rafmacalaba/datause-encoder-data huggingface dataset:
Delhi, have the highest electrification rate (almost 100 percent in 2012). Among the regions, South India has the highest electrification rate—about 87 percent in 2005 and 96 percent in 2012. East India, which includes the states of Bihar, Jharkhand, Orissa, and West Bengal, has the lowest rate among the regions. But electrification increased quite a bit in these states during the seven years from 2005 to 2012, rising from a rate of 37 percent to almost 65 percent. Overall in rural India, the electrification rate increased from 62 percent in 2005 to 77 percent in 2012, implying annual growth of about 2 percentage points.

Data on the duration of power outages (in hours per day) for the two survey years show that outages worsened during the sample period: While 9 percent of connected households reported no outages in 2005, that share dropped to 7 percent in 2012 (table 3). And while about 35 percent of the households reported power outages of at least 13 hours a day in 2005, almost 45 percent experienced outages of similar duration in 2012. The average duration of outages was 9.3 hours a day in 2005 and 10.7 hours a day in 2012. The hours of electricity availability per day vary substantially across states (figure 2). While connected households in union territories such as Daman and Diu have more than 20 hours of electricity per day on average, those in such states as Arunachal Pradesh and Assam have less than 6 hours per day on average.

Summary statistics for outcome variables show that grid‐connected households consume less kerosene and spend less time collecting fuel than off‐grid households do (table 4). They also have higher incomes and expenditures and a lower poverty rate. In households with an electricity connection both boys and girls spend more time studying and complete higher grades. Labor force participation is lower among adults in grid‐connected households than among those in off‐grid households—for both men and women. But when employment is measured by hours worked per month (in both wage and self‐ employment activities ) , grid‐connected households have more hours of employment than their off‐grid counterparts. These results are statistically significant and hold in both survey years. Also noticeable is that the trend in outcome variables differs between grid and off‐grid households. For example, while kerosene consumption drops over time for all households, it drops more for grid‐connected households.

Another way to look at the summary statistics for outcome variables is by duration of electricity availability. We compare two groups of households: those that have access to electricity for at least 20 hours a day and those that do not (table 5). Unsurprisingly, households with more hours of electricity available spend less on kerosene, have higher incomes and expenditures, and see their children achieve better education outcomes. Employment outcomes are somewhat mixed. Men in households with shorter electricity outages have higher labor force participation and fewer hours of employment in 2005 8
regional fixed effects – identical to the reduced form probit model estimated above. A few household and community characteristics enter into the first stage selection model but not the second-stage regression, since they are likely to influence a household’s choice of facility but _not_ its satisfaction with the facility (see Appendix C for a more detailed discussion of the identification strategy). The assumption stated earlier – that the services from a facility is an “experience” good, whose actual quality is known to only those who have _used_ that facility – implies that the variables measuring facility quality influence satisfaction with the facility but _not_ the household’s choice of facility.<sup>16</sup> We do not find significant selection bias from our estimated results,<sup>17</sup> which implies that the reduced form discrete choice model discussed above is appropriate for our analysis. The detailed model and results from our 2 stage-Heckman selection model are reported in Appendix C (Table C-4). The results on determinants of satisfaction are quite similar for the reduced form model and the 2-stage Heckman selection model, which lends confidence to the reduced form model results.

# _Satisfaction with public schools_

The model for satisfaction with public education facilities (schools) is similar to that for health facilities. The estimated equations examine how the binary variable of satisfaction ( _S_ ) with public schools varies with objective quality of the facility and the governance environment

> 16 This assumption can be justified since accurate information on the quality of a facility is likely to be available only to the users of the facility. That said, it is somewhat restrictive since the actual quality of the facility can have an impact on its reputation, which in turn can influence the household’s choice of a facility.

> 17 The coefficient of Inverse Mill’s Ratio (λ) in the second stage regression in each case is insignificant in two cases, and only weakly significant for the pooled sample. These results are also quite robust to changes in specifications of the 1<sup>st</sup> stage selection model.

20
move away from wheat, maize, millet-ground nuts, or millet-sorghum. Instead, they choose fruits-vegetables, fruits-vegetables-maize, millet, or ground nuts-maize. If precipitation increases, farmers move away from groundnut-millet and maize towards fruits-vegetables-maize, maizegroundnut, and millet.

These behavioral changes at the African level, however, do not hold for all Agro-Ecological Zones. Although the choice of fruits-vegetables is expected to increase when rainfall increases, it is reduced in dry zones such as deserts, lowland dry savannah, and lowland semi-arid zone. Similarly, millet will decrease even though temperature increases if the farm is located in lowland moist savannah or lowland sub-humid AEZs. Maize will decrease with higher temperature, but the exceptions are deserts and high elevation dry savannah.

# **5. Forecasting Climate Change Impacts on Irrigation and Crop Choices**

As climate change unfolds over the coming century, farmers are likely to adapt to it by switching crops or irrigating their land. In this section, we use the results from the previous section to predict how farmers might adapt in the future. We explore how adaptations might be different depending on the climate scenario. We also explore how they might be different depending on the AEZ of each farm. In practice, future farm practices will also depend on economic development, technological changes, and price changes. We do not examine these other important influences but rather focus simply on the role of climate change. We are predicting how climate influences farm choice, not each farmer’s actual future choices. We assume all other factors remain unchanged and examine the effects of climate change alone.

We examine a set of climate change scenarios that are consistent with the range of likely outcomes predicted in the most recent Intergovernmental Panel on Climate Change report (IPCC 2007). Specifically, we use the A1 scenarios from the following two models: CCC (Canadian Climate Centre) (Boer et al. 2000) and PCM (Parallel Climate Model) (Washington et al. 2000).

Table 8 presents the mean temperature and rainfall predicted by the two models for the years 2020 and 2100. In Africa in 2100, PCM predicts a 2°C increase and CCC a 6°C increase in temperature. Rainfall predictions vary. PCM predicts a 10% increase in rainfall in Africa and CCC a 10% decrease by 2100. Even though the mean rainfall in Africa is predicted to increase/decrease depending on the scenario, there is also substantial variation in rainfall across countries. Examining the path of climate change over time reveals that temperatures are 10
<mark>WPS3459</mark>

# **Economic Volatility and Returns to Education in Venezuela: 1992-2002**

**Harry Anthony Patrinos**<sup>*</sup> World Bank Washington DC <u>hpatrinos@worldbank.org</u>

## **Chris Sakellariou**<sup>∗</sup>

School of Humanities and Social Sciences Nanyang Technological University, Singapore <u>acsake@ntu.edu.sg</u>

**Abstract:** Preliminary evidence suggests that the rates of return to education in Venezuela have been declining since the 1970s.  This paper rigorously estimates the returns to education in Venezuela for the period 1992-2002 and links them to earlier available estimates from the 1980s. Consistent cross-sections from the _Encuesta de Hogares por Muestro_ are used to document falling returns to schooling and educational levels until the mid-1990s, followed by increasing returns thereafter. Quantile regression analysis is used to provide further insight into the within skill group changes in returns over time.

JEL Classification Codes: I21, J31 Keywords: Returns to schooling, wages, quantile regressions World Bank Policy Research Working Paper 3459, November 2004 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> ∗ The authors would like to thank Emiliana Vegas and George Psacharopoulos for useful comments, as well as discussants and participants at the Latin America and the Caribbean Economic Association meetings, Puebla, Mexico, October 2003.
that all prices are in 1998 (January) Dong. The price deflator does not vary by region. Thus, the variation in real rice prices observed in the data stems from movements in rice prices rather than the price index. The mean rice price in 1993 was 2,600 dong per kilogram of ordinary rice (author's calculation from the 1992/93 Vietnam Living Standards Survey). One U.S. dollar corresponds to approximately 14,000 Dong in 1998, so the price of rice in 1993 is approximately 19 cents per kilogram. The average domestic price of ordinary rice increases by 28% relative to the rise in the consumer price index between 1993 and 1998 to approximately 24 cents per kilogram (author's calculation using the 1997/98 Vietnam Living Standards Survey).

Benjamin and Brandt (2004) describe the sources of rice price variation in greater detail. They highlight two reasons for why rice prices rise. First, the increase in the overall level of prices may be attributable to the decline in the rice export quota that took place during the 1990s. Out of a concern for domestic food security, Vietnam implemented a rice export quota in the late 1980s in order to suppress the domestic price of rice. During the time of the data, Vietnam gradually liberalized the quota so that by 1997, it was no longer binding. The lifting of the quota may lead to asymmetric price changes if communities vary in their exposure to the effects of lifting the quota. This may occur if there are important transport costs that are a function of the value of the rice being exported out of the exporting community or if the transport costs are on the quantity of rice exported which is in turn associated with the baseline price.

The second major source of rice price variation across communities is Vietnam’s lifting of restrictions on the flow of rice across communities in 1997. Prior to 1997, communities that would become rice exporters in 1998 received relatively lower prices than rice importers. The easing of restrictions on the flow of rice across communities, then allowed price changes that are largest in the net exporting regions. Overall, though, there does not appear to be national 12
# **Annex Table 1 Capital flight and external debt in a selected sample of countries (in million 2004 US $)**

||**Cumulative real**<br>**capital flight with**<br>**imputed interest**<br>**earnings**|**Real external**<br>**debt**|**Ratio of interest**<br>**rate adjusted real**<br>**stock of capital**<br>**flight to external**<br>**debt**|
|---|---|---|---|
|**Botswana**|-1,087|510|-2.13|
|**Comoros**|-169|309|-0.55|
|**Swaziland**|1,343|471|2.85|
|**Mauritius**|650|2296|0.28|
|**Congo Republic**|17,475|6,741|2.59|
|**Zambia**|19,814|7,279|2.72|
|**Mauritania**|4,006|2,312|1.73|
|**Mozambique**|14,273|5,047|2.83|
|**Total Sub-Saharan Africa**|**602,063**|**229,756**|**2.62**|

# **Capital flight and external debt in a selected sample of countries (in percentage of GDP 2004)**

||**Cumulative real**<br>**capital flight with**<br>**imputed interest**<br>**earnings**|**Real external**<br>**debt**|**Ratio of interest**<br>**rate adjusted real**<br>**stock of capital**<br>**flight to external**<br>**debt**|
|---|---|---|---|
|**Botswana**|-11|5|-2.13|
|**Comoros**|-47|85|-0.55|
|**Swaziland**|53|19|2.85|
|**Mauritius**|11|38|0.28|
|**Congo Republic**|402|155|2.59|
|**Zambia**|365|134|2.72|
|**Mauritania**|259|149|1.73|
|**Mozambique**|241|85|2.83|
|**Total Sub-Saharan Africa**|**199**|**93**|**2.62**|

25
for selected investment employees.<sup>8</sup> There is also an investment firm offering advice to SP members and an investment company that operates 4 mutual funds with several subfunds in which SP balances are invested.

# **7. Implementation of Investment Strategy**

Implementation of investment strategy is the responsibility of the Executive Committee and executive management. To strengthen asset management, ATP adopted in 1999 a policy of upgrading the investment skills of its staff, hiring external asset managers, using appropriate benchmarks, establishing effective internal controls, and ensuring a separation of functions between trading on the one hand and record keeping, monitoring and reporting to the Board on the other. Expected tracking error (deviation from benchmark) for different asset classes was clearly specified and closely monitored.

The strategic asset allocation that was articulated in 2000 indicated a growing emphasis on investments in foreign equities and bonds. The asset allocations were set as targets to be reached in 2005. However, in 2004, a decision was taken to adopt active management of total balance sheet risk with a view to ensuring a swift and flexible adjustment of investment policy to changing market conditions and risk capacity.

The share of equities, especially foreign ones, fell in the aftermath of the bursting of the high tech bubble and accounted for 25 percent of all assets in 2006, half the level reached in 2001 (Table 4). Holdings of domestic bonds also fell from 31 percent in 2001 to 17 percent in 2006. Investments in real estate and private equity remained at a low level despite an expressed desire to increase significantly the allocation to alternative asset classes. In contrast, the share of foreign bonds increased rapidly and now represents half the total investment portfolio.

**<u>Table 4: Asset Allocation</u>**<sup>**9**</sup> **<u>, 2001-2006</u>**

||2001|2002|2003|2004|2005|2006|
|---|---|---|---|---|---|---|
|Domestic Equities|16.8|11.8|11.3|9.6|11.2|11.8|
|Foreign Equities|30.8|13.4|2.2|7.1|11.1|13.4|
|Domestic Bonds|30.9|32.9|30.3|26.7|20.3|17.4|
|Foreign Bonds|16.7|31.0|39.5|38.5|39.2|50.4|
|Real Estate|3.6|4.1|3.9|3.1|2.9|3.5|
|Private Equity|1.1|1.1|1.2|1.3|1.7|2.5|
|Hedging Instruments||4.1|2.8|9.2|11.4||
|Other||1.6|8.7|4.5|2.2|1.0|
|Total Assets (DKK bn)|247.5|237.7|259.7|304.4|359.8|396.0|
|Total Assets (% GDP)|18.5|17.3|18.5|20.9|23.2|24.2|

Total assets refer to total investment assets, which are slightly below total group assets. Source: ATP 8 These arrangements entail a loss of transparency and create a risk of malfeasance. However, no indication is provided that these partnerships are subject to intensified supervision from internal and external auditors. 9 The annual allocations reported in this table are based on the analyses of investments contained in the annual reports of ATP, supplemented with information contained in the notes to the financial statements. There are some small discrepancies in some of these numbers, but these are unlikely to have a significant effect on the overall assessment and conclusions.

12
and so lack of impact on other outcomes may reflect measurement problems, or that the information alone was not enough to change intentions.

Finally, column 9 examines our action-oriented measure of interest in migration. 11.4 percent of the control group took the action of sending an SMS message to find out more about wages for Gambians in Europe. We find that the Senegal treatment significantly lowered this by 4 percentage points, suggesting less interest in considering Europe as a migration destination. Taken together, we view these short-term results as suggesting that the information part of the interventions was able to generate new knowledge, and, for the Senegal migration alternative at least, change intentions to migrate.

# **5.2 Impacts on migration knowledge and intentions over the longer-term**

We begin our analysis of the follow-up survey data by asking whether the interventions had lasting impacts on knowledge about migration to Europe and Senegal, and migration intentions over the next five years.

The first three columns of Table 3 examine impacts on knowledge. We find no significant impact on knowledge about backway migration (column 1), nor in the likelihood that the individual is over-optimistic about the wages they can earn abroad or the likelihood of asylum (column 2). Column 3 shows the Senegal treatment resulted in a lasting improvement in knowledge about migration to Senegal. The effect size of 0.14 questions out of five is similar in magnitude to the impact measured immediately after showing the informational video, and shows that any knowledge depreciation from the video has been offset by experiential learning from more lasting migration to Senegal.

Given the issues we experienced with asking probabilistic likelihoods of migrations at baseline, our registered report specified that we would switch to using a 5 category Likert scale to measure migration intentions at endline. We then look at impacts on saying they will “surely” move in the next five years (5 on the Likert scale), or that they will “likely or surely” move (4 or 5 on the Likert scale). Columns 4 to 6 examine impacts on longer-term intentions of migrating to Europe and using the backway. The desire to migrate to Europe remains very high, with 28 percent of the control group saying they will surely move in the next five years; 52 percent saying they are sure or likely to migrate; and 42 percent saying they would consider the backway. We find all three treatments have some negative impact on these expressed likelihoods. The largest and most 28
# **I. Introduction**

While global income inequality has declined since the 1990s, income inequality levels within countries have been rising for a wide range of developed and developing countries, capturing the attention of social activists and policymakers alike. More than half of all countries and close to 90 percent of advanced economies have seen an increase in income inequality since 2000, with the income Gini increasing by more than two points in some instances (IMF 2023). Yet inequality is present not only in the space of incomes; it inhabits other key dimensions such as health, education, and social services, whose dimension-specific inequalities may reinforce or dampen the impact of income inequality. Empirical data on income inequality offers only a partial view of what Amartya Sen has termed “economic inequality” and can limit the scope and accuracy of a country’s policy responses (Sen 1997, 1999).

Dashboards and weighted averages of dimension-specific inequalities can help paint a broader picture of economic inequality within a country, and its evolution through time. However, they completely ignore the joint distribution of dimensional variables, which conveys important information on how people in the country are experiencing inequality. For example, it could be the case that most people with lower levels of one variable have lower levels of the others, resulting in a rigid hierarchy of achievement vectors and, arguably, greater economic inequality. Alternatively, people could exhibit mixed levels of achievements, dampening positive association and its impact on inequality. Measurement tools sensitive to the joint distribution can distinguish between these situations, and better gauge the extent of economic inequality.<sup>1</sup> Following the pioneering work of Kolm (1977), Atkinson and Bourguignon (1982), and Maasoumi (1986), there have been significant advances in the range of tools available for measuring inequality when there are multiple dimensions, including new classes of measures, axioms for discerning among measures, and dominance methods for ensuring comparisons are robust.<sup>2</sup> In their survey on multidimensional poverty and inequality, Aaberge and Brandolini (2015, p. 201) note a strong demand for multidimensional analyses by policymakers and other stakeholders, and indeed, empirical and policy applications of multidimensional poverty indices

> 1 For Aaberge and Brandolini (2015, p. 146) this sensitivity is “the single feature that distinguishes multidimensional from unidimensional analysis.” Note that it also places additional requirements on the data that can be used. 2 See the recent surveys of Aalberg and Brandolini (2015), Andreoli and Zoli (2020), Glassman (2019), and Seth and Santos (2018).

2