rafmacalaba/datause-encoder-data huggingface dataset:
information (in addition, item-level data not analyzed in this paper contribute another 90 variables), (b) 57 teacher background variables, (c) 27 teaching process variables (not including 144 "opportunity to learn" variables not analyzed here), and (d) 75 school variables; many indicators are redundant. The specific variables included in our analytic models were identified after screening all variables included in the IEA study, eliminating at the outset variables for which no variance was observed, those having excessive numbers (more than 20% of the cases) of missing data, and those that were unrelated to the objectives of this study. For student-level data, this screening of variables was completed before aggregation at the classroom level. To reduce further the variables to a reasonable number for analysis, the following procedure was employed.

First the 419 classrooms were classified according to the mean science score of the students in the class. Five groups were formed: (a) high: mean score greater than 1.5 standard deviation above the group mean, (b) medium high: mean score between 0.5 and 1.5 standard deviations above the group mean, (c) medium: mean scora between 0.5 and -0.5 standard deviations from the group mean, (d) medium low: mean score between -0.5 and -1.5 standard deviations below the group mean and (e) low: mean score less than -1.5 standard deviation below the group n-tan. Next, multiple Anova (for continuous variables) or Chi-square (for iategorical variables) analyses were conducted with classroom science classification as the "independent" variable and the school, teacher or aggregated student variable as the "dependent" variable; variables unrelated (p>.0<sup>5</sup> ) to differences among the five classroom classifications were discarded. While 22
<mark>Baird, S, C. McIntosh, and B. Ö</mark> zler (2019): “When the money runs out: Do cash transfers have sustained effects on human capital accumulation?” Journal of Development Economics, vol. 140, 169185.

<mark>Baird, S., F. Ferreira, B.</mark> Özler, and M. Woolcock (2013): “Relative Effectiveness of Conditional and Unconditional Cash Transfers for Schooling Outcomes in Developing Countries: A Systematic Review,” Campbell Systematic Reviews, vol. 9(1), 1-124.

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Bandiera, O., N. Buehren, R. Burgess, M. Goldstein, S. Gulesci, I. Rasul, and M. Sulaiman (2020): “Women’s Empowerment in Action: Evidence from a Randomized Control Trial in Africa,” American Economic Journal: Applied Economics, vol. 12(1): 210–259.

Banerjee, A., E. La Ferrara, V. Orozco-Olvera (2019): “The Entertaining Way to Behavioral Change Fighting HIV with MTV,” World Bank Policy Research Working Paper 8998.

<mark>Barham, T., K. Macours, and J. Maluccio (2018): "E</mark> xperimental Evidence of Exposure to a Conditional Cash Transfer During Early Teenage Years: Young Women's Fertility and Labor Market Outcomes," CEPR Discussion Paper <mark>s 13165, C.E.P.R. Discussion Papers.</mark> Barrera-Osorio, F., A. de Barros, and D. Filmer (2018): “Long-Term Impacts of Alternative Approaches to Increase Schooling: Evidence from a Scholarship Program in Cambodia”, World Bank Policy Research Working Paper 8566.

Batyra, E. and L. Pesando (2021): “Trends in child marriage and new evidence on the selective impact of changes in age-at-marriage laws on early marriage,” SSM - Population Health, vol 14.

Beaman, L., E. Dulfo, R. Pande, and P. Topalova (2012): “Female Leadership Raises Aspirations and Educational Attainment for Girls: A Policy Experiment in India,” _<u><mark>Science, w</mark></u>_ <mark>ol. 335(6068), 582-586.</mark> Behrman, Jere R., Susan W. Parker, and Petra E. Todd. 2011. “Do Conditional Cash Transfers for Schooling Generate Lasting Benefits? A Five-Year Follow up of PROGRESA/Oportunidades.” Journal of Human Resources 46(1): 93-122.

Bellés-Obrero, C., and M. Lombardi (2020): “Will You Marry Me, Later? Age-of-Marriage Laws and Child Marriage in Mexico”, CRC TR 224 Discussion Paper Series.

Bellows, B., Bulaya, C., Inambwae, S., Lissner, C. L., Ali, M., & Bajracharya, A. (2016). “Family Planning Vouchers in Low and Middle Income Countries: A Systematic Review”, Studies in family planning, 47(4), 357–370.

Bénabou, R., and J. Tirole (2011): “Identity, Morals, and Taboos: Beliefs as Assets”, The Quarterly Journal of Economics, vol. 126, 805–855.

26
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# **Table 8: Nonlinear Results with Total Public Expenditures (Dynamic Panel - GMM)**

|**_Dependent variable: Growth rate of GDP per capita_**|||||
|---|---|---|---|---|
||Fast growing<br>countries|Comparison<br>group|Whole sample<br>pooled|Whole sample<br>with dummies|
|Openness|0.044|-0.281|0.068|0.014|
|(% of GDP)|(2.106)**|(-1.344)|(1.971)**|~~(~~0.171)|
|Total government revenue|-0.492|0.192|-0.443|-0.393|
|(% of GDP)|(-1.58)|~~(~~0.332)|(-1.062)|(-0.653)|
|Productive + nonproductive expenditure (-1)|0.508|0.775|1.098|...|
|(% of GDP)|~~(~~0.547)|~~(~~0.735)|~~(~~1.104)||
|Square term of productive + nonproductive expenditure (-1)|-0.997|-3.210|-3.408|...|
|(% of GDP)|(-0.465)|(-1.004)|(-1.349)||
|DFAST*Productive + nonproductive expenditure (-1)|...|...|...|2.593|
|(% of GDP)||||~~(~~0.63)|
|DFAST*Square term of Productive + nonproductive expenditure (-1)|...|...|...|-2.972|
|(% of GDP)||||(-0.339)|
|DCOMP*Productive + nonproductive expenditure (-1)|...|...|...|2.943|
|(% of GDP)||||~~(~~1.468)|
|DCOMP*Square term of Productive + nonproductive expenditure (-1)|...|...|...|-11.894|
|(% of GDP)||||(-1.565)|
|Budget surplus|-0.089|0.051|0.278|0.099|
|(% of GDP)|(-0.261)|~~(~~0.081)|~~(~~0.563)|~~(~~0.157)|
|Inflation - consumer price index|-0.388|-0.052|-0.065|-0.055|
||(-4.015)***|(-2.672)***|(-2.341)**|(-1.521)|
|Growth rate of GDP per capita (-1)|0.082|-0.400|-0.650|-0.386|
||~~(~~0.484)|(-2.513)**|(-4.742)***|(-1.544)|
|No. of observations|99|171|270|270|
|Overidentification statistics|1.035|2.514|1.346|2.996|

Note: The estimation method is a dynamic GMM. Annual data are used. (-1) indicates variables lagged one period. DFAST is the dummy variable which is 1 for fast growing countries and 0 otherwise. DCOMP is the dummy variable which is 1 for the comparison group and 0 otherwise. t-statistics are given in parenthesis. * indicates 10% significance level, ** indicates 5% significance level, and *** indicates 1% significance level. These significance levels are equal to one minus the probability of rejecting the null hypothesis of zero coefficients. Overidentification statistics is for instrumental variables where H0: there is no overidentification problem. We fail to reject in each case.

39
25 _Table 13: Results for change in log out-of-pocket medical expenditures_

|VARIABLE|_Δ lo_<br>|_g out-of-poc_<br>|_ket medical_<br>|_expenditur_<br>|_es_<br>|
|---|---|---|---|---|---|
||(A)|(B)|(C)|(D)|(E)|
|SAH_CHANGE|**0.060**||**0.074**|**0.056**|**0.056**|
||**8.780**||**7.740**|**7.060**|**7.140**|
|SAH_POS_CH_LGE||**-0.216**||||
|||**-5.240**||||
|SAH_POS_CH_SML||**-0.066**||||
|||**-3.630**||||
|SAH_NEG_CH_SML||**0.038**||||
|||**2.260**||||
|SAH_NEG_CH_LGE||**0.087**||||
|||**2.460**||||
|POOR X SAH_CHANGE|||**-0.032**|||
||||**-2.110**|||
|HI X SAH_CHANGE||||0.016||
|||||0.930||
|URB_HI X SAH_CHANGE|||||**0.046**|
||||||**2.280**|
|RUR_HI X SAH_CHANGE|||||-0.046|
||||||-1.660|
|**_LINEAR COMBINATIONS_**||||||
|_SAH_NEGCHANGE - SAH_POSCHANGE_|**-0.052**|||||
||**-2.209**|||||
|_SAH_POS_LGE - 2 * SAH_NEG_SML_||-0.085||||
|||-1.675||||
|_SAH_NEG_LGE - 2 * SAH_NEG_SML_||0.011||||
|||0.244||||
|_SAH_CHANGE + POOR X SAH_CHANGE_|||**0.042**|||
||||**3.931**|||
|_SAH_CHANGE + HI X SAH_CHANGE_||||**0.072**||
|||||**4.835**||
|_SAH_CHANGE + URB_HI X SAH_CHANGE_|||||**0.102**|
||||||**5.614**|
|_SAH_CHANGE + RUR_HI X SAH_CHANGE_|||||0.011|
||||||0.415|
|N|7,486|7,486|7,486|7,486|7,486|

_Note: Results estimated with household-level fixed effects. Coefficients on controls (household mean age (level, squared, cubed), household size (level, squared, cubed), and wave dummies are not reported. t- statistics are reported under each coefficient. Coefficients in bold are significant at the 10% level (twosided). The test for symmetry between positive and negative shocks is based on a regression where positive (SAH_POSCHANGE) and negative (SAH_NEGCHANGE) health change are included as separate variables, ranging from one to three. Results are not reported_
# **References**

- Baquie, Sandra and Habtamu Fuje (2020) “Vulnerability to Poverty Following Extreme Weather Events in Malawi.”

- Barrett, Christopher B and Michael R Carter (2013) “The economics of poverty traps and persistent poverty: empirical and policy implications,” _The Journal of Development Studies_ , 49 (7), 976–990.

- Barrios, Salvador, Luisito Bertinelli, and Eric Strobl (2010) “Trends in rainfall and economic growth in Africa: A neglected cause of the African growth tragedy,” _The Review of Economics and Statistics_ , 92 (2), 350–366.

- Calcutt, Evie, Katja Vinha, Conor Meenan et al. (forthcoming) “Predicting the Welfare Impacts of Droughts in Sub Saharan Africa.”

- Christiaensen, L. and K. Subbarao (2004) “Towards an Understanding of Household Vulnerability in Rural Kenya,” _World Bank Policy Research Working Paper_ (3326).

- Dell, Melissa, Benjamin F Jones, and Benjamin A Olken (2012a) “Temperature shocks and economic growth: Evidence from the last half century,” _American Economic Journal: Macroeconomics_ , 4 (3), 66–95.

- Dell, Melissa, Benjamin F. Jones, and Benjamin A. Olken (2012b) “Temperature Shocks and Economic Growth: Evidence from the Last Half Century,” _American Economic Journal: Macroeconomics_ , 4 (3), 66–95, 10.1257/mac.4.3.66.

- (2014) “What Do We Learn from the Weather? The New Climate–Economy

- Literature,” _Journal of Economic Literature_ , 52 (3), 740–798, `http://www.jstor.org/ stable/24434109` .

- Dercon, Stefan (2002) “Income risk, coping strategies, and safety nets,” _The World Bank Research Observer_ , 17 (2), 141–166.

- Deschênes, Olivier and Michael Greenstone (2007) “The economic impacts of climate change: evidence from agricultural output and random fluctuations in weather,” _American economic review_ , 97 (1), 354–385.

- Essama-Nssah, R, Roy Shuji Katayama, and Emmanuel Skoufias (2011) “Too little too late: welfare impacts of rainfall shocks in rural Indonesia,” _Bulletin of Indonesian economic studies_ , 48 (78987), 1–24.

- Fafchamps, Marcel, Christopher Udry, and Katherine Czukas (1998) “Drought and saving in West Africa: are livestock a buffer stock?” _Journal of Development economics_ , 55 (2), 273–305.

- FAO (2019) “Afghanistan - Situation report March 2019.”

- Fensholt, R. and I. Sandholt (2005) “Evaluation of MODIS and NOAA AVHRR vegetation indices with in situ measurements in a semi-arid environment,” _International Journal of Remote Sensing_ , 26 (12), 2561–2594.

21
<!-- Start of picture text --> Correlation of Orig. Commitments w/ Quarterly Correlation of Disbursements w/ Quarterly<br>Real GDP (2000-2019) - Pre and Post Global Real GDP (2000-2019) - Pre and Post Global<br>Financial Crisis Financial Crisis<br>0.29 0.20<br>0.11<br>0.12 0.09 0.08 0.03 0.06 0.03 0.05<br>0.03 0.00<br>-0.02 -0.01 -0.05<br>-0.05 -0.05 -0.13<br>-0.14 -0.16<br>EAP ECA LAC MENA SAR SSA EAP ECA LAC MENA SAR SSA<br>Pre Crisis Post Crisis Pre Crisis Post Crisis<br><!-- End of picture text -->

# **4.3 Sectoral Comparison**<sup>**14**</sup>

At the sector level, commitments were consistently countercyclical, yet disbursements were consistently procyclical (Figure 3). This pattern could imply an “intent/implementation mismatch,” in which investments that are designed to be countercyclical become procyclical due to prolonged lags between commitment and disbursement. Disbursement procyclicality was also persistent, as all sectors displayed positive correlations between disbursements and economic activity in the recipient country both before and after the GFC. In the manufacturing, agribusiness, and services (MAS) and disruptive technology and funds (CDF) sectors, commitments turned countercyclical after the GFC, while commitments in the financial institutions group (FIG) remained countercyclical across the entire period. Infrastructure and natural resources (INR) sector commitments were almost acyclical in the post-GFC period.

**Figure 3: Sector-Level Correlation Results**

<!-- Start of picture text --> Correlation of Orig. Commitments w/ Correlation of Disbursements w/ Quarterly<br>Quarterly Real GDP (2000-2019) Real GDP (2000-2019)<br>0.05<br>0.03<br>0.02<br>-0.01 -0.01<br>-0.02 0.01<br>-0.03<br>MAS FIG INR CDF MAS FIG INR CDF<br>MAS FIG INR CDF MAS FIG INR CDF<br><!-- End of picture text --> 14 NB: Splitting country-level investments by sectors reduces the number of workable observations needed to run two decompositions for GDP (seasonal and cyclical) as not all sectors do business each quarter. This alters the end points of the time series based on when investment in that sector was recorded and implies that the cyclical components of GDP used in the overall case may be different from cyclical components of GDP in the sectoral split. Therefore, the countercyclicality of commitments at the level of each sector (aggregated across countries) should not be viewed as a contradiction to the overall procyclicality result for commitments.

11
24 stemming from shifts in EGS wages. However, we find no sign of an equilibrium relationship between the two wage rates. Little more than lCX of an increase in the EGS wage was passed on in the agricultural wage rate in either the short-run or long-run. Rationing of EGS emplovment appears to have dampened the expected second-round income effects arising through agricultural labor markets.

Whether one deems these changes to have been desirable or not depends crucially on one's judgement of the desirability of an employment guaiantee. The concept of assured employment, albeit at a low wage, does have a number of potentially attractive features from the po'nt of view of poverty alleviation: it generally allows scarce resources to go to the poorest first (at least those able to work), it maximizes the insurance benefits to the poor, and it helps undermine some of the possibilities for their corruption on such schemes, and for their exploitation in labor ;irkets and tenancy contracts. However, achieving these benefits with limited budgetary resources must entail a sufficiently low wage rate. There can be no presumption that higher wage rates without the necessary budgetary resources will be in the interests of the poor.
# **TABLE OF CONTENTS**

|INTRODUCTION................................................................................................................ 3|
|---|
|THEPMR METHODOLOGY.............................................................................................. 4|
|BENCHMARKINGPRODUCTMARKETPOLICIES INBULGARIA.......................................... 9|
|_1. Outward-oriented policies .................................................................................... 13_|
|_2. Inward-oriented policies....................................................................................... 17_|
|CONCLUSION.................................................................................................................. 27|
|ANNEXI: COMPARISONS WITH FULL SAMPLE................................................................. 33|
|ANNEXII: DETAILEDRESULTS OFPMRSURVEY FORBULGARIA.................................. 41|

2
14 high-income country guidelines that arguably are more aligned with pure need. Specifically, we now use the rate of women in the 20-69 age group who had a pap smear in the three years prior to the survey interview. The three-year interval is used in the United States as well as in most countries in the European Union (International Agency for Research on Cancer 2017). There is more heterogeneity in the age-groups recommended for screening across high-income countries, but the guidelines typically recommend screening from a woman’s 20s to her 60s.<sup>12</sup> Whenever the raw survey data permit, we therefore use the 20-69 age-range, which is also used by OECD (OECD 2018).

We continue to apply the same method to standardize pap smear rates with different reference periods in the original survey data as for the 2018 HEFPI database.<sup>13</sup> Nevertheless, the shortening of the reference period from five to three years and the extension of our preferred agegroup from 30-49 to 20-69 years changes the values of all previously presented pap smear data points in the 2019 HEFPI version.

The 2019 HEFPI database makes a more minor change to the definition of two additional indicators: skilled birth attendance and having 4 or more antenatal care checks. Here, we change the age-range<sup>14</sup> of women for whom we compute the indicators from 18-49 to 15-49. Most surveys with maternal and child health modules like the DHS, MICS and Reproductive Health Surveys (RHS) use the 15-49 age-range to collect data on pregnancy outcomes. We nevertheless used the 18-49 age-

> 12 In the United States the recommended screening age-range is 21-64 and the commonest range in the European Union is 25-64, although only 28% of countries use this range (no European Union country guideline recommends ending screening at age 49) (International Agency for Research on Cancer 2017).

13 When a survey used a pap-smear utilization question with a reference period other than 3 years, we transform the survey utilization rates to a 3 year reference period using the formula for the probability of an <u>�</u> event over multiple trials, 1 ��1 �x ~~�~~ � where x is the percentage of women obtaining pap smears over the survey’s reference period z (in years), and y is our target reference period of 3 years. For surveys where the <u>�����</u> reference period is unspecified (“Have you ever had a pap smear/mammogram?”), we assume z� � �β where α and β are the upper and lower bounds, respectively, of the age-group for which the survey question is asked (e.g. 65 and 21).

14 Age-ranges always refer to the age at the time of the survey interview (i.e. not the age during which the pregnancy the woman is asked about occurred).
worry could be that the technology growth for cities in model (5) is endogenous. The concern would be that Germany is rapidly developing innovations and new technologies for the automobile industry, and this expansion is simultaneously leading to greater exports from Germany and the migration of German scientists that are patenting automobile technologies to the United States.

This concern is addressed in several ways throughout this study, including sample decomposition exercises, lag structure tests, and similar exercises. The most straightforward safeguard, however, is already built into model (5). The patenting data, as described below, allow us to separate the probable ethnicities of inventors in the United States. By focusing on inventors of Anglo-Saxon ethnic heritage, one can remove much of this reverse causality concern. The Anglo-Saxon group accounts for about 70% of US inventors during the time period studied, and so this group reflects the bulk and direction of US technological development. Extensions will further consider settings where patent citation records suggest that the Anglo-Saxon inventors are mainly drawing on other Anglo-Saxon inventors in their research.<sup>9</sup> Addressing these concerns also provides the approach (5) with a conceptual advantage with respect to the fixed effect estimation strategy. The first differencing in specification (3) controls for the initial distributions _M_ % _i_ , _c_ ,1980 , and the importerindustry-year fixed effects δ _jtk_ control for the technology growth ratio for industry _k_ . This separation is not perfect due to the summation over cities, but it is closely mimicked. Thus, the identification in these estimations comes off these particular interactions. This provides a strong lever against concerns of omitted factors or reverse causality, and the well-measured US data can provide instruments that overcome the downward bias in coefficients due to measurement error.

II. DATA PREPARATION

> 9. Very strong crowding-in or crowding-out of natives by immigrant scientists and engineers would create a bias in the Anglo-Saxon trend itself. Kerr and Lincoln (2010) find very limited evidence of either effect at the city level for the United States during this time period and for the time horizons considered here (i.e., first differencing over five-year periods).
considerable variation across countries. There are several countries (Tajikistan, Romania, Latvia, Russia, Georgia, Estonia, Moldova, Kyrgyz Republic, Serbia, Turkey, Czech Republic and Hungary) that experienced rapid wage bill expansions that outpaced GDP growth (see Figure 2). In many of these cases, wage bill growth also exceeded total expenditure growth. Other countries, such as Albania, Poland, and Slovenia experienced much more restrained wage bill growth, broadly in line with GDP growth. At the same time, the wage bill grew significantly less than GDP growth and total expenditures in Azerbaijan, Kazakhstan, Belarus, Lithuania, Bulgaria, and the Slovak Republic.

**Figure 2. Real Growth Index 2000-2008 (year 2000=100)**

<!-- Start of picture text --> 700<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>GDP Expenditure Wage Bill<br>HUN CZE TUK SVN POL SVK SRB BGR KGZ ALB MDA EST GEO LTU RUS LVA ROM BLR TJK KAZ AZE<br><!-- End of picture text --> Source: ECA Fiscal Database, Country sources During the crisis, many countries, especially those that had experienced rapid expansions prior to the crisis, were forced to make drastic cuts to the overall size of their wage bill to restrain the fiscal deficit. Some of the countries that were hit hardest by the economic crisis, such as Latvia, Romania, Hungary, and Lithuania, experienced particularly large declines in real wage bill expenditures (see Figure 3). In 2011, Latvia’s real wage expenditures stood at only 65% of their 2008 level, while Romania’s real wage expenditures were only 72% of their 2008 level.

**Figure 3. Real Growth Index 2008-2011 (year 2008=100)**

<!-- Start of picture text --> 180<br>160<br>140<br>120<br>100<br>80<br>60<br>GDP Expenditure Wage Bill<br>LTV LTU ARM SLV EST HRV HUN CZE ROM BIH SLK MNE SRB BGR UKR RUS MKD KGZ GEO POL ALB KSV TUR MDA AZE BLR KAZ TJK<br><!-- End of picture text --> Source: ECA Fiscal Database, Country sources 5
increase in rice prices. Thus these areas may not need larger safety-net types of public interventions to help them cope with the shock, but on the other hand they would benefit (as would the country as a whole) from policies designed to increase rice production. What the data suggests in Guinea is that safety-net types of policy interventions may focus in part on urban and peri-urban areas, and rely on the detailed information available in the poverty map to target those rural areas which are not part of the rice producing economy.

15