rafmacalaba/datause-encoder-data huggingface dataset:
Table 9. Macroeconomic and fiscal indicators during government debt booms (crises vs. non-crises)

||World<br>Comple|Advanced<br>economies<br>ted booms wi|EMDEs<br>th crises|World<br>Complete|Advanced<br>economies<br>d booms with|EMDEs<br>out crises|
|---|---|---|---|---|---|---|
|**Macroeconomic outcomes**|||||||
|Output growth|2.2|0.3|2.3|2.8***|2.4***|2.9**|
|(Percent)|[-0.1, 3.8]|[-0.6, 2.5]|[0.3, 3.9]|[1.0, 4.5]|[1.2, 3.6]|[0.9, 4.7]|
|Investment growth|-0.4|-2.6|0.7|2.7***|1.8***|5.8***|
|(Percent)|[-4.4, 3.9]|[-4.7, 1.1]|[-3.8, 4.1]|[0.7, 6.8]|[0.3, 2.8]|[1.3, 8.8]|
|Consumption growth|1.1|0.3|1.7|2.9***|2.2***|3.5***|
|(Percent)|[-0.2, 3.9]|[-0.5, 2.2]|[-0.1, 4.2]|[1.8, 4.6]|[1.7, 3.2]|[2.1, 6.0]|
|**Debt sustainability**|||||||
|Primary balance, initial|0.1|1.1|-0.5|0.7|0.6|0.7**|
|(Percent of GDP)|[-2.0, 2.7]|[-0.7, 3.2]|[-2.4, 0.9]|[-1.1, 2.8]|[-0.7, 2.0]|[-1.5, 3.5]|
|Primary balance, changes|-0.1|-0.3|0.0|-0.3*|-0.2*|-0.3***|
|(Percentage points of GDP)|[-0.8, 0.4]|[-1.0, -0.1]|[-0.6, 0.6]|[-0.9, 0.1]|[-0.6, 0.0]|[-1.5, 0.1]|
|Sustainability gap, initial|2.9|2.8|4.6|0.9**|0.7***|1.9|
|(Percent of GDP)|[1.1, 5.1]|[1.4, 5.1]|[0.8, 5.1]|[-1.0, 3.4]|[-1.0, 1.2]|[-0.8, 5.2]|
|Sustainability gap, changes|-0.8|-1.1|-0.4|-0.4|-0.4**|-0.5|
|(Percentage points of GDP)|[-1.8, -0.2]|[-1.4, -0.4]|[-2.0, 0.1]|[-0.9, 0.0]|[-0.5, 0.1]|[-1.6, -0.2]|
|Interest rate-growth differential, initial|-1.8|-1.3|-12.4|-1.9|0.0|-2.9|
|(Percentage points)|[-12.4, 0.7]|[-2.4, 0.9]|[-27.7, -4.6]|[-6.7, 1.8]|[-2.7, 2.7]|[-13.8, 1.0]|
|Interest rate-growth differential, changes|0.3|0.2|1.8|0.3|0.3|0.4|
|(Percentage points)|[-0.2, 2.3]|[-0.1, 0.7]|[-0.5, 5.4]|[-0.6, 0.9]|[-0.3, 0.9]|[-0.6, 2.2]|

Note: Table shows medians, as well as interquartile ranges (i.e., two numbers in brackets), of macroeconomic and debt sustainability indicators across completed government debt booms with and without crises in respective country groups. Indicators on macroeconomic outcomes are annual averages during debt booms. In indicators on debt sustainability, initial and changes refer to, respectively, the level at the beginning, or trough, of debt booms and average annual changes during debt booms. *, **, and *** denote that medians are statistically significantly different between booms with and without crises in respective country groups at the 10 percent, 5 percent, and 1 percent levels, respectively.

29
FIGURE A3 **(Continued)** NIGERIA: REAL MONEY M2 <!-- Start of picture text --> MD-<br>U<br>00.4~,.<br>I~~~~~~~~~~~~ L1 ;1U 84<br>eo<br>70 7t 72 7J 74 75 70 n7 73 7S Kt 31 32 3. 04 35 a 37<br><!-- End of picture text --> <!-- Start of picture text --> ZAMBIA: REAL MONEY M2<br>a 070-35<br>- 2<br>08<br>21-<br><!-- End of picture text --> Ne:Thc~;e nanels show the end-veai stock of MZ deflated by the local CPI to 1970 prices. 1970-88 80
# **1. Introduction**

The subject of development economics has evolved from a largely theoretic discipline focused on the process of economic transformation in developing countries to a multi-faceted field in which there may be no single big idea (Lindauer and Pritchett, 2002), but in which there is active debate about the role of macro versus micro approaches (e.g. Rodrik, 2008; Bardhan, 2013), and in which there is so much data and empirical work that there is debate as to whether there has been enough emphasis on theory (e.g. Mookherjee, 2005, Banerjee, 2005).

How is this state of the field reflected in what is taught as development economics in developing countries? The answer to this question is important for several reasons. First, the next generation of policy makers responsible for implementing key development policies are likely to have their views of what policies they should pursue heavily influenced by what they have been taught. Second, the answer is instructive about the ease at which ideas and knowledge flow from the research frontier, which is of more general interest for development. Third, what is taught as development economics around the world should be of interest to researchers producing development research, and to professors teaching the subject who may wish to benchmark their own courses against what is done elsewhere. Finally, it is important for determining the extent to which development economics education is meeting the key learning goals of economics education (Allgood et al, 2015).

We collected original data on what is taught as development economics and how it is taught through a combination of surveys of instructors and standardized coding and analysis of course syllabi and examinations.<sup>1</sup> We were able to collect this for 145 undergraduate courses in 54 developing countries, and 81 masters courses in 34 developing countries. We benchmark these courses against undergraduate development economics classes taught at top-20-ranked U.S. economics departments, and at a selection of other U.S. economics departments.

> 1 We focus on development economics courses. In some countries the content of other economics classes such as macroeconomics, public finance, and labor economics may include applications of these topics to the home country or developing countries in general. Given the heavy reliance of many undergraduate classes on U.S.-produced textbooks and the emphasis on teaching the principles and methods of each subdiscipline at the introductory level, our sense is that this coverage is rather limited in many countries, but does occur more in Latin America, especially in the top economics programs in this region.

2
# **I.  Introduction**

More than a billion people around the world still lack access to electricity. For untold others who are nominally connected to the grid, access to electricity is often uneven and unreliable, characterized by frequent, long‐lasting power outages.<sup>2</sup> Outages often occur because of technical failures. In many developing countries, they also reflect the efforts of utilities to cope with persistent power shortages through load shedding (or rolling blackouts). According to business surveys, firms in about a third of developing countries experience at least 20 hours of power outages a month on average. The situation is even worse in South Asia, where firms report almost one outage a day, with an average duration of 5.7 hours.<sup>3</sup> Providing access to reliable electricity is vital for ending extreme poverty and boosting shared prosperity. Many studies have examined the welfare effects of grid connections on households (for example, Dinkelman 2011; Khandker, Barnes, and Samad 2012 ; Lipscomb, Mobarak, and Barham 2013; Banerjee et al., 2015, and Chakravorty, Emerick, and Ravago 2016). While earlier analyses focused on credibly estimating the causal relationship between household welfare and a binary variable of electricity access, few have taken into account whether the “connected” household actually receives an adequate level of service.

Going beyond basic connectivity is essential to understanding the effects of modern energy in the developing world. Even where electrical wires are present, children cannot study in the evenings if there is no power to provide light—nor can people keep their businesses open. Poor‐quality electricity service also means that households must continue to rely on costly backup services. And because consumers often pay a fixed monthly charge regardless of their actual consumption, long power outages increase their effective electricity tariff. All this suggests that models ignoring the quality of electricity service may underestimate the net benefits associated with reliable electricity supply. A recent World Bank study concluded that reliable supply is the defining issue in achieving the economic benefits of grid‐ connection (Pargal and Banerjee 2014).

The purpose of this paper is to examine the impact of rural electrification on household welfare while paying special attention to how the quality of electricity supply affects the results. Our study takes advantage of newly available panel data from the Indian Human Development Survey (IHDS), which includes a question asking households to estimate the daily average duration of outages. Using hours of

> 2 In the following, we use the terms “access” and “grid‐connection” interchangeably.

> 3 World Bank Enterprise Surveys (http://www.enterprisesurveys.org).

2
_Table 6: Loss size by shock type_

|**Dependent variable:**Percent of harvest lost|(1)<br>Ethiopia|(2)<br>Malawi|(3)<br>Mali|(4)<br>Niger|(5)<br>Nigeria|
|---|---|---|---|---|---|
|Drought|6.570***|7.767***|2.536|3.183|-4.939|
||(1.853)|(2.668)|(3.966)|(2.235)|(4.152)|
|Pests|5.955***|-4.283|11.48*|-6.561**|-10.28**|
||(1.731)|(2.867)|(6.384)|(2.710)|(4.711)|
|Flood||15.11***||-1.935|9.263*|
|||(2.995)||(5.656)|(4.984)|
|Constant|34.08***|47.68***|45.31***|64.71***|31.80***|
||(1.145)|(1.944)|(3.875)|(2.292)|(4.936)|
|Observations|10,478|2,180|7,389|6,537|449|
|Crop FE|YES|YES|YES|YES|YES|
|Survey Wave FE<br>Note: Results from OLS regressions with the sha|YES<br>re of potential ha|YES<br>rvest lost as ou|YES<br>tcome variable.|YES<br>Base category|YES<br>for shock|

Note: Results from OLS regressions with the share of potential harvest lost as outcome variable. Base category for shock dummies is other shock. The estimates are weighted to be nationally representative. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 27
# _1.2. The Health and Development Impacts of HIV/AIDS in Southern Africa_

# HIV **prevalence**

The estimated adult prevalence of HIV in selected countries of Southern Africa is shown in Figure 1-1. South Africa is by far the largest of the countries listed, with about 61 percent of the adult population of the region, and therefore dominates the overall average rate of 21.4 percent. There are, however, considerable regional variations within South Africa, with rates above 30 percent in KwaZulu Natal, and below 10 percent in the Western Cape. The highest regional rate of 35.8 percent is found in Botswana.

The figure also shows the populations (both total and adult - i.e., aged 1549), the per-capita GDP and the net secondary school enrollment ratios. All of the totals shown in the table are weighted by population from the countries where data were available.

# **Demographic impact**

The demographic impacts of HIV/AIDS arise through its impact on mortality and fertility. Deaths due to AIDS can be expected to cause a dramatic increase in the mortality rates in the 15-49 age group, where they would otherwise be expected to be very low. In the case of infant mortality, there is clear evidence from Botswana and other African countries that HIV/AIDS has caused a stagnation or even a rise in infant and child mortality rates (Timaeus 1998).

The demographic impact of HIV/AIDS is often measured in terms of life expectancy. Life expectancy is, however, an unsatisfactory indicator, since it depends upon assumptions about age and sex specific mortality rates. Accurate measures of age specific mortality are difficult to obtain, and most published estimates of life expectancy make use of mortality rate projections. These projections have not yet been validated against national population census data since such data are usually only collected every 10 years. Both South Africa and Botswana have recently conducted population censuses, and results from these will in due course yield estimates of mortality. Early estimates from the Botswana census (no official publication is yet available) indicate that the projections used to date in life expectancy calculations are not valid.

The evidence from survey data gives some indication of impact. Figure 1-2 below shows measures of adult mortality by age and sex from Botswana (1997 demographic survey) as compared with mortality (in dotted lines) as measured in the 1991 population census. There is clear evidence of substantially raised mortality in the 25 to 40 age groups.

_2_
Figure 4: Parallel trends assumption <!-- Start of picture text --> Panel A: Lights per capita growth Panel B: Population denisty<br>No mining Active mining No mining Active mining<br>.4<br>200<br>150<br>.2<br>100<br>0<br>50<br>-.2 0<br>1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br>Year Year<br>Panel C: Spatial Gini Panel D: Spatial Sen index<br>No mining Active mining No mining Active mining<br>3.5<br>0<br>3<br>-.02<br>2.5<br>-.04<br>2<br>-.06<br>1.5<br>-.08<br>1<br>-.1<br>.5<br>-.12<br>1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 0<br>1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br>Year<br>Source: Authors' construction using lights and population data. Year<br>Population density<br>changes relative to 1997 changes relative to 1997<br>Mean per capita lights growth<br>Sen Index<br>Spatial light Gini index<br>changes relative to 1997<br>changes relative to 1997<br><!-- End of picture text --> Panel B of Figure 4 shows the average annual changes in population density – our migration measure – relative to 1997. That panel shows that the annual population density changes before the accession were steady and trending in parallel between the treated and control districts. Significant changes in population densities are observed in the treated districts in the postaccession period. These upward trends suggest that in the wake of booming mining activities, treated districts experienced increased inflows of people; these people were, arguably, migrating in search of jobs in the mining sector (as well as into other occupations in the local economy, which benefits from the rise in purchasing power induced by mining operations). Surges in population density are typically induced by agglomeration and spillover effects common in economically booming areas.

Panel C shows the average annual changes in spatial Gini – a measure of district level inequality. The panel reveals similar pre-accession trends, followed by a widening gap in inequality between the treated and control districts post-accession: treated districts experienced significant reductions in inequality relative to control districts. With a rise in mining activities, it is not implausible to conjecture that the treated districts had more economic activities than the control districts, allowing 16
to construct ICT stocks.<sup>7</sup> Following the methodology in the construction of the Penn World Table (PWT) starting with version 8.0 (Feenstra, Inklaar and Timmer, 2013; Inklaar and Timmer, 2013) we use constant depreciation rates that differ for telecommunication (11.5% p.a.) and other information technology (31.5% p.a.).<sup>8</sup> To estimate NICT investment, we subtract nominal ICT investment from aggregate investment measured in the PWT. Similar to the procedure for ICT, we deflate the resulting investment series using the BEA-based NICT price index from Eden and Gaggl (2014) and compute NICT capital stocks using the PIM, with a depreciation rate of 6% p.a., as estimated by Eden and Gaggl (2014) for the US.<sup>9</sup>

# 2.1. Comparison With Existing Datasets

At this point, it is perhaps useful to compare our measurement strategy with existing datasets that include measures of ICT capital. There are two main datasets containing ICT capital measures: (a) the Groningen Growth and Development Center’s KLEMS datasets, and (b) the Conference Board’s Total Economy Database (TED).

The key difference with the KLEMS datasets is country coverage. The EU KLEMS covers 27 high income countries between 1970-2013 (O’Mahony and Timmer, 2009) and the WORLD KLEMS database provides additional ICT/NICT data for Canada (Gu, 2012) and Russia (Voskoboynikov, 2012). In contrast, our dataset on ICT/NICT capital stocks covers 70 countries at various levels of development (see Panel D of Table 1) and is—to the best of our knowledge—the most compre-

> 7Note that we explicitly choose to use a single price index to deflate all four investment series, even though we could in principle estimate the disaggregated price indexes from the BEA’s fixed asset accounts (e.g., Figure 2). We do so for two reasons: first, it allows us to aggregate the four categories simply by adding the four resulting constant dollar stocks within each category. With time varying investment-type specific price indexes aggregation would be less straight forward. Moreover, while Figure 2 shows that there were some differences in the trends of price indexes for different ICT categories, the general trend is relatively uniform. Second, and more importantly, it is not clear how representative the differential trends between detailed investment categories in the US are for other (especially poor) countries.

> 8While Eden and Gaggl (2014) estimate time varying depreciation rates for the US, and we could have used their procedure to estmate these here as well, we choose to stick with the same methodology as in the PWT for two reasons: first, at several points in our data construction we use PWT aggregates to imupte residual quantities, and second, we would like to keep our dataset conceptually comparable to the PWT.

> 9Note that we kept our description of the data construction brief here and we provide additional details in Appendix A.

7
_ANDR EE_<sup>_ ́_</sup> _AND PAPE_ _SEPTEMBER 5, 2023_ _4_ systems. To handle the relatively large temporal gaps in the IFPRI data, the paper incorporates exchange rate data, showing that the methods can also produce estimates of unofficial parallel-market exchange rates that allow dollarizing the price data streams in real-time. Finally, the paper shows that the methods are applied successfully to short time series, opening the door to piloting the methods in conjunction with ongoing mobile phone surveys in a high-frequency setting.

The application to PNG data is valuable as formal traditional methods suitable for high frequency price tracking are not implemented in the Pacific Island region for a number of reasons, including low capacity, challenging geography and incomplete digitalization of market price information. Furthermore, prices collected with traditional methods are released often only after the data are already outdated. The Pacific Islands are generally import dependent for food products such as grains, meats, dairy products and vegetable oils, which all rose sharply during the previous major global food price spike in 2008 (McGregor et al., 2009). However, without adequate price monitoring capabilities, it is difficult to assess how the development context is changing in the region while countries across the world are grappling with falling living standards (Egger et al., 2021), high inflation (Etang et al., 2022; World Bank, 2022), and volatility in commodity prices (World Bank Group, 2022). The paper concludes that the explored methods may have wider applicability and could help to fill crucial data gaps in the Pacific Islands, especially in conjunction with specifically designed continuous surveys.

The remainder of the paper is as follows. Section II discusses the survey data and imputation methodology. Section III presents imputation results for different setups in terms of the number of food items, markets and the temporal dimension of the survey data. Section IV concludes on the viability of the methods and the potential use of machine-learning augmented high frequency surveys in the Pacific Islands.

# **II. Methods**

# _A. Imputation strategy at a high level_

Table 1 visualizes the general missing data problem when gathering price data for the use of tracking inflation. In particular, the example considers tracking the basket-price in a simple three item setting, showing that even when there is a reasonable amount of price data, it may not be possible to observe the change in basket price at any given moment. The overall idea behind the suggested solution is to fill price gaps by leveraging prices of the same item in different markets or of other items in the same market.

Completing the missing entries is challenging using standard imputation tools. For instance, carrying the last observation forward suggests zero change and so results in a major bias in an application that aims to monitor inflation (price change), particularly in high inflationary environments. This is an issue when
**Figure 23** _Flow of returning temporary migrants by last destination_

<!-- Start of picture text --> year of ret migration from last time<br>Total Greece Italy<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>30000<br>20000<br># of temporary migrants 10000<br>0<br><!-- End of picture text -->

# **Modeling migration and return decisions**

# _The empirical model_

The descriptive analysis set out above has revealed an extremely dynamic process, both in terms of migration trends as well as their correlates. From a policy perspective, it becomes crucial to relate determinants to outcomes and to understand how changes over time have influenced migrants’ decisions. Migration is a repeated decision which an individual continues to make every year when he or she decides to either migrate or stay in Albania; and for those who have migrated, whether to return or stay abroad. As such, modeling the decision to migrate using dichotomous choice models, as often encountered in the literature, may be limited and misleading as such models ignore the dynamic and repetitive nature of the process. We therefore suggest that the migration process is more properly modeled as a repeated decision conditional on past occurrences and affected by changing conditions over time. In addition, dichotomous models fail to provide information on the diffusion of migration and thus on the role played by changing conditions on the spreading, or thinning out, of the phenomenon. In this study, we model both the decision to migrate, as well as the decision to return, using a single-spell duration model framework.<sup>9</sup> The pre-migration and migration spells will be analyzed in relation to a number of time-variant (and invariant) covariates in order to shed light on the dynamics of migration, its diffusion over time, and the efficacy of policy interventions in regulating migration flows.

17
**Flies Without Borders:* Lessons from Chennai on Improving India’s Municipal Public Health Services** Monica Das Gupta<sup>1</sup> , Rajib Dasgupta<sup>2</sup> , P. Kugananthan<sup>3</sup> ,Vijayendra Rao<sup>4</sup> , T.V. Somanathan<sup>5</sup> , and K.N. Tewari<sup>6</sup>

**_Keywords:_** <mark>Public health, Sanitation, Urban, Local government, India, Governance</mark>

**<mark>JEL Codes</mark>** <mark>: D</mark> 73, H11, I18, O18

**_Acknowledgements:_** Valuable suggestions from Pranab Bardhan, Shashank Bhide, Jean Dreze, K.P. Krishnan, Peter Miovic, Dilip Mookherjee, and J. Radhakrishnan are gratefully acknowledged. *We thank “Doctors Without Borders” for inspiring the title of this paper.

- 1 (Corresponding Author) Research Professor, Department of Sociology, University of Maryland, College Park, USA. Email: mdasgupta@gmail.com

- 2 Professor, Centre of Social Medicine and Community Health, Jawaharlal Nehru University, New Delhi, and earlier Deputy Health Officer, Municipal Corporation of Delhi. Email: <u>dasgupta.jnu@gmail.com</u>

- 3 City Health Officer, Chennai Municipal Corporation (retired). Email: <u><mark>drkugan@yahoo.com</mark></u>

- 4 Lead Economist, Development Research Group, The World Bank, Washington DC, USA. Email: <u>vrao@worldbank.org</u>

- 5 Joint Secretary, Government of India, New Delhi, formerly Director at the World Bank. Email: <u><mark>tvsomanathan@gmail.com</mark></u>

- 6 Municipal Health Officer, Municipal Corporation of Delhi (retired). Email: <u><mark>kntewari@yahoo.com</mark></u>