rafmacalaba/datause-encoder-data huggingface dataset:
# **3.  Data and Methodology**

This section presents the data sources and methodologies used in this paper to analyze poverty and labor markets in rural Argentina.

## **Data**

Argentina has no comprehensive household survey that covers both rural and urban areas. Therefore, analyses in this paper are based on available data: urban households survey (EPH) from 1990 to 2003; Censuses (1991 and 2001); educational data from the Ministry of Education; and health data from the Ministry of Health. The Agricultural Census was used sparsely in this paper because we did not have access to the micro dataset but only to highly inconsistent tabulations.

Additionally, this paper applies information from a special rural household survey (RHS) undertaken by the World Bank in 2003 in dispersed rural areas. The survey was undertaken in four provinces: Chaco, Santa Fe, Santiago del Estero, and Mendoza. It covers a third of Argentina’s rural population. The RHS includes 441 households.<sup>8</sup> Data provided by the RHS is critical for making informed decisions on rural poverty alleviation in Argentina. It is the first time in Argentina’s history that a survey of this magnitude has been conducted.<sup>9</sup> Consumption data in the RHS is measured in a broad sense, i.e., it includes selfconsumption and any kind of consumption including clothing, food, rent, gas, etc. The consumption series are developed using the “Guidelines for constructing consumption aggregates for welfare analysis” or LSM135.<sup>10</sup> The reason for analyzing consumption in this way is that people tend to more easily recall what they consume than what they earn. The income measure includes all income sources such as transfers, remittances, selfconsumption, labor income, and production income. The way in which the consumption and income data are constructed may explain why consumption poverty is higher than income poverty in some provinces (see Section 4), because it is well known that income often tends to be underreported.

The RHS also includes information on demographics, employment, education, and health for all household members. Furthermore, a special module with agricultural production questions was applied to farming households. The survey was conducted with 8 To design the sample, a database with the fractions and radius of each department in each province was considered. In each fraction, a random weighted sample of 8 to 10 points, depending on the number of rural people in the province, was conducted. Once the fraction and points sampled were identified the final sample points were defined considering the number of rural inhabitants in each radius.

9 Previous studies on livelihoods in rural areas used small samples of data. Therefore, these take the form of case studies: for example, the study of citrus workers or of a geographic area.

10 Another recent study using this approach is the “Panama Poverty Assessment: Priorities and Strategies for Poverty Reduction" (SKU 14716).

13
- O’DONNELL, M., U. NWANKWO, A. CALDERON, C. STRICKLAND, ET AL. (2020): “Closing Gender Pay Gaps,” .

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WORLD BANK, G. (2013): “Gender at Work: A Companion to the World Development Report on Jobs,” https://www.worldbank.org/content/dam/Worldbank/document/Gender/GenderAtWork_web.pdf.

- WORLD ECONOMIC, F. (2017): “The Global Gender Gap Report 2016 - World,” https://reliefweb.int/report/world/global-gender-gap-report-2016.

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States. As shown in Tables 7, 8, and 9, stamp duties are typically among the third or fourth most important source of own-source revenues, with some exceptions.

|**Table 7. Revenues from **|**Stamp Duties and Registration **|**Fees, 2000-2001 **(in Rs.millions)|
|---|---|---|
|**State**|**Non-judicial Stamp Duties**|**Stamp Duties and Registration Fees**|
|AndhraPradesh|7663.0|6709.3|
|Arunachal Pradesh|---|2.5|
|Assam|268.2|386.4|
|Bihar|1366.9|3434.8|
|Chattisgarh|---|623.7|
|Delhi|---|1916.8|
|Goa|155.5|219.1|
|Gujarat|4778.3|5374.2|
|Haryana|3791.2|4192.4|
|Himachal Pradesh|---|292.2|
|Jammu andKashmir|---|210.6|
|Jharkhand|---|0|
|Karnataka|7078.1|6381.2|
|Kerala|3323.6|3411.0|
|MadhyaPradesh|4094.2|4770.8|
|Maharashtra|19,018.6|22,009.2|
|Manipur|---|18.0|
|Meghalaya|---|30.1|
|Mizoram|---|0.6|
|Nagaland|---|42.9|
|Orissa|860.6|1085.2|
|Punjab|3498.2|4240.6|
|Rajasthan|2701.2|4367.3|
|Sikkim|---|5.0|
|TamilNadu|9969.7|9102.6|
|Tripura|---|59.4|
|Uttar Pradesh|9261.1|12,697.6|
|Uttaranchal|---|424.0|
|WestBengal|3346.8|4740.1|
|TotalAllStates|1007823|967470|
|, <br>Sources: For non-judici<br>and Policy from State<br>ReserveBankof India.|,.<br>al stamp duties, data collected by t<br>budgets; for stamp duties and reg|,.<br>he National Institute of Public Finance<br>istration fees, data collected from the|

and registration fees was gathered from Reserve Bank of India sources. I am grateful to Tapas Sen of the National Institute of Public Finance and Policy for providing this information.

18
_Note:_ Figures in parentheses are robust t-statistics. *, **, ***: significant at 10, 5, and 1 percent respectively. Annual data for 1996–2013 for low- and middle-income countries. RMSE = root mean square error, or sample standard deviation.

Any device that is effective in reducing inflation, and therefore seigniorage revenue, might have the effect of improving the fiscal balance in the same way as hard pegs. Table 4 shows the effect of adding a dummy for an inflation-targeting regime to the table 1 regression. This variable is never significant, however.

Do countries behave differently when they first become commodity exporters? To examine this issue, we construct a dummy variable that is equal to one in the five years after a country first exceeds the 25 percent threshold for fuel exports, or the 10 percent threshold for ores and metals (even if the other threshold is already exceeded). Thus this dummy is zero for countries that do not specialize in natural resource exports and also for those that have been exporting resources of the specified type for more than five years. In table 5 this dummy is added to the regressions in table 1, together with an interaction term between this dummy and political stability, which is designed to test whether political stability affects the behavior of new commodity exporters. Neither of these new variables is significant, even at the 10 percent level, for total revenues or total expenditures, but they are each significant at 10 percent level for the fiscal balance. The interaction term has a positive coefficient, and the new commodity exporter dummy has a negative one. This suggests that with low political stability (below 11.91/0.176 = 68) new commodity exporters have somewhat worse fiscal balances, whereas those with high political stability have slightly better ones.

These results imply that new fuel exporters shift to the higher revenue and expenditure patterns of fuel exporters as soon as they become significant fuel exporters. We have also investigated whether there is an anticipation effect whereby new fuel exporters start to take on the fiscal characteristics of fuel exporters in the three years before extraction begins, but we found no evidence of it (results not shown).

10
Chart 9 shows the relative magnitude of changes in terms of trade and long-term capital inflows for LMIC as a whole. On a gross basis (net aggregate resource flow, as shown in Chart 3), capital flows have been a dominant force in the transfer of purchasing power to developing countries, except for the periods of the OPEC oil price hikes. On a net basis (net aggregate transfer), however, dominance did not appear until the early 1990s due to a net resource outflow from the LMICs at the height of the debt crisis (1982-87). In both net and gross terms, the dominance is very pronounced as we observe the effective end of debt crisis and a renewed, much larger surge in capital flows into the LMICs.

Chart 9. Transfer of Purchasing Power to Developing Countries through Terms of Trade Changes and Long-Term Capital Flows <!-- Start of picture text --> USS billion<br>120 -<br>80.-<br>0 Ilk~~~~~~~~~~~~~~~~~~~~~'<br>-40 -<br>-80-i . . . . . . . . . . .<br>70 72 74 76 78 80 82 84 86 88 90 92<br>| ---Net Aggregate Transfer* |<br>l Terms of Trade Gains"'<br>*Net Aggregate Transfer = Net Aggregate Resource Flow<br>- Interest on Long-Term Debt - Profit Remittances on FDI<br>Source: World Debt Tables 1994-95, World Bank.<br>-Terms of Trade Gains =(Export Price - Export Price[-1 ])*Export Volume<br>(Import Prce - Import Pr1ce[-1 ])*Import Volume<br>Source: International Economics Department, World Bank<br><!-- End of picture text -->

Chart 10 shows contributions to world import volume growth by **three LMIC** regions, Asia, Latin America, and Middle East and North Africa. A surge in import demand in the early 1990s is visible for Asia and Latin America. The movements in import demand in these regions largely correspond to movements in output growth. However, upon closer examination, the chart also reveals movements in import demand 14
# **4. Estimation Method**

In this section, we describe our estimation methods. Because the allocation of TEEP was purposive, the initial school conditions are likely to have different distributions in the treatment and control groups. If the initial conditions affect subsequent changes of the outcome variables, DD would give a biased estimate of the TEEP impacts. We use two strategies to deal with the potential bias due to nonrandom program placement. First, we use the sample from Visayas only. As shown in Figure 2.1, TEEP divisions are relatively evenly distributed throughout Visayas compared with the other two macro-regions. We therefore expect that the TEEP and non-TEEP provinces are more comparable in Visayas, and hence our extra data collection and cleaning efforts were focused on Visayas. Second, we use propensity score (PS) matching to balance observable cohort characteristics and initial conditions between the treated and the control groups.

Because the original phase-in plan of TEEP was not strictly followed in the first two batches in practice, we cannot explore the pipeline design to identify the impact of TEEP on school performance. In this process, however, all schools in TEEP provinces were covered in the program. We also use all schools in non-TEEP provinces in the same region (regions 6, 7 and 8 of Visayas in our case). Therefore, we formed a control group based on all the schools in the non-TEEP provinces to estimate the counterfactual of the TEEP schools.

Three caveats exist in our method. First, our baseline is not free of contamination. Table 3.2 showed that, in Visayas region, TEEP had been at least introduced in many treatment schools by SY 2002/03. Thus, the initial level of test scores in the treatment group reflects 12
the projection of future city growth to the year 2050 at the global scale while following the city growth modeling framework and key variables developed by Henderson and Wang (2007). The model is set up as a three-stage procedure. In the first stage, we develop a city growth empirical model of “core cities” with more than 100,000 population-essentially re-estimating a modified version of the Henderson–Wang model and use the estimated parameters to produce corresponding city population projections to 2050. In the second stage, we extend the projections to “broader cities” of more than 50,000 population by extrapolating city growth dynamics in different city size groups. These smaller cities are important because many will enter our category of larger cities within the next four decades. In the third and final stage, we use the UN Population Division country level urban population projections (to 2050) to make our city population projections conform to these national urban totals.

We extend Henderson and Wang’s (2007) modeling framework and datasets covering core cities with more than 100,000 population (as of year 2000) and estimate corresponding city population projections in ten year intervals from 2010 to 2050. The projection in this stage covers 2,186 cities.

The core city growth model is estimated using the Ordinary Least Squares estimation. The dependent variable is the city population growth rate of city _i_ in country _j_ over a 10-year period (∆ ln _nijt_ = ln _nijt_ − ln _nijt_ − 1 ) . The independent variables include both country and city level characteristics. At the country level, we add the national population growth over the same period (∆ ln nat_pop _jt_ ) , the share of urban population ( urban_rate _jt_ − 1 ) , the share of population between 15 to 24 years of age ( r_pop_15_24 _jt_ − 1 ) , the percentage of adults with secondary education ( pct_sec_edu _jt_ − 1 )<sup>in the base year, and a dummy indicating landlocked countries.</sup>

> At the city level, we include the city population growth in the previous period (∆ ln _nijt_ − 1 ) in order to capture strong time persistence often observed in the city growth empirical literature. In addition we consider factors that determine the economic attractiveness of a city relative to its national peers. The

> growth of a city-specific market potential measure in the previous period (∆ ln _MPijt_ − 1 ) is added as a

31
exercises that follow, we include all these variables in the prediction model even though not all of them are statistically significant at the 10 level or higher.

# **Table 2:  Estimated Prediction Model**

|`Probit regression Number of obs = 1561`|
|---|
|`LR chi2(6) = 153.84`|
|`Prob > chi2 = 0.0000`|
|`Log likelihood = -825.00538 Pseudo R2 = 0.0853`|

|`iegsat`<br>|`Coef. Std. Err. z`|`P>|z| [95% Conf. Interval]`|
|---|---|---|
|`lntotalcom`<br>|`.015695 .0320012 0.49`|`0.624 -.0470261 .0784162`|
|`preptime`<br>|`-.0029434 .0021185 -1.39`|`0.165 -.0070957 .0012088`|
|`effdelay`<br>|`-.0101739 .0074377 -1.37`|`0.171 -.0247516 .0044038`|
|`planlength`<br>|`-.0077196 .0021558 -3.58`|`0.000 -.0119449 -.0034943`|
|`ttlqual`<br>|`1.032869 .1265106 8.16`|`0.000 .7849125 1.280825`|
|`cpiaav`<br>|`.4187925 .0755984 5.54`|`0.000 .2706224 .5669625`|
|`_cons`<br>|`-.8541903 .292965 -2.92`|`0.004 -1.428391 -.2799894`|

# **5.  Predicting Project Outcomes**

Table 3 summarizes the in-sample (first column) and out-of-sample (second column) predictive performance of the model. Specifically, the first column summarizes how well the fitted values from the probit regression estimated over the period 1995-2005 predict project outcomes during the same period, while the second column asks how well the fitted values predict project outcomes during the next six years, 2006-2012. For reference, the first row of the table provides the historical average rates of unsatisfactory projects. The subsequent rows present predictions from the benchmark model described above, followed by predictions using the model with different combinations of the most significant correlates, the CPIA ratings and TTL track record.

Looking first within the sample, 26 percent of projects (413/1561) were rated as unsatisfactory during 1995-2005. The full model, including all correlates, correctly predicts 46 percent of these unsatisfactory outcomes. The out-of-sample predictions are naturally somewhat less accurate, but still correctly predict 40 percent of unsuccessful projects. A model with TTL record as the only variable correctly predicts 42 (38) percent in sample (out of sample), whereas a model with the CPIA alone correctly predicts 36 percent. The predictive performance of the model that combines the CPIA and TTL 8
# Natural Disaster Damage Indices Based on Remotely Sensed Data: An Application to Indonesia<sup>_∗_</sup>

## Emmanuel Skoufias (World Bank) Eric Strobl (University of Bern) Thomas Tveit (University of Cergy-Pontoise)

**JEL Classification:** Q54, C63, R11, R5,O18 **Keywords:** Remotely Sensed Data, Natural Disasters, Natural Hazard model, Damage Index, Floods, Earthquakes, Volcanic Eruptions

> _∗_ This paper was financed in part by the Disaster Risk Finance Impact Analytics project of the Disaster Risk Financing and Insurance Program of The World Bank Group
Second, there has been a continuing trend in the industrialized countries, to deregulate and break up the large public or private monopolies. This has become feasible as well as desirable as technological progress has gradually eroded the economies of scale inherent in some natural monopolies. A good example of this is the telephone industry in the United States.

Third, rapid economic growth in many developing nations has forced the private sectors in these economies to undertake significant investments in infrastructure services and facilities in order to compensate for deficiencies in the publicly supplied infrastructure services (World Bank, 1994).

These developments mean that infrastructure cannot be viewed as an economic activity exclusively in the public domain. Results of studies of the effects of public investment on productivity (e.g. Aschauer, 1989) may not fully correlate with the effects of infrastructure on productivity to the extent that studies do not include the significant infrastructure investments in the private sector and the complex synergisms and complementarities between public and private infrastructure investments.

A second important bias in thinking about infrastructure, stems from the belief that the impacts of infrastructure investment on an economy are best understood by looking at the macroeconomy. This view, in turn, may be widely held because of the tendency to think of infrastructure investment as public investment. When analyzing the role of public investment in an economy, a macroeconomic approach is the traditionally dominant perspective and this has carried over to analyses of infrastructure as well.

The macroeconomy bias masks several crucial aspects of the complex process by which infrastructure affects an economy. For example, urban economies play an increasingly important
There are limits to how far cross-country comparisons, with limited degrees of freedom, can be considered conclusive about the determinants of the SPEC. However, by pooling crosscountry observations with time series data, we will have 2,481 observations for assessing the relative importance of these various factors. On average, we have 17.5 time-series observations per country with non-missing shares of SP expenditure. We focus mainly on total SP spending, though we note any important differences between pensions and other SP spending, since public pension schemes often appear to be serving a somewhat different role to other types of SP spending (notably the more poverty-focused category of “social assistance”). The sources allow us to distinguish pension from non-pension SP spending for more than 1,500 observations across 104 countries, although coverage is less complete for low-income countries.

The panel data are unbalanced. A saturated panel would have 3692 (=142 x 26) observations. So, we have about two-thirds (0.67=2481/3692) coverage for the basic SPEC (though this falls further when we introduce covariates). The number of country observations per year is increasing, from 75 in 1995 to a maximum of 112 in 2017. In no year do we have SP spending data for all 142 countries; 45 countries have 26 years of data, and 73 have 20 or more years. The Addendum provides a series of graphs (Figure A2) that show the missing observations are more likely to be lower-income countries. (A similar pattern is found for most other covariates discussed below.)

# **3.2 Descriptive statistics on SP spending**

Some simple descriptive statistics based on the (pre-pandemic) panel data set are instructive. On average, we find that SP spending has risen over time as a share of GDP, and that this has been more pronounced among initially low- and middle-income countries. This can be seen in Table 1, which tabulates the SP spending share by countries classified according to their initial (1995) GDP per capita. For the lowest quintile of countries, the share of GDP going to SP increased by a factor of three between 1995 and 2015, from 1.4% to 4.2%.<sup>22</sup> By contrast, the share has been quite stable over time for the upper two quintiles.

The rise in the SP share among initially poorer countries seen in Table 1 could reflect either economic growth in those countries or a change in policy priorities, whereby the share rose

> 22 Much of the rise in SP spending by initially poorer countries was coming from public outlays on pensions; the Addendum provides details in <u>Table A2.</u>

14
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