rafmacalaba/datause-encoder-data huggingface dataset:
Table 5: Predictors of Job Quality

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||_JQ_<sup>_S_</sup>|_JQ_<sup>_R_</sup>|_k >_1_/_2|_k_ = 1|
||b/se|b/se|b/se|b/se|
|Age|0.024***<br>(0.001)|0.003***<br>(0.000)|0.027***<br>(0.001)|0.041***<br>(0.004)|
|Male|0.620***|0.088***|0.612***|0.217***|
||(0.054)|(0.009)|(0.115)|(0.039)|
|Educ - High School|0.504***|0.070***|0.633***|0.453***|
||(0.018)|(0.003)|(0.042)|(0.065)|
|Educ - College|-0.025|-0.010|0.176***|0.006|
||(0.054)|(0.006)|(0.060)|(0.090)|
|Migrant|0.136***<br>(0.018)|0.022***<br>(0.003)|0.162***<br>(0.020)|0.045***<br>(0.016)|
|Public Firm|1.360***|0.158***|1.649***|0.564***|
||(0.191)|(0.021)|(0.123)|(0.204)|
|Large Firm|1.042***|0.125***|1.264***|0.168|
||(0.208)|(0.025)|(0.188)|(0.138)|
|Formal|1.159***|0.165***|0.681***|1.084***|
||(0.183)|(0.021)|(0.156)|(0.144)|
|Self-Employed|-0.091|-0.004|-0.091|-3.353***|
||(0.226)|(0.032)|(0.509)|(1.040)|
|Employer|-0.805***|-0.096**|-0.432|-3.486***|
||(0.267)|(0.044)|(0.641)|(1.139)|
|Union|0.591***|0.103***|1.128***|0.844***|
||(0.125)|(0.010)|(0.135)|(0.054)|
|Urban|0.322***|0.043***|0.294***|0.265***|
||(0.053)|(0.008)|(0.050)|(0.075)|
|ln(GDP/capita)|0.033|0.016|0.037|0.105|
||(0.204)|(0.011)|(0.263)|(0.177)|
|Adj. _R_<sup>2</sup>||0.487|||
|Pseudo-_R_<sup>2</sup>|0.185||0.343|0.345|
|Number|6,375,826|6,375,826|6,375,826|6,256,021|

_Notes_ : Authors’ calculations using SEDLAC data. Robust standard errors clustered at the country level are in parentheses. All specifications include year, country, and sector fixed effects. Significance: *** 1%, ** 5%, * 10%.

27
<!-- Start of picture text --> 2014<br>2011<br>2008<br>2005<br>2002<br>1999 Year<br>1996<br>1993<br>1990<br>1987<br>1984<br>9 8 7 6 5 4<br>Employee Non-Wage Labor Costs (as% of Wage)<br><!-- End of picture text --> <!-- Start of picture text --> 2014<br>2011<br>2008<br>2005<br>2002<br>1999 Year<br>1996<br>1993<br>1990<br>1987<br>1984<br>56 54 52 50 48 46<br>Employer Non-Wage Labor Costs (as% of Wage)<br><!-- End of picture text --> <!-- Start of picture text --> 2014<br>Calculations Subsidized Calculations<br>the<br>2009 to<br>Author’s Author’s<br>access<br>costs. 2001.<br>2004 Health<br>have<br>these before<br>of who<br>1999 Year<br>surveys<br>Subsidized<br>Workers the<br>Surveys<br>of in<br>construction 1994<br>detailed Values Household<br>a 1989 availability<br>Evolution of Employee Non-Wage Labor Costs Evolution Informal-Sector<br>Wage)for of data<br>%of Table12 Statutoryon 1984 12: No Colombianon<br>Figure 10:(asSee based Figure Percentage Regime- based<br>Costs Calculations 2014q1 dataNo<br>Labor<br>Author’s 2009q1<br>Non-Tradables.<br>costs. to Bank<br>Non-Wage 2004q1 Prices<br>these<br>of relative Central<br>1999q1 Year<br>Employer Relative Tradables Colombian<br>of<br>of construction<br>1994q1<br>Index- Source:<br>detailed Values 1998<br>a<br>1989q1 Evolution Average<br>Evolution for<br>Wage) before<br>9: of 13 Statutory 11:<br>CPI Index Base:Dec 2008=100 (All items less Food)<br>Figure%(asSeeTable basedon 1984q1 Figure QuarterlyCPI availability<br>50 40 30 20<br>Subsidized Health (as % Informal Sector Workers)<br>1.2 1.1 1 .9 .8<br>CPI Quarterly Avg Index: Tradables/Non-Tradables<br><!-- End of picture text --> 46
15

# **Table 1**

||DATA USED IN INDONESI|AN FERTILITY|**-** MORT|ALITY MODEL||
|---|---|---|---|---|---|
|Svyuol|Variable|Units|Yr|Nean|STD DEV|
|IMR|Infant Deaths per Birth|Proportion|1980|.108|<br>.023|
|TFR|Children born per Woman 15-45|Children|1980|5.228|<br>.815|
|TOIL|Percent of HH with Private<br>Waste Disposal (Toilet<br>or Latrine)||1980|47.838|<br>12.046|
|REXNP|Government Non Hospital<br>Recurrent Health<br>Expenditures<br>per Capita|Rp 000|1984|1.272|<br>.485|
|REXHP|Government Recurrent Hospital<br>Expenditures<br>per Capita|Rp 000|1984|.802|<br>.687|
|REXFPP|Gov. Expenditures<br>on Family<br>Planning per Capita|Rp 000|1980|.427|<br>.191|
|ILLIT|Adult Illiteracy Rate|t|1980|26.870|<br>10.190|
|PURB|Percent Urban Population||1980|21.978|<br>18.248|
|YP|Monthly Household Expenditures<br>Per Capita|<br>Rp 000|1983|17.609|<br>5.318|
|CAL|Daily Calories per Capit%|Calories|1980|1906.2|207.2|

Sources: Indonesia; Department of Health, Bureau of Planning; Central Bureau of Statistics, Census; National Family Planning, Coordinating Board; SUSENAS (National Household Survey, February 1984)

## IV. EmRirical Results

17. Applying the conceptual framework in Figure one to cross province data for Indonesia reveals that observed pairs of IMR and TFR for individual provinces are the result of the intersection of the two simultaneous
# **2 Reserve requirement data**

Our starting point is the quarterly RR database put together by Federico _et al._ (2014), which identifies different types of RR in terms of maturity and currency of denomination.<sup>8</sup> Based on this, we construct a metric of effective RR that also takes into account the structure of deposits for each of the three countries included in this paper (Argentina, Brazil, and Uruguay).<sup>9</sup> As shown in Table 1, Panel A, we identify a total of 93 quarterly changes in RR. Specifically, Argentina, Brazil, and Uruguay changed RR on 49, 31, and 13 occasions, respectively. In other words, Argentina, Brazil, and Uruguay changed their RR, on average, about once every 7, 9, and 26 months, respectively.

To set the stage, we begin by briefly discussing some broad features of the data; in particular the varieties of RR (Section 2.1), long- and short-run properties (Section 2.2), and the variable effectively used to conduct the empirical analysis (Section 2.3).<sup>10</sup>

> 8The current version of this database comprises 66 countries, 15 industrial and 51 developing (see Federico _et al._ , 2014, for details) and is available at www.guillermovuletin.com.

> 9The analysis covers the period 1990:1-2018:1 for Argentina, 1994:3-2018:2 for Brazil, and 1990:1-2018:2 for Uruguay. This dataset is also available from www.guillermovuletin.com.

> 10Our study also uses other macroeconomic variables such as real GDP, inflation, central bank interest rates, and market interest rates, all at quarterly frequency. Most of these data were gathered from the IFS (IMF), the Global Economic Monitor (World Bank), and local sources. See Appendix 8 for a description of data and sources.

6
43 schools (85%; Smith 2000) suggest that 80% use of time could be realistically attained. _This 80% figure could be used as a benchmark for lower-income countries to attain._ _Project evaluation challenges._ The World Bank and other agencies try to evaluate project outcomes by comparing baseline to post-intervention data. Instructional time use is rarely mentioned explicitly during various interventions, and baselines are typically not available. How can a single measure at the end of a project help evaluate outcomes when the counterfactual is not known?

If project interventions resulted in better management, the amount of time used for instruction should increase over a baseline and approximate 100%, the amount that governments expect schools will devote to teaching students. Without a baseline it is hard to determine the amount of increase, but a criterion-referenced approach could be taken. Curriculum developers in a country can be asked to estimate how many class hours students would need to master objectives of specific subjects in various grades and also estimate the number of objectives likely to be missed if the available time were reduced by 20%, 40%, or 60%. In some subjects the loss would be limited to that subject (e.g. social studies), but in others, like math, losses in grade 1 would impact learning in higher grades and result in cumulative losses. It is possible to estimate and model these losses for evaluation purposes.

There is a further need to improve time measurement not only for greater reliability but also for ease of use and cost effectiveness. The concepts are complex, and different studies have used different methods. This project has resulted in improved modular surveys that obviate the need to craft new questionnaires every time a new study is initiated. Currently, data that would give a sense of baselines, range, and likelihood of improvement are limited, but as more are collected, these indicators will become more meaningful.

_Research prospects._ Much needs to be learned about the effects of time use on test scores and reading fluency acquisition in the early grades. It must be better understood how the various systemic inefficiencies impact the amount of time ultimately available for students in different countries. Clearly causality must be established to link time use and learning outcomes in longitudinal studies and clarify the relative importance of inputs such as textbooks and other instructional materials. The advantages and disadvantages of group work in time use must be also studied. Instructional time use in private and public schools within the same areas must be compared and patterns must be better understood. Memory research issues, such the rate and efficiency of consolidating new information need to be better understood vis-à-vis the amount and placement of instructional time as well as the activities that maximize long-term recall.

The future of the Education for All initiative may depend on how seriously governments and donors take instructional time wastage. Hopefully this research is only the beginning.
# **Figure 2 Impact of Banking Crises on Net Disbursed Aid Provided by Crisis-Affected Donors, 1977-2007**

<!-- Start of picture text --> 0 5 10 15 20<br>Years after onset of crisis<br>.4<br>.2<br>0<br>Banking crisis coefficients<br>-.2<br>-.4<br><!-- End of picture text --> Source: Authors’ calculations using LV data, as described in the text Note: The figure shows the coefficient on the individual‐year counter dummy for years after crisis, as estimated in regressions controlling for donor‐country per‐capita income and population, as well as country fixed effects and year dummies across all donors. Year t=1 corresponds to the first year of the crisis.

33
Table 7: Effect of SNAP-benefit changes on consumption of households

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Specifcation|OLS, log-log|IV, log-log|OLS, level-level|IV, level-level|
|VARIABLES||Con|sumption||
|Both ineligible|0.0444|0.193|0.106*|0.200|
||(0.0587)|(0.180)|(0.0571)|(0.148)|
|Household eligible only|0.0635|0.208|0.170*|0.254|
||(0.109)|(0.219)|(0.0908)|(0.172)|
|Product eligible only|0.0231|0.193|0.0376|0.128|
||(0.0376)|(0.167)|(0.0420)|(0.140)|
|Both eligible|0.252***|0.411**|0.354***|0.442**|
||(0.0557)|(0.164)|(0.0866)|(0.170)|
|Observations|2044662|2044662|2044666|2044666|
|R-squared|0.561|0.561|0.573|0.573|
|Prob _>_ F|0.000|0.000|0.000|0.000|
|Number of units|23911|23911|23911|23911|
|Number of clusters|49|49|49|49|
|First stage F-stat||12.258||15.642|

Notes: Robust standard errors are in parentheses, clustered by state. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Each coefficient comes from a regression of (log) consumption on (log) SNAP benefits per recipient, interacted with 4 group indicators. Both ineligible refers to expenditures by SNAP-ineligible households on SNAP-ineligible products, household eligible only refers to expenditures by SNAP-eligible households on SNAP-ineligible products, product eligible only refers to expenditures by SNAP-ineligible households on SNAP-eligible products, and both eligible refers to expenditures by SNAP-eligible households on SNAP-eligible products. Control variables as well as household-month and period fixed effects are also included. Observations are weighted by sampling weights.

Table 8: Incidence of an additional dollar of SNAP benefits for SNAP-eligible goods

|MPC elasticity|Pass-through elasticity|Shift magnitude|PS|CS|CS (SNAP)|CS (non-SNAP)|
|---|---|---|---|---|---|---|
|0.339|0.0724|0.0802|0.440|0.0466|0.405|-0.358|
|0.522|0.0724|0.124|0.492|0.3051|0.664|-0.358|

Notes: MPC elasticities are obtained from Section 4.2 and from HS. A market conduct parameter of 0.2 is assumed using markups as shown in Hottman (2016). A demand elasticity of -0.709 is obtained using panel variation as described in Section 5.4 and falls within the typical range reported in the literature as surveyed by Andreyeva et al. (2010). Proportion of SNAP sales of 0.168 is obtained from USDA data. Pass-through elasticity is obtained from Section 4.1 and the shift magnitude is the predicted pass-through elasticity obtained using equation (8) assuming a unit-subsidy pass-through rate of 1. Surplus calculations are changes in surplus per marginal dollar of SNAP disbursed. PS refers to producer surplus and CS refers to consumer surplus, CS (SNAP) and CS (non-SNAP) refers to consumer surplus for SNAP consumers and consumer surplus for non-SNAP consumers, respectively. See Section 5.4 for a detailed explanation.

43
avoid excessive imprudent access to credit.

The concept of the Access Possibilities Frontier is also useful for empirical attempts to measure outreach and access, bankable and banked population, for specific financial products.<sup>56</sup> A combination of household and supplier surveys can help compute demand and supply side factors and constraints, as we want to illustrate with the following example for the case of savings and payment services. First, household surveys can be used to determine the share of the population that uses a specific service, such as checking account, savings account, or ATM services (Point III in Graph 2). Second, using survey questions on the reasons why certain people do not use a specific service, one can identify the (i) unbankable (no sufficient income or economic activity), (ii) unbanked due to voluntary exclusion (by choice) and (iii) unbanked due to involuntary exclusion (access related obstacles such as costs, lack of necessary documentation, distance etc.). Adding the banked population, the unbanked by choice and the unbanked due to involuntary exclusion, one can get to a first approximation to the bankable population (point I in Graph 2) or the frontier. The relative shares of unbanked due to voluntary and involuntary exclusion also allow quantifying the effect to be expected by market-enabling policies aimed at the demand and supply side.<sup>57</sup> The tool of access possibilities frontier can also be used to assess the impact of cost- or risk-reducing technological change and of changes in state variables such as the legal system on the bankable population. The asset holdings of enterprises and households can be compared to

> 56 Porteous (2005) provides an example for such a calculation for the Mzansi account in South Africa, while Melzer (2006) offers an example for the housing loan market in South Africa.

> 57 Alternatively, the bankable population could be computed through estimates of the costs of delivering certain payment services, affordability assumptions (x% if monthly income) and information on the income distribution in the country. These estimates can then be compared to a supplier survey on the actual prices for the same products to calculate the share of the unbanked population that is excluded due to supply constraints.  The notorious unreliability of such cost estimates, due to the important role of overhead costs in financial institutions, is an important caveat for such an exercise.

48
Policy ReseaRch WoRking PaPeR 4919

# **Abstract**

An AIDS epidemic threatens Ethiopia with a long wave of premature adult mortality, and thus with an enduring setback to capital formation and economic growth. The authors develop a two-sector model with three overlapping generations and intersectorally mobile labor, in which young adults allocate resources under rational expectations. They calibrate the model to the demographic and economic data, and perform simulations for the period ending in 2100 under alternative assumptions about mortality with and without the epidemic. Although the epidemic does not bring about a catastrophic economic collapse, which is hardly possible in view of Ethiopia's poverty and high background adult mortality, it does cause a permanent, downward displacement of the path of output per head, amounting to 10 percent in 2100. An externally funded program to combat the disease is socially very profitable.

This paper—a product of the Global HIV/AIDS Program, Human Development Network-—is part of a larger effort in the World Bank to assess the economic effects of HIV/AIDS, especially in Africa. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at clive.bell@urz.uni-heidelberg.de or anastasios.koukoumelis@urz.uni-heidelberg.de.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
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3.2 Regression analysis on the impact of the PARE program on learning and achievement In this subsection, we assess the impact of the PARE intervention on students' scores controlling for supply and demand indicators. The results are shown in Tables 6 through 8.

The indicators, constructed through principal components analysis, include:

- Family's cultural capital: index based on parents' schooling, reading habits, television and radio programs listened, and number of books at home;

- Teacher performance: index based on teacher's attendance and other practices;

- Quality of school director: variable indicating favorable school conditions for teaching and learning, such as qualification of principal, his knowledge update, and the distribution students in the classroom.

- Supervision quality: a composite indicator based on frequency of supervisor visits, duration, occupations of people interviewed, and themes discussed; and

- Parents' participation: a measure of parents' attitudes to teachers' attendance, participation in school activities, and relevance of parents' school association.

**Table 6. Student's change in performance, 1992 and 1994.**

||**Native**|<br>**Rural**||
|---|---|---|---|
||**Beta coefficient**|**tvle**<br>**Beta coefficient**<br>**t-valuet-au**|**t-value**|
|Control|0.245348|4.698a<br>0.11465|2.695a|
|Teachefs Performance 6th grade|-0.002794|-0.060<br>0.074814|1.691c|
|Teachers Performance 5th grade|-0.004594|-0.102<br>0.107404|2.485a|
|Director's Quality|0.171017|3.709a<br>0.138362|3.040a|
|Supervision Quality|0.121867|2.302b<br>0.013111|0.283|
|Parents' Participation|0.072675|1.565c<br>-0.133568|-3.048a|
|Child'spartAcademic Record|0.043542|0.984<br>0.061537|1.441d|
|R2-adjusted||0.12097|0.06234|
|F||10.437a|6.205a|
|N|480|548||
|Students self-esteem at 5th grade|-0.088019|-2.032b<br>-0.044318|-1.039|
|Availability & quality of urban infrstructure|-0.166759|-3.120a<br>0.006836|0.153|
|Memorandum item:||||
|Maximum total contribution of PARE program|0.530844|0.448341||

a - Significant atthe 1% level ormore b - Significant at the 5% level or more

- c - Significant at the 10-% level or more

- d - Significant at the 20% level or more

Dependent vanable: Difference in normalized test scores between 6th and 4th grade 8
# **Table C.4. Robustness check with additional telephone survey round**

|||||Event|type|Perp|etratorty|pe|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|VARIABLES|||||||||
|Conflict events|0.071***<br>(0.026)|0.045**<br>(0.020)|0.052***<br>(0.016)||||||
|Conflict events property||||0.091***<br>(0.026)|||||
|Conflict events violence|||||0.046<br>(0.038)|<br>|||
|Insurgents||||||0.081***<br>(0.020)|||
|Bandits/criminals|||||||0.202**<br>(0.089)||
|Pastoralists/nomads||||||||0.018*<br>(0.009)|
|Observations|3,458|2,802|2,802|2,802|2,802|<br>2,802|2,802|2,802|
|R-squared|0.323|0.023|0.045|0.043|0.042|<br>0.045|0.044|0.042|
|Number of households|581|581|581|581|581|581|581|581|
|Controls|NO|YES|YES|YES|YES|YES|YES|YES|
|Year FE|YES|YES|NO|NO|NO|NO|NO|NO|
|HH FE|YES|YES|YES|YES|YES|YES|YES|YES|
|Region-round FE|NO|NO|YES|YES|YES|YES|YES|YES|

_Note_ : Dependent variable is the standardized CSI. Data used are from the six visits of the GHS and telephone survey for conflict and an additional telephone survey with the CSI administered to 581 households in 2017. All regressions are conducted using weights specifically calibrated for the sample. Controls include all household and geographical variables listed in appendix table C.1. Standard errors clustered at the local government area level. Significance: *** _p_ <0.01; ** _p_ < 0.05; * _p_ < 0.1 xiv