.576)|(-1.211)|(1.746)|(-1.229)|(0.019)|
|Hired labor|<br>0.040***|<br>0.003|<br>-0.029*|<br>0.007|<br>-0.016|<br>-0.009|
||(2.995)|(0.245)|(-1.922)|(0.307)|(-0.902)|(-0.482)|
|Family labor|<br>-0.001|<br>0.020**|<br>0.007|<br>0.012|<br>-0.025|<br>-0.022*|
||(-0.141)|(2.508)|(0.777)|(0.753)|(-1.586)|(-1.670)|
|Observations|1,231|817|523|825|718|673|

_Note:_ Small is below 2 ac., medium between 2 and 5 ac., and large above 5 ac. Asymptotic t-ratios in parentheses. Constant and household characteristics, input/output prices & village characteristics included but not reported. *** p<0.01, ** p<0.05, * p<0.1 20
financial institution and many of them are financially illiterate (see the next section). The community with formal financial institutions may have keen competition with larger enterprises. In a less-educated community, entrepreneurs are more likely to stay in nonfarm business, whereas in more educated community, they are more likely to close their business and find other source of income.

# **4.2Enterprise Performance**

The hypothesis of enterprise performance model is to study what RIC components at the enterprise level would be the possible determinants of performance to be better off.

# **4.2.1** **_Performance Model_**

In order to analyze rural enterprise performance, we employ the standard economic theory related to TFP model (Bosworth and Collins 2003). For simplicity, a Cobb-Douglas production function is assumed here.

where _Y, K, L, H,_ and denote for total revenue, physical capital, labor, a measure of educational attainment, and capital share respectively. We further decompose this equation by labor and take log form.

To integrate equation (4) with the RIC variables, we describe performance in terms of the log of total revenue per full time employee equivalent as:

**_y_** _m = β0 + βk_ **_k_** _m + βc_ **_c_** _m+ βx_ **_x_** _m + εm_ (5)

where _m_ denotes enterprise _m_ ; **_y_** denotes revenue per full time equivalent employee; **_k_** denotes capital input; **_c_** denotes a vector of observed factors, a vector of control variables such as enterprise characteristics and manager’s education level; **_x_** denotes a vector of unobserved factors, in this case, RIC components; and _ε_ is error terms.

Revenue of enterprise has been used as a key indicator to measure enterprise performance in most literature (World Bank 2004; 2007b; 2007c; and Bosworth and Collins 2003). The enterprise performance in this study is measured by total annual revenue per fulltime equivalent number of employees including managers. By considering the key RIC components, women entrepreneurs perform much better than men entrepreneurs, and finance (money transfer), security (face payment dispute), electricity, and transport show strong correlation with 14
# **Introduction**

In this paper we examine the effects of the commodity price boom, running from around 2004 to 2014, and what it meant for the economies of Sub-Saharan Africa. We consider how this period marked a change from the pre-boom years, and also the legacy of the boom once commodity prices fell post-2015. We examine the record of resource-rich (RR) countries in harnessing this opportunity and consider how their economic performance compares to their non-resource-rich (non-RR) neighbors in the region. We investigate how these groups fared in terms of rising resource dependence, overall record of economic growth, the impacts on poverty and inequality and diversification of economic activity.

Our work follows the recent economic literature in considering the overall performance economic of resource-rich countries compares to non-resource-rich counterparts. Here the hypothesis taken by studies in this area is to consider whether resource abundance undermines economic performance or enhances it and treats non-resource rich countries as a useful benchmark or control group to consider possible economic counter-factual experiences.

Resources generate export revenue, government revenues and can secure access to foreign currency. As such developing countries often celebrate major resource discoveries as the first step on a path to middle income status and sustainable prosperity. However, there is no guarantee resources will deliver on this promise, and instead economic performance in resource-rich countries has often been much worse than their neighbors.

Previous studies have discussed the potentially positive effects of price booms in developing resource-rich countries while balancing this against the historical record of weaker economic performance in resource rich developing countries. For example, Warner (2015) traces more recent economic performance in resource rich countries around the world and considers how poorly the non-resource economy fared during resource booms. Venables (2016) finds that it has proven challenging in recent decades for resource-rich developing countries to translate their relative abundance of subsoil assets into aboveground prosperity and asset accumulation.

The historical record of resource-rich countries globally is mixed, with many countries that have struggled to harness booms and that have recorded lower average growth rates than their non-resource-rich counterparts. This phenomenon has become known as the ‘resource curse’, for which there exists an extensive empirical literature, summarized in surveys such as by (Van der Ploeg 2011) and (Ross 2015). Earlier work analyzed the relationship between resource dependence and economic growth starting with the seminal studies by Sachs and Warner (1997) to compare growth between economies with and without substantial natural resources over the 1970-1990 period. More recent work such as van der Ploeg and Poelhekke (2009) explore the effect of resource dependence on growth during the last decades of the 20th century and find similarly disappointing growth 2
The empirical model is as follows: _Lidst_ = _γ_ 0 + _γ_ 1 _PhaseI ∗ Post_ + _γ_ 2 _PhaseII ∗ Post_ + _γ_ 3 _Zds ∗ Tt_ + _Tt_ + _Ids_ + _εidst_ (2) where _Lidst_ is the reported labor outcome of child _i_ in district _d_ in state _s_ at time _t_ . _PhaseI_ and _PhaseII_ are the indicators for the respective phases; _Zds_ is a vector of district-level controls for demographic characteristics, and is interacted with year indicators to control for trends in these characteristics; _Tt_ and _Ids_ are year- and districtfixed effects, respectively, and _εidst_ is the idiosyncratic error term. After the program, there is a 1.33 percent increase in the likelihood of child reporting working in Phase I districts relative to Phase III districts (Column (i) of Table 6). This is significant at the 5 percent significance level. In Phase II districts, this effect is 1.17 percent increase marginally significant at the 10 percent significance level. We cannot reject the equality of these two coefficients. In column (ii), the coefficient for doing chores is small and positive but indistinguishable from 0. Our findings indicate that child labor supply increases in response to the program.

# **6 Conclusion**

We use the phased roll-out of MGNREGA to estimate the impact of employment based safety net programs on schooling and labor market outcomes of children. We find that fewer children enroll in schools in primary grades due to the introduction of the program and their likelihood of being employed increases. This is surprising especially because the schooling aspirations for children have become stronger in the country and large strides have been made in ensuring universal enrollment in primary schools. The drop in enrollment is driven by low quality private schools implying that school quality affects enrollment choices when the opportunity cost of attending school shifts.

We also find that among the enrolled, school pass rate with more than 60 percent 18