rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 11 Welfare implications of Tunisian unilateral and bilateral services trade reform initiatives ($US million)**

|_Reform scenario_||_Welfar_|_e in_||
|---|---|---|---|---|
||_Tunisia_|_EU_|_GAFTA_|_Rest of world_|
|Unilateral telecommunications reforms|21|-13|0|-1|
|Increased B2B ecommerce|31|4|1|0|
|Unilateral banking reforms|3|-2|0|0|
|Unilateral and bilateral reforms in the professions|17|2|0|-5|
|Open skies agreement with the EU|4|14|-2|-37|
|**Total**<sup>**a**</sup>|**77**|**4**|**-1**|**-42**|
|Full reform in these sectors|175|-18|-2|-42|

> **a** Individual entries may not add to total because of interaction effects.

_Source_ : FTAP model projections.

41
The analysis of returns to individual noncognitive skills in the analysis of individual countries presents a more complex picture compared with the pooled sample regressions, as patterns across countries differ regarding which skills are valued in the labor market and which skills obtain different returns for men and women. For example, while openness is strongly significant in the pooled sample of countries, its coefficient is significantly positive and varies for either men or women in only five countries. We find that taking risks increases log-earnings for men and women, but not in the same countries. Hostile attribution bias is negatively associated with log-earnings, except in Georgia, where hostile attribution bias raises men’s log earnings.<sup>64</sup> For cognitive skills, some of the results obtained in the pooled sample disappear or are significant only in a few countries. Similarly, skills which did not yield significant returns in the pooled sample of all countries are significant in country-specific estimations (e.g. grit has a negative return in Ghana). We consider that these differences support a composition effect when all countries are combined, but note _that our results relating to openness, risk-taking and hostile attribution bias are robust across several countries._

**<u>Table 9. Log earnings returns to noncognitive skills, adults aged 25-54</u>**

|||**M**<br>**OLS**|**en**<br>**Selectivity-**<br>**corrected**|**W**<br>**OLS**|**omen**<br>**Selectivity-**<br>**corrected**|
|---|---|---|---|---|---|
|**All countries**|Openness|0.055***|0.055***|0.031**|0.030**|
||Conscientiousness|0.005|-0.001|0.004|0.030**|
||Extraversion|0.018|0.015|0.018|0.034**|
||Agreeableness|0.013|0.015|-0.011|-0.021|
||Emotional stability|0.008|0.006|0.028**|0.041***|
||Grit|-0.018|-0.019|-0.005|0.011|
||Decision making (1)|0.005|0.004|0.017|0.015|
||Hostile attribution bias (1)|-0.030**|-0.031**|-0.028**|-0.023*|

64 Emotional stability is statistically significant in four countries, but with mixed positive and negative returns for men and women. Agreeableness and conscientiousness are statistically significant in two countries each, with no overlap in the countries for men and women and also a mix of positive and negative returns, and extraversion is statistically significant in just two countries and is positively associated with hourly earnings in both. Finally, grit is statistically significant in two countries, with no overlap in the countries for men and women and mixed positive and negative returns.

48
The results from the audit study show that reference letters are valuable to both job seekers and hiring firms. For the _same_ applicant, attaching a letter increases the probability of receiving a response from the firm from 4.2% to 6.7% (a 60% increase) and the rate of interview requests from 2.4% to 3.9% (a 62% increase). The overall effect is driven by female applicants.

As reference letters become more common, applications attaching a letter may be less distinctive. We shed some light on this by randomly varying the share of applicants for whom we submit reference letters. We find no evidence that increasing the share of reference letters sent to a given vacancy affects the letter’s impact.

Firms use the information provided by past employers to update their beliefs of applicants and are more likely to respond to applications with positive letters. In addition, we find that reference letters in which the former employer gives the highest rating in every category are ineffective. We interpret this as a perceived lack of credibility, which harms employment prospects.

Our design includes an aptitude test that job seekers complete at the Labour Centre. This provides a measure of ability that is observable to us but not to the employer. Using the applicant’s score on this test, we show that reference letters lead firms to select candidates of higher ability. Performing one standard deviation higher on the aptitude test increases the likelihood of an employer response by 2.6 percentage points (a 62% increase) for applications that include a reference letter, as opposed to 0.6 percentage points (a 15% increase) for those not including a letter. This improvement in the firm’s capacity to screen better applicants suggests that letters are informative of workers’ unobserved ability. Indeed, we show that ratings from previous employers are highly correlated with aptitude scores, even after controlling for information that can be easily observed from the resume or school transcripts.

In sum, the results from the audit study (Experiment 1) show that reference letters can reduce information asymmetries, especially for women, and improve the employment prospects of higher ability candidates. Although our design cannot explicitly test for general equilibrium effects, theory predicts that the identification of higher ability workers should increase firm demand (Pissarides, 1985).

Since Experiment 1 uses employers’ callback as the main outcome, it does not estimate employment effects. Moreover, it abstracts from job seekers’ behavioral responses, as applications are submitted on their behalf. While these are limitations that are common to most audit studies (e.g. Bertrand and Mullainathan, 2004; Belot et al., 2016), we 2
purpose. Estimation results show that the poverty decline between 2009/10 and 2011/12 is not severely over-estimated (or equivalently, the design-based poverty estimate using the 2011/12 survey round is practically comparable to those from previous rounds).

Our methods are promising of richer analysis for welfare dynamics that can further exploit the richness of the NSS data. For example, future research can provide more disaggregated analysis within each state, and analyze either more survey rounds to study transition trajectories between more than two periods or survey rounds that are farther apart to investigate longer-term transitions. Another direction is to make better use of the “thin” rounds, in addition to the “thick” rounds, to build a more comprehensive picture of these dynamics over time.

27
Having compared geospatial and census-based small area estimates of head count poverty in Burkina Faso, this section describes the generation of experimental small area estimates of head count poverty for all five countries. Estimates are produced using model (1) with geospatial covariates as described in Table A1. Because the estimates we produce are experimental and not official, the results we present do not identify target areas in the focus countries. Model-based estimates under model (1) with geospatial covariates are compared to direct estimates both at target area and at aggregate, regional levels. In addition, MSE estimates of the model-based estimates are compared to the estimated variances of the direct estimates.

Figure 4 shows the relationship between the EBP estimates under model (1) and the direct estimates at the target area level. In general, model-based estimates are strongly correlated with direct estimates and exhibit less variation than direct estimates, as one would expect due to the impact of shrinkage.

Figure 4: Direct estimates vs. model-based (under the unit context model with weighting following Guadarrama et al., 2018) estimates at target area level.

<!-- Start of picture text --> Burkina Faso Chad Guinea<br>1<br>.75<br>.5<br>.25<br>0<br>0 .25 .5 .75 1 0 .25 .5 .75 1 0 .25 .5 .75 1<br>Mali Niger<br>1<br>.75<br>.5<br>.25<br>0<br>0 .25 .5 .75 1 0 .25 .5 .75 1<br>Direct estimates<br>Geospatial model estimates<br><!-- End of picture text --> Figure 5 shows the relationship between the EBP estimates under the weighted and unweighted version of model (1). The results show that weighted model-based estimates are systematically higher than the unweighted estimates, while the unweighted estimates are closer to the direct estimates. This may be due to the approach to weighting taken by the Guadarrama et al. (2018) method. In future research it will be interesting to compare the current estimates against estimates obtained by using other weighting methods, including the method that accounts for informative sampling proposed by Cho et al. (2024). For the remaining of this section, we will use the term model-based estimates to refer to those obtained under the unit context model using the weighting proposed in Guadarrama et al. (2018).

17
- Peters, G. P., Marland, G., Le Qu ́er ́e, C., Boden, T., Canadell, J. G., and Raupach, M. R. (2012). Rapid growth in CO2 emissions after the 2008-2009 global financial crisis. _Nature Climate Change_ , 2(1):2–4.

- P ̈otscher, B. M. and Prucha, I. R. (1991). Basic structure of the asymptotic theory in dynamic nonlineaerco nometric models, part i: consistency and approximation concepts. _Econometric Reviews_ , 10(2):125–216.

- P ̈otscher, B. M. and Prucha, I. R. (1997). _Dynamic Nonlinear Econometric Models_ . Springer Berlin Heidelberg, Berlin, Heidelberg.

- Rothenberg, T. J. (1971). Identification in Parametric Models. _Econometrica_ , 39(3):577– 591.

- Sch ̈olkopf, B. and Smola, A. J. (2001). _Learning with kernel: Support Vector Machines, Regularization, Optimization and Beyond_ .

- Shahbaz, M., Shafiullah, M., Papavassiliou, V. G., and Hammoudeh, S. (2017). The CO2–growth nexus revisited: A nonparametric analysis for the G7 economies over nearly two centuries. _Energy Economics_ , 65:183–193.

- Sin, C.-Y. and White, H. (1996). Information criteria for selecting possibly misspecified parametric models. _Journal of Econometrics_ , 71(1):207–225.

- Sleeter, B. M., Sohl, T. L., Wilson, T. S., Sleeter, R. R., Soulard, C. E., Bouchard, M. A., Sayler, K. L., Reker, R. R., and Griffith, G. E. (2012). Projected Land-Use and LandCover Change in the Western United States. _Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Western United States_ .

- Smarzewski, R. (1986). Strongly unique best approximation in Banach spaces. _Journal of Approximation Theory_ , 47(3):184–194.

- Stern, D. I. (1998). Progress on the Environmental Kuznets Curve? _Environment and Development Economics_ , 3(2):173–196.

- Stern, D. I. (2004). The Rise and Fall of the Environmental Kuznets Curve. _World Development_ , 32(8):1419–1439.

- Stern, D. I., Common, M. S., and Barbier, E. B. (1996). Economic growth and environmental degradation: The environmental Kuznets curve and sustainable development. _World Development_ , 24(7):1151–1160.

- Tallis, H., Hawthorne, P., Polasky, S., Reid, J., Beck, M., Brauman, K., Bielicki, J., Binder, S., Burgess, M., Cassidy, E., Clark, A., Costello, C., Fargione, J., Game, E., Gerber, J., Isbell, F., Kiesecker, J., McDonald, R., Metian, M., Molnar, J., Mueller, N., O’Connell, C., Ovando, D., Troell, M., Boucher, T., and McPeek, B. (2017). Meeting Economic Growth and Multiple Environmental Elements of Sustainable Development. _Forthcoming_ .

34
surveys of relatively large manufacturing firms generalize to rural areas, where firms tend to be smaller. Yet, diversification beyond agriculture is often considered a promising pathway out of poverty for poor rural economies and there is a widespread belief that small enterprises may play an important role in especially the early stages of diversifying beyond agriculture (see the discussions in Barret et al, 2001, Lanjouw and Lanjouw, 2001, Reardon et al., 2000).

To address these lacunae in the literature, the World Bank has recently launched five rural investment climate pilot surveys. These surveys have demonstrated that rural non-farm enterprises in developing countries are typically small, not very profitable and unlikely to grow (World Bank, 2005b), and point towards market fragmentation and weak institutions as a plausible explanation for the dismal performance of rural non-farm firms. Fragmentation and institutions are also considered a key part of the explanation for Africa’s poor economic performance (Collier and Gunning, 1999, Collier and Venables, 2008). Low levels of economic density and interaction may lead to small, diffuse pockets of demand, which in turn result in small, localized production (Tybout, 2000). More subtly, thin markets may undermine enterprise performance by raising the cost of capital, disincentivizing investment and innovation, and increasing the scope for opportunistic behavior. Moreover, the average cost of supplying public goods and infrastructure, such as electricity, to thin markets is typically high since the provision of infrastructure and public goods are often subject to economies of scale (Collier and Venables, 2008). Rural enterprises are thus likely to operate in less favorable, fragmented business environments.

This paper assesses the hypothesis that market fragmentation and the investment climate are the key constraints to the development of the rural-non-farm economy by comparing and contrasting the performance of rural non-farm enterprises in Ethiopia with their urban counterparts using the most recent rural and urban Ethiopian investment climate surveys, the Rural Investment Climate Survey (RICS) and the Productivity and Investment

-3-
_Figure 1: Performance criteria_ <!-- Start of picture text --> A B<br>Non-referral failure = A/(A+B)<br>Referral threshold (Pseudo-PMT) Non-referral success = B/(A+B)<br>Referral failure = C/(C+D)<br>Referral success = D/(C+D)<br>D C Non-referral of eligible<br>cases = A/(A+D)<br>Referral of ineligible cases = C/(B+C)<br>Predicted consumption from full PMT<br>Absorbed by full PMT Rejected by full PMT<br>Not referred to MoLSA<br>Eligibility threshold (Full PMT)<br>Predicted consumption from pseudo-PMT<br>Referred to MoLSA<br><!-- End of picture text --> Table 6 exhibits the performance/referral statistics for the North VM and SEVAT models for a MoLSA’s program targeting bottom 18 percent nationally. Minimizing the non-referral rate of eligible cases will prevent leaving cases that are in chronic poverty without state-distributed benefits after the humanitarian support stops. Nevertheless, the constraint is the national SP system’s ability to screen all the referred cases on time and take them into the system. Too many referrals may cause a backlog, while not all the referred cases will qualify for the MoLSA benefits.

As seen in the table, the rate of non-referral of eligible cases falls dramatically as the referral confidence level is relaxed, while the referral rate of non-eligible cases rises but not by the same extent. There is a significant heterogeneity in the accuracy of the referral/non-referrals across the confidence levels. Decreasing confidence level implies increasing the referral threshold and thus referring more beneficiaries. Depending on the correlation between the pseudo-PMT and full-PMT scores, we may decrease the referral failure rate by lowering the confidence level or increasing threshold but may increase referral failure rates. Statistics for the Kurdistan and Center-South models are reported in Table MM A 3. WW _Table 6: Performance criteria for VM and SEVAT north models for MoLSA program targeting the poorest 18 percent_

|Referral confidence level =|90%|80%|70%|60%|50%|
|---|---|---|---|---|---|
|VM||||||
|Non-referral failure|23.1|21.0|20.5|17.3|15.9|
|Non-referral success|76.9|79.0|79.5|82.7|84.1|
|Referral failure|32.1|32.4|34.7|29.3|31.1|
|Referral success|67.9|67.6|65.3|70.7|68.9|
|Non-referral of eligible cases|91.8|79.5|76.2|61.5|54.5|
|Referral of non-eligible cases|1.2|3.2|4.1|5.2|6.6|

13
PPR Working Paper Series

|Title|Author|Date|Contact|
|---|---|---|---|
|WPS93The Effects of Education,<br>Health and<br>Social Seculity on Fertility in<br>Developing<br>Countries|Susan H. Cochrane|September1988|S.Ainsworth<br>31091|
|WPS94The World Bank's Population<br>Lending<br>and Sector Review|George B. Simmons<br>Rushikesh Maru|September1988|S.Ainsworth<br>31091|
|WPS95International<br>Trade and Imperfect<br>Competition: Theory and Application<br>to the Automobile<br>Trade|Junichi Goto|September1988|J. Epps<br>33710|
|WPS96 The Private Sector and Family Planning<br>in Developing<br>Countries|Maureen A. Lewis<br>Genevieve Kenney|September1988|S.Ainsworth<br>31091|
|WPS97Export-Promoting<br>Subsidies and What<br>to Do About Them|Richard H. Snape|September1988|J. Sweeney<br>31021|
|WPS98Diversification<br>in Rural Asia|Agriculture<br>and Rural<br>Development<br>Staff|October1988|S.Barghouti<br>38408|
|WPS99 Trade Policies and the Debt Cr,sis|Sam Laird<br>Julio Nogues|September1988|S.Torrijos<br>33709|
|WPS100 Public Infrastructure<br>and Private<br>Sector Profitability<br>and Productivity<br>in Mexico|Anwar Shah|September1988|A. Bhalla<br>60359|
|WPS1O1 Measuring the Impact of Minimum Wage<br>Policies on the Economy|Luis A. Riveros<br>Ricardo Paredes|October1988|R. Luz<br>61762|
|WPS102Effects of the Multifibre Arrangement<br>(MFA) on Developing<br>Countries:<br>A Survey|Junichi Goto|||
|WPS103 Macro Shocks and Industrial<br>Portfolio<br>Responses: An Econometric<br>Model<br>for LDCs|James Tybout<br>Taeho Bark|||
|WPS104Financial Crises in Developing<br>Countries|Manuel Hinds|||
# 4 Empirical Strategy

We begin by estimating the impacts of offering NEP-B and NEP-I on parental behaviors and child development, what is usually referred to as the intention-to-treat parameter (ITT). Impacts on child outcomes are estimated using data at the child level, while impacts on parental outcomes such as behaviors, beliefs and well-being are estimated using data at the household level.

We use the following specification: where Y is the outcome of interest at endline, which varies by child / household i, in health center, c. NEP is an indicator which takes value 1 if the caregiver was invited to participate in NEP-B, and NEP is an indicator which takes value 1 if the caregiver was invited to NEP-I. X is a set of control variables including children’s attributes such as sex and age, household characteristics such as family structure, household’s per capita income, caregiver’s education, and the outcome of interest at baseline. Γ are health center fixed effects which capture unobservable differences in program quality. Coefficients β and β7 are the ITT parameters of interest.

Since participation in NEP was voluntary, we complement the ITT analysis with the estimation of instrumental variables (IV) estimates of the impacts of NEP. If these impacts are heterogeneous in the population, these estimates are usually interpreted as Local Average Treatment Effects (LATE). The first and second stage equations for this estimator are: where random assignment dummies NEP and NEP are used as instrumental variables for participation in each treatment arm, T and T (Basico and Intensivo, respectively).

## 5 Results

This section presents the estimated impacts of NEP-B and NEP-I on child outcomes and parenting attributes. Because the endline survey was conducted between 30 to 36 months after the end of the interventions for households in our sample, these can be interpreted as medium-term effects of the program. In this section we focus on the simplest specification where we only control for health center fixed effects. In tables A1-A4 in Appendix 2 we present specifications that include age, gender, household characteristics, maternal cognition and personality traits, and baseline outcomes as controls. Our results are robust to the inclusion of controls. We supplement standard inference procedures with multiple hypothesis testing (Romano and Wolf 2005), where all outcomes, including child development, parental behaviors and parental beliefs are considered simultaneously. Appendix 4 discusses in detail the construction of the final measures of child development, parenting, and beliefs used to estimate impacts in this section.

13
regressions because our main focus is not on how gender inequality affects or correlates with economic growth but on how the correlation between gender inequality and growth varies with the income level of countries. The interaction term is akin to a difference-in-difference estimation strategy that suffers less from spurious correlation than a cross country regression of levels on levels. For example, one might argue that income inequality may be correlated with certain cultural factors that may also have a direct effect on growth. However, there is little reason to believe why these cultural factors should have an impact on growth that differs between rich and poor countries. Even so, to further raise our confidence against the omitted variable bias problem, we control for a number of variables that are known to be correlated with gender inequality and/or growth. The choice of controls is motivated by existing studies and is as follows.

To begin with, we follow Dollar and Gatti (1999) and control for differences in civil liberty across countries. Civil liberty could be important for economic growth by allowing individuals to freely explore their talent. Civil liberty is also likely to be a useful proxy measure of the broader institutional environment for the protection of property rights against public and private expropriation. That civil liberty could be correlated with gender equality is more likely than not. Countries that value civil liberty are also likely to be particularly concerned about ensuring that females are not disadvantaged in political empowerment, labor market, etc. To rule this source of omitted variable bias with our main results, we control for civil liberty using year 2005 values of Freedom House’s civil liberty index ( _Civil liberty_ ) as our first control. The index runs from 1 (most free) to 7 (least free).

Next, cultural diversity or differences along cultural lines within a country are often the source of conflict in a country and this is likely to have an adverse effect on growth (Easterly and 9