rafmacalaba/datause-encoder-data huggingface dataset:
in the past. Smoking is sometimes used as a proxy for (im)patience (Fersterer and Winter-Ebmer, 2003) and the decision to quit is seen as as a sign of willpower (Kan, 2007), two characteristics that are correlated with both health conditions and labor market outcomes. Their inclusion has not altered significantly neither the impact of _chronic_ nor that of education, supporting our assumption that only a small share of individual specific ability remains outside the family component that is captured by the within-siblings estimators and the proxy _order_ (order of birth).

Following the same idea of focusing on siblings of similar age, we have estimated the employment equation by age intervals. This procedure implies a drastic reduction in the number of observations, so that the choice of age intervals had to accommodate the need of a reasonable sample size. Indeed, the model has been estimated in each of the following age intervals: 16-20, 21-25, 26-30, 31-40, 41-64. Results are reported in Table 2 (line *). Unsurprisingly, the precision of the estimates is much smaller and their magnitude is increasing with age, a pattern already observed above. The benchmark against which to compare these estimates are those of equation (1), augmented by the interaction _chronic X age_ (which provides age specific effects of chronic diseases)<sup>8</sup> , obtained by using the whole “siblings” sample. In the lower part of Table 2 we have reported such age specific effect (line **), calculated at the mean age of the corresponding interval, and its standard error. Moreover we add the p-value of a test where we check if age-specific estimates (**) differ from estimates (*). The two are not significantly different at 95% level of confidence. Actually, except for the age interval 21-25 the null of equality cannot be rejected at much lower levels of confidence.

Finally we have checked whether there are long-term effects of the business cycle that an individual finds at 16, the minimum legal age for being employed, and at the time when he actually enters the labor market, after the end of his education period. For this purpose we have included the per capita GDP growth rate in these years (sources: WDI 2007 and Maddison). They always resulted in non-significant and comparatively uncorrelated with all the other variables included into the model.

**Table 2 – Employment equation. Estimates by age intervals**

||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|---|---|---|---|---|---|
|VARIABLES|16-20|21-25|26-30|31-40|41-64|
|*****<br>Chronic|-0.0376|-0.0577|-0.2121|-0.4761|-0.4968|
|s.d.|(0.1729)|(0.1916)|(0.3022)|(0.3174)|(0.3291)|
|Observations|1314|1204|506|407|200|
|Number of generations|609|569|243|190|96|

> 8 This model extension is described in detail in the next section.

15
# | Povicy RESEARCH WORKING PAPER 1616

## Summary findings

Jalan and Ravallion study transient poverty in a six-year panel dataset for a sample of 5,000 households in postreform rural China.

Half of the mean squared poverty gap is transient, in that it is directly attributable to fluctuations in consumption over time. There is enough transient poverty to treble the cost of eliminating chronic poverty when targeting solely according to current consumption — and to tilt the balance in favor of untargeted transfers. Transient poverty is low among the chronically poorest, and tends to be high among those near the poverty line.

Using censored quantile regression techniques, Jalan and Ravallion find that systemic factors determine transient poverty, although they are generally congruent with the determinants of chronic poverty. There is little to suggest that the two types of poverty are created by fundamentally different processes.

It appears that the same things that would help reduce chronic poverty — higher and more secure farm yields and higher levels of physical and human capital — would also help reduce transient poverty.

This paper — a product of the Poverty and Human Resources Division, Policy Research Department — is part of a larger effort in the department to use improved data to understand the causes of poverty and implications for policy. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Patricia Sader, room N8-040, telephone 202-473-3902, fax 202-522-1153, Internet address psader@worldbank.org. June 1996. (35 pages)

> 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 used and cited accordingly. The findings, interpretations, and conclusions are the authors’ own and should not be attributed to the World Bank, its Executive Board of Directors, or any of its member countries.

Produced by the Policy Research Dissemination Center
the linear IV model builds on well-established textbook results for Bayesian analysis of the multivariate regression model (for textbook treatments of the latter see Zellner (1971), Ch. 8 and Poirier (1996), Ch. 10). In particular, the multivariate regression model admits a natural conjugate prior, meaning that the prior and posterior distributions have the same analytic form. Moreover, there are analytic results providing the mapping from the parameters of the prior distribution to the parameters of the posterior distribution, which make transparent how the observed data is used to update prior beliefs.

Hoogerheide, Kleibergen and Van Dijk (2008) extend these tools to analysis of the linear IV regression model. Their key insight is that, since there is a one-to-one mapping between the structural and the reduced-form parameters, the familiar prior and posterior distributions for the reduced form parameters in the multivariate regression model induce well-behaved prior and posterior distributions over the structural parameters. They analytically characterize these distributions for the structural parameters for a number of particular cases, and provide an application to the AngristKrueger data. I follow their approach, but with a further extension to allow for prior uncertainty over the validity of the exclusion restriction.

# **3.3  The Prior Distribution**

I begin by specifying the same prior distribution over the correlation between the reduced-form error and the instrument, φ, that was used in the previous section of the paper, i.e. g(φ)∝ (1− φ2 )η over the support (-1,1) where η is a parameter that governs the strength of the prior belief that this correlation is zero. For the remaining parameters, I make the standard multivariate analog of the diffuse prior assumptions for these parameters in the OLS case. In particular, define ω11 = (1− φ2 ) ⋅ σu2 , −3 / 2 distribution for the elements of Ω be Ω . This prior corresponds to the Jeffrey's prior for the multivariate regression model when φ=0. And, as in the OLS case, this choice of prior distribution ensures that the Bayesian results mimic the frequentist ones for the 18
Mehrotra, S., and Parida, J. K. (2017). “Why is the LFP of women declining in India?” _World Development_ , Vol. 98, No. C, pp. 360-380.

Mishra, V; Nielsen, I., and Smyth, R. (2010). “On the relationship between female labour force participation and fertility in G7 countries: evidence from panel cointegration and Granger causality”, _Empirical Economics_ , Vol. 38, No.2, pp. 361-372.

Mishra, V., and Smyth, R. (2010). “Female labor force participation and total fertility rates in the OECD: New evidence from panel cointegration and Granger causality testing”, _Journal of Economics and Business,_ Vol. 62, No. 1, pp. 48-64.

Narayan, S., Nguyen, T., and Nghiem, X. (2021). Does Economic Integration Increase Female Labour Force Participation? Some New Evidence from Vietnam. _Bulletin of Monetary Economics and Banking_ , Vol. 24, No. 1, pp. 1-34.

Olivetti, C. (2014). "The Female Labor Force and Long-Run Development: The American Experience in Comparative Perspective," NBER Chapters, in: Human Capital in History: _The American Record_ , pp. 161-197, National Bureau of Economic Research, Inc.

Ostry, J.D., Alvarez, J. A., Espinoza, R. A., and Papageorgiou, C. (2018). "Economic Gains from Gender Inclusion: New Mechanisms, New Evidence," _IMF Staff Discussion Notes_ 2018/006, International Monetary Fund.

Pachón, M. and Aroca, M. (2017). “Effects of Institutional Reforms on Women’s Representation in Colombia, 1960-2014”, Latin American Politics and Society, Vol. 59, No. 2, pp. 103-121.

Pérez-Reyna, D. (2017). Historia del Banco de la República. Crisis de 1999. In Uribe, J.D. (ed.) _Historia del Banco de la República, 1923-2015_ . Banco de la República, Bogotá.

Pinzón de Lewin, P. and Rothlisberger, D. (1977). Participación política de la mujer. En M. León de Leal (Research director), _La mujer y el desarrollo en Colombia_ , Asociación Colombiana para el Estudio de la Población (ACEP), p. 38-64. Taken from: http://bdigital.unal.edu.co/41976/. Accessed on 15 May 2020.

Prüser, J. and Schlösser, A. (2020). On the Time-Varying Effects of Economic Policy Uncertainty on the US Economy. _Oxford Bulletin of Economics and Statistics_ . Vol. 82, pp. 1217-1237. https://doi.org/10.1111/obes.12380 Ribero, R., and Meza, C. (1997). Determinantes de la participación laboral de hombres y mujeres en Colombia: 1976-1995. Documento No. 063, _Archivos de Macroeconomía_ , Departamento Nacional de Planeación, Bogotá.

Romero-Prieto, J., and Meisel-Roca, A. (2019). Análisis demográfico de la Violencia en Colombia. _Cuadernos de Historia Económica_ , No. 50, Banco de la República.

33
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- Bernanke, Ben S.,and Alan S., Blinder (1988), "Credit, Money and Aggregate Demand," _AEA Papers and Proceedings,_ 78, pp. 435-39.

- , "The Federal Funds Rate and the Channels of Monetary Transmission",

- _American Economic Review,_ 82, pp. 901-21

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- of Monetary Policy? ", _NBER Working Paper_ (No. 6056).

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30
CAPE VERDE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE ELECTRA’s operational performance leads to substantial hidden costs compared to those of utilities in other African middle-income countries. Operational inefficiencies of power and water utilities cost Cape Verde a further $23 million a year, equivalent to 1.5 percent of GDP. The annual value of inefficiencies in the power sector (at $19 million) is substantially higher than for the water sector (at $4 million). The burden of utility inefficiencies in Cape Verde is substantially higher than for the middleincome peer group (figure 15). In both sectors, it is high distribution losses (as opposed to low revenue collection) that are primarily responsible for the weak performance.

**Figure 15. Cape Verde’s utilities are highly inefficient relative to middle-income peers**

a. Uncollected bills and unaccounted losses in the power sector, as a percentage of GDP b. Uncollected bills and unaccounted losses in the water sector, as a percentage of GDP <!-- Start of picture text -->
1.5 0.20<br>0.15<br>1.0<br>0.10<br>0.5<br>0.05<br>0.0 0.00<br>Cape Verde MIC Cape Verde MIC<br>collection inefficiencies collection inefficiencies<br>unaccounted lossses unaccounted lossses<br>% of GDP % of GDP<br><!-- End of picture text -->

_Source_ : Derived from Briceño-Garmendia, Smits, and Foster (2008). _Note:_ GDP = gross domestic product; MIC = middle-income countries.

In sum, Cape Verde dedicates an exceptionally high share of its national resources to the infrastructure sectors, reflecting in part the exceptionally high costs presented by the country’s challenging economic geography.

25
# **Appendix A: Additional Tables and Figures**

# **Table A.1: Overview of subjective scales estimated from panel data**

|**Author**|**Data**|**Welfare indicator**|**Subsample**|**Specification**|<br>**Weight give**|<br>**n to the additio**<br>**member**|<br>**nal household**|
|---|---|---|---|---|---|---|---|
||||||**2nd adult (1st**<br>**adult = 1)**|**1st child (in**<br>**2/1 adult hh)**|**2nd child (in**<br>**2/1 adult hh)**|
|||Stifti ith lif/||Pooled ordered logit|0,5|0.23/0.34|0.15/0.23|
|**Charlier (2002)***|GSOEP, 1984–91|asacon w e<br>Satisfaction with hh income|Sample of household heads|FE ordered logit(Das and van Soest(1999)approach)|0,43|0.20/0.28|0.10/0.16|
|||||RE ordered logit|0,5|0.26/0.39|0.19/0.28|
|**Sh 2003**|GSOEP 199299|Stifti ith hh i|Respondents who answered|Pooled ordered logit|0,34|0.17/0.30|0.08/0.14|
|**cwarze ()**|, –|asacon w ncome|at least twice are included|FE binarylogit|0,28|0.13/0.24|0.06/0.11|
|||||OLS|0,43|0.25/0.39|0.14/0.22|
|**Flt 2006****|SHP 19992002|Satisfaction with hh income|Households who answered at|Orderelprobit<br>|0,43|0.26/0.4|0.15/0.23|
|**aer ()**|, -||least twice are included|FE linear model|0,48|0.28/0.43|0.14/0.24|
|||Minimum IncomeQuestion||FE linear model|0,07|0.09/0.1|0.11/0.11|
|||Self-assessed financial|Individuals 18-80 yo<br>who are household heads or|FE ordered logit (Baetschmann et al. (2015) approach), men|0,15|1.12/1.64|0.68/1.22|
|**Bollinger et al.(2012)**|BHPS, 1991-2008|situation|the partner of the head with<br>or without minor children|FE ordered logit (Baetschmann et al. (2015) approach),<br>women|0,31|1.17/1.52|0.41/0.78|
|**Biewen and Juhasz**<br>**(2017)*****|GSOEP, 1999–2009|Satisfaction with hh income|Individuals≥17 yo living in<br>households with less than 6<br>members|Nonlinear FE ordered logit (based on Baetschmann et al.<br>(2015))|0,35|0,13|0,13|
||||Individuals≥18 yo, one- or|Pooled ordered logit|0.31-0.36|0.18-0.29/<br>0.29-0.37|0.10-0.27/<br>0.16-0.30|
|**Borah et al. (2018)******|GSOEP, 1984-2013|Satisfaction with hh income|two-adult-households with or|Nonlinear least squares|0.31-0.25|0.12-0.31|0.12-0.31|
||||without minor children|FE ordered logit (Baetschmann et al. (2015) approach)|0.26-0.15|0.16-0.13/<br>0.24-0.17|0.10-0.12/<br>0.15-0.14|

**Note:** GSOEP - German Socio-Economic Panel; SHP - Swiss Household Panel; BHPS - British Household Panel Survey

*Equivalence weights reflect period-specific equivalence scales based on satisfaction with income in case when the first child is 12 years old and the second child is 6 years old.

**Equivalence weights refer to the model with control variables

***Equivalence weights refer to OECD-type scale

****Equivalence weights reflect the cases without and with reference effect measured with household Mincer equation

36
# **_Empirical evidence_**

It is important to note up front that there are serious limitations that impede rigorous empirical analyses of the impacts of services trade reforms. Information on both policies and outcomes (performance) is patchy and often the best that can be done are crosssectional analyses. As a result of data constraints the dependent variable in analyses varies across the limited extant literature. Some studies focus on overall growth of economy-wide output, others on output per worker at the industry or firm level.

In a cross-section, cross-country regression analysis, Mattoo, Rathindran and Subramanian (2006) find that controlling for other determinants of growth, countries with open financial and telecommunications sectors grew, on average, about 1 percentage point faster than other countries. Fully liberalizing both the telecommunications and the financial services sectors was associated with an average growth rate 1.5 percentage points above that of other countries. Eschenbach and Hoekman (2006a) utilize three indicators of the “quality” of policy in banking, non-bank financial services and infrastructure, constructed by the EBRD spanning the period 1990-2004 to investigate the impact of changes in services policy, including liberalization, on economic performance over this period for a sample of 20 transition economies.<sup>17</sup> They find that changes in policies towards financial and infrastructure services, including telecommunications, power and transport, are highly correlated with inward FDI. Controlling for regressors commonly used in the growth literature, they conclude that measures of services policy reform are statistically significant explanatory variables for the post-1990 economic performance of the transition economies in the sample.

The positive association between policy reforms in services and inward FDI in services, and between TFP growth performance of downstream firms and FDI is perhaps the most robust finding to emerge from the limited empirical research on the impacts of services reforms. Arnold, Javorcik and Mattoo (2007) analyze the effects of allowing foreign providers greater access to services industries on the productivity of manufacturing industries relying on services inputs. The results, based on firm-level data 15
71

# **Table A1c**

## **Dependent Variable: Growth Rate of Real GDP Per Worker Educational Pillar Indicators**

|**Years : 1960 - 2000**|**Reg A1c.1 **|**Reg A1c.2 **|**Reg A1c.3 **|**Reg A1c.4 **|**Reg A1c.5 **|**Reg A1c.6 **|**Reg A1c.7 **|**Reg A1c.8**|**Reg A1c.9 **|**Reg A1c.10 **|**Reg A1c.11**|**Reg A1c.12**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Growth of Capital**<br>**Per Worker**|**0.2772*** **|**0.2277*** **|**0.2003*** **|**0.2095*** **|**0.2624*** **|**0.2131*** **|**0.1981*** **|**0.2298*** **|**0.2010*****|**0.1940*****|**0.2197*****|**0.2128*****|
|**Initial GDP (1960)**|**-2.3912*****|**-3.9242*****|**-4.2335*****|**-3.7197*****|**-3.7624*****|**-3.8508*****|**-4.1472*****|**-3.7342*****|**-3.9820*** **|**-4.2469*** **|**-3.6560*** **|**-3.9317*****|
|**HC25**||**0.4463**|**0.3377**|**3.8946***|||||||||
|**HC25 with**<br>**educational returns**|||||**-0.0175**|**-0.1512**|**1.0705**||||||
|**HC25 with extended**<br>**educational returns**||||||||**0.1877**|**-0.0081**|**1.2305**|**0.1115**|**1.5373***|
|**Sq of HC25**||||**-0.9925****|||||||||
|**Sq of (HC25 with**<br>**educational returns)**|||||||**-0.2284**||||||
|**Sq (HC25 with extended**<br>**educational returns)**||||||||||**-0.2433***||**-0.2845***|
|**Log (Educational Quality)**|||**0.6822**|**0.4324**||**1.263***|**1.2928**||**0.7790**|**0.6691**|||
|**Log (Educational Quality**<br>**extended)**|||||||||||**0.5522**|**0.4288**|
|**Educational Quality (BC)**|||||||||||||
|**Log (Patents)**|**0.1524** **|**0.1973*** **|**0.1880** **|**0.1984*****|**0.1229**|**0.1122**|**0.0932**|**0.2062*** **|**0.1977*****|**0.1860****|**0.2028*****|**0.1873****|
|**Phones**|**0.0041*** **|**0.0042*** **|**0.0040*** **|**0.0040*** **|**0.0048*** **|**0.0037*** **|**0.0040*** **|**0.0047*** **|**0.0042*****|**0.0043*****|**0.0041*****|**0.0043*****|
|**Constant**|**0.5058****|**0.1965**|**-1.8520**|**-4.066***|**1.2033****|**-2.8994**|**-4.2047**|**0.4789**|**-1.7686**|**-2.5648**|**-1.3626**|**-2.2720**|
|**R squared**|**0.6148**|**0.5414**|**0.5695**|**0.5842**|**0.6317**|**0.6548**|**0.6629**|**0.5411**|**0.5683**|**0.5768**|**0.5462**|**0.5578**|
|**Number of Countries**<br>*, **, *** denotes significance at th<br>Robust standard errors obtained|**107**<br>e 90, 95 and<br>using White's|**90**<br>99 percent c<br>correction fo|**84**<br>onfidence lev<br>r heterosceda|**84**<br>el respectively<br>sticity.|**52**<br>|**52**|**52**|**90**|**84**|**84**|**90**|**90**|
mainly, but not exclusively, on the topic for that cycle. She also compared these video clips with selected videos from a large “library” of videos from Ecuador that had been prepared for this purpose. Coach and teacher then had an in-person meeting, viewed the video and the relevant clips from the library together, and agreed on concrete actions that the teacher could take in her classroom.

# B. <u>Data</u>

We use the CLASS (Pianta, LaParo, and Hamre 2007) to measure the quality of teacher-child interactions. The CLASS is based on developmental and education theories that argue that the daily interactions between teachers and children are the “primary engine” for child development and learning in preschool and early elementary school (Hamre and Pianta 2007; Leyva et al. 2015).

The CLASS measures teacher behaviors in three broad domains: _Emotional Support_ , _Classroom Organization_ and _Instructional Support_ . The behaviors that coders are looking for in each dimension are quite specific—Appendix Table A1 in Araujo et al. (2016) gives an example. For each of these behaviors, the CLASS protocol gives coders concrete guidance on whether the score given should be ‘‘low’’ (scores of 1–2), ‘‘medium’’ (3–5), or ‘‘high’’ (6–7).

The CLASS has been widely used both for research and policy purposes in the U.S. Head Start grantees need a minimum score on the CLASS to be re-certified for funding, and several states have integrated the CLASS into their quality rating and improvement systems. The CLASS has also been used as a measure of teacher quality in research on several Latin American countries, including Chile (Leyva et al. 2015; Yoshikawa et al. 2015; Bassi, Meghir, and Reynoso 2020), Ecuador (Araujo et al. 2016; Campos et al. 2020), and Peru (Araujo, Dormal, and Schady 2019).

We carefully followed CLASS protocols to code the videos of treated and control teachers recorded at the end of 1<sup>st</sup> grade.<sup>5</sup> Specifically, each day of film was cut into 20-minute segments. We took the first four segments, and each segment was coded by two separate coders. Coders were blinded to treatment status. The correlation in the scores given by different coders is high—the inter-coder reliability ratio is 0.84, on average. On the other hand, there is more variation in the CLASS scores given to different segments from the same day—the inter-segment reliability ratio between the 1<sup>st</sup> (earliest) and 4<sup>th</sup> (latest) segments that were coded is only 0.35, on average. This pattern of results suggests there is substantial measurement error in the CLASS, principally because the behaviors teachers exhibit at any time are not a perfect measure of the behaviors they engage in over the course of a day, leave alone over the course of a school year.

> 5 The videos of teachers in the treatment group filmed every two weeks were not formally coded with the CLASS. Thus, we do not have data on the evolution of teaching practices between the beginning and the end of the school year for teachers who received coaching.

7