rafmacalaba/datause-encoder-data huggingface dataset:
three years of higher secondary schooling. In addition, there is a variable reporting years of schooling available for some samples (17 out of 21 samples).

**Linking generations.** The data collection is organized at the household level,<sup>3</sup> so it is possible to link individuals who live with their parents in the same household at the time of the interview using a variable that details the relationship between each individual and the head of the household. For simplicity, I use individuals linked to their probable father and probable mother according to the procedures used by IPUMS for family interrelationships.<sup>4</sup> This method of linking generations differs from the one used in Munoz (2021a) for the main analysis, which links individuals ages 14-25 to all the older relatives living in the same household, however, as mentioned in the robustness section of Munoz (2021a), both methods give indicators of mobility that are close to being perfectly correlated.

For the main analysis, I use a sample of individuals co-residing with at least one “probable” parent, I focus on individuals with age in the range 14-25 in the main analysis where completion of primary education is the outcome of interest. However, when I explore completion of secondary education as the outcome of interest, I use individuals with age in the range 18-25. These age ranges are chosen to target individuals with an age sufficiently large so they can complete the educational level of interest if progressing in line with the official age for which the educational system is designed but young enough so they are likely to still live with their parents. The effort of maximizing the co-residence rate attempts to minimize potential truncation bias in the estimates of intergenerational mobility (see Emran, Greene, & Shilpi, 2018; Francesconi & Nicoletti, 2006). Nonetheless, Munoz and Siravegna (2021) show that the bias is small for estimates of absolute mobility that use census data. In addition, there is a low level of re-ranking when these estimates are used to rank economies across time and space by level of mobility relative to the ranking obtained with estimates that use retrospective information (i.e., surveys that ask all individuals for the level of education of their parents).

> 3I exclude several census samples for which this is not the case.

> 4More details can be found in the following link: https://usa.ipums.org/usa/chapter5/chapter5.shtml.

5
11

**Table 2**

**Inflation equation for domestic industrial goods**

||(1)|(2)|(3)|
|---|---|---|---|
|**constant**|1.428|0.826|1.665|
||_(1.28)_|_(0.76)_|_(1.60)_|
|**_Ac_**|**0.634**|**0.688**|**0.516**|
||_(9.98)_|_(13.57)_|_(6.38)_|
|**_Ap*_**|0.113|0.1.27|-0.039|
||_(1.39)_|_(1.57)_|_(-0.36)_|
|y (a)|-2.275||-1.523|
||_(-1.81)_||_(-1.30)_|
|x|0.604|0.071|0.419|
||_(2.02)_|_(0.95)_|_(1.50)_|
|**AGDP**(a)||**-0.313**||
|||_(-2.03)_||
|[p f3c-(1-p)p*]Il|-0.118|-0.069|-0.139|
||_(-1.27)_|_(-0.76)_|_(-1.60)_|
|**_with_**||||
|**P**(b)|0.635|0.631|0.645|
||_(11.16)_|_(6.24)_|_(16.19)_|
|**i**|||0.004<br>**(1.984)**|
|**AdjR**<sup>**2**</sup>|**0.891**|**0.893**|**0.905**|
|**DW**|2.33|2.01|2.304|

**Notes: t-statistics in parenthesis. (a) Instnnnented by lagged industrial GDP (up to four quarters), and the variations in the lagged real exchange rate and the lagged nominal** wage;(b)the **coefficients on c-l and** P$ 1 are constrained to sum to one.

The long-term markup equation in the Argentina's industrial sector can be expressed as follows:

# Equation 3

**_pi.-_** _c,= 0.57 (p*p,) -_ 3.36 _Ap,,_

The first term captures the long-run effect of foreign prices on domestic industrial prices. An increase in foreign prices leads to some, but less than one-for-one increase in domestic prices and, thus, translates partly into an improvement in competitiveness and
Table 7 Securities Holdings of Financial Institutions (trillion yen)

||Total|Banks|Trust<br>Fund<br>Bureau|Gov<br>Fin<br>Inst|Insur<br>Comp|Agric<br>Coop|Other|
|---|---|---|---|---|---|---|---|
|3945|0.14|0.06|0.048|||||
|1950|0.27|0.14|0.078|||||
|1960|3.2|2.2|0.4|..|0.3|..|0.06|
|1970|17.8|9.7|3.4|0.03|1.7|0.2|0.6|
|1980|119.8|62.9|20.8|0.3|10.2|2.0|5.9|
|1988|338.6|160.3|62.4|0.7|51.3|2.0|12.6|

Notes: Total includes overlapping accounts, i.e. , certificates and bank debentures held by other financial institutions.

Banks include city banks, regional banks, trust banks, long-term credit banks, sogo banks and shinkin banks.

Other includes the Shokochukin and Norinchukin banks.

The series is discontinued after 1988.

Source: Bank of Japan, _Economic Statlatics Annual,_ 1989 (pp. 169-70) and 1968 (pp. 167-168).

38
# Introduction

Data on export prices of manufactured goods for the industrial countries have long been used throughout the World Bank to assess and project trends in the dollar costs of imports needed in Bank-financed projects in the developing countries. These data also help to shape the Bank's view of trends in the barter terms of trade between commodities and manufactures in world markets, i.e., trends in "real" commodity prices. In recent years there have been growing concerns that trends in these price data are biased upward. This paper addresses these concerns.

The issue of long-term changes in the terms of trade between primary products and manufactures has a long history, going back at least to Torrens (1821), Ricardo (1832), and Mill (1848). It engaged the attention at various times, of Jevons (1866) and Keynes (1912), and of Singer (1950), Prebisch, in United Nations (1950), Lewis (1952), Kindleberger (1956), Haberler (1959, 1961) and Viner (1953), among more recent commentators. The classical views were based mostly on theoretical considerations of the inevitability of increasing costs, while the later debate has been centered around empirical questions.

Many improvements have taken place in the measures of prices of primary products, but the manufactures side of the debate has rested on a collection of inappropriate or low-quality series stitched together in a variety of ways by different analysts. The purpose of this paper is to examine the implications for this discussion of some efforts to construct better price series for exports of manufactures by developed countries for the period since 1953. Among the issues that are examined here with the new price data are:

- (1) The effects of shifting the dates of the export weights used; (2) The effect of changing the country on whose export pattern the weighting is based;
<u>rT}</u> ~~a~~ <0 ~~mBK~~ _ CF) 4 F I <u>Tj</u> i ~~?~~ <u>UF</u> I <u>nf</u> F I a i (2) mt} (@B) > D; i= ~~(=~~ F + cf - cl) x 5D 5D 5D 5D 5D <u>aak</u> 708 <u>Ger</u> POs <u>Scr</u> Os ~~Se~~ < 95 <u>Ser</u> <0 <!-- Start of picture text --> i<br>IF<br>l<br>F<br><!-- End of picture text --> I l (7;) (éi;) ij = 1 + Ei Tj

1..j—1
Policy Research Working Paper 10127

# **Abstract**

This paper uses a hedonic property price function to estimate the relationship between flood risk and rents in four Sub-Saharan Africa cities: Accra, Antananarivo, Dar es Salaam, and Addis Ababa. The analysis relies on household survey data collected after flood events in the cities. Flood risk is measured with self-reported data on past flood exposure and perception of future risk of flooding of households. The study finds that flood risk is associated with lower rents in Accra, Antananarivo, and Addis Ababa, ranging from 14 to 56 percent lower. In contrast, risk is associated with higher rent in Dar es Salaam, which could be potentially attributed to a combination of lack of awareness of flood risk among renters, high transaction costs and omitted variable bias. For example, only 12 percent of households living in flood-prone areas were aware of the flood risk when they moved in. In Antananarivo, job density is associated with higher rents while in Accra and Addis Ababa, higher job density is associated with lower rents. Results are negative but not significant in Dar es Salaam. When interacting job density with flood risk for each city, the negative effect of job density on rents is higher (in absolute value) when flood risk is high in Accra and Addis Ababa, and the positive effect of job density on rents becomes negative when flood risk is high in Antananarivo. This relationship is not found in Dar es Salaam. The finding seems to suggest that access to jobs is a factor driving people to settle in flood-prone areas.

This paper is a product of the Urban, Disaster Risk Management, Resilience and Land Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at aerman@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
to municipalities) or more than 20 percent of federal tax revenue. State transfers to municipalities accounted for an additional 2.5 percent of GDP, or 30 percent of state tax revenue. Intergovernmental transfers therefore represent an important source of financing for sub-national governments, especially municipal governments. Comparisons with other federal countries show that the Brazilian states have considerable capacity to raise their own tax revenues and are less dependent on transfers than state governments in other countries. However, municipalities in Brazil are more dependent on transfers than their counterparts in other federal countries (Table 1).<sup>4</sup>

**Table 1: Revenue Composition of Subnational Governments**

|||**Taxes**|**Transfers**|**Other **|**Total **|
|---|---|---|---|---|---|
|Brazil(2004)|States|68%|20%|12%|100%|
||Municipalities|24%|66%|10%|100%|
|Mexico (2000)|States|41%|51%|8%|100%|
||Municipalities|58%|27%|14%|100%|
|Canada (2002)|States|60%|16%|24%|100%|
||Municipalities|43%|39%|19%|100%|
|United States (2001)|States|51%|26%|23%|100%|
||Municipalities|38%|40%|22%|100%|
|Australia (2001)|States|31%|50%|19%|100%|
||Municipalities|38%|17%|45%|100%|
|Germany (2002)|States|71%|17%|12%|100%|
||Municipalities|37%|35%|28%|100%|
|Colombia (2000)|States|17%|50%|33%|100%|
||Municipalities|28%|31%|41%|100%|
|Spain (2000)|States|33%|61%|6%|100%|
||Municipalities|51%|36%|13%|100%|
|South Africa (2002)|States|1%|96%|3%|100%|
||Municipalities|23%|18%|59%|100%|
|Average(without Brazil)|Municipalities|38%|46%|16%|100%|
||States|40%|30%|30%|100%|

Source: World Bank (2008a) Since their inception the vast majority of intergovernmental transfers have been financed through revenue-sharing rules laid down in the Constitution. The automatic and formulabased nature of these rules guarantees transparency and autonomy, keeping political interference at bay. The use of revenue-sharing rules has increased in recent decades. A

> 4 There are significant regional disparities in Brazil: while the more developed states can be considered largely self-sufficient, the great majority of the more than 5,500 municipal governments are highly dependent on transfers.

7
# **1. Introduction**

Much has been written recently about equity or fairness in health financing, the financial protection function of health systems, "catastrophic" health care costs, and the impoverishment associated with health care outlays. The World Health Organization (WHO), for example, in its 2000 World Health Report (WHR) _Health Systems: Improving Performance_ (World Health Organization 2000) proposed and estimated values of a fairness of financing contribution (FFC) index, and argued that providing financial protection to households is an important goal of any health system. The International Labour Organization (ILO), in a forthcoming report _Toward Decent Work: Social Protection in Health for all Workers and their Families_ (Baeza et al. 2001) discusses the importance of considering "catastrophic" health care costs and of modifying insurance systems to provide protection against them. Reflecting the importance of the theme in its _Voices of the Poor_ consultative exercise (Narayan et al. 2000), the World Bank in its 2000/2001 World Development Report (WDR) _Attacking Poverty_ (World Bank 2000) emphasized the impoverishing effects of ill health in general and of the costs of health care in particular. Furthermore, the 1997 strategy paper for its health sector (World Bank 1997) committed the Bank to "working with countries to reducing the impoverishing effects of ill health...." Two distinct strands of thinking are evident in this debate. One is based on egalitarian notions of equity or fairness. A common theme here is that payments for health care ought to be linked not to usage of health services but rather to ability to pay, and the concern is with the degree of inequality in one or other variable. The other focuses on minimum standards. Here there is some divergence of view, but in each case the concern is not with inequality in any variable but rather with a variable exceeding or falling short of a threshold. One approach sets the threshold in terms of proportionality of income. The concern is to ensure that households do not spend more than some prespecified fraction of their income on health care (call it z). Spending in excess of z is labeled "catastrophic". The idea is, in effect, to ensure that households have at least (I _-z)_ of their income to spend on things other than health care. The other approach sets the minimum in terms of the absolute level of income. The concern here is to ensure that spending on health care does not push households into poverty-or further into it if they already there. These two approaches are fundamentally different-neither is "right", and the choice between them must be made on normative and ideological grounds.

I
|Require government<br>agencies to collect data<br>on hate crimes commit-<br>ted on ethnicgrounds?|<br> <br> <br>Yes|Judiciary Sys-<br>tem Act<br>(2007), Arts.<br>377–384|N/A|N/A|N/A|N/A|N/A|
|---|---|---|---|---|---|---|---|
|Does the law mandate t<br>|rainings of t<br>|he following professi|onals on recognizing<br>|and identifying hate crimes?<br>||||
|Paramedics?|No||N/A|N/A|N/A|N/A|N/A|
|Police officers?|No||N/A|N/A|N/A|N/A|N/A|
|Prosecutors?|No||N/A|N/A|N/A|N/A|N/A|
|Judges?|No||N/A|N/A|N/A|N/A|N/A|
|Social workers?|No||N/A|N/A|N/A|N/A|N/A|
|Other?|No||N/A|N/A|N/A|N/A|N/A|
|Services to Victims of H<br>Does the law mandate t|ate Crimes<br>heprovision|<br>of anyof the followi|ngservices to victim|s of hate crimes:||||
|Legal assistance (includ-<br>ing asylum applica-<br>tions)?|Yes|Legal Aid Act<br>(2005), Arts.<br>21, 22<br>Asylum and<br>Refugees Act<br>(2002), Art.<br>20|N/A|N/A|N/A|N/A|N/A|
|Shelter/Housing?|No||N/A|N/A|N/A|N/A|N/A|
|Forensic or medical<br>examinations?|Yes|Law on Sup-<br>port and Fi-<br>nancial<br>Compensa-<br>tion to Crime<br>Victims<br>(2006), Art. 8|N/A|N/A|N/A|N/A|N/A|

Anti-Discrimination Law and Shared Prosperity 49
the household and by electronic stove-use monitors (SUMs) attached to each stove to record time and temperature readings. Interviewers returned to the households to record SUM measurements four times throughout the first year following the randomization. Adoption was high and remained high throughout the year—12 months after the intervention use rates were approximately 90 percent with no statistically significant differences across the treatment arms. The frequency of use remained high as well, with treatment households averaging 2.5 cooking events per week on the _Mirt_ stoves, a rate consistent with the traditional frequency for baking and storing _injera_ (Beyene et al., 2015).

Important for interpretation of the health results presented below, baseline results suggest households chose to continue using traditional three-stone open-fire stoves for all non- _injera_ cooking. _Mirt_ stoves are highly specialized for cooking _injera_ , with the main burner approximately 50 cm in diameter, leaving it necessary to have a second stove to cook other foods such as stews and coffee. The _Mirt_ technology was adopted for its specific use, with overall pollution and health improvements constrained by the continued use of open fires by both treatment and control households.

# **2.3 Three-year follow up and health outcomes**

This study reports on the three-year follow-up investigation conducted in late 2016, approximately 40 months after the establishment of the baseline and the initial distribution of the stoves. Given the initial high adoption rates, we added collections of household indoor air pollution and health outcomes in the follow-up survey to assess the secondary outcomes potentially associated with adoption of the improved biomass stoves. We focused on children and adult cooks—the two groups thought to be most at risk from exposure to indoor air pollution from biomass cooking.

Households in the follow-up survey were re-interviewed with minimal and balanced attrition across treatment and control arms. Of the original 504 households, 480 were re-interviewed, yielding a 95 percent re-contact rate.<sup>5</sup> Uptake of the original stoves remained quite high as 60 percent of the treatment households continued to use their original _Mirt_ stoves with no observed differences across the price, incentive, or networking groups ( _p_ -value = 0.67).

In the current study, we specifically focus on three sets of secondary health outcomes linked to indoor air pollution and, potentially, _Mirt_ stove use: child growth, symptom reports of respiratoryrelated conditions, and measures of physical function captured through activities of daily living.

First, young children during the key early-life developmental period are thought to be the most

> 5 Appendix Table A1 reports tests of differential attrition across treatment and control groups at both the household and individual levels, and shows that attrition is orthogonal to treatment status as well as a baseline wealth index.

6
Figure 2. Dynamic treatment effects on payroll reported by firm <!-- Start of picture text --> Soft letter Strong letter<br>Filing period Filing period<br>.3 .3<br>.25 .25<br>.2 .2<br>.15 .15<br>.1 .1<br>.05 .05<br>0 0<br>-.05 -.05<br>Marginal effect on payroll (w.r.t control)<br>-.1 Marginal effect on payroll (w.r.t control) -.1<br>-.15 -.15<br>-.2 -.2<br>Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec<br><!-- End of picture text --> Source: Authors' calculations using historical tax declaration records. Coefficients from random effects model plotted.

# Evaluating impacts on employment & average wages

The impacts on the firm wage bill highlighted above can be decomposed into two changes: a change (likely positive) in the average wage per employee and a change (likely negative) in the total number of workers employed ( _reported_ ) by the firm. The letters were meant to address suspiciously low wages for select employees, so the expected impact of these letters was an increase in the total wage bill due to a higher wage reported for targeted employees. However, at the time of the study there was anecdotal evidence that some firms continue to report wages for employees no longer employed by the firm (administrative error in filing), so some employees would be “let go” as a result of these letters. However, a rational decision by the firm would also be to either terminate employees because of the letters (to hide the suspiciously low pay from the authorities) or as a result of the pandemic (lower firm operations, so some employees get let go). For simplicity, we focus on the dynamic treatment effects (using the entire series of data) rather than the cross-sectional ones.

Figure 3 below presents the dynamic treatment effects on the total number of employees (in logs) reported by the firm. Interestingly enough, the impacts from treatment have an immediate effect on behavior that is independent on the impact of the global pandemic: The interaction between the soft letter and the February filing period (pre-pandemic employment, though with a due date after the pandemic started and the letters were received) is strong and negative (1.6% reduction in employees reported, p<0.05), particularly for firms 13
In this model, y denotes real per capita income in federal region I, y�,��� represents 2001, the initial year, human capital is measured by the percentage of population enrolled in secondary school, FDI is measured in U.S. dollars per capita, business climate is based on scores from consulting firm Expert RA for 2003, mineral exports are expressed as a share of total merchandise exports, and urban population as a share of total population. For dependent variables other than income, t-1 denotes the period averages.

Along with attracting more FDI, more rapidly growing regions are likely to have higher business climate scores and are more likely to have rural than urban populations—other things being equal, poorer regions grow more rapidly than rich regions (“beta convergence”). The other variables (human capital, total merchandise exports, share of mineral exports) are not independently significant as determinants of regional growth; however, regions that attract more FDI also tend to export more.

As an illustration of the importance of FDI, if annual FDI in Krasnoyarsk Republic ($14.90 per capita in 2011) were increased to the level of St. Petersburg City ($219.20 per capita), its annual growth in per capita income would increase by an estimated 2–2.27%, raising income by 30 percent after 10 years. And if the initial business climate score of the Komi Republic, 3B2 (“marginal potential-moderate risk”) were raised by one point, to 3B1 (“reduced potentialmoderate risk”), estimated annual growth would have increased from 4.27 to 4.55 percent, raising per capita income by an additional 32 percent after 10 years.

Despite the finding of beta convergence, Russia’s poorest regions have not caught up to the richer regions in absolute terms on average in recent years. Sigma convergence, measured by the trend in standard deviation of log per capita income across regions, fell from 0.585 in 1996 to 0.511 in 2001, but increased to 0.528 in 2011.<sup>4</sup> (Thus, regional inequality <mark>has gotten worse over the period of the regression.) The level of income inequality</mark> across regions in Russia is higher than that in some other countries. Beta convergence, which has been found for subnational regions in the EU, Japan, and the U.S. (Barro and Sala-i-Martin, 1995), is necessary but not sufficient for sigma convergence (these regions, other than Russia, also experience sigma convergence). That is to say, these countries have less regional income inequality than Russia, and regional incomes have become more equal over time. Comparisons for available time periods are as follows (Barro and Sala-i-Martin, 1995, and authors’ calculations for Russia):

|Russian Federation (1996) .58 Russian Federation (2011) .53|
|---|
|United States (1940) .35 United States (1988) .19|
|Japan (1940) .63 Japan (1987) .15|
|Italy (1950) .42 Italy (1987) .27|
|France(1950).21 France(1987).14|

4 This calculation is made for 83 federal subjects with complete data from 1996-2011. Since the subjects with partially missing data are at the extreme ends of the income distribution, actual regional inequality is somewhat greater than that reported here.

9