rafmacalaba/datause-encoder-data huggingface dataset:
# **Appendix Table 2**

|Variable Definitions and Sources|||
|---|---|---|
|**Variable name**|**Variable definition **|**Sources**|
|**Dependent variables:**|||
|Unemployment rate (UnempRate)|The percentage of the labor force that is without work but available for and seeking<br>employment.|IMF IFS<br>World Bank WDI<br>ILO KILM<br>OECD LFS<br>Regional agencies<br>Country-specific sources|
|Employment (Emp)|In millions, includes employees, the self-employed, unpaid family members that are<br>economically engaged, apprentices, and the military (aged 15 and older for series from<br>theWorld BankWDI).|The conference Board TED<br>World Bank WDI|
|Female employment(FemaleEmp)|In millions,aged 15 and older.|World BankWDI|
|Wage Index(WageIndex)|Index of wage earnings with 2005 as the baseyear equal to 100.|IMF IFS|
|Labor force participation rate<br>(LFPRate)|Labor force as a percentage of the working age population. Total labor force comprises<br>people ages 15 and older who meet the ILO definition of the economically active<br>population: all people who supply labor for the production of goods and services<br>during a specified period. It includes both the employed and the unemployed. While<br>national practices vary in the treatment of such groups as the armed forces and<br>seasonal or part-time workers, in general the labor force includes the armed forces, the<br>unemployed, and first-time job seekers, but excludes homemakers and other unpaid<br>caregivers and workers in the informal sector.|World Bank WDI<br>IMF IFS|
|Female labor rorce participation rate<br>(FemaleLFPRate)|Female labor force as a percentage of the female working age population.|World Bank WDI|
|**Endogenous independent**<br>**variable:**|||
|POST|A dummyvariable that takes the value of 1 every year after a country’s reformyear.|Wacziargand Welch(2008)|
|POST_Dictatorship|POST interacted with a dummy variable that takes the value of 1 for countries with a<br>dictatorship. Countries with a dictatorship are identified as those with a score of four<br>on PolityIV's regulation of participation (PARREG) variable, defined as countries<br>where regulation of participation is restricted. The PARREG variable can take a value<br>between one and five. A value of one indicates unregulated political participation, two<br>indicates relatively stable but enduring political groups, three indicates oscillation<br>among multiple identity groups that restrict competing groups’ political activities, four<br>indicates some organized political participation permitted but significant groups/issues<br>are excluded from the political process, and five indicates regulated political<br>participation but with little use of coercion. Totalitarian party systems, authoritarian<br>military dictatorships, personal dictatorships and despotic monarchies are typically<br>coded with a score of four.|PolityIV|

39
farm management decisions. This occurred for three-fourths of the farm households with married women. Almost always these women manage their farms jointly with their husbands. In fact, more than 90 percent of the women do so.

Our working sample is based on about 500 joint farm manager couples from 146 communities for whom data on non-cognitive skills are available for both spouses.<sup>4</sup> This sample allows us to estimate the effect of the non-cognitive skills of women farmers on the adoption of tobacco, holding constant the non-cognitive skills of their husbands. This is important in light of evidence from rich country marriage markets documenting that men and women match assortatively on personality traits [Dupuy and Galichon 2014]. In fact, in Section 3.3 we will show that in our data higher non-cognitive ability women in rural Malawi tend to be married to higher non-cognitive ability men.

# **2.2. Measuring Non-Cognitive Skills**

The IHPS collected information on the non-cognitive skills of the farm managers. The questions were developed by entrepreneurial psychologists [see Frese and Gielnik 2014 for a review], and have been used in recent studies on microentrepreneurs in developing countries [e.g. De Mel _et al._ 2009, 2010]. We focus on 14 questions capturing mental attitudes related to perseverance in challenging tasks and environments (e.g., “I continue to work on hard projects even when others oppose me,” and “I can think of many times when I persisted with work when others quit”), and the related constructs of optimism (e.g., “In uncertain times I usually expect the best”) and passion for work (e.g., “I look forward

> 4 Reasons for missingness may have been related to respondent unavailability at the time of the interview as well as to interviewer effort. We compared summary statistics between our working sample and the remaining households in the IHPS rural sample. We find that the communities in our sample are not randomly selected. For example, on the one hand our sample over-represents communities in which there is a tobacco club and an agricultural extension officer. On the other hand it under-represents matrilocal communities, on which there is a commercial bank or MFI. Within communities however households in our sample do not appear to be strongly selected. While we are careful to note that our inferences only apply to the population we examine, reassuringly our main estimates are qualitatively similar when weighted by the inverse of the probability of complete non-cognitive data for both spouses to systematically correct for potential sample selection bias [Wooldridge 2007].

6
ZIMBABWE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Bibliography

This country report draws upon a wide range of papers, databases, models, and maps that were created as part of the Africa Infrastructure Country Diagnostic. All of these can be downloaded from the project Web site: www.infrastructureafrica.org. For papers go to the document page (www.infrastructureafrica.org/aicd/documents), for databases to the data page (www.infrastructureafrica.org/aicd/tools/data), for models go to the models page (www.infrastructureafrica.org/aicd/tools/models), and for maps to the map page (www.infrastructureafrica.org/aicd/tools/maps). The references for the papers that were used to compile this country report are provided in the table below.

## General

AICD (Africa Infrastructure Country Diagnostic). Africa‘s Infrastructure: A Time for Transformation (AICD Web site). www.infrastructureafrica.org.

- Foster, Vivien, and Cecilia Briceño-Garmendia, eds. 2009. _Africa’s Infrastructure: A Time for Transformation._ Paris and Washington, DC: Agence Française de Développement and World Bank.

## Financing

- Briceño-Garmendia, Cecilia, Karlis Smits, and Vivien Foster. 2009. ―Financing Public Infrastructure in Sub-Saharan Africa: Patterns and Emerging Issues.‖ AICD Background Paper 15, Africa Region, World Bank, Washington, DC.

## Growth

- Calderón, César. 2009. ―Infrastructure and Growth in Africa.‖ Policy Research Working Paper 4914, World Bank, Washington, DC.

- Escribano, Alvaro, J. Luis Guasch, and Jorge Pena. 2010. ―Assessing the Impact of Infrastructure Quality on Firm Productivity in Africa.‖ Policy Research Working Paper 5191, World Bank, Washington, DC.

Yepes, Tito, Justin Pierce, and Vivien Foster. 2009. ―Making Sense of Africa‘s Infrastructure Endowment: A Benchmarking Approach.‖ Policy Research Working Paper 4912, World Bank, Washington, DC.

## Information and communication technologies

AICD (Africa Infrastructure Country Diagnostic). 2009. ―Connecting the Continent: Costing the Needs for Spending on ICT Infrastructure in Africa.‖ Background Paper 3 (Phase II), Washington, DC.

51
- 10

-

correlation patterns appear to be consistent with Doukas' (1989), findings that sovereign interest rate spreads for Argentina, Brazil, and Mexico have a contagious element and that "news" about the creditworthiness of a sovereign borrower affects the spreads charged to others. 1/

# IV. Foreign Shocks and More Persistent Forms of Contagion

In the remainder of the analysis, we turn our focus to Latin America and investigate whether there is evidence of more durable forms of spillover effects, over and beyond those that typically arise in moments of crises. It is noteworthy that the surge in capital inflows during 1990-1994 in Latin America has not been limited to a few relatively large countries-there were significant improvement in the capital account bal4nces of the smaller countries as well. While the orders of magnitude of the change in the capital account balance as a percent of GDP are, on average, somewhat less for the smaller countries, these nonetheless amount to a hefty 5 percent of GDP. In this section, we first revisit the topic of the sensitivity of cross-border capital movements to international interest rates along the lines of Calvo and Reinhart (1995). Secpnd, we test for "spillover" effects in the capital account from the large countries to the smaller ones.

## 1. Preliminaries

The group of the larger countries includes Argentina, Brazil, Chile, Colombia, Mexico, and Peru while the "small" country group refers to Costa Rica, Ecuador, El Salvador, and Uruguay. Since several of these countries have no quarterly balance of payments statistics, we use annual data for the balance on the capital account over the period 1970-93. This is a much longer period than is examined in other studies such as, Calvo, Leiderman, and Reinhart (1993), Chuhan, Claessens, and Mamingi (1993), and Fernandez Arias (1994), which focus on the recent episode. The main

- 1/ He used spreads on publicly guaranteed Euro-syndicated loans.
24 incomes by only between 3 and 5 percent. Similarly, increasing non-farm physical assets by one standard deviation is associated with only a 19 to 22 percent increase in expenditures.

Table 5: Magnitude of the estimates of various determinants of income.

|Table 5: Magni|tude of the estimates o|f various d|eterminants|of income.||l|
|---|---|---|---|---|---|---|
|Variable:|Source of<br>estimates|Point<br>Estimate|Standard<br>Deviation|In<br>expendit<br>dev. incr|crease i<br>ures fro<br>ease:|n<br>m one std.|
||||||Dollar|s<sup>2</sup>|
|||||Percent'|||
||||____|____<br>__<br>____|H<br>___<br>___|Per<br>person'|
|Social capital|SCPS, household|_.56_|1|56|655|101|
||SCPS, cluster|.49|1|49|690|106|
||HRDS, household|.19|1|19|222|34|
||HRDS, cluster|.21|1|21|245|38|
|Education|SCPS, household|.009|3.21|2.9|34|5.2|
||HRDS, household|.019|2.70|5.1|60|9.2|
|Assets|SCPS, household|.18|1.24|22|261|40|
||HRDS, household|.14|1.36|19|222|34|

Notes: 1) Using natural log change as approximation to percentage change. 2) Based on the assumption of mean per capita consumption of $180. 3) At the household average of 6.5 members per household.

_Incidental association, is this really social capital?_ There remains the possibility that the estimated effects of village social capital are merely an artifact and that social capital is proxying for some unobserved characteristic of villages (the omitted variable must be at the village, not household level to explain the HRDS results). It is impossible to reject this possibility econometrically, because village level 'fixed effects' which would eliminate the
country _j_ in year _t_ as additional robustness checks.

In line with the empirical strategy by Kahn (2005), our second set of estimates analyzes the determinants of the annual national death toll from each of the three different natural hazards. The EM-DAT disaster dataset only includes major disasters that have exceeded a certain threshold in magnitude (e.g. number of fatalities, state of emergency). A nation could experience a natural disaster but a sufficient amount of protective measures can limit the overall losses and fatalities and thus prohibit the event being included in the dataset. Constructing a panel dataset allows us to analyze this first-stage process as well and use this information as an additional control in a second stage regression of the disaster fatalities.

Using the negative binomial model would assume that the observations with zero death-counts are generated by the same underlying process. This might not apply to our analysis. A zero outcome in the death count can arise from two regimes: Either no disaster took place (regime 1) or a large scale disaster took place and protective measures prevented any fatalities (regime 2). We therefore apply a zero-inflated negative binomial model (ZINB) which allows for overdispersion in the dependent variable and treats the zero outcomes of the dependent variable different from the positive counts. The model combines a binary variable<sup>_c_</sup> _i_<sup>with a standard</sup> count variable _y_<sup>*</sup> _i_ such that the observed variable is given by The probability that _c_ = 1 is denoted by ω _i_ . We apply the following probit model to estimate the influence of covariates<sup>_z_</sup> _i_<sup>on</sup> ω _i_ .

The corresponding log-likelihood function is given by The first stage probit model captures the process governing that a potential deadly disaster takes place in country _j_ at time _t_ . The second stage estimates the death count once a disaster has taken place by applying a negative binomial estimator.

# **6  Results**

13
_Social Capital, Household Welfare and Poverty in Indonesia_ 12

- For the most important local institutions, interviews were held with leaders and members, as well as with non-members, in order to get a balanced view of the role of the institutions in the village, their development over time, their main activities, relations with other institutions and government, and their main strengths and weaknesses.

At the _district_ level (defined as the administrative level above the village or community), data were collected about the extent of service coverage and the institutional arrangements for the provision of services. Information was also obtained about the general functioning of the district administration and its relation with civic organizations, through interviews with general and sectoral managers at the district level.

The third and critical part of the data collection was a _household survey_ which aimed to capture households' actual participation in local institutions, their use of services, and information that identifies the welfare level of households and their coping strategies. The questionnaire consisted of six sections:

- demographic information on household members

- participation in local institutions

- characteristics of the most important groups

- service provision profiles

- perceptions of community trust and collaboration

- household economy and coping strategies.
# **Impact Investments**

Impact investments generate both social value and financial returns. “Impact investment is private equity, debt and/or real estate investment that generates measurable financial as well as social or environmental returns beyond comparable industry standard investments. Impact investors and their investees explicitly seek to generate both financial and these extra-financial returns.”<sup>99</sup> The difference to social responsible investing is that investors search for investment opportunities that actively seek impact. A recent report estimates that impact investing could grow to 1 percent of total professionally managed assets within the next five to ten years with a market of about US$500 billion.<sup>100</sup> Examples include microfinance bonds, which securitize micro credits and sell them on the international capital markets or direct investments in microfinance institutions; the Media Development Loan Fund (MDLF), which invests in media institutions in emerging markets; and a US$500 million equity investment fund to invest in companies in the health sector, which IFC plans to set up. New communication technology also enables person-to-person socially responsible investments. Kiva.org (without interest payments) and MyC4.com (partially concessional with lower than market rate interest payments) allow investors to give micro loans directly to entrepreneurs in developing countries on a personal basis.

Further information: <u>http://www.mdlf.org http://www.kiva.org http://www.myC4.com http://www.rockfound.org/efforts/impact_investing/impact_investing.sht ml</u> Reading: Monitor Institute 2009 See also: Blended Value Investments Person-to-Person Giving

# **Index-based Insurance**

See: Parametric insurance

# **Indexed Bonds**

Indexed bonds tie the performance (schedule or amount of payment of interest and/or of repayment of principal) to the performance of an index. The instrument allows debtors to hedge against risks deriving from fluctuations of the index. Common indices are inflation and GDP but also a carbon-indexed bond has been piloted by the World Bank in cooperation with a private sector partner.

Reading: Fischer 1975, Shiller 2003b, 2005 See also: Carbon-indexed Bonds Commodity-indexed Bonds GDP-indexed Bonds Inflation-indexed Bonds

> 99 http://www.rockfound.org/efforts/impact_investing/RIIC_recommendations_final.pdf. 100 Monitor Institute 2009.

74
9

# II. INTRODUCTION

1. The purpose of this paper Is to conskier whether the hstitutional factors associated with the Mexican land reform (1Jkb) sector constrain agrkcultural growth. Parthg from the assumption that the ejido may be considered<sup>a more or</sup> **less** fixed **eement** of Mexico's cuWtural and politial scene, the paper wUI focus on **the** pecomeal changes that could help to enhance growth and productivity within the existing structure.

2. The ejldo Is a rural community, possessing land and water resources conferred upon it by the Mexican government; ejido members (ejldatarlos) have usufruct rights over the land but are prevented by the agrarian reform law from selling it. The term "ejido" originally referred to communa **l** ands outside (at the "exit") of the medieval Spanish village; as an institution, the ejldo also has antecedents In pro-Columblan land-holding patterns (Eckstein,1966:9-13).

3. There is little rellable, recent Information on the extent of the ejido system (see Appendix A). In 1970 (the last date for which systematic farm census<sup>data</sup> Is available), about 60 percent of farm families were elidatarlos (Yates,1 981 a: 1 45). At that tkie there were over 23,000 ejidos In Mexico, possessing 56 percent of arable land and accounting for almost 40 percent of the combined value of<sup>crop</sup> and livestock output (Yates,1981a:160).

4. In considering the constraints posed by the ejido sector It Is necessary to ask whether the institutional differences between ejldo enterprlses and skmilarly-sized private farms give rise to significant variations in the economic performance of enterprises In each tenure category. To enhance the legitimacy of the comparison, references to the ejido sector will focus on Individual ejldos:
|District dummies (27 -1 dummies)|Included|Included|Included|Included|
|---|---|---|---|---|
|Time average of explanatory variables|Included|Included|Included|Included|
|rho||-0.145***<br>[0.000]||-0.150***<br>[0.000]|
|Constant|-0.885|-2.354**|-0.890|-2.358**|
||[0.437]|[0.013]|[0.434]|[0.012]|
|Observations|5,286|5,286|5,286|5,286|

Source: Generated by authors using LSMS data Note: The symbols ***, **, *, and + indicate that the corresponding regression coefficient is statistically significant at the 1%, 5%, 10%, and 15% levels, respectively. P-values between brackets. The GHK estimates are generated using the mvprobit command with 100 draws in STATA. The bivariate ML estimates are generated using the biprobit command in STATA. The parameters in the table are coefficients and not APEs.

49
effects they find in evaluating an unconditional cash transfer program in Morocco, “labeling” a program as support for education can affect perceptions of the importance of schooling. Similarly, the program we evaluate may have signaled the value that the government places on girls’ secondary education—making such education salient to households and communities and inducing girls’ secondary school enrollment independent of the size of the benefit.

Alternatively, greater exposure of households and communities to girls’ secondary education in the early years of the program, induced by the economic incentive of the program benefit, may have generated a durable, positive change in the “taste” for girls’ secondary education or helped correct for preexisting biases in beliefs or preferences (Kremer, Rao, and Schilbach 2019). And these adjustments in beliefs and preferences may have catalyzed a positive shift in social norms around girls’ secondary education.

# **2. Related literature**

Most evaluations of education CCT programs in developing countries assess program effects on the likelihood of school participation, typically in the first few years after the program has been initiated (Fiszbein et al. 2009; Baird et al. 2014; Garcia and Saavedra 2017). A recent meta-evaluation study of the effects of education CCT programs finds a significant, positive average effect on the likelihood of school participation, at both the primary and secondary levels (Garcia and Saavedra 2017). The study also finds a larger average effect on the likelihood of participation at the secondary level than at the primary level. Effects found by studies included in the meta-evaluation sample vary substantially, however.

Among education CCT programs that, like the one we evaluate, are targeted to girls and condition cash benefits on secondary school participation, almost all those that have been evaluated are in Asia. Here too, evaluations generally find that the programs have significant, 6
# **Table 1.1 Some Trade and Competition Regulations (comparison between Chile and OECD Countries)**

# **(Scale: 0 represents a very low level, while 6 is a high incidence)**

|||**Chile**|**Emerging**<br>**Markets**<br>**(1)**|**Mexico**|**EU-15**|**US**|
|---|---|---|---|---|---|---|
|**GOV’T**<br>**INVOL**|**OWNERSHIP AND**<br>**VEMENT**||||||
|•|State control|2.1|3.0|1.7|2.4|0.9|
|•|Public ownership|1.7|3.4|1.7|2.3|0.8|
|•|Size of public enterprise sector|1.5|2.5|1.4|1.9|0.0|
|•|Involvement in business operations|2.7|2.4|1.7|2.4|0.9|
|•|Price controls (2)|2.0|1.2|1.3|1.2|0.0|
|**ADMI**|**NISTRATIVE BARRIERS**||||||
|•|Barriers to entrepreneurship|2.3|2.0|1.7|1.8|1.3|
|•|Regulatory and administrative<br>opacity|3.5|2.2|2.3|1.9|2.1|
|•|License and permits system|4.0|3.0|4.0|2.9|4.0|
|•|Communication and simplification of<br>rules and procedures|3.1|1.2|0.5|0.8|0.0|
|•|Administrative burdens on start-ups|2.2|2.2|1.8|2.1|0.8|
|•|Administrative burdens for<br>corporation|2.3|2.7|2.1|2.3|0.5|
|•|Administrative burdens for sole<br>proprietor firms|4.0|2.1|1.6|2.1|1.3|
|•|Sector specific administrative<br>burdens (3)|0.6|2.1|1.6|1.9|0.5|
|**TARIF**|**F AND TRADE RESTRICTIONS**||||||
|•|Barriers to trade and investment|2.7|2.5|2.2|0.7|0.9|
|•|Explicit barriers|1.5|2.8|3.1|0.9|1.3|
|•|Ownership barriers|0.0|2.6|2.7|0.3|2.2|
|•|Discriminatory procedures|2.3|2.0|1.4|0.6|0.3|
|•|Tariffs|3.0|3.7|5.0|2.0|1.0|
|•|Other barriers|4.3|2.0|1.1|0.4|0.3|
|•|Regulatory barriers|5.3|1.7|0.3|0.2|0.0|
|**REGU**<br>**COMP**|**LATORY FRAMEWORK FOR**<br>**ETITION**||||||
|•|Economic regulation|1.9|2.5|1.5|2.0|1.0|
|•|Regulation of economic structure|2.0|2.7|1.7|2.1|1.0|
|•|Regulation of economic behavior|2.5|3.1|1.8|2.7|1.2|
|•|Regulation of competition|1.0|1.4|0.7|1.0|0.8|

Data for Chile take into account the state of the legal framework in May 2003 and for the other countries, 1998. (1) Emerging markets: Czech Republic, Hungary, Korea, Mexico, Poland, Turkey; (2) Includes sector specific information on road freight, air transport, retail distribution and some telecom services; (3) Exemptions to public enterprises and state-mandated actions only. Source: OECD 2003 4