rafmacalaba/datause-encoder-data huggingface dataset:
15 also makes sense to rescale the complements of both, since the original catastrophic and impoverishment indicators do not, in practice, ever reach anywhere near one. Our rescaled versions of (the complements of) the catastrophic and impoverishment are _FPCATA_ = ((1- _Cata_ )-(1- _CataMAX_ ))/((1- _CataMIN_ )-(1- _CataMAX_ )) and _FPIMPOV_ = ((1- _Impov_ )-(1- _ImpovMAX_ ))/((1- _ImpovMIN_ )-(1- _ImpovMAX_ )), where _Cata_ and _Impov_ are the fractions of households incurring catastrophic and impoverishing payments, and the MIN and MAX subscripts denote minimum and maximum values.<sup>12</sup> To capture the fact the policy makers are more concerned about catastrophic payments among poor households than among rich households, we replace the fraction not experiencing catastrophic payments, (1- _Cata_ ), by the corresponding achievement index.<sup>13</sup> Last – given that they capture different aspects of financial protection, and policy makers are presumably willing to trade one off against the other at a diminishing rate – we combine the two dimensions of financial protection into a single financial � ��� protection index: ��������� ∙�������, where  is the weight attached to catastrophic payments (the weight could, of course, be a half).

> 12 A similar rescaling exercise is performed in the UNDP’s HDI.

> 13 The achievement index in this case is the fraction of the population not experiencing catastrophic payments multiplied by the complement of the corresponding concentration index. The latter is equal to -(  /(1-  )) _CICATA_ , where  is the fraction of households experiencing catastrophic payments and _CICATA_ is the corresponding concentration index (cf. Erreygers 2009 eqn (11)).

15
# **Appendix 2: Public-use database with estimates of adult mortality**

In the course of carrying out this study, we created two databases of adult mortality estimates based on the original DHS datasets, both of which are publicly available for analysts who wish to carry out their own analysis of the data. The databases are available at http://go.worldbank.org/RIIWM53VS0.

The first database ( **survey_mortality.dta** ) reports adult mortality estimates derived from each survey that was carried out.

The second database ( **country_mortality.dta)** reports the same adult mortality estimates but averages the estimates for the same country and period from different rounds of the DHS. The weights used to average across survey rounds are the inverse of the variance of each estimate, so that more precise estimates are given more weight. This second database only includes the “adjusted” mortality estimates (the ones we use in our analysis) but also includes the national level social and economic indicators that we use in the analysis. Note that this database includes countries for which we do not have adult mortality estimates, but for which we have under-5 mortality.

The naming conventions for the adult mortality-related are as follows. Variables are named:

# **GGG** _ **MC** _ **AAAA**

**GGG** refers to the population subgroup.  The values it can take, and the corresponding definitions are in the following table:

|All|All|
|---|---|
|Fem|Female|
|Mal|Male|
|Rur|Rural|
|Urb|Urban|
|Rurm|Rural/Male|
|Urbm|Urban/Male|
|Rurf|Rural/Female|
|Urbf|Urban/Female|
|Noed|No education|
|Pri|Some or completedprimaryonly|
|Sec|At least some secondaryeducation|
|Noedm|No education/Male|
|Prim|Some or completedprimaryonly/Male|
|Secm|At least some secondaryeducation/Male|
|Noedf|No education/Female|
|Prif|Some or completedprimaryonly/Female|

30
- 39-

FIgue 6.2 Poland <!-- Start of picture text --> 0.4<br>0.4 ...... .... ''''''''' ................ ' ..... ''"''''''''il'''''/ .<br>0.3 ... ...<br>002 .. ..... ... . . .... ... .. .... ............ ............ ......<br>0V.8<br>-0.1<br>1983 1064 IMS 9i8 1987 IS88 1989<br><!-- End of picture text --> <!-- Start of picture text --> 10. 000 .. . ..<br>0.50 ......<br>=045~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~/<br>|_ DICPI .... D .PI<br><!-- End of picture text -->

<!-- Start of picture text --> 0.100<br>0.07'5 . ,. ,.<br>-0-075' ....: ....<br>-0.020<br>19S3 1984 1985 1986 1987 ' 1988 1989<br>|_ DLPRO<br><!-- End of picture text -->
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Sloane, P. (2003): Much ado About Nothing? What does the Overeducation Literature Really Tell Us? Chapter 2 in: Büchel, F.; A. de Grip and A. Mertens (eds.): _Overeducation in Europe. Current Issues in Theory and Practice_ . Cheltenham: Edward Elgar.

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33
de Gouvello, C., Saores Filho, B., Nassar, A., Schaeffer, R., Alves, F., and Alves, J. (2010). _Brazil Low-carbon Country Case Study_ . Energy Sector Management Assistance Program, The World Bank, Washington, DC.

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(http://spot.colorado.edu/~markusen/teaching_files/applied_general_equilibrium/GAMS/Examples/m92.g ms) 27
**(16.23) (.2103) (.1929)**

R<sup>2</sup> _ .774, F2 - .754, DW - 2.23, **aCY** - 7.805, Data - 1962:1 - 1987:1 Estimated Equation for RCOSTCGS RCOSTCGS - -14.45 + 1.011 YEAR + 18.53 RFPC (23.19) (.2571) (2.423) R2 _ .722, R<sup>2</sup> - .698, DW - .951, cry - 9.077, Data - 1962:1 - 1987:1

# **Annual Xquations for the Boybean SuPPlV Block**

The soybean supply block has a structure similar to feed grains except that no participation equation is included since there has been no voluntary program. Hence, the acreage equation follows the free market form in (1). The yield equation follows a simple time trend with variations in response to feed grain diversion (which presumably removes poorer acreage from soybean as well as corn production) and the ratio of profit per acre for feed grain production to that for soybean production (representing the shift of higher quality land toward the more profitable crop).

# Estimated Equation for AS

AS - -1.859 + .08838 PROFS - .07905 PROFFG + .9783 AS(T-1) (2.401) (.01473) ( 01681) (.03948) =-2 .971, R<sup>2</sup> = .967, DW - 3.00, **ay** = 2.482, Data - 1962:1 - 1987:1 Estimated Equation for YLDS YLDS = -1.621 + .4072 YEAR - 2.811 RATIO + .07692 DIV (4.405) (.06707) (1.867) (.04955) R2 _ .680, R<sup>2</sup> - .637, DW - 2.34, _ay_ - 1.908, Data - 1962:1 - 1987:1 Estimated Equation for RCOSTS RCUSTS - -21.26 + .5249 YEAR + 5.973 RFPS (14.21) (.1600) (.6522) R<sup>2</sup> _ .786, R<sup>2</sup> - .767, DW - 1.50, **_oay_** - 5.849, Data - 1962:1 - 1987:1 17
and imports to GDP, (iv) the inflation rate and (v) the ratio of government expenditures to GDP.<sup>6</sup> All data are averaged over the sample period 1994 to 2005, with the exception of initial GDP per capita, measured in 1994. We include most regressors, including Enterprise Credit to GDP and Household Credit to GDP in logs, to take account of potential non-linearities in their relationship with GDP per capita growth.

GDP per capita growth varied significantly across our sample over the period 1994 to 2005, ranging from -0.6% in Russia to 5.5% in Korea. While it is positively correlated with Bank Credit to GDP only at the 12% significance level, its correlation with Enterprise Credit to GDP is positive and significant at the 1% level. There is no significant correlation of GDP per capita growth with Household Credit to GDP.

OLS regressions suffer from several biases, including omitted variable, measurement and endogeneity biases. We therefore use instrumental variable regressions to extract the exogenous components of bank lending to enterprises and households and relate them to GDP per capita growth. Following the seminal work by La Porta et al. (1997, 1998) who identified variation in countries' legal origin of countries as a historical exogenous factor explaining current variation in countries' level of financial development, an extensive literature has utilized this variable to extract the exogenous component of financial development.<sup>7</sup> Stulz and Williamson (2003), on the other hand, suggest religious composition as important driver of cross-country differences in financial development.<sup>8</sup> Table 3 presents regressions of the logs of Enterprise Credit to GDP and Household Credit to GDP on the included exogenous variables from the second stage regressions, legal origin dummies and indicators of religious composition. Specifically, we include dummy variables indicating British Common Law countries, French Civil Code countries, German Civil Code countries and Scandinavian Civil Code countries, with transition economies being the omitted category. We include the share of population belonging to the Catholic, Muslim and Protestant denominations in 1980, with data from La Porta et al. (1999).

> 6 Similar sets of conditioning information were used by Beck, Levine and Loayza (2000) and Beck and Levine (2004).

> 7 Specifically, an extensive literature has discussed the empirical observation that British Common Law countries have better developed financial systems than French Civil Code countries, with German and Scandinavian Civil Code systems falling in between. As most countries acquired their legal system through colonization or occupation several centuries ago, legal origin can be considered an exogenous variable.   See Beck and Levine (2005) for an overview.

> 8 Specifically, they show that countries with predominantly Protestant population have stronger creditor rights.

7
# **Figure 8. Source of Debt for Tamil Nadu and Indian States**

<!-- Start of picture text --> Tamil Nadu All States<br>14% 14% 14%<br>29%<br>72%<br>57%<br> Central Government Central Government<br> Market Financing Market Financing<br> Provident Funds Provident Funds<br><!-- End of picture text --> Source: Government documents.

# **_3.3 Fiscal reform and outcome_**

Reversing fiscal deterioration and putting a state on a sustainable fiscal path is not about finding a magic threshold of debt over GSDP ratio. Instead, it is about reforms aimed at realigning major expenditures in support of economic growth, eliminating structural deficiencies in the tax system, which may not improve primary balance but can accelerate growth and increase the state’s ability to service debt. It is also about managing key fiscal risks including contingent liabilities.

The fiscal reform program of the Government of Tamil Nadu, launched in late 2001, was aimed at controlling recurrent expenditures, improving the efficiency of the tax system, restructuring the SOEs, and improving fiscal transparency. Substantive reforms were undertaken from late 2001/02 to 2003/04.<sup>20</sup> A framework for reform was articulated in the government’s medium-term fiscal framework. The framework aimed to achieve a primary balance, eliminate the revenue deficit and reduce the consolidated fiscal deficit to 2 percent of GSDP (Table 2).<sup>21</sup> While reducing the consolidated fiscal deficit, the framework expected further re-orientation of expenditure from salaries, pensions, and subsidies to non-wage O&M and capital investments and deepening tax reform.

Fiscal adjustment would be illusionary without addressing the presence and impact of hidden and contingent liabilities. In India, the main hidden liabilities include those associated with pension liabilities, the power sector cross subsidy financing, off-budget borrowing, and guarantees issued to back the market borrowing of loss-making public enterprises. Tamil Nadu’s medium-term fiscal plan explicitly incorporated important off-

> 20 The new government of Tamil Nadu was elected in 2001 and launched an ambitious fiscal reform program. The government’s White Paper of August 2001 analyzed the systemic causes of the fiscal deterioration and articulated the need for fiscal reform and stabilization in the state. For a more detailed analysis of Tamil Nadu’s reform program, see World Bank (2004a, 2004b, 2005a).

> 21 The first medium-term fiscal plan was presented to the state legislature in February 2004, along with the state’s budget for 2004/05.  Since then it has been updated annually.

15
The results of this experiment are shown in Table 2. The direct effect of the increase in aid is on the budget. On the one hand it lowers the fiscal deficit (because it adds to resources “above the line”), and on the other it increases it, because it raises overall public investment, as discussed in the previous section. The increase in public investment is initially of the order of 0.7 percent of GDP, but it rises over time (as a result of the increase in tax revenue), becoming almost one to one. The initial reduction in the budget deficit therefore gradually disappears. The increase in public investment in infrastructure “crowds in” private investment and leads to a higher growth rate in output of about 0.2 percentage points in the long run. The increase in private capital accumulation tends to raise the demand for educated labor, given the low degree of substitution between these factors. Because the increase in public investment is allocated across all components of government capital formation (according to initial shares), the greater demand for educated labor is matched at least in part by an increase in supply. Over time, the increase in the stock of public capital in health raises the efficiency of educated labor, whereas the increase in public capital in infrastructure raises the marginal productivity of all other production factors (including “effective” labor). These productivity gains contribute also to higher domestic output, which in turn raises consumption spending and lowers poverty. The growth rate of real disposable income per capita rises at its peak by almost 0.6 percentage points; the poverty rate, based on survey data, falls by 3.6 percentage points by 2015, whereas the estimate based on a partial elasticity of -1.8 falls by about 4.3 percentage points by 2015. The order of magnitude is thus similar in both cases. However, with a partial elasticity of -0.5, poverty falls by only 1.1 percentage points at the same horizon.

Throughout the adjustment period, the real exchange rate depreciates as a result of both an initial reduction in composite prices and a nominal depreciation. Downward pressure on the price of domestic goods is the result of a supply response that dominates the increase in aggregate demand resulting from higher government spending. Consequently, production is reallocated toward exports, which increase by about 0.7 percentage points in the long run. At the initial levels of imports and foreign borrowing, the expansion of exports and the aid inflow tend to appreciate the 36
rural areas due to a very sharp decline in poverty among Hindus, perhaps due to the rapid decline in poverty among the SC and the ST.

**Table 4** : National rural and urban poverty rates by religious groups at Lakdawala lines

|Religion|1983|1987-88|<br>1993-94<br>2004-05|2009-10|
|---|---|---|---|---|
||||**Rural**||
|Buddhism|59.4|57.7|53.8<br>43.4|33.6|
|Christianity|38.3|33.2|34.9<br>19.6|12.9|
|Hinduism|47.0|40.0|36.6<br>28.0|20.4|
|Islam|51.3|44.1|45.1<br>33.0|21.7|
|Jainism|12.9|7.8|14.1<br>2.6|0.0|
|Sikhism|12.0|10.1|11.7<br>10.4|3.7|
|Others|46.1|46.9|41.5<br>51.4|24.2|
|Total|46.5|39.8|37.0<br>28.2|20.2|
||||**Urban**||
|Buddhism|51.1|62.1|51.9<br>42.2|39.3|
|Christianity|30.7|30.1|24.5<br>15.3|13.0|
|Hinduism|38.8|37.5|31.0<br>23.8|18.5|
|Islam|55.1|55.1|47.8<br>40.7|33.7|
|Jainism|18.5|17.7|6.4<br>4.5|2.1|
|Sikhism|19.7|11.3|11.1<br>3.2|5.5|
|Others|35.9|45.5|34.2<br>18.1|7.9|
|Total|40.4|39.8|33.1<br>26.1|20.7|
||||**Rural + Urban**<br> <br>||
|Buddhism|57.5|58.9|53.2<br>43.0|36.0|
|Christianity|36.3|32.3|31.6<br>18.2|13.0|
|Hinduism|45.5|39.5|35.3<br>27.0|20.0|
|Islam|52.2|47.5|46.0<br>35.5|25.8|
|Jainism|16.8|14.2|8.3<br>4.1|1.9|
|Sikhism|13.4|10.4|11.6<br>8.8|4.2|
|Others|42.7|45.7|39.4<br>47.0|20.1|
|Total|45.4|39.8|36.0<br>27.7|20.4|

15
- 36 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity

Bunnel, T; Drummond, LBW; Ho, KC (2002). Critical Reflections on Cities in Southern Asia. Brill Academic Press, Singapur.

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- Center for International Earth Science Information Network - CIESIN - Columbia University, International Food Policy Research Institute - IFPRI, The World Bank, and Centro Internacional de Agricultura Tropical - CIAT. 2011. Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Settlement Points. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H4M906KR.

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**Table B1: Characteristics of the first medium-term cycle**

||**_-------- Expansion_**|**_phase --------_**|**_------- Contractio_**|**_nphase -------_**|
|---|---|---|---|---|
|_Peak month_|_Duration(years)_|_Amplitude_|_Duration(years)_|_Amplitude_|
|**ENERGY**|||||
|Coal<br>1979:08|9.6|2.82|7.1|1.87|
|Crude oil<br>1980:06|10.4|3.57|17.6|2.42|
|Natural gas<br>1982:01|12.0|3.22|9.4|2.57|
|**AVERAGE**<br>**1980**|**10.7**|**3.20**|**11.4**|**2.29**|
|**BASE METALS**|||||
|Aluminum<br>1978:06|5.9|3.32|5.6|3.13|
|Copper<br>1976:12|6.9|0.50|6.8|1.04|
|Lead<br>1978:07|6.4|2.78|6.1|2.88|
|Nickel<br>1978:04|6.8|1.86|5.6|1.82|
|Tin<br>1980:03|10.2|3.18|9.8|2.20|
|Zinc<br>1976:09|5.6|1.77|6.0|3.08|
|**AVERAGE**<br>**1978**|**7.0**|**2.24**|**6.6**|**2.36**|
|**PRECIOUS METALS**|||||
|Gold<br>1981:07|11.5|2.85|7.1|1.26|
|Platinum<br>1981:03|11.2|2.35|5.3|0.78|
|Silver<br>1980:12|11.0|3.21|10.1|2.15|
|**AVERAGE**<br>**1980**|**11.2**|**2.80**|**7.5**|**1.40**|
|**FERTILIZERS**|||||
|Phosphate<br>1977:07|7.5|3.77|15.8|1.98|
|Potassium<br>1980:01|10.0|2.21|6.5|1.14|
|TSP<br>1976:09|6.7|3.28|5.4|1.52|
|Urea<br>1977:09|7.7|3.94|11.4|1.98|
|**AVERAGE**<br>**1978**|**8.0**|**3.30**|**9.8**|**1.66**|
|**ANNUAL AGRICULTURE**|||||
|Cotton<br>1977:11|7.8|2.95|8.4|2.48|
|Maize<br>1976:02|6.1|2.67|12.7|0.54|
|Rice<br>1977:12|7.9|2.97|7.1|2.50|
|Soybean meal<br>1977:04|7.3|3.33|7.4|1.88|
|Soybean oil<br>1977:02|7.1|3.13|12.3|2.05|
|Wheat<br>1976:08|6.6|3.19|12.4|1.62|
|**AVERAGE**<br>**1977**|**7.1**|**3.04**|**10.1**|**1.85**|
|**PERENNIAL AGRICULTURE**|||||
|Cocoa<br>1977:11|6.8|4.34|14.0|3.28|
|Coffee Arabica<br>1977:08|6.4|3.14|13.8|3.04|
|Coffee Robusta<br>1977:06|7.1|2.95|14.1|3.05|
|Natural rubber<br>1978:07|8.5|2.52|9.9|1.17|
|Tea<br>1979:08|8.6|2.85|12.4|3.01|
|**AVERAGE**<br>**1977**|**7.5**|**3.16**|**12.9**|**2.71**|
|**_ALL AVERAGE_**<br>**_1978_**|**_8.6_**|**_2.96_**|**_9.7_**|**_2.04_**|

**Notes** : Each group average has been calculated based on the weights reported in Table 2 (main text). The average corresponding to the year reflects the median year on which the peak month occurred.

_— 39 —_