rafmacalaba/datause-encoder-data huggingface dataset:
RCI (rental cost index) = the ratio of per square rental cost in this region to the national average; SDI (student disability index) = the ratio of the percentage of students with physical disabilities in this region to the national average; PFI (poor family index) = the ratio of the percentage of students from low-income families in this region to the national average.

The weights attached to the four factors should add up to one, i.e., a, + a2 + a3 + a4 = **1.** These weights can be derived from an econometric estimation using cross-region or penal data (cross region and time series) from the past years. Shah (1994a) provides an example of such an estimation. Many countries try arbitrary values of weights based on the designers' intuition about the importance of different factors in affecting the costs of services. Assigning these weights can also be a method for the designers to emphasize certain factors in grant distribution.

Figures used to calculate the indices (WI, RCI, SDI, and PFI) are those of the previous year or past few years' averages.

# (2) Health

# Measurement unit = total population in this region

# Average per unit cost = the country's per capita public expenditure on health care

# Adjustment index = a1HPI + a2IMI + a3ILEI + a4IPDI

- where HPI (health price index) = the ratio of health care cost in this region to the national average; IMI (ifant mortality index) = the ratio of infant mortality rate in this region to the national average;

- ILEI (inverse life expectancy index) = the ratio of national average life expectancy to life expectancy in this region;

IPDI (inverse population density index) = the ratio of national average population density to that in this region; a, +a 2 +a 3 +a 4 = 1.

# (3) Transportation

# Measurement unit = total length of roads in this region

# Average per unit cost = the country's per capita public expenditure on transportation

## Adjustment index = a,WI + a2GRI + a3SNI + a4IPDI

- where WI (wage index) = the ratio of wage level in this region to the national average; GRI (grade index) = the ratio of average road grade in this region to the national average; SNI (snow index) = the ratio of annual snowfall in this region to the national average;

40
continuity of the assignment variable around the eligibility threshold, and (ii) in the absence of the treatment, track choices would not display discrete changes around the threshold. Assumption (i) is akin to imposing that households cannot manipulate the assignment variable in order to result eligible for the transfer, whereas (ii) implies that the distribution of other variables that might be potentially relevant for track choices does not change discontinuously in the surroundings of the eligibility threshold. Two additional assumptions are needed in order to identify the LATE parameter in equations 3-4: (iii) monotonicity of the treatment assignment, and (iv) scoring above threshold cannot impact track choices except through the effect of the transfer (exclusion restriction).

# **3.2 Validity of the RD Design**

We start by assessing the continuity of the assignment variable. Martinelli and Parker [2009] finds evidence that in order to increase the chances of being eligible for _Oportunidades_ , households tend to under-report goods and desirable home characteristics. In the absence of perfect monitoring of the self-reported information, we might thus observe a discrete jump in the distribution of the household poverty score immediately above the eligibility threshold, thereby suggesting that households can manipulate the assignment variable. Using the sample of all households in our sample with at least one child in the COMIPEMS exam we formally test for the presence of a discontinuity in the assignment variable at the cutoff of program eligibility, in the spirit of McCrary [2008]. Figure 2 depicts a kernel regression interpolation along with the confidence intervals of the empirical distribution of the assignment variable at the points below and above the cutoff. There is no evidence that in our population of interest households are manipulating the score in order to increase the probability of being eligible for the transfer. Visual inspection reveals the presence of a small drop (rather than a bump) immediately above the eligibility cutoff, although the null hypothesis of a smooth density cannot be rejected: the point estimate of the log difference in height between the two interpolating kernel regressions is -0.119 (std. err.= 0.075).

We next provide evidence in support of the assumption that in the absence of the treatment, track choices would have not changed discretely around the threshold. Figure 3 displays six sociodemographic pre-determined characteristics that are plausibly correlated with track choice decisions: students’ age, ethnicity, household income, parental education, the number of siblings and 13
Pakistan: Sumary and TiMefzame for Structural and Policy **Reforms** in Agriculture, continued

|PolicyArea|Objective|Measurem|Proposed<br>Timing of<br>Initiating Measures and<br>Expected Implementation<br>Period|
|---|---|---|---|
|Government<br>Expenditure|Reorient public<br>expenditure towards<br>changing needs of<br>agriculture and<br>enhance efficiency of<br>expenditure program|Reduce burden of administrative<br>expenses on research and extension<br>budget and increase operational<br>funding.<br>Introduce new research priorities<br>including research on farming<br>systems, growth-enhancing<br>public<br>goods, and the environment.<br>Induce private sector to undertake<br>privately profitable research.<br>Introduce patent protection fcr<br>seed varieties<br>Reduce duplication and increase<br>coordination of research<br>institutions to minimize wastage of<br>resources<br>*Reduce the number of front-line<br>extension workers and replace them<br>with fewer, better trained workers<br>more responsive to the needs of<br>farming_systems_and not just given<br>crops<br>*Open<br>consulting services<br>by<br>adaptive research institutes to<br>better off farmers on a cost-<br>sharing basis<br>*Extensive use of mass media and<br>other group approaches for basic<br>messages about available technology<br>and better farming systems.<br>Increase investment in education,<br>including functional education of<br>farmers|1995/96, short-run<br>1994/95, short-run<br>1994/95, short- to<br>medium-run<br>1994/95, short-run<br>1995/96, short- to<br>medium-run<br>1995/96, medium-run<br>1994/95. medium-run<br>1994/95, medium-run<br>1995/96, medium- to<br>long-run|

Note: For implementation periods, short-run implies 1 to 2 years, medium-run, 3 to 5 years, and long-run, 5 tc 7 years.

* Element of high priority

V
_Statistik_ ). The survey contains detailed information on industry, employment, production, trade participation and other firm characteristics. It covers all formal manufacturing establishments with more than 20 employees, which account for about 80 percent of all manufacturing output in Indonesia.<sup>4</sup> For each year, we have approximately 20,000 plant-level observations.<sup>5</sup> Our sample spans 1993–2009, which enables us to study competition during the last years of Suharto’s reign and the decade following his departure. Following Blalock et al. (2008), we exclude the crisis years and the first year of the recovery, i.e. 1997–99, because they are characterized by high turmoil and volatility.

These data are used to construct measures of competition. We use entry and exit rates,<sup>6</sup> price costs margins (PCM), the profit elasticity of demand (PE), the Herfindahl-Hirschman Index of concentration (HHI), the aggregate market share of the biggest 4 firms (MS4), the number of firms in an industry and prices.<sup>7</sup> To draw general conclusions about the evolution of competition, we create a summary index of competition following Kling et al. (2007). Specifically, our competition index Z is simply the sum of equally weighted average z-scores of each of these 8 indicators, with the sign of each measures oriented so that higher values signal more intense competition (e.g. more competition is associated with _more_ entry, exit, and market participants but a _lower_ price-cost margin, profit elasticity, market share of the 4 largest firms, concentration, and prices).

To identify which firms are politically connected, we use data from Mobarak and Purbasari (2006a). They identify firms with family connections to Suharto, i.e. firms that are directly owned or managed by a Suharto family member (either directly or being owned by firms held by a family member). They also identify firms with other, i.e. cultivated connections to him. We will primarily focus on family connections, as this is the most conservative and strongest measure of connectedness.

The online appendix B provides supporting evidence that these variables capture connections that were specific to Suharto. It extends Fisman’s (2001) findings by showing that Suharto connections neither 4 We obtain this number by dividing total output produced by all firms in our sample by total manufacturing output reported by the World Development Indicators from the World Bank.

> 5 We use the terms _plant_ and _firm_ interchangeably.

6 Note that because we do not observe firms with fewer than 20 workers entry and exit rates may in part reflect movements in economic activity rather than actual firm creation or foreclosure.

7 These indicators are important markers of competition and allocative efficiency as well as determinants of productivity growth (Aghion et al., 2005; Jerbashian and Kochanova, 2017).

9
**Fiure1**

<!-- Start of picture text --> AGRICULTURAL PROTECTION:<br>TARIFF EQUIVALENT<br>3 0 0 ........ .. .. .... .. ............... .. .. ........<br>250 200 ~ ~ ~~ 1979-85............. 1986-88~~~~~~~~~~~. * a .. .... .......... .. ........ ..... .... ....<br>w~~~~~~~~~~~~~~~~~~~~~~~...... ~~~~~~~~~ ~ 150 ~ ~ ~ ~ ~ ~ ~ .... ~~.............. ..... ... ... .. .. ......<br>~~~~ 01989-92 ~ ~ ~~.... ... .. .......<br>50~~~ ~~~~~~~~~~~~....... ....... .. ..............<br>0~~ ~~~~~~~~~~~~.............. ... ... ............ ........... ...<br>i a- Cu .. . .. . . C U C W o ... ~~~~~~~~~~~~~~~~. ... . Cu~~~~~~...... o.... .. ....... . .. . .. .. . . . ....... .0 . ............. .. .. ......... . . . . . ... . . ). .. .<br><!-- End of picture text -->

**250 -E**

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and with other elements in the health system, in the production of health outcomes. First, health facility data can be collected with a view to better understand how health services affect health related behavior and outcomes, in particular by collecting data on health care quality, access, and user charges. Second, health facility surveys can be used to monitor and analyze the performance of health care providers. In this case, the focus is on measuring different dimensions of health facility performance, as well as institutional and organizational determinants of performance. This may concern characteristics of the facility itself, or its interaction with other providers-whether between different levels of the public system, or between public and private providers. Facility performance may also depend on features of the administrative and support system "upstream" from the facility, such as government spending, policy, and regulation.

IFAguire i - Thoe routes to better nealRth outcomnes <!-- Start of picture text --> ~gProviders<br>Goverimment<br>o expenditure -------- _ ---- E[ouseholdth<br>• policy ~~~~ be h orv outcoom2<br>o reguatnion @ 0 \7/<br><!-- End of picture text --> In many surveys, the motivation for collecting health facility data is to understand better the link between health facilities, on the one hand, and the health-seeking behavior and health outcomes of households, on the other (see Fig. 2). Indeed, a number of health facility surveys have been implemented with the explicit purpose of feeding into the analysis of household level data. Although household-level behavior and outcomes are shaped in large part by factors within the household (income, education, location, etc.), health care qualitye.g., input availability, amenities, and clinical and inter-personal skills of health workershave an impact not only on utilization of health services, but also on the effectiveness of health care interventions and client perceptions. As a consequence, poor quality can drive a sizeable wedge between actual and potential cost-effectiveness of interventions (Wouters 1991).

2
19 Table 3. Economic Performance Indicators

||1965|-73|1973|-80|198|0-87|
|---|---|---|---|---|---|---|
||Asia|LAC|Asia|[AC|Asia|<br>LAC|
|Growth of GDP|5.3|5.8|5.6|5.0|6.8|<br>1.5|
|GDP output deflator|1.8|3.5|6.8|15.9|6.1|<br>39.8|
|Growth of Merchandise Export|7.1|-0.4|<br>8.5|2.2|9.0|<br>3.4|
|Growth of Investment|7.8|7.1|8.7|5.4|7.9|<br>-4.0|
|Growth of Per Capita Consumption|<br>2.1|2.9|3.2|2.9|3.8|<br>-0.7|
|Current Account Balance/GDP|-2.0|-2.6|-6.3|<br>-3.1|-2.0|<br>-2.7|
|Share of Manufacturing<br>in Exports|29.4|10.9|37.5|17.7|50.2|<br>23.8|

Source: World Bank data.
<!-- Start of picture text --> 55 Standard© 0,390 deviation in 2030<br>50 @ 2.000<br>(@® 4.000<br>(TM) 6.000<br>45 (Him 8.310<br>(ose Data: Sample covered<br>40 © 1980_2014<br>(| 0+ 2000_20141990_2014<br>> Lose X 2005_2014<br>RFfe}£255 3530 x{tose Countrywalmec typology<br>3 tos¢ MHosc<br>q<br>gere83e& 20 TM@@ mocc Locc<br>=<br>45 ROC<br>10<br>All@MOGCMOGC<br>S givroc<br>of @<br>0 5 10 5; 20 25 30 35 40 45 sO 55<br>Median in 2015 (percentage)<br><!-- End of picture text -->

<!-- Start of picture text --> 45 Standard deviation in 2030<br>— @ 1510<br>44 Loci (@®) 4.000<br>() 6.000<br>(MM) 8.650<br>43 Data:© 1980_2014Sample covered<br>42 1 1990_2014<br>41 HOGCx a cone+X 2000_2014 2005_2014 typology<br>3 m ROC<br>a Roc<br>&££3s 4030 HOGC HOGC xOntose WHoccTMLoseMocc<br>= NROC<br>38 aL8)><a<br>37 MoscOmoes|xX an Hose<br>mos] xX<br>Be + roc<br>Mose<br>x<br>35 moce ROC<br>ROC<br>34<br>34 35 36 37 38 39 40 41 42 43 44 45<br>Median in 2015<br><!-- End of picture text -->

<!-- Start of picture text --> 900M Stancors10.200;exter n.2020)<br>3 100© 2.000<br>8 800m (Gl) 6.000<br>oS (7) 2310<br>= 5 oar patt<br>3 700m an © 1980_2014<br>3 canta<br>=3 Qofl asim-<br>® 600M Country typology<br>3§E©22 500m we x marIHeedRocNRoc<br>Zl nok Cmose “ Bi ose<br>a<br>8g5ry85 400M noe(0)° MOGCXmose+<br>2 300M<br>Fy5<br>= Oneoc<br>© 200m Dnroc<br>2 +NROc<br>oO25 100M NROCx<br>2<br>om @<br>OM 100M 200M 300M 400M 500M 600M 700M _ 800M900M<br>Number of extreme poor corresponding to a median headcount in 2015<br><!-- End of picture text --> <!-- Start of picture text --> 24 0.00 Standard deviation in 2030<br>@ 194<br>© 400<br>22 (® 60.0<br>20 (®) 800<br>() 100.0<br>18 (9) 123.6<br>16 Data: Sample covered<br>1412 ARE + xO ROC ©1+ 2005_2014 1990_2014 1980_20142000_2014.<br>a Roc 7S []roc Country typology<br>@ 10 Mall<br>aiN2883 6 HOGCHAl weoc x Gc LoseO mMi: nRoc eeRoc<br>= rn IO occ 0] ml Mocc<br>2 Qi Hose<br>9 2 NROC<br>2<br>NRoC p<<br># occ HOGC<br>6<br>8<br>-10 10.00<br>-10-8 6 -4 2 0 2 4 6 8 10 12 14 16 18 20 22 24<br>Median in 2016<br><!-- End of picture text -->
# Table 4: Regression Results for Test of Basis Risk

|Regression|J|D|R2|D-W|
|---|---|---|---|---|
|(St+ I- S)= c+8 (F,+I - F)|-.09|.34*|.30|1.30|
||(-.32)|(6.14)|||
|(S+ I - S) = r + B (I,+,- I)|-.04|.80*|.80|2.08|
|.i,|_(-.25)_|(19.04)<br>**1~.**|||

Notes: 1. Monthly data for the period May '85 - Jan. '93 (93 observations) were used in both regressions. S stands for the spot price, F for the futures price and I stands for the price of the Cotlook A Index and D-W for the Durbin-Watson Statistic. T-statistics are in parenthesis and starred variables indicate significance at the 99% level.

2. The Cotlook A Index is published daily by Cotlook Limited, a cotton information service in the United Kingdom. The A index is an average of the 5 lower quotes in USC/lb for cotton being offered in significant quantities from 14 cotton growing regions in 13 producing countries. The Index is based on cotton comparable to middling 1-3/32 inch quality by the "Liverpool" concept, delivered C.I.F. North Europe, cash against documents on arrival of vessel, including profit and agent's commission. The Index is presented as an indication of the competitive level of offering prices.

3. All variables are stationary in first differences.
<mark>ensure that in the very long run imports remain as a steady share of GDP �β</mark> � <mark>�1� as in the first order condition. For the panel, it is estimated freely to test this hypothesis. It is also expected that</mark> ε�,� �0 suggesting that a rise in import prices relative to domestic prices will reduce imports.

<mark>The short-run elasticities and the error-correction parameter vary across countries. There are no a priori expectations about the size of the parameters, but it is expected that they have the correct theoretical sign (i.e. positive for domestic demand and negative for relative prices).</mark> To ensure stability of the current account balance under simulation (the Marshall-Lerner condition) the sum of the absolute elasticities of exports and imports to relative prices should be greater than unity ( |ε�| �|ε�| �1� .

All the estimated coefficients reported in Table **_4_** 4 are statistically significant, and the error correction estimate suggests that there is a common stochastic trend between import volumes, domestic demand and relative prices. The long-run elasticities meet a priori expectations and there is evidence for the Marshall-Lerner condition ( |ε�| �|ε�| �1� . As with export volumes, the equilibrating mechanism is close to 10 years – thus shocks that cause imports to deviate from their long-run path will take a considerable time to move back to that path.

As discussed in World Bank (2019) the coefficient on the ECM likely reflects rising import (and export) to GDP ratios during the 1990s and early 2000s. Indeed, the discussion in the Appendix demonstrates that the long-run elasticity is not constant without the trend – thus violating an important condition for interpreting the elasticities as structural. The historical increase in import penetration likely reflects a range of actors including: reduced shipping costs, access to credit markets, accession of China into the WTO, and economies of scale (Evenett and Keller, 2004). The third column in Table 4 summarizes results from an alternative specification, where a trend is assumed to capture the innovations. The income elasticity is still positive but slightly lower than one in Column 2, while the coefficient on the ECM is much larger, suggesting that, once the trend rise in import shares is accounted for, adjustment to equilibrium occurs much more quickly, in about four years _ceteris paribus._

**Table 4: Import volume coefficients**

|Long-run|Standard Specification|Specification includingtrend|
|---|---|---|
|y�,���<br>���|1.22***|0.78***|
|ln~~�~~<sup>P�,���</sup><br>~~�~~<br>P�,���<br>~~�~~|-0.57***|-0.35***|
|θ�<sup>�</sup>|-0.12***|-0.22***|
|trend||0.01***|
|Short-run(average over co|untrylist)||
|∆�y�,���<br>����|1.31***|1.31***|

14
# **Poverty and flood exposure.**

By combining flood exposure estimates with survey-based sub-national data on poverty, we further estimate flood exposure among the world’s poor. Our estimates show that 132 million people living in extreme poverty (living on less than $1.90 per day) are directly exposed to flood risk; of these, 72.5 million (or 55 percent) are in Sub-Saharan Africa (figure 16). Approximately two out of ten people exposed to flood risk globally are living in extreme poverty.

When poverty is defined using less extreme (i.e. higher) thresholds, the number of flood-exposed poor people increases significantly. Around 343 million are estimated to live in high-risk flood zones while living on less than $3.20 a day. The number increases to 587 million when considering incomes below $5.50 a day. Increasing the poverty threshold from $1.90 to $5.50 doubles the number of poor people exposed to floods in Sub-Saharan Africa from 73 million to 147 million. However, in South Asia the number of the poor and flood-exposed increases seven-fold from 41 million to 289 million; in East Asia the increase is nine-fold from 9 million to 80 million. but increases the poor people exposed in SAR by a factor of 6.55 to 286.8 million people. In other words, four out of every ten people exposed to flood risk globally are living on less than $5.50 a day.

||**Poverty t**<br>**$1.90**|**hreshold (consumptio**<br>**$3.20**|**n per day)**<br>**$5.50**|
|---|---|---|---|
|Number of poor (millions)|741.8|1,812.0|2,986.6|
|Number of poor with flood exposure<br>(millions)|132.0|343.4|586.8|
|Share ofglobalpopulation that ispoor|9.4%|23%|37.8%|
|Share of population that is poor and<br>flood exposed|1.7%|4.4%|7.4%|

_Table 1. Absolute and relative flood exposure headcounts at different poverty thresholds_ <!-- Start of picture text --> LAC SSA MENA ECA SAR EAP<br>$5.50 586.8 million<br>$3.20 343.4 million<br>$1.90 132.0 million<br>0 100 200 300 400 500 600 700 800 900 1,000 1,100 1,200 1,300 1,400 1,500<br>Millions<br><!-- End of picture text --> _Figure 16. The number of poor people with high flood exposure, using different poverty thresholds and disaggregated by regions_

**Country-level poverty exposure.** Of the 132 million people living in extreme poverty and high flood exposure, almost half are located in just three countries. With over 35 million, India has the highest number of extremely poor people exposed to flood risk of any country in the world; corresponding to 2.5

19