rafmacalaba/data-use-mentions-extended huggingface dataset:
*2.50**|**2.35**|

<mark>*</mark> _<mark>Licenciaturas</mark>_ <mark>in physics, chemistry, biology, history and geography. NA: Not available (less than five observations).</mark> _<mark>Note.</mark>_ <mark>Value Added: Difference Between Observed and Expected Performances Indicator (Indicador da Diferença entre os Desempenhos Observado e Esperado [IDD]).</mark> _<mark>Source.</mark>_ <mark>ENADE/IDD microdata, INEP/MEC.</mark> In aggregate, private institutions tend to add as much value as federal institutions, and non-for-profit institutions are more capable of adding more value in either training modalities. This advantage is even higher for face-to-face _licenciatura_ programs in pedagogy and Portuguese. On the other hand, public institutions perform better in math programs, but nonprofit private institutions add more value that state institutions. Finally, distance learning tends to add more value only for _licenciatura_ in math for private

> 8 The National Student Performance Test (ENADE) evaluates the skills and competences acquired by the graduates of all the undergraduate courses in Brazil. The first application of ENADE occurred in 2004 and each area of knowledge is evaluated triennially. The most recent edition that evaluated the _licenciatura_ programs occurred in 2014.

> 9 Indicator of Difference Between Observed and Expected Performances (IDD), a value-added indicator calculated by INEP/MEC used to control the performance in the ENADE by pre-university learning level of the incoming students shows the details on the calculation formula are available in INEP technical note No 33/2017/CGCQES/DAES.

6
25 years old or more. Omitted categories: Below 2.15 USD-a-day, female, youth. Robust standard errors clustered at the subnational geographic units between brackets. *** significant at 1%, ** 5%, * 10%.

_What is the correlation between the use of coping strategies and declines in enterprise income?_ To directly test if coping strategies were adopted in response to a decline in household enterprise earnings, Table 9 presents how the probabilities of reporting different coping strategies differed for enterprise-owning households who reported farm or non-farm income declines from those that did not experience an income loss. Only households with enterprises are included in the sample. As in the previous sub-section, country surveys included different coping strategies, which affects the sample for each estimate.

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available for investments. Hence, total national income goes to government expenditures, household expenditures, and investments.

International trade is modeled by a system of Armington demands that give rise to flows of goods and services between the regions. At the national/regional level, import demand is driven by CES functions of domestic and imported components of demand for Armington commodities. Export supply is depicted by a two tier constant elasticity of transformation (CET) function, where, on the first tier, the total output of a sector is designated either to total exports or to domestic supply, and, at the second tier, total exports are partitioned according to their destinations.

The endogenous driver of the dynamics in the model is the vintage capital structure. The capital stock is composed of old and new capital, where new corresponds to the capital investments at the beginning of the period and old corresponds to the capital installed in previous periods. The ratio of new to old capital is also a measure of the flexibility of the economy as new capital is assumed to be perfectly mobile across sectors. Furthermore, a fraction of the old capital depreciates each period.

Population and productivity growth are exogenous drivers of the model’s dynamics. The former is taken from the projections of the United Nations Population Division, where labor force growth corresponds to the growth of the population aged 15-64 years. Productivity growth is modeled as factor neutral for agricultural sectors and labor augmenting for industrial and service sectors. Productivity of energy follows an autonomous energy efficiency improvement (AEEI) path so that there is no endogenous technological change in the model.

To ensure equilibrium in the model, three sets of market clearing conditions are met. First, total production of each commodity equals the sum of domestic consumption and exports so that the goods and services markets clear. Second, total investment equals total savings, where savings are composed of private (household) savings, public (government) savings and exogenously fixed foreign savings. Third, factor markets clear, which implies full employment.
|wage|Percen|tage|
|1|82.5%|85.4%|Min.|Max.|Men|Women|
|1<br>2|48.5%|51.4%||1|74.4%|79.3%|
|2<br>3|50.5%|54.4%|1|2|61.1%|65.9%|
|3<br>4|53.5%|57.4%|2|3|64.1%|68.9%|
|4<br>5|56.5%|60.4%|3|4|67.1%|71.9%|
|5<br>6|59.5%|63.4%|4|5|70.1%|74.9%|
|6<br>8|62.5%|69.4%|5|6|73.1%|77.9%|
|8<br>9|68.5%|72.4%|6||75.1%|79.9%|
|9<br>Source: (Núñez 2009)|71.5%|75.4%|||||

In the household survey, there are 1,803,623 pensioners, whose total pension value is 32.8 trillion Colombian pesos, a figure very close to the total government budget for pensions (98 percent). Once the percentages of Table 1 are applied, a total subsidy of 21,6 trillion pesos is obtained. Figure 2 presents the results of the distribution of transfers for pensions throughout the income distribution and also presents the results of progressivity in relative terms of these transfers.

<!-- Start of picture text --> Figure 2. Absolute and relative progressivity of government
_The husband and wife share the same bed, but they have different dreams. (Chinese proverb)_

# **1. Introduction**

Employment and earnings statistics are among the most important variables used to identify the causes of poverty as well as the pathways out - for households and for an economy. Therefore, employment outcomes are especially important to monitor consistently because they are the key link between the size and structure of economic growth and the welfare of households - the ultimate goal of development policy. The quality of employment outcomes has become a key issue for governments around the world. Gallup World Poll recently reported that jobs and economic security are the number one issue for people all over the world, replacing other concerns such as war, crime, and security (Clifton, 2012).

Policy makers in Sub-Saharan African countries, facing a large youth bulge, are increasingly focusing their national development strategies on creating more productive employment. Yet data on the nature and intensity of economic activities undertaken by the labor force in low-income Sub-Saharan-African countries, and the returns to these activities are hard to find. For example, the 2011 edition of the World Bank’s _African Development Indicators_ (ADI) reports data on the structure of employment by sector - one of the most basic statistics - for only 14 of the 48 countries included. In the same volume, data on labor force participation rate (LFPR) is available for all 48 countries, but the values vary improbably between countries. For example, Sudan, a very poor country where most adults cannot afford to be out of the labor force, is reported to have a labor force participation rate of 52 percent in 2009, while a rate of 85 percent is reported for Ethiopia, Sudan’s neighbor, and a rate of 81 percent is reported for Angola, a relatively richer country.<sup>1</sup> Even for countries where micro data from household surveys are widely available, the methods of questionnaire design and implementation generate indicators which fail to capture
20 Also, as discussed in the previous section, working women’s personal beliefs are strongly negative about the risks of working in the private sector (primarily from public sector workers) as shown in Column 9.

For male respondents, social empirical expectations are correlated for the working women and gender role themes (Columns 2 and 7), showing that men also know more working women, and other households who deviate from expected gender roles. Their expectations of female counterpart views are also positively significantly correlated with the gender role theme as shown in Column 7. This suggests that men may in some cases pay attention to the female counterpart’s views as well, alongside their own views.

The results taken together suggest that women who work and their counterparts tend to have somewhat different views and expectations across these thematic areas. When it comes to women working, in general, it appears that working women are more aware of others who work, but are also more likely to think that others will speak badly of working women. When topic areas such as gender roles (and the private sector as discussed above) it appears that along with what others do, individual perceptions and expectations of counterpart are different for working women, i.e. both societal expectations, as well as individual and household perceptions (or cultural beliefs in the household) are different. While these are derived from correlations, it still suggests that individuals and their counterparts in households with working women were amenable to changing these perceptions in the ways outlined above.

# **3.2.3 Job Seeker Effort: Persistence and Channels to Finding Work**

Job search intensity, including both persistence in looking for work, “intention-action” gaps (i.e. what people say they want to do, and what they actually do) and exploring additional channels to finding work are further barriers to finding work for women in KRI. We find evidence of these barriers through responses in the social norms sample, as well as an additional sample of
and developed countries. Although economists have argued against agricultural subsidies and trade barriers for decades (e.g., Irwin 2002), vested interests’ political forces continue to dominate domestic agricultural policy in both rich and poor countries.

The objective of this paper is to review and synthesize the literature that investigates trends and fluctuations in agricultural- and food-policy distortions as well as the politicaleconomic theories that have been advanced to explain such distortions. We identify hypotheses that have been explored in the literature concerning the extent of price distortions and the potential for the adoption of sustainable unilateral and multilateral policy reforms in developed, developing, and transition economies. We emphasize that although many countries have recently begun to adjust their agricultural and trade policies, these reforms have not kept up with the effects of globalization in the non-agricultural sectors of the world economy. We examine the extent to which these more-recent agricultural-policy reforms have succeeded in reversing the prior era’s policy distortions, and we explore the sustainability of these reforms.

Throughout our assessment, we emphasize the potential empirical insights that are embedded in a global five-decade database of evidence recently compiled by the World Bank. This database updates and dramatically expands our understanding of the distortions to market incentives across the globe. The recently improved political-economic conceptual lenses, combined with the new global database, allow the empirical testing of a rich menu of hypotheses about patterns across countries, commodities, and policy instruments.

Recent political-economic frameworks have focused on issues beyond the structural economic factors on which most earlier research concentrated.<sup>3</sup> These conceptual frameworks

> 2 Using a stochastic model of world food markets, Tyers and Anderson (1992) found that instability of international food prices in the early 1980s was three times greater than it would have been under free trade in food products.

> 3 Our paper is a successor to the paper published by _JEL_ by Binswanger and Deininger (1997). It makes three additional contributions. First, we cover the full spectrum of countries (not only developing countries)
# Research Working Paper 12512. Available at:

http://faculty.chicagobooth.edu/christian.broda/website/research/unrestricted/TradeEl asticities/TradeElasticities.html.

**Table C1: Estimated Elasticities of Substitution for Varieties in Tanzanian Imperfectly Competitive Goods Sectors**

|**Sector in our Model**|**Matching HS-3 Code from Broda et al estimates weighted elasticity of substitution**|
|---|---|
|Processed food|110,150,151,160,170,180 190,200,210<br>10|
|Beverages & tobaccoproducts|220,240<br>2|
|Textile & leatherproducts|510-630,650,420<br>4|
|Manufacture of basic & industrial chemicals|280-391<br>3|
|Manufacture of fertilizers &pesticides|310<br>2|
|Petroleum refineries|271<br>4|
|Glass & cement|680-702<br>5|
|Iron steel & metalproducts|720-831<br>7|
|Mining&quarrying|270,271<br>4|
|Grain milling<br>Source: Authors calculations based on estim|110<br>3<br>ates from Broda,Greenfield and Weinstein(2006).|

95
column 1), an index for connection to health facilities with an all-weather road (column 2), an index for public program implementation in a village (column 3), an indicator for the presence of Auxiliary Nurse Midwives in a village (column 4), and an indicator for the presence of a female doctor in a village (column 5). All regressions include district controls, and state fixed effects. District-level controls include the share of female population, the share of urban population, and the share of SC-ST population in the district, and the female and male literacy rates. Robust standard errors are clustered at the district level. ***, **, and * denote significance at the 1, 5, and 10 percent levels.

21
14

# **4 Toward an Empirical Application**

The socio‐economic data to estimate equation (4) were obtained from the Vietnam Household Living Standard Surveys (VHLSS) of 2002, 2004, and 2006. In each round, households were selected using a 3‐stage sampling process.<sup>30</sup> These three survey waves provide high quality and nationally representative household information on household consumption and income, their demographic characteristics, health status, educational achievements, asset holdings and the availability of public services in the community. The latter is obtained from the community survey which was fielded in parallel to the household surveys. The questions in the VHLSS were similar throughout the different surveys rendering them highly comparable. The surveys were conducted each year between May and November of the survey year, with expenditures referring to the past 12 months.

Real total per capita expenditure less per capita expenses on health care is taken as the dependent variable. Health expenditures are subtracted as the study also controls for the effect of idiosyncratic health shocks, proxied by the number of days spent ill in bed over the past year by senior adults, adults and children respectively. Inclusion of health expenditures in the overall expenditure measure, which are highly correlated with illness and not routine or maintenance health outlays, might perversely suggest an increase in welfare following sickness. The expenditure data are expressed in January 2002 prices using the CPI provided by the Government Statistical Office. They are also corrected each year for regional and rural/urban differences across the 8 regions.

A series of standard household demographics (household size, the dependency ratio, the age, gender, educational status of the household head<sup>31</sup> ) are included to control for household characteristics in determining a household’s consumption level. In this context, controlling for ethnicity is especially important as poverty is increasingly concentrated among ethnic minorities, often located in Vietnam’s mountainous areas. While productive assets affect the capacity of a household to generate income and
Our study faces several limitations. The valuation of losses relies on human reporting which is subject to human error, respondents’ incentives, misreporting, and misperceptions. The data allows for a detailed analysis of disaster impacts on crop production, but other aspects of disaster impacts on agriculture are absent. This concerns, for example, damages to agricultural assets, storage losses, or impacts on livestock. Further, the data is representative of the household sector in study countries but misses commercial and larger farms. This implies that we are likely underestimating the full extent of disaster crop losses. As the analysis is focused on agriculture, we do not discuss damages incurred in other sectors.

# Methods

## A. LSMS-ISA microdata

We use plot-level survey data from the Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) in Ethiopia, Malawi, Mali, Niger, Nigeria, and Tanzania. The LSMS-ISA comprise a series of harmonized, national, multi-topic household panel surveys with a distinct focus on agriculture. We harmonized data on agricultural outputs, inputs, and plot characteristics for close to 30,000 households over the six countries for a total of 18 survey waves collected between 2008 and 2019. The combined dataset contains over 120,000 plot observations. More specifically, the dataset includes data from the Ethiopian Social Survey (waves 1 to 4), Malawi’s Integrated Household Panel Survey (waves 1 to 4), Mali’s _Enquête Agricole de Conjoncture Intégrée_ (waves 1 and 2), Niger’s _Enquête National sur les Conditions de Vie des Ménages et Agriculture_ (waves 1 and 2), Nigeria’s General Household Survey (wave 4) and Tanzania’s National Panel Survey (waves 1 to 5). Households are selected to be representative of the population at the national and sub-national level using a two-stage stratified sampling design <mark>with census enumeration areas (EAs) as primary sampling units and households as secondary sampling units.</mark> Households are then tracked through time, except for
Table 9: OLS and IV Estimates of Father’s Labor-Supply Models in Albania (2002 - 2012)

|Method||OLS|IV|IV|
|---|---|---|---|---|
|Instrument for Fertility:<br>|||||
|_(Number of children ever born)_<br>||—|_Same-sex_|_Two girls_|
|Survey Fixed-Efects<br>||Yes|Yes|Yes|
|District Fixed-Efects||Yes|Yes|Yes|
|Controls||Yes|Yes|Yes|
|Dependent variable<br>Mean|Obs.|(1)|(2)|(3)|
|_Panel A: All data_|_bases (LS_|_MS and DHS)_<br>|||
|_Worked in last_ 7 days<br>0.790|5460|0.0124*<br>(0.0067)|0.0581<br>(0.0445)|0.0257<br>(0.0314)|
|_Worked of-farm_<br>0.394|5460|-0.0234***<br>(0.0080)|0.0365<br>(0.0537)|0.0671*<br>(0.0401)|
|_Worked on-farm_<br>0.218|5460|0.0347***<br>(0.0066)|0.0125<br>(0.0396)|-0.0019<br>(0.0280)|
|_Self-employed_<br>0.198<br>_Panel B: Onl_|5460<br>_y LSMS _|-0.0137**<br>(0.0063)<br>_databases_|0.0215<br>(0.0437)|-0.0072<br
<br>-0.05<br>-0.1<br>-0.15<br>-0.2<br>-0.25<br>Contributory pensions as deferred income Contributory pensions as direct transfers<br>Source: The Impact of Taxes and Social Spending on Income Distribution and Poverty in Latin America. An Application of the<br>Commitment to Equity Methodology (CEQ), 2017. Colombia World Bank group staff estimates using the 2017 ENPH<br>Note: The results for all countries other than Colombia were obtained by applying the old methodology of the CEQ, which<br>among other things did not include indirect effects of indirect taxes, accounted for income in kind differently, while<br>the results for Colombia were obtained by applying the new methodology of the CEQ.<br>Argentina, 2012 Brazil, 2009 Costa Rica, 2010 Colombia, 2017 Uruguay, 2009 Mexico, 2010 Chile, 2013 Ecuador, 2011 LAC Venezuela, 2013 Dominican Rep., 2016 Bolivia, 2009 Nicaragua, 2009 El Salvador, 2011 Peru, 2009 Honduras, 2011 Guatemala, 2011<br><!-- End of picture text --> methodological differences and the second of them to the differences in public policy. Given the unavailability of all information it is impossible to isolate these two effects.

30
in Ghana is 2.7% for women and 1.5% for men (Ghana and ORC Macro 2004). The corresponding figures for Kenya are 8% and 4.5% (Kenya and ORC Macro 2009) and for Malawi are 13% and 10% (Malawi and ORC Macro 2005).

> 8 See Corno and de Walque (2007) for additional details related to the HIV testing sample selection in the Lesotho DHS.

7
on the assumption of a capital cost of 40 cents per cubic meter.

**Figure 19. Urban versus rural access to water supply and sanitation, 2007**

<!-- Start of picture text --> a. Water supply b. Sanitation<br>100 100<br>90 90<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>Piped Stand Posts/ Wells and Surface Water Septic thanks Improved Traditional Open<br>Shared Boreholes Latrines Latrines Defecation<br>Urban Rural Urban Rural<br>% population % population<br><!-- End of picture text --> _Source:_ AICD water supply and sanitation utilities database (www.infrastructureafrica.org/aicd/tools/data). _Note:_ Access figures calculated by AICD using data the 2007 Malaria Indicator Survey.

# Information and communication technology

## **Achievements**

Access to ICT has improved markedly since the end of Angola’s civil war in the early 2000s. Mobile penetration rose from less than 1 subscriber per 100 people in 2000 to almost 50 subscribers per 100 people in 2009. Fixed-telephone access has risen marginally, but this is offset to some extent by the development of wireless access (table 15) (Narain 2009).

Angola has made reforms to its telecommunications sector. The Ministry of Telecommunications and Information Technology is responsible for overall sector oversight, while the Angolan Institute of Communications (INACOM), created in 1999, is the industry regulator. Two mobile operators have been licensed: Movicel, an Angolan firm, and Unitel, partly owned by Portugal Telecom, which entered the market in April 2001. In addition to the incumbent, four fixed-line operators have been licensed (Mercury, Nexus, Mundo Startel, and Wezacom).

32
6 In this paper, we are interested in analyzing the individual and household-level correlates of infant mortality. For this purpose, we work with actual survival data of individuals rather than aggregate figures. This corresponds to the true cohort life table approach. Specifically, we use observations on children born alive 12-35 months before the interview. This analysis uses principally data from Kenya Demographic and Health Surveys (DHS) conducted in 2003 and 2008-09 (hereafter 2008). Given the precise dates of the DHS interviews, the infant survival data is based on a set of births that occurred April 2000-August 2002 and November 2005-February 2008. The sample consists of 2295 observations in the 2003 DHS and 2373 observations from the 2008 DHS. See the Annex for further details on sample restriction and data imputation procedures.

The specific choice of time window for eligible births (12-35 months before the DHS interview) was determined by a number of factors. The period must end 12 months before the survey date so that survival up through the age of one year can be determined for each observation. A time period extending more than 35 months would expand the size of the sample. However, some of the possible correlates for mortality—such as ITN ownership and water and sanitation access— are measured on the date of the survey. Consequently, the reliability of this information as a guide to the circumstances of the infant‘s first year of life declines as the window for observations is extended into the past. The 12-35 month window was chosen to balance the competing concerns of sample size and relevance of data observed on the date of the survey.

The analysis is restricted to infant mortality and does not examine the broader question of under5 mortality because under-5 mortality can only be determined for individuals once five years have passed since the child‘s birth. This means that, for example, an analysis of child survival based on individual data from the 2008 DHS could only use data on births that took place in 2003 or earlier. However, such births would have taken place