rafmacalaba/datause-encoder-data huggingface dataset:
**Table 1. OLS estimates for ECA countries and Western Europe: IVS data (1981–2009)**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||ECA|Western Europe|ECA|Western Europe|ECA|Western Europe|
|Age|-0.0478***<br>(0.005)|-0.0294***<br>(0.004)|-0.1367***<br>(0.022)|-0.1140***<br>(0.024)|||
|Age squared|0.0005***|0.0004***|0.0024***|0.0022***|||
||(0.000)|(0.000)|(0.000)|(0.000)|||
|Age cubic|||-0.00001***<br>(0.000)|-0.00001***<br>(0.000)|||
|Age 23-27|||||-0.2740***<br>(0.074)|-0.1158*<br>(0.059)|
|Age 28-32|||||-0.4131***<br>(0.066)|-0.1848**<br>(0.059)|
|Age 33-37|||||-0.4818***|-0.2055*|
||||||(0.068)|(0.101)|
|Age 38-42|||||-0.5272***<br>(0.094)|-0.2374<br>(0.128)|
|Age 43-47|||||-0.5741***<br>(0.085)|-0.2021<br>(0.126)|
|Age 48-52|||||-0.5096***<br>(0.081)|-0.1749<br>(0.109)|
|Age 53-57|||||-0.4985***<br>|-0.0666<br>|
||||||(0.072)|(0.120)|
|Age 58-62|||||-0.4050***|0.1273|
||||||(0.070)|(0.152)|
|Age 63-67|||||-0.3085***|0.3423*|
||||||(0.074)|(0.163)|
|Age 68-72|||||-0.2791***|0.3759**|
||||||(0.086)|(0.155)|
|Age 73-77|||||-0.1847*|0.4839*|
||||||(0.098)|(0.206)|
|Age 78-82|||||-0.2115*|0.6625**|
||||||(0.110)|(0.191)|
|Age 83-87|||||-0.1106|0.7177*|
||||||(0.154)|(0.324)|
|Age 88-108|||||-0.3627|0.5997**|
||||||(0.359)|(0.253)|
|Minimum|48.8|35.4|42.9|36.6|||
|Maximum|||82.4|93.8|||
|N|77527|17389|77527|17389|77527|17389|
|_Adj._R<sup>2</sup>|0.246|0.241|0.246|0.242|0.246|0.242|

_Note:_ The dependent variable is self-rated individual life satisfaction (10-point scale). Age ranges from 18 to 108, with age 18–22 as the reference in (5) and (6). Controls are gender, employment status (full-time employed, part-time employed, self-employed, with not employed as the reference), marital status (married/living together, divorced/separated, widowed, with single/never married as the reference), education (medium level education and high level education, with low level education as the reference), income quintiles, self-reported health (5-point scale), country dummies, and year dummies. Robust standard errors clustered at the country level in parentheses; * _p_ < 0.1, ** _p_ < 0.05, *** _p_ < 0.01.

10
status, whether individual consider themselves a member of an ethnic or racial minority group, socioeconomic status (upper, upper middle, lower middle, or lower), place where individual lives (rural, small, or large city), whether individual has citizenship or a valid residence permit for the country in which they live, whether individual lives in a place with outdoor spaces during COVID-19, log of pre-pandemic GDP per capita of 2019, Purchasing Power Parity (PPP) (constant 2017 international $), and regions (South Asia, East Asia and Pacific; Europe and Central Asia; Latin America and the Caribbean; Middle East and North Africa; North America; and Sub-Saharan Africa)<sup>7</sup> ; and is a vector of errors. Moreover, to understand the mental health of the respondents during the COVID-19 pandemic, εε Moreover, to understand the mental health of the respondents during the COVID-19 pandemic, an ordered probit model is estimated based on the anxiety and depression measure constructed using the Patient Health Questionnaire (PHQ-4) scale. For the ease of administering the survey over an app, the questionnaire includes standardized questions for constructing the PHQ-4 scale. PHQ-4 scale is used extensively in research and clinical settings as a general indicator for anxiety and depression. The advantage of the PHQ-4 over the other mental distress measurements is that it is an ultra-brief screener, which can be used to indicate whether individuals may benefit from a further in-depth assessment (Stanhope 2016).

The four standardized indicators used for constructing the PHQ-4 scales are: (1) feeling of depression or hopelessness over the last 2 weeks, (2) feeling of disinterest or displeasure over the last 2 weeks, (3) feeling of nervousness, anxiety, or on edge over the last 2 weeks, and (4) feeling of inability to stop or control worrying over the last 2 weeks. For each of the four indicators, the respondents were asked to score how often they were bothered by the problem: not at all (score 0), several days (1), more than half the days (2), and nearly every day (3). The total score for anxiety and depression in the full PHQ-

> 7 Because of the limited sample size, we use control of aggregated regions for estimations instead of countries. Using countrylevel control in the estimations reduces the sample size further as many country dummies get dropped during probit optimization. Note that, to check the sensitivity of the results, even with a small sample size constraining the empirical analysis, we attempt to estimate the probit models including country fixed effects instead of aggregated regions. We confirm that the results with country fixed effects are qualitatively similar and consistent with the findings presented in this paper.

8
|Privatization and CapacityBuildingProject|Claude P. Sorel|
|---|---|
|Privatization and CapacityBuildingProject|Claude P. Sorel|
|Privatization and CapacityBuildingProject|Claude P. Sorel|
|Privatization and CapacityBuildingProject|Claude P. Sorel|
|Public Enterprise Institutional Development Project|Demba Ba|
|Public Enterprise Institutional Development Project|Demba Ba|
|Public Enterprise Institutional Development Project|Demba Ba|
|Public Enterprise Institutional Development Project|Demba Ba|
|Public Enterprise Institutional Development Project|Demba Ba|
|Public Enterprise Institutional Development Project|Demba Ba|
|Public Enterprise Institutional Development Project|Demba Ba|
|Public Enterprise Institutional Development Project|Demba Ba|
|Public Enterprise Institutional Development Project|Demba Ba|
|Public Enterprise Institutional Development Project|Demba Ba|
|Coconut Rehabilitation and Rodent Control Project||
|Coconut Rehabilitation and Rodent Control Project||
|Coconut Rehabilitation and Rodent Control Project||
|Coconut Rehabilitation and Rodent Control Project||
|Coconut Rehabilitation and Rodent Control Project||
|Education Project||
|Education Project||
|Education Project||
|Education Project||
|HighwayMaintenance Project(02)||
|HighwayMaintenance Project(02)||
|HighwayMaintenance Project(02)||
|Development Bank of the Comoros Project||
|Development Bank of the Comoros Project||
|Development Bank of the Comoros Project||
|Development Bank of the Comoros Project||
|Development Bank of the Comoros Project||
|Rural Services Project||
|Rural Services Project||
|Rural Services Project||
|Rural Services Project||
|Rural Services Project||
|Rural Services Project||
|Rural Services Project||
|Rural Services Project||
|Health and Population Project(01)||
|Health and Population Project(01)||
|Health and Population Project(01)||
|Health and Population Project(01)||
|Health and Population Project(01)||
|Health and Population Project(01)||
|Health and Population Project(01)||
|Education Project(02)|Daniel Viens|
# **4. Instrumental Variables**

Even though we concede the difficulty in accounting for endogeneity issues in our estimations given the data at hand, as a robustness we present instrumental variables estimations that may not be compelling, but do provide some insight that may be beneficial for future research. We use two instruments for police strength– a dummy variable indicating whether the party of the chief executive is right-wing or not and the stock of international migrants as a % of the population. The right-wing indicator is obtained from the database of political institutions (DPI) and is defined as conservative, religious or right-wing. On the one hand right, wing governments may prefer less government intervention and are thus more likely to decrease the police force. On the other hand, although right wing governments may be in favor of budget cuts, they may also prefer a larger crackdown of police on criminal activity, consequently increasing the police budget. A priori, it is difficult to see which effect will dominate. Furthermore, it has been shown that conservative voters typically perceive immigrants as illegal, and thus a larger migrant stock may push conservative governments to spend more on increasing the police force (Fennelly and Federico, 2008). Thus we expect the international migrant stock in the country to be positively correlated with the size of the police force.

The first-stage estimates are presented in column 2 of table 3, with a negative coefficient for right wing governments and a positive coefficient for migration stocks, both with a significance of at least 5%. This shows some support of a negative association of right wing governments with police size for the sample of countries in this study. We report the second-stage results using instrumental variables in table 3, column 1. The coefficient of police per 100,000 of population using instrumental variables retains the sign and significance of the base estimations. The magnitude increases in absolute terms from -0.0011 in the base estimations to -0.0025 in the estimations using the instrumental variables. As indicated in the bottom of table 3, the Sargen-Hansen test of 19
**Table 2**

|Weddine Celebration Expenses - OLS Regressions|
|---|
|(Robust standard errors)|

|**Variables**|Weddinz<br>No Interactio|Celebr<br>ns|ation ExDens<br>With Interac|es<br>tions|
|---|---|---|---|---|
|_l_<br>______________________________|_Coefficient_|_JtL_|<br>_Coefficient_|J_L_|
|Marriage Squeeze Ratio|-15595.980|0.87|-15496.910|0.92|
|Year of Marriage|-140.266|2.52|-154.520|2.57|
|Wife's Schooling (Yrs)|-177.396|0.84|-175.045|0.82|
|Husband's Schooling (Yrs)|267.253|1.10|59.506|0.33|
|Muslim|840.806|0.48|1119.523|0.71|
|Scheduled Caste/Tribe|*<br>-82.179|0.12|131.148|0.15|
|Wife's Land at Marriage - Dummy|80.108|0.09|434.018|0.56|
|Husband's Land at Marriage - Dummy|1393.610|2.12|-371.459|0.42|
|Husband's Age at Marriage|12.333|0.19|-12.152|0.19|
|Wife's Age at Marriage|171.708|2.14|199.380|2.25|
|Village Exogamous|1537.037|2.76|-1043.381|1.08|
|Exogamous x H. Schooling|---|--|276.141|1.84|
|Exogamous x H. Land?|---|--|2577.297|2.28|
|Bidar|494.008|0.42|256.660|0.16|
|South Kanara|2152.321|0.38|2244.290|0.22|
|Kodagu|4280.982|3.33|4076.240|3.23|
|Kolar|-2279.403|2.73|-2383.694|2.83|
|Constant|24676.820|1.08|27369.130|1.21|
|F for Joint Significance|6.52||5.58||
|R-Squared|0.21||0.23||

18
impact of the brain drain, as suggested by Commander, Kangasniemi and Winters (2004); and extension of the cross-country comparisons. In particular, due to data limitations, existing empirical studies (Beine et al., 2001 and 2003) are based on cross-section regressions, meaning that they neglect the dynamics of migration rates as well as the dynamics of education levels; in addition, in the absence of a time series dimension, it is impossible to control for individual-country e¤ects in the regression estimates. Given the strong heterogeneity of developing countries in terms of sizes, levels of development, etc., such country-...xed e¤ects are likely to play some role in the value of the estimates. However, the new estimates computed for 1990 and 2000 by Docquier and Marfouk (2004) should make it possible for further empirical research to go part of the way toward addressing these concerns satisfactorily.

# 6 References

Adams, R. (2003): International migration, remittances and the brain drain: a study of 24 labor-exporting countries, World Bank Policy Research Working Paper, n. 2972 Arora, A. and A. Gambardella (2004): The Globalization of the Software Industry: Perspectives and Opportunities for Developed and Developing Countries; in A.B. Ja¤e, J. Lerner and S. Stern, eds.: Innovation Policy and the Economy, Vol. 5, Cambridge, Ma.: The MIT Press, forthcoming.

Azariadis, C. and A. Drazen (1990): Threshold externalities in economic development, Quarterly Journal of Economics, 105, 2: 501-26.

Barro, R.J. and J.W. Lee (1993): International Data on Educational Attainment, Journal of Monetary Economics, 32, 363-394.

Bauer, T., Epstein, G.S., Gang, I.N. (2002): Herd e¤ects or migration networks? The location choice of Mexican immigrants in the US, IZA Discussion Paper No 551, August.

Beine, M., F. Docquier and H. Rapoport (2001): Brain Drain and Economic Growth: Theory and Evidence, Journal of Development Economics, 64, 1: 275-89.

Beine, M., F. Docquier and H. Rapoport (2003): Brain Drain and LDCs’ Growth: Winners and Losers, IZA Discussion Paper No 819, July.

Bhagwati, J. N. and K. Hamada (1974): The brain drain, international integration of markets for professionals and unemployment, Journal of Development Economics, 1, 1: 19-42.

Blomqvist, A.G. (1986): International migration of educated manpower and social rates of return to education in LDCs, International Economic Review, 27, 1: 165-74. Borjas, G.J. and B. Bratsberg (1996): Who leaves? The outmigration of the foreign-born, Review of Economics and Statistics, 78, 1: 165-76.

Carrington, W.J. and E. Detragiache (1998): How Big is the Brain Drain?, IMF Working Paper, No 98.

Carrington, W.J. and E. Detragiache (1999): How extensive is the brain drain, Finance and Development, June: 46-49.

35
**<mark>Table 3: Specification 2 – Correlates of stunting with exposure to E Coli contamination at source point (N=1292 children)</mark>**

|**Dependent Variable: Stunting**|**6 to 23**|**months**|**6 to 60**|**months**|**12 to 60**|**months**|**24 to 60**|**months**|**0‐60**|**months**|
|---|---|---|---|---|---|---|---|---|---|---|
||(2.1a)|(2.2a)|(2.1b)|(2.2b)|(2.1c)|(2.2c)|(2.2d)|(2.2d)|(2.1e)|(2.2e)|
|High E. Coli Contamination at Source (=1)||0.022||0.077**||0.076**||0.099**||0.094***|
|||(0.059)||(0.035)||(0.038)||(0.047)||(0.034)|
|Some E. Coli Contamination at Source (=1)|‐0.015||0.046||0.043||0.062*||0.051*||
||(0.048)||(0.028)||(0.030)||(0.036)||(0.027)||
|Appropriate water treatment (=1)|0.091|0.082|0.009|0.003|0.020|0.012|‐0.042|‐0.051|0.010|0.000|
||(0.077)|(0.078)|(0.052)|(0.052)|(0.056)|(0.056)|(0.066)|(0.067)|(0.051)|(0.051)|
|**Water Sources**|||||||||||
|_Tube Wells_|‐0.071|‐0.053|‐0.002|0.018|‐0.008|0.014|0.027|0.055|‐0.024|0.001|
||(0.169)|(0.168)|(0.087)|(0.088)|(0.092)|(0.093)|(0.099)|(0.101)|(0.082)|(0.083)|
|_Piped Water_|‐0.083|‐0.067|0.028|0.043|0.027|0.042|0.088|0.105|‐0.005|0.013|
||(0.176)|(0.175)|(0.091)|(0.091)|(0.097)|(0.097)|(0.105)|(0.105)|(0.086)|(0.086)|
|_(baseline: Unimproved Water)_|||||||||||
|Log of Child Age|‐0.281|‐0.281|1.419***|1.433***|2.043***|2.066***|‐0.368|‐0.605|0.144**|0.151**|
||(0.836)|(0.832)|(0.261)|(0.260)|(0.508)|(0.509)|(1.837)|(1.842)|(0.068)|(0.067)|
|Log of Child Age Squared|0.119|0.119|‐0.216***|‐0.219***|‐0.308***|‐0.312***|0.024|0.055|‐0.015|‐0.017|
||(0.165)|(0.164)|(0.041)|(0.041)|(0.076)|(0.076)|(0.250)|(0.251)|(0.013)|(0.013)|
|Female Child (=1)|‐0.046|‐0.047|‐0.004|‐0.002|‐0.009|‐0.006|0.015|0.017|‐0.009|‐0.006|
||(0.047)|(0.046)|(0.026)|(0.026)|(0.028)|(0.028)|(0.033)|(0.033)|(0.025)|(0.025)|
|Child was ever breastfed (=1)|‐0.082|‐0.088|‐0.191**|‐0.186**|‐0.201**|‐0.196**|‐0.223**|‐0.211**|‐0.214***|‐0.208**|
||(0.170)|(0.170)|(0.080)|(0.081)|(0.083)|(0.083)|(0.090)|(0.093)|(0.082)|(0.083)|
|**Mother's Education**|||||||||||
|_Primary Education_|‐0.031|‐0.030|‐0.070**|‐0.072**|‐0.088**|‐0.090**|‐0.089**|‐0.093**|‐0.062*|‐0.064**|
||(0.057)|(0.057)|(0.034)|(0.034)|(0.037)|(0.037)|(0.042)|(0.042)|(0.032)|(0.032)|
|_Secondary Education or Higher_|‐0.301***|‐0.297***|‐0.272***|‐0.271***|‐0.287***|‐0.286***|‐0.247***|‐0.246***|‐0.255***|‐0.253***|
||(0.064)|(0.065)|(0.046)|(0.045)|(0.050)|(0.050)|(0.060)|(0.060)|(0.044)|(0.044)|
|_(baseline: No Education)_|||||||||||
|Community Improved Sanitation Ratio|‐0.077|‐0.073|0.055|0.075|0.073|0.093|0.115|0.141*|0.072|0.095|
||(0.110)|(0.111)|(0.066)|(0.066)|(0.070)|(0.071)|(0.083)|(0.085)|(0.064)|(0.064)|

15
# APPENDIX 3. Methodology and database

# Methodology

# Panel regressions

If long-term expectations are well-anchored, they will not be highly responsive to macroeconomic news. Figure 4, panel A, presents the results of a panel regression model that estimates the sensitivity of changes in long-term inflation expectations to macroeconomic news shocks. The change in long-term inflation expectations (dependent variable) is measured by the difference between five-year-ahead inflation expectations in the current period and five-year-ahead inflation expectations in the previous period. The macroeconomic news shock corresponds to an inflation shock (a regressor) that is measured by the difference between realized inflation and short-term inflation expectations in the previous period.<sup>1</sup> The model includes an interaction dummy variable to allow for different elasticities of inflation expectations in advanced economies and EMDEs: where i denotes country and t refers to time. _Et πi,t_ +5 and _Et_ -1 _πi,t_ +5 are five-year-ahead inflation expectations in the current and previous periods, respectively. _π_ i,t refers to realized inflation and _Et_ -1 _πi,t_ is short-term inflation expectations in the previous period. _Di_ is a dummy variable that is equal to 0 for advanced economies and 1 for EMDEs, implying that _β_ 1 and ( _β_ 1 + _β_ 2) are the estimated sensitivities for advanced economies and EMDEs, respectively. When the estimated sensitivity is small (that is, _β_ 1 is not statistically significantly different from zero), inflation expectations are well anchored. The model includes country fixed effects ( _μi_ ) and time fixed effects ( _τt_ ) that are estimated for three periods: 1990H2-2004H2, 2005H1-18H1, and 1990H2-2018H1.<sup>2</sup>

# Regressions with time-varying parameters: Country-specific models

Figure 4, panels B, C, and D, presents the results of a time-varying model, estimated using a Kalman filter, that captures the time variation in the sensitivity of changes in long-term inflation expectations to inflation shocks. The model is a version of model (1), but it includes time-varying coefficients: where the measures of expected and realized inflation are the same as those in model (1). The model is estimated for each of the 24 advanced economies and 23 EMDEs in the sample, using semiannual data for 1990H1-2018H1 and 1995H1-2018H1, respectively. The

> 1 The model follows Beechey, Johannsen, and Levin (2011); Gürkaynak, Levin, and Swanson (2010); and De Pooter et al. (2014).

> 2 Because there are no available data for most EMDEs in the early 1990s, the panel data set is unbalanced. The sample was split at 2004 to produce two samples of roughly equal length.

34
ensure that only low-cost private schools self-select to participate in the program; the original amount was also purposely set at half the estimated per-student cost in the public school system.<sup>1</sup> In return for receiving the subsidy benefit, the program school has to, among other things, waive tuition and fees for all students and ensure that the school achieves a minimum student pass rate in the Quality Assurance Test (QAT). Program schools that satisfy the above conditions are also eligible for other substantial cash benefits offered on an annual basis: group-based bonuses for teachers in schools that achieve high QAT pass rates/mean scores and competitive bonuses for schools that rank highest in the QAT in each main program district.

Each of the above incentive features (the structure of the benefits as well as the benefit eligibility rules) represents key program innovations in the education field that may induce participation, equity, and learning gains.<sup>2</sup> We are not aware of any other program in a developing country which combines these innovations into a single intervention. As of June 2010, the FAS program has proceeded through six phases of expansion and supports 798,000 students in 1,779 schools in 29 of the 36 districts in the province, making it one of the largest PPP initiatives in education in the developing world.

The learning data used in the study come from the QAT. The QAT is a curriculum-based, multi-subject, written test offered biannually in program schools. It is designed by professional subject specialists in PEF, and, starting with QAT 4, the administration of the test was competitively outsourced to independent testing agencies. The testing agencies are expected to adhere to the testing protocols set by PEF, with testing agency compliance monitoring by PEF at all program schools. Importantly, the testing agencies are instructed by PEF to follow preestablished procedures to control for certain types of potential strategic responses of schools (i.e., cheating in a broad sense) that may artificially raise test scores.

To date, nine rounds of the QAT (QAT 1-QAT 9) have been administered, of which data from five rounds, QAT 4-QAT 8, administered between November 2007 and November 2009, are used in this study. QAT 4 is the first round of test data used in the study due to sample size considerations: it is the first QAT administered after the first major expansion of the FAS program.

> 1 Setting the subsidy level in such a way was considered important by PEF for securing political buy-in for the use of public funds for the initiative.

> 2 The typical subsidy program is designed to finance stipulated education inputs (Gauri and Vawda 2003).

3
Table 4. The Average Marginal Effects from Multinomial Probit Regression of an Individual Farmer’s Exit Choice Model

|VARIABLES|(1)<br>Individual|(2)<br>New smaller<br>group|(3)<br>Stay in WUG|
|---|---|---|---|
|Volumetric price of reservoir water|-5.983|13.07***|-7.088*|
||(-1.548)|(2.950)|(-1.941)|
|Volumetric price of reservoir water squared|48.99*|-87.56***|38.57|
||(1.763)|(-3.056)|(1.433)|
|Group size||||
|No. of parcels|-0.000524|0.00120|-0.000678|
||(-0.528)|(1.433)|(-0.694)|
|Homogeneity||||
|Gini coefficient of parcel size|0.262|0.340|-0.602*|
||(0.756)|(1.224)|(-1.810)|
|Infrastructure condition||||
|Simplicity of water flow direction|-0.0570|-0.682|0.739|
||(-0.0712)|(-1.093)|(0.946)|
|Earth canal length|-0.0228|-0.0247|0.0475|
||(-0.677)|(-0.985)|(1.444)|
|Exit options||||
|No. of ponds per member (pre-reform)|0.238***|-0.0211|-0.217**|
||(2.827)|(-0.306)|(-2.512)|
|No. of pumps per member (pre-reform)|0.0137|0.157*|-0.170*|
||(0.127)|(1.938)|(-1.685)|
|Surrounding parcel characteristics||||
|No. of surrounding parcels|-0.0357|0.254**|-0.218*|
||(-0.304)|(2.336)|(-1.786)|
|No. of surrounding parcels squared|0.000830|-0.0395**|0.0387*|
||(0.0403)|(-2.092)|(1.786)|
|Years knowing owners of surrounding parcels|0.00297|-0.00267|-0.000307|
||(0.836)|(-0.917)|(-0.0854)|
|Proportion of surrounding parcels whose owners send|-0.0227|0.159***|-0.137|
|their children to the same school|(-0.244)|(2.698)|(-1.541)|
|Management skill and water demand<br>||||
|Average schooling years of working members|0.0208|0.0274|-0.0481**|
||(0.836)|(1.613)|(-2.058)|
|Parcel size of paddy|0.0975|0.185|-0.282|
||(0.189)|(0.529)|(-0.575)|
|Sandy/high seepage soil dummy|0.352**|-0.105|-0.247|
||(2.414)|(-0.823)|(-1.586)|
|Threshold value: the price level that gives the<br>max/min probability of the dependent variable|0.061|0.075|0.091|
|Observations||135||

_Note_ : WUG-level cluster robust _z-_ statistics are in parentheses. *** p<0.01, ** p<0.05, * p<0.1. _Source:_ Authors’ analysis based on data sources discussed in the text.

26