rafmacalaba/datause-encoder-data huggingface dataset:
# TABLE OF CONTENTS

|1. Introduction|1|
|---|---|
|2. Analytical Framework|5|
|The Basic Structure|5|
|First-Order Conditions|11|
|Comparative Statics|13|
|- Price Elasticities of Schooling and Health|14|
|- Income Elasticities of Schooling and Health|15|
|- Sector-Share Elasticities of Schooling and Health|15|
|Introducing Gender Differences|17|
|- Differences between Industry and Services|17|
|- Health Investments, Rates of Return and Longevity|19|
|3. Estimation Results|23|
|Definitions of Variables|23|
|Disclission<br>of Regression Results|26|
|- Primary School Enrollment Regressions, 1965 and 1987|28|
|- Secondary School Enrollment Regressions, 1965 and 1987|28|
|- Life Expectancy (Health) Regressions, 1965 and 1987<br>- Fixed Effects Regressions (1965-1987)|29<br>30|
|Su-mmary<br>Statistics|33|
|Correlations Between Important Variables|34|
|Primary School Enrollment Cross-Section Results|35|
|Secondary School Enrollment Cross-Section Results|36|
|Life Expectancy (Health) Cross-Section Results|37|
|Secondary School Fixed-Effect<br>Results|38|
|Life Expectancy (Health) Fixed-Effect Results|39|
|4. Policy Implications and Conclusions|40|
|References|42|

Helpful discussions with Ken Chapman, Govind Hariharan, Barbara Herz, Ravi Kanbur, Randy Lutter and T. Paul Schultz are gratefully acknowledged. We also benefited from comments by participants at a World Bank seminar.
<!-- Start of picture text --> Tube 3.2 Reslts or slected seaMes or<br>aid, savin nud investmn<br>Data Coefficient<br>-Savings<br>-Griffi (1970) Cross-section: 32 LDCs -0.73<br>: - -: : -:- - ~~~~~~~~~~~~~~~~~(6.64)<br>Time series: Colombia 0k814<br>(2,90)<br>-Weiskopf (1972) Pooled: 44 LDCs, 1950s and early -0.23<br>1960s (5.30)<br>Gupta and Is1am (1983) Cross-section: 52 LDCs -0.47<br>. . : : - - - ~~~~~~~~~~~~~~~~(n.a.)<br>'Morisst (1989) Timn series: Argentina -0.98<br>(0.91)<br>Lvy -(1988) :-Aggregatecross-section for sub- 1.08<br>Sah-ara Africa (4.40)<br>s-^Bhalla (1990) Time series: Sri Lanka 0.94'<br>WVhite (1992d) -Time series: Sri Lanka<br>-Public investment 0.32'<br>- =<Notes: 'cszickics.'Coefficient from White (1992d) isthat-from :te levels regression fior<br>.n ercorion mode!, i.e.ft is--the long-runirelatonship. Figres--in parentheses are<br>>~~~~~~i :;esl-ti.--:-::---:--: ipf -i<br>abiso-luditetsttis<br><!-- End of picture text --> Sources:-asshown-in,table.; he does not look at the dynamic aspects of his model - that is the possibility that future savings may be higher despite current displacement.

Analysis of theories of consumption (the converse of savings) are, of course, rooted in more complex formulations than the simple savings function used by Levy (and others). Gupta and Islam (1983) estimate a simultaneous model of savings and growth which contains a number of demographic relationships - but the model is void of economic analysis.

The aid-savings relationship is a debate very much in need of some attention to quality, rather than quantity, of analysis. One direction for such attention is to analyze the relationship at

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understandable school performance information, they reallocated resources and school enrollment for higher-performing children.

Taken together, the existing research highlights how parents utilize indicators of their child’s abilities, to inform educational investments. Therefore, understanding parental perceptions of children’s developmental abilities, including intelligence, is important for informing effective parenting programs; however, research in low-income contexts is limited. Existing studies from LMICs primarily focus on perceptions of weight and nutritional status (Wang, et al., 2018, Bertrand & Walmus, 1983, Hochdorn, et al., 2014, Mwangome, et al., 2015, Roy, et al., 1993, Syahrul, et al., 2017), and in general, find parental perceptions in low-resource settings do not align with objective measures. These misperceptions may lead to behaviors and practices that have long-term negative consequences for child health and development.

The present study is based in Madagascar, a low-income country where 77.8% of the population lives on less than $1.90 a day (World Bank, 2017) and where 49.2% of children under five years are stunted (WHO, 2017). The government’s nutritional program has been in effect since 1999 and is founded on frontline community health workers hosting monthly growth monitoring sessions, during which they also provide mothers of children under five years with nutrition, feeding, and hygiene education. To examine the difference between Malagasy caregivers’ perceptions of child intelligence and developmental abilities, we asked caregivers to rank their child’s intelligence in comparison with other children in the community, and compared this ranking with how children scored on the Ages and Stages Questionnaire: Inventory (ASQ-I), an assessment of children’s developmental abilities across multiple domains including gross and fine motor, language, problem-solving, and social skills. In Malagasy, the term ‘intelligence’ translates to ‘sharp mind’ and caregivers generally understand it as a broad concept of child development 4
- McGuire, Greg and Michael Schuele (2000), “Restrictiveness of International Trade in Banking Services,” in Findlay, Christopher and Tony Warren eds., _Impediments to Trade in Services: Measurement and Policy Implications_ , London; Routledge, 201-214.

- McGuire, Greg, Michael Schuele, and Tina Smith (2000), “Restrictiveness of International Trade in Maritime Services”, in Findlay, Christopher and Tony Warren eds., _Impediments to Trade in Services: Measurement and Policy Implications_ , London; Routledge.

- Nordhus, Hildegunn Kyvik, Massimo Geloso Grosso and Enrico Pinali (2008), “Market Structure in the Distribution Sector and Merchandise Trade,” OECD Trade Policy Working Paper No. 68.

- Nguyen- Hong, Duc (2000), “Restriction on trade in professional services,” Productivity Commission Staff Research Paper, AusInfo, Canberra, August. Available at: <u>http://www.pc.gov.au/__data/assets/pdf_file/0016/8080/rotips.pdf</u>

- Shepotylo, Oleksandr and Volodymyr Vakitov (2012), "Impact of services liberalization on productivity of manufacturing firms: evidence from the Ukrainian firm-level data," World Bank Policy and Research Working Paper Number 5944, January.

- Stern, Robert (2002). “Quantifying Barriers to Trade in Services,” in Bernard. Hoekman, Aaditya Mattoo and Philip English (eds.), _Development, Trade and the WTO_ , Washington D.C.: The World Bank, 247-258.

- Walsh, Keith (2006), “Trade in Services: Does Gravity Hold? A Gravity Model Approach to Estimating Barriers to Services Trade,” IIIS Discussion Paper Number 183. Available at: https://www.tcd.ie/iiis/documents/discussion/pdfs/iiisdp183.pdf

- Warren, Tony (2000a), “The identification of impediments to trade and investment in telecommunications services”, in Findlay, Christopher and Tony Warren (eds.), _Impediments to Trade in Services: Measurement and Policy Implications_ , London: Routledge.

- Warren, Tony (2000b), “The impact on output of impediments to trade and investment in telecommunications services,” in Findlay, Christopher and Tony Warren (eds.), _Impediments to Trade in Services: Measurement and Policy Implications_ , London: Routledge.

- Whalley, John (2004), “Assessing the Benefits to Developing Countries of Liberalization of Services Trade,” _The World Economy_ , Vol. 27(8), 1123-1153.

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Candidate variables were selected from the NHBPS 2014/2015 based on a review of the existing literature, hypothesized correlations to consumption, and the following criteria important for the field implementation of the PMT (Coady, Grosh, and Hoddinott 2004; Brown, Ravallion, and Van de Walle 2018; Sebastian et al. 2018):

1. It should be feasible and cost-effective to collect the required data on a variable for a significant share of the population in a timely manner.

2. Each variable in the PMT should be easy to measure and observe.

3. Each variable in the PMT should be difficult for the household to manipulate.

While it may be the case that surveyed household consumption is a more accurate measure of welfare, it is often difficult and costly to obtain such information from a large number of program applicants.<sup>10</sup> Additionally, if variables in the PMT are not observable and verifiable, households may have an incentive to hide or understate their assets and wealth in order to qualify for public assistance.

Based on these criteria, a broad range of candidate variables were selected from the household survey covering household head characteristics, household population characteristics, asset ownership, and employment. Table A1.1 in Annex 1 provides a list of the candidate variables included in this analysis along with their definitions. Summary statistics of for all candidate variables are presented in Table A1.2 in Annex 1.<sup>11</sup> In this paper, we propose a PMT and show how the use of a PMT could improve program targeting. The final design of the PMT targeting system and final administration, including the final choice of variables and survey forms, should be developed in consultation with the government agency tasked with managing the targeting system. Certain variables may be more easily manipulatable or difficult to verify in different country contexts.

Guided by the expected program design in Sudan and robustness analyses, we exclude state and area (e.g., rural and urban) indicator variables, as well as employment variables, since welfare programs in Sudan will be administered at the state level,<sup>12</sup> and employment information is often difficult to obtain and potentially manipulatable by households. The impact of these decisions on the PMT are presented in Section (5).

> 10 For example, the purchases and consumption module in the Sudan NHBPS 2014/2015 is approximately 20 pages long and consists of hundreds of questions.

> 11 A small percentage of observations were missing for several variables. We explore model results for dropping missing observations and imputation. We found minor changes and high parameter stability between the two methods (Annex 2, Table A2.1). Therefore, all missing observations are imputed based on modes and means, where applicable.

> 12 Geographic targeting will be a first tool to identify areas with a high prevalence of potential beneficiaries, e.g., areas with high poverty rates or nutritional risks, and allocate program budget shares based on program goals.

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the top manager to be a woman as well. More closely related to this study, Amin et al. (2015) use firm-level data across 58 economies and find that the presence of non-discrimination clauses in hiring increases women’s employment. Amin et al. (2016b) also uses firm-level data to uncover the effects of mandated paternity leave on women’s employment. This study builds on this strand of research by combining micro firm-level data across countries to explore the link between overall gender discrimination in laws and goes beyond women’s employment by exploring indicators of women’s empowerment - women’s presence in top managerial positions and ownership of firms. The use of micro firm-level data allows the analysis to account for firm-level heterogeneities given that several firm-level characteristics such as sector, size, access to finance and so forth can influence the nature of employment, firm ownership, and firm management. Furthermore, the use of firm-level data alleviates simultaneity bias as it is unlikely that an individual firm can affect overall country-level institutions (Paunov, 2016).

In summary, this study makes the following contributions to the literature. First it utilizes an aggregate measure of legal gender discrimination and links it with a set of labor market outcomes for women, including employment, managerial positions, and firm ownership. Second, the study conducts a global analysis of 104 economies using rich micro-level firm data that follow the same methodology and use the same survey instrument across all countries to allow for comparisons across economies. Firm-level data allow the analysis to account for several firm-level heterogeneities, including firm sector and size. Finally, the study also attempts to uncover the pathways by which systematic legal gender discrimination that permeates through several laws can disempower women in the labor market.

The paper is organized as follows. Section 2 provides the conceptual underpinnings of the analysis, section 3 provides the data and empirical strategy, section 4 presents the results. Robustness checks 5
**Table 6. Financial Structure, Export Sector Concentration, and Second Year Survival**

||_Export Con_<br>Herfindahl-|_centration_<br>Share of top 5%||_Second Year Surviv_|_al_|
|---|---|---|---|---|---|
||Hirschman Index|Exporters|Baseline|Smaller Exporter|s<br>Bigger Exporters|
||(1)|(2)|(3)|(4)|(5)|
|||_Panel A_|_: Banks vs. Stock_|_Market_||
|(Bank Credit / (Bank Credit + Stock))*ExtFin|-0.129***|-0.073**|0.027|-0.065|0.039|
||(0.035)|(0.037)|(0.026)|(0.074)|(0.028)|
|Observations|17,561|15,098|11,654|3,221|8,344|
|R-squared|0.453|0.441|0.325|0.316|0.373|
|||_Panel B: Fore_|_ign Banks vs. Do_|_mestic Banks_||
|(Foreign Bank / Total Bank Assets)*ExtFin|0.026|-0.070**|-0.014|-0.074**|0.028|
||(0.027)|(0.028)|(0.019)|(0.035)|(0.028)|
|Observations|17,233|13,888|10,432|4,681|5,738|
|R-squared|0.413|0.440|0.283|0.252|0.341|

Note: This table reports the effects of financial structures on concentration of the exporters in 2-digit ISIC industries (columns 1-2), and on the second year survival rate (columns 3-5). Export concentration is captured by the Herfindhal index (column 1) or the share of export sales of the top 5% of exporters (column 2). The financial structure is captured by the ratio of bank credit to the sum of stock market capitalization and bank credit (panel A); the share of foreign bank assets in total bank assets in the country (panel B). In columns 4 and 5, smaller exporters are those with export values below the median, and bigger exporters are those with export values above the median. All regressions include FinDev*ExtFin, country-year fixed effects and industry-year fixed effects, where FinDev is measured as all credit over GDP to control for total financial development. Regressions in panel B also include Bank Concentration*ExtFin to control for the concentration of the banking sector. Robust standard errors, clustered at the country-year level, are in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% level, respectively.
agriculture in wage employment), data become more scarce. In order to maximize the data that enter into each calculation, the estimates are made in a sequential way. So for each income group or region: First, aggregate population data to get group totals. Second, estimate the share of the working age population that is in the labor force. Third, estimate the share in the labor force that is unemployed. Fourth, estimate the share of the work force that is in each sector. Fifth, within each sector, estimate the share that is in wage employment. The following section describes these steps in more detail.

# 2. Methodology

## 2.1. The working age population and activity rates

Population data for 1995 are obtained from age specific population estimates from the ILO. These are ILO updates to the data presented in the _ILO Estimates and Projections 1950-2025_ [ILO 1986]2 and are the latest available as of December 1994. The working age population for each country is calculated as the total population minus ages 0-14, and ages 65 and over. The labor force (ages 15-64) is calculated as the sum of the Economically Active Population for each age group between 15 and 64. These are derived by ILO from age-specific estimates of activity rates which are reported in _ILO Estimates and Projections 1950-2025._ Country level data for working age population, labor force (ages 15-64) and activity rates are given in Table 1. Activity rates are calculated as the share of the working age population that is economically active.

Defining who is economically active is not an uncontroversial task. Typically, women who 2According to personal communication with someone at the ILO, the "updated" nature of the data is new population estimates from the United Nations.

2
represents the short-term flexibility of the production system with inventories, while CGE models (e.g., Rose et al., 2005) use constant-elasticity substitution depending on price levels.

# _3.2 Data and parameters_

The economic data is the same than in H08, with two major differences. First, as already stated, the housing service is now included in the table. Second, new estimates of the amount of productive capital in each sector have been produced. In H08, a unique ratio of sector productive capital to sector value added was used, in all sectors. In this new version, US-scale data on the amount of private and government capital, from the Bureau of Economic Analysis, are used to assess national sector-scale ratios of productive capital to value added. These ratios are then used for Louisiana, assuming that the structure of each economic sector is similar in Louisiana and in the US as a whole. These ratios are reproduced in Tab.1.

No data are available on the distribution of damages to productive capital among sectors. The Congressional Budget Office produced estimates in broad categories: 17 to 33 billion US$ in housing; 5 to 9 US$ in consumer durable goods; 18 to 31 US$ in the energy sector; 16 to 32 US$ in other private sector businesses; 13 to 25 US$ for the government. Taking the conservative side of these estimates, these losses were distributed among the different sectors according to their respective size and other information, to assess productive capital losses in all sectors. Housing losses and consumer durable goods were included in the financial and real estate sector; Energy sector losses were distributed in the utilities and mining and extraction sectors ($5 billion and $15 billion, respectively); Government losses were distributed in the energy, transport, and government sectors; Other private-sector losses were distributed in all other sectors, following their respective productive capital. All losses are reproduced in Tab. 1. Aggregated direct losses amount to $63 billion, and are significantly lower than estimates used in H08 ($107 billion).

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_DICCb_<sup>_m,whereIRS_</sup> _s_<sup>_sandDICC_</sup> _b_<sup>_mbind,andthecompensationschemeischaracterizedby:_</sup> _and cooperation is achievable ∀ δ ∈_ <u>[</u> _<u>δss,</u>_ 1) _, where_ _<u>δss</u>_ = (1+ _c_ <u>(</u> _<u>q</u>_ <u>)+</u> _aau_ )(+ _Va_ ( _aqV_ ) _−_ <u>(</u> _<u>qπ</u>_ <u>))</u><sup>_._</sup> Proposition 8 presents the total payment and its structure given a spiteful seller and a self-interested buyer. It is easy to see that all payments are increasing in the seller’s spite coefficient. That is the more spite the seller feels towards the buyer, the higher the total compensation needs to be for the seller to participate and engage in a long-term relationship. Although the fixed payment is also increasing in the spite coefficient, giving the assumptions of the model, it equals zero while the bonus contains all payments.

Additionally the seller’s spite is also reflected in the long-term cooperation opportunities. It is easy to see that with an spiteful seller, the discount factor is directly related _s_<sup>_s_</sup> to the parameter representing spite as _∂δ /∂as >_ 0. The more spiteful the agent is, the narrower the range of discount factors that sustains cooperation because the threshold for cooperation is higher. The intuition in this case is similar to the case of a spiteful buyer. An spiteful seller gets a lower per-period payoff when compared to payoffs derived only from material resources. Therefore, she needs a higher payment to compensate the utility loss she gets from the buyer’s material gains. As a consequence the buyer gets lower payoffs from the relationship and as a consequence a higher discount factor is needed.

If parties cooperate, the seller gets a higher material payoff than if she were purely selfinterested. However, the seller receives the same discounted value of her outside opportunity (44) because of the disutility derived from spite. The buyer gets the discounted value of the net social surplus adjusted for the seller’s spite (43). The buyer receives lower material payoff than if the seller were self-interested because he has to pay a higher compensation to induce a spiteful seller to participate.

# **3.2 Inequality Aversion and Relational REDD Contracts**

Fehr and Schmidt (1999) developed a theory of fairness in which they assume that a fair 23
# **Figure 2.   Demand for Representative Dixit-Stiglitz (IRTS) good g in Regional Market r**

Dixit-Stiglitz Aggregate of IRTS good g in RMr <!-- Start of picture text --> CES<br>σ = 3 = esub a/<br>Imports of g from Goods g from<br>Goods g from Goods g from<br>Rest of World RM q<br>RM 1 RM m<br>CES<br>CES CES<br>σ =3 σ =3 CES σ =3 σ = 3 = esub<br>Rest of World firms Firms in RM m<br>Firms in RM 1 Firms in RM q<br>selling g in RMr selling g in RMr selling g in RM selling g in RM<br><!-- End of picture text --> <u>a/ We take σ = 3, except based on Ivanova (2005), we take σ = 3.1 in MWO; σ = 2.6</u> in TPP; σ = 2.5 in CNM; and σ = 1.8 in OTI.

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Figure 7. Policy volatility in Costa Rica using the R-squared measure <!-- Start of picture text --> R-squared<br>1.0<br>0.8<br>0.5<br>0.3<br>0.0<br>1923:<br>2010:<br>1914: Presidential 1939: 1953: 1962: 1982: 1994:<br>PE<br>WWI Election (PE) WWII PE PE PE PE<br>1878 1887 1894 1899 1904 1909 1914 1921 1928 1933 1940 1945 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007 2012<br><!-- End of picture text --> Note: This Figure shows the R-squared obtained by regressing θ�—the topic proportions of the speech from year t—on θ���—the topic proportions of the speech from the preceding year.

Although for many of the years policy volatility is low (R-squared is close to one), it is interesting to note that in periods in which a significant exogenous “event,” such as a presidential election or the two World Wars took place, the R-squared falls abruptly. This result makes intuitive sense, as a change of government often brings policy changes, which are likely reflected in the content of speeches. Similarly, one would expect that a global event of such magnitude as a World War would induce presidents to devote attention to this subject in their speeches, which would represent a clear deviation from the issues discussed in the previous year.

To synthetize Figure 7 into an index, for each country we calculate both the average R- squared, and the coefficient of variation of the R-squares. Another alternative measure of policy volatility consists in computing the average coefficient of variation of the topic proportions (θ) across all topics, a measure of variability in the relevance of the topics in each speech. Finally, we also look at the percentage of speeches in which the main topic— the topic with the highest θ—changes, a measure of volatility in policy prioritization.

Although these indicators are useful for measuring the degree of policy stability, we must note that they suffer from one important limitation: they fail to differentiate between the continuity of good and bad policies. Clearly, the type of policy continuity that exerts a positive effect on economic growth is the continuity of _good_ policies. An authoritarian government that has pursued the same set of poorly-planned policies for decades has a high degree of policy continuity, but logically, this continuity is not beneficial for growth. We acknowledge that our measures are imperfect because they fail to capture these qualitative differences, but we believe that they are still useful as general indicators of policy continuity.

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