rafmacalaba/datause-encoder-data huggingface dataset:
Table 3: The divergent impact on imports from third countries

|Regressand: ln(import)|I|II|
|---|---|---|
|Harmonization on intra-regional trade|0.3002***|0.2825***|
|(HAR)|(0.010)|(0.011)|
|importers with stricter initial standards||0.0951***|
|(HAR�strictness)||(0.023)|
|Harmonization on imports from the ROW|0.2912***||
|(HAR_M)|(0.007)||
|from developing countries|-0.4587***||
|(HAR_M �developing)|(0.012)||
|from developed countries to importers with stricter initial standards||0.4491***|
|(HAR_M with developing = 0 & strictness = 1)||(0.014)|
|from developing countries to importers with stricter initial standards||0.0458***|
|(HAR_M with developing = 1 & strictness = 1)||(0.017)|
|from developed countries to importers with less strict initial standards||0.2369***|
|(HAR_M with developing = 0 & strictness = 0)||(0.008)|
|from developing countries to importers with less strict initial standards||-0.2421***|
|(HAR_M with developing = 1 & strictness = 0)||(0.010)|
|Harmonization on exports to the ROW|0.6357***|0.6315***|
|(HAR_E)|(0.005)|(0.005)|
|MRAs with rules of origin on intra-regional trade|2.3185***|2.3233***|
|(MRA_RO)|(0.031)|(0.031)|
|MRAs with rules of origin on imports from the ROW|-0.3294***|-0.3334***|
|(MRA_RO_M)|(0.041)|(0.041)|
|from developing countries|-0.1792***|-0.1763***|
|(MRA_RO_M �Developing)|(0.044)|(0.044)|
|MRAs with rules of origin on exports to the ROW|0.3204***|0.3213***|
|(MRA_RO_E)|(0.036)|(0.036)|
|MRAs without rules of origin on intra-regional trade|0.6612***|0.6641***|
|(MRA_NRO)|(0.075)|(0.075)|
|MRAs without rules of origin on imports from the ROW|0.8237***|0.8379***|
|(MRA_NRO_M)|(0.040)|(0.040)|
|from developing countries|0.2267***|0.2296***|
|(MRA_NRO_M �Developing)|(0.041)|(0.041)|
|MRAs without rules of origin on exports to the ROW|1.5031***|1.4952***|
|(MRA_NRO_E)|(0.037)|(0.037)|
|RTA on intra-regional trade|1.7331***|1.7371***|
|RTA on imports from the ROW|0.0347***|0.0295***|
|RTA on exports to the ROW|0.004|0.0027|

22
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access to electricity except for the percentage of households headed by men. This similarity in characteristics lends support to the idea of the treatment and control groups having similar trends in the absence of the oil.

# **5. Empirical Results**

# _5.1 Effects on Income and Consumption_

The results from estimating the impacts on real income and consumption are presented in Table 3.<sup>15</sup> The coefficient of interest is T*Post interaction, which is the effect of the oil discovery on outcome variables of interest. All specifications include district fixed effects and household controls. The coefficient on the log of real income is positive and significant. Household income increased by 39 log points (or about 47 percent) for the households in the oil region compared to the households in non-oil regions. Given the large magnitude of the effect, we investigate why households in the oil region experienced large changes in income. Our results indicate increases in the average employment income per household<sup>16</sup> at both the extensive and the intensive margin. First, the number of income earners per household increased by about 0.2 for households in the treatment group (in Appendix Table A-6). Given the average number of income earners in the pre-treatment period, this effect translates to about a 10 percent increase in the number of income earners. Second, we find an increase in household employment income by more than 30% (in Appendix Table A-7). This implies that employment income per income earner also increased.

In contrast, the effect on consumption (column 2) is statistically insignificant. We also investigate the effect on household poverty status by using a dummy variable P P , which is equal to one if a household is below the poverty line and zero

> 15 Data on price index used to compute the real income in this study is compiled by Ghana Statistical Service (GSS). This data is available in the 3 rounds of GLSS data.

> 16 Employment income includes wage and self-employment income. We considered other components of household income such as transfers separately. Although not presented in the paper, we do not find significant increases in transfers.

13
only -0.14, which is lower in magnitude as compared to the lowest magnitude of long-run own price elasticity of gasoline reported by the literature. There also exist some studies that estimate both own-price elasticities and cross-price elasticities of fuels used in the transport sector. For example, Serletis et al. (2010) estimate own and cross price elasticities of four fuels, fuel oil, electricity, gasoline and diesel, using data from selected OECD countries.

The main objective of our study is to compare two specific policies in the transport sector, fuel efficiency improvement and fuel switching, to reduce GHG emissions. To do this, we estimate own and cross price elasticities for various OECD countries. For this purpose, we used a translog cost function, which is widely used in the literature (see e.g., Berndt & Woods,1975; Fuss, 1977; Griffin, 1977; Pindyck, 1979; Uri, 1979; Hall, 1986) for the estimation. First, we hold the total demand for fuel in the road transportation sector constant and calculate the Allen partial elasticities of substitution (Uzawa, 1962). We then use these Allen partial elasticities to calculate the partial own- and cross-price elasticities of the various fuels in different OECD countries. In the second step, we relax the assumption that total demand for fuel in the road transportation sector is constant, and estimate total own- and cross-price elasticities. While the estimated values of partial crossprice elasticity measure fuel switching possibilities, the differences between partial and total ownprice elasticities estimate the effect of fuel efficiency improvements and the reduction in energy consumption in the transportation sector.

Some existing literature (Greening et al. 1999; Greening, 2004; Lu et al. 2007; Paravantis and Georgakellos, 2007; Timilsina and Shrestha, 2008 and Timilsina and Shrestha, 2009) has used the identity approach to compare the roles of various factors, such as change in fuel mix, improved fuel efficiency, change in transportation modes, etc. in influencing the historical trends of transport sector energy consumption or CO2 emissions from this sector. Although the identity approach has 4
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**Figure 2.5: Rural Hourly Wage Distribution by** **<u>Sector of Activity (2002 Pesos), Mexico, 2003</u>**

<!-- Start of picture text --> 0 30 60 90 120<br>Hourly Wage<br>Agriculture Industry<br>Services<br>Source: ENE 2003, 2nd quarter.<br>.04<br>.03<br>.02<br>Density<br>.01<br>0<br><!-- End of picture text --> Off-farm labor income is the most important income source for the rural population in Mexico. The share of off-farm labor income has increased its share of total income from 49 percent in 1992 to 66 percent in 2002 (see Figure 2.6 and Table 2.5). The high-productivity nonfarm income share increased nearly five-fold, while the low productivity income share fell during 1992-2002. In 1992, none of the seven regions had high productivity rural nonfarm income shares accounting for more than 7.5 percent of total income. Over the past decade these shares reached 51, 22, and 21 percent in the Pacific, Capital, and Gulf regions, respectively. In the Center and South region, the highproductivity rural nonfarm income share is still low, reaching only 5.6 and 9.9 percent respectively in 2002.

The farm-income share of total rural income fell dramatically during 1992-2002 according to the 1992-2002 ENIGH surveys (Table 2.4 and Figure 2.6). The share of agricultural income in total rural income fell from 50.8 percent in 1992 to 23.8 percent in 2002. The regions that experienced the largest reduction in the agricultural income share were the North, Capital, and Pacific regions with close to a 70 percent reduction in the agricultural income share. The South and Center regions experienced the lowest reduction, namely 38 and 30 percent, respectively. The South region still has the largest agricultural income share of all regions, reaching 41.2 percent in 2002.

13
Guyana National Insurance Scheme. 1987. _Annual Report 1987._ Guyana.

Halter, William A.; Richard Hemming. The impact of Demographic change on social security financing.

Hannah, Leslie. 1986. _Inventing Retirement: The Development of Occupational Pensions in Britain._ Cambridge: Cambridge University Press.

Hauser, Richard, Peter Meyer, and Urs Ober Hansli. 1983. "Die Obligatorische Altersvorsorge in der Schweiz: Rentabilitatsuberlegungen und Einkomensverteilungsapekte." _Revue Suisse d'Economie Politique et de Statistique_ (June): 139-61.

Hepp, Stefan. 1990. _The Swiss Pension Funds._ Zurich: Haupt.

Holzmann, Robert. 1992. "Pensions in the Ex-Socialist Countries". World Bank, Washington, D.C.

-. 1994. "Funded and Private Pensions for Eastern European Countries in Transition." Astria: University of Saarland.

Hong Kong, Government of. 1992. "A Community-wide Retirement Protection System." Education and Manpower Branch. Hong Kong.

Hurd, Michael and John B. Shoven. 1983. "The Distributional Impact of Social Security". (June) Chicago National Bureau of Economic Research.

Ibbotson, R. and Sinquefield. 1989. "Stocks, Bonds, Bills, and Inflation: Historical Returns 1926-1987." Research Foundation of the Institute of Chartered financial Analysts, Charlottesville, Va.

IESS. 1990. Instituto Ecuatoriano de Seguridad Social, _Boletin Estadistico No.5._ IGSS. 1986. Instituto Guatemalteco de Seguro Social. _Informe Anual de Labores._ Iglesias, A. and Acuna, R. Chile: Experiencia con un Regimen de Capitalizacion 1981- 1991. Santiago, Chile: CEPAL; 1992; ISBN: 956-7122-04-k.

IMF (International Monetary Fund). Various years. _International Financial Statistics._ Washington, D.C.

India Employees' Provident Fund. 1991. _Annual Report 37, 1989-90._ New Delhi, India: Employees' Provident Fund Organization.

Institute de Prevencion Sociale du Senegal. 1990. Annuaire Statistique.

Instituto Ecuatoriano de Seguridad Social. 1985. Boletin Estadistico No. 5. Ecuador: IESS. Direccion de Asesoria Matematico Actuarial.

Instituto Nazionale di Statistica. 1990. Italia. Dimensione d'imnpresa e grande mercato. Rassegna di Statistiche del Lavoro. N.3.

130
34

# Table 12: Estimates of NCMS impact on health facilities

|||County ho<br>|spitals<br>|Central T<br>|HCs<br>|General<br>|THCs<br>|
|---|---|---|---|---|---|---|---|
|||ATT%|t-stat|ATT%|t-stat|ATT%|t-stat|
|Finances|Total revenues|-1%|-0.21|18%|7.96|13%|2.67|
||of which subsidies|-9%|-0.82|26%|6.18|-5%|-0.66|
||Total expenditures|-3%|-0.53|15%|6.15|15%|2.81|
||of which salaries|-1%|-0.13|11%|5.20|7%|1.55|
||expenditure per case (log)|-3%|-0.17|4%|0.43|7%|1.27|
|Inputs|Staff|-1%|-0.38|6%|4.08|3%|1.09|
||of which retirees|-7%|-1.31|5%|2.32|5%|1.34|
||Items equipment > 10,000 RMB|-12%|-1.31|8%|5.36|1%|0.22|
|Activity|Inpatient discharges|13%|1.47|8%|2.12|44%|5.04|
||Bed occupancy rate|2%|0.91|11%|2.07|13%|3.89|
||Length of stay|7%|0.62|11%|1.12|-11%|-1.10|
||Outpatientvisits|-5%|-0.98|10%|3.20|0%|-0.01|
for displaced sitting tenants are elderly or other socially vulnerable households that are sufficiently cash income - poor to be attracted to vacate their units, usually against a side payment.<sup>56</sup> However, relying on a stock conversion mechanism alone, when new construction is economically not feasible, leads to inefficiency over time: the number of sitting tenants who can be outbid by migrants shrinks to zero. The higher minimum wage level required by migrants to compensate for the resultant rising rents will eventually stop migration.

Analysis from a different empirical perspective supports the perception of housing supply inelasticity. The urban hierarchy - measured by the relationship of a city’s rank in the nation to its size - has barely changed during the first decade of transition, which was characterized by significant regional labor market changes. In contrast, city hierarchies changed drastically during the last decade before transition, the 1980s, which was characterized by high housing construction activity levels.

**Table 8. City Rank and Size—Little Change during Transition**

|**Year**|**Pol**<br>**Beta**|**and**<br>**Year**|**Rom**<br>**Beta**|**ania**<br>**Year**|**Rus**<br>**Beta**|**sia**<br>**Year**|**Se**<br>**Beta**|**rbia**<br>**Year**|
|---|---|---|---|---|---|---|---|---|
|~1980|0.85|1982|0.89|1977|0.77|1979|0.86|1981|
|~1990|0.80|1992|0.86|1992|0.735|1989|0.85|1001|
|2002|0.81|2002|0.87|2002|0.735|2002|0.84|2002|

_Source_ : Statistical offices, the authors’ estimates. _Note_ : The equation tested is ln (city size) = α + β* ln (city ranking). Sample sizes: Poland = 100, Romania = 70, Russia = 109, Serbia = 49. Fit quality: all R<sup>2</sup> greater than 0.95. Armenia and Lithuania are excluded because of low sample sizes. City size is defined by administrative borders, which tends to lead to lower betas relative to wider metropolitan definitions.

Regression analysis is normally used to test an empirical regularity between city size and city rank, called Zipf’s Law.<sup>57</sup> For all four countries analyzed, the 1980s brought certain decentralization, most pronounced in Poland and Russia, while the 1990s brought no change or only a small recentralization (Table 8). Considering the declining relevance of mono-industrial cities and the increasing relevance of agglomeration effects in a market economy, a strong recentralization and closer approximation of Zipf’s Law (beta = 1) would have been expected had labor and residential mobility been unconstrained. Deichmann and Henderson (2000) confirm for Poland that rural-urban migration has significantly declined during the transition,

> 56  In Lithuania, for example, many elderly privatized tenants in Vilnius, who moved to the city after World War II, are enticed by real estate brokers to sell off and return to the much cheaper towns and villages of their childhood for retirement. Their Vilnius dwellings are then sold to small investors for renting out to in-migrants pulled to the city by the strong labor market.

> 57  See Fujita, Krugman, and Venables (1999), Chapter 12.

35
level of income regardless of whether it is from Honduras or Chile for example. In what follows, we refer to this as the global quantiles (deciles / percentiles) distribution.<sup>5</sup> This way of defining income class allows for easy comparisons across countries, and for the disentangling of country- vs. individual determinants of households’ situation. Based on this, the next section examines access to infrastructure across countries and at different levels of the income distribution.

# **4. The Demand for Infrastructure**

The last decade witnessed significant changes in the shape of the income distribution of developing and emerging countries (Lakner and Milanovic 2015). In Latin America specifically, Ferreira et al. (2013) estimated that the number of people that can be considered as middle class increased by 50% over the 2003 to 2009 period. They define the concept of middle class following a criteria of economic security, whereby a household is considered middle class if it has a probability of falling back into poverty no larger than 10% over a fiveyear interval. This implies a middle class income threshold of $10 a day, which is at the 68th percentile of the Latin American income distribution in 2009, while the moderate poverty line of $4 leads to 30.5% of the population falling below it.

Looking at our data and relying on a consistent sample of 14 countries for which data are available from 2002 to 2012, Latin America was characterized by important income gains across the income distribution during the 2002-2012 period. Indeed, over that ten-year period, the annual growth rate of the incomes of the lowest three deciles was above 5%, that of vulnerable households at above 4%, and that of the top three deciles, which includes the middle class and rich households at almost 3%.<sup>6</sup>

## **4.1. Access Patterns in Latin America: The Situation as of 2012**

> 5 We often take the semantic shortcut of referring to households “in a given quantile” when talking of those below a given threshold (e.g., in quantile 1 for those below the first quantile threshold) or between two successive ones (e.g., in quantile 5 for those between the quantiles 4 and 5 thresholds).

> 6 Household surveys of the type used here typically do not include very rich individuals because of random sampling, non-response, or large under-reporting. This may lead to the figure for the growth of income of the top decile being lower than the actual one, but it is inconsequential for our purpose.

6
THE CENTRAL AFRICAN REPUBLIC’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 18. Power spending needs**

## <u>$ millions per year</u>

||Expansion|Rehabilitation|Capital|Operations and<br>maintenance|Total|
|---|---|---|---|---|---|
|Generation|28|1|29|3|32|
|Hydro|28|1|29|||
|Transmission and distribution|20|1|21|2|23|
|Distributiongrid|1|||||
|Urban connection|19|||||
|Total|48|2|50|5|55|

_Source:_ Rosnes and Vennemo 2009.

The CAR needs to spend about $47 million per year (or 2.4 percent of the GDP) to meet the MDG of halving the percentage of people without access to improved water and improved sanitation. These estimates assume that the new customers will be provided with middle-range water supply and sanitation modalities. In particular, in urban areas, new customers will be using standposts and ventilated-improvedpit (VIP) latrines; in rural areas, new customers will rely on wells, boreholes, and traditional latrines. As a result, no expansion of upper-end modalities such as piped water supply or sewer connections is envisaged. As the current provision of improved services is low, the CAR needs to make an effort to expand and rehabilitate water supply and sanitation assets, at an estimated cost of $36.6 million per year. The O&M of existing service would require spending about $10.5 million per year (table 19).

**Table 19. Water supply and sanitation spending needs**

<u>$ millions per year</u>

||Expansion|Rehabilitation|Capital|Operations and<br>maintenance|Total|
|---|---|---|---|---|---|
|Water supply|14.4|10.2|24.6|8.0|32.7|
|Sanitation|5.6|6.3|12.0|2.5|14.4|
|Total|20.1|16.5|36.6|10.5|47.1|

_Source:_ AICD water supply and sanitation spending needs model, 2010.

To expand the irrigated land by 1,824 hectares through small-scale projects, the CAR would need to spend about $1 million per year. The total spending needs for the irrigation sector were calculated assuming an IRR cutoff of 12 percent and the investment required for additional land, plus requirements for the rehabilitation and maintenance of existing irrigation infrastructure (recall table 6 in the irrigation section).

# Spending allocated to address infrastructure needs

When all traceable financing sources—public sector, private sector, ODA, and non-ODA—are put together, existing spending in infrastructure amounts to $134 million per year (table 18). The public sector has the lion’s share of infrastructure spending, at about two-thirds of total funds ($87 million). The remaining third is sourced externally, mainly by ODA ($43 million), and to a smaller extent, by the private sector ($4.5 million). There are no data on spending by non-OECD financiers in the CAR.

39
# **3.3 Empirical Strategy**

We aggregate the data (using sampling weights) by district, location (rural/urban) and sex to estimate the following cross-sectional specification that examines how labor market outcomes at the district level are affected by the length of exposure to mandated political representation for women.

_Ls,i_ captures the log-count of individuals in a given labor market activity in state _s_ and district _i_ of in 2009. _Es_ represents the number of years of exposure to mandated political representation for women under Panchayati Raj since its implementation at the state level, _Ds,i_ captures the cumulative number of years in which the district chairperson seat was reserved for a woman, and _Xs,i_ represents district specific controls including the initial (1999) value of the left hand-side variable.<sup>8</sup> Equation (1) is estimated separately for men and women and standard errors are clustered at the state level. We estimate this equation using NSS data from 2009-10 to allow for the longest possible time since the introduction of Panchayati Raj to assess its impact; this year choice for the data also guarantees at least some exposure to PR across as many districts in our sample as possible.

The 1993 reform, as it pertained to rural and urban areas, is legislated under the 73rd and 74th amendments respectively. Given that there was considerable variation in the actual implementation of the 73<sup>rd</sup> Amendment we estimate the equation above using data from rural areas only. As explained in section 2.1, the date of implementation of this reform varied across different states of India, but for reasons unrelated to women’s LFP and employment patterns in these states. Therefore, the number of years of exposure to women political representatives is arguably exogenous to female employment patterns. We exploit this variation in exposure to women political representatives both at the state and the district level, given that they imply different comparison margins. For the districts within a given state, variation in exposure to the program is only with respect to the presence of a woman _Sarpanch_ (leader) of the district

> 8 We are constrained to use 1999 as our “initial” year for this analysis due to the fact that the data available for purchase from the NSSO for the previous household survey  round (conducted in 1993-94) do not contain districtidentifying information.

11
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