rafmacalaba/data-use-mentions-extended huggingface dataset:
Industry-level output data come from United Nations (2007), Industrial Statistics Database (UNIDO). This database reports various measures of industry-level activity, such as output, value-added, and industrial production, for 28 three-digit ISIC manufacturing industries comprising the entire manufacturing sector across the world with variable coverage during the period 1960-2003. From this database we keep the data on all countries that JP Morgan classifies as emerging markets, and for which there is industrial data during the post Bretton-Woods period, 1975-2003. This leaves us with a sample of 45 countries. To measure the activity of each industry in each country we use the index of industrial production. Despite some coverage and consistency problems with this measure (see Yamada, 2005), it does not require deflating the nominal values reported, for instance, for value added. This is an important advantage when working with annual data, since it has been documented that price responses to sudden stops may be significantly different across sectors. Table 2 reports some summary statistics for the number of industries and growth rates of industries in the various countries included in the sample. In most countries, there is almost full coverage across the 28 industries and there is positive average industrial growth.

We measure the degree of external dependence of an industry using balance sheet data of listed manufacturing industries in the US, following Rajan and Zingales (1998) approach of considering these needs to be at least partially technologically determined, and measuring them in the United States, a country with well developed financial markets. The data used to build these measures comes from Compustat. To ease comparison and interpretation of the results reported below, the measure of external dependence, and all other industry level characteristics that are interacted with the sudden stop indicators (see next paragraph), are normalized to have zero mean and unit standard deviation. Having a zero mean eases the interpretation of the coefficient for the average effect of sudden stops in equation (1). Having a unit standard deviation eases the comparison 14
Schneider, Pia, Barry Popkin, Meera Shekar, Julia Dayton Eberwein, Charlotte Block, and Kyoko Shibata Okamura. 2020. “Health and Economic Impacts of Overweight/Obesity.” _Human Development Perspectives_ , February, 69–94. https://doi.org/10.1596/978-1-4648-1491-4_CH3.

Shapiro, Myra J, Shauna M Downs, Haley J Swartz, Megan Parker, Diana Quelhas, Katharine Kreis, Klaus Kraemer, Keith P West, and Jessica Fanzo. 2019. “A Systematic Review Investigating the Relation Between Animal-Source Food Consumption and Stunting in Children Aged 6–60 Months in Low and Middle-Income Countries.” _Advances in Nutrition_ , June. https://doi.org/10.1093/advances/nmz018.

Siekmans, Kendra, Patrizia Fracassi, Tomoko Kato, Ti Kian Siow, Diana Carter, and Susan Horton. forthcoming. “Narrative Review on the Hidden Health and Nutrition Costs of Unhealthy Diets in LMIC (Draft).” Rome.

Stanton, Alice V, Frédéric Leroy, Christopher Elliott, Neil Mann, Patrick Wall, and Stefaan De Smet. 2022. “36-Fold Higher Estimate of Deaths Attributable to Red Meat Intake in GBD 2019: Is This Reliable?” _The Lancet_ 399 (10332): e23–26. https://doi.org/10.1016/S0140-6736(22)00311-7.

Stevens, Gretchen A., Ty Beal, Mduduzi N.N. Mbuya, Hanqi Luo, Lynnette M. Neufeld, O. Yaw Addo, Seth Adu-Afarwuah, et al. 2022. “Micronutrient Deficiencies among Preschool-Aged Children and Women of Reproductive Age Worldwide: A Pooled Analysis of Individual-Level Data from PopulationRepresentative Surveys.” _The Lancet. Global Health_ 10 (11): e1590–99. https://doi.org/10.1016/S2214109X(22)00367-9.

The Food and Land Use Coalition. 2019. “Growing Better: Ten Critical Transitions to Transform Food and Land Use.” https://www.
will be near zero in those countries. In high income countries more workers, particularly among the more able, can afford to trade off income against moral or other psychic considerations when choosing a job. The morality-ability correlation will be positive in those countries.

We address these endogeneity concerns in several ways. First, as explained in more detail below, we exploit time series variation in the data, by modifying the sample or alternatively by modifying the specification. Subsequently, we instrument for the morality variables using 2SLS and limited-information maximum likelihood (LIML) in Table 5. Coefficients for the moralityability correlation variable remain positive and highly significant in these tests, while results for the morality proxy are mixed.

Income is measured for 2008, while the morality variables are measured from surveys conducted between 1995 and 2008 but with variation among countries. The potential for estimates to reflect reverse causation from income to morality is reduced somewhat by measuring morality further back in time, e.g. for 1995 instead of for 2008. Accordingly, in equation 4-1 of Table 4 we drop the 19 observations for which morality is measured using only the 5<sup>th</sup> WVS survey wave (2005-2008). This change reduces the mean survey year represented in the data by two years, from 2002 to 2000. The model specification is from equation 2-4, based on RST but with the morality variables added. The coefficient for the morality proxy in equation 4-1 is insignificant and only one-third as large as in equation 2-4, consistent with the view that the latter estimate mostly reflects reverse causation from income to morality. The coefficient on the morality-ability correlation remains positive and highly significant, however, in equation 4-1. Moreover, its coefficient is one-third larger in equation 4-1 than in equation 2-4.

In equation 4-2 we drop an additional 13 observations with a mean survey year greater than 2001. This change reduces the mean survey year by one additional year, from 2000 to 1999. Re23
PASS-THROUGH OF COMPETITORS’ EXCHANGE RATES 17

# 6. Extension: PT of other competitors’ prices to import prices

While pass-through of foreign competitors’ exchange rates (and prices) are the focus of this paper, the simple model (Section 2) and the full model (Section 4) also suggest that import prices will respond to other components of the sectoral price: (i) to the US producer price index (βP P I ) and (ii) to other compatriots’ prices (βOCP ). In this section, I add measures of these two prices to the standard import price pass-through regression (Equation 8). GI2011 competitors’ price index is a combination of foreign competitors’ prices and other compatriots’ prices.

The first two columns of Table 4 show that a 1% increase in other compatriots’ prices (other firms from the same exporting country) is associated with an increase in US import prices of around 0.5% (significant at the 1% level). Controlling for compatriots’ prices also reduces pass-through of the bilateral RER (0.23 → 0.17) in column 1. However, once competitors’ RERs are included as a control, the addition of compatriots’ prices only has a small effect on estimates of bilateral pass-through, suggesting modest rates of “indirect” bilateral pass-through (Section 2).

Columns 3-4 of Table 4 show that pass-through of sectoral US producer prices to US import prices is extremely large: a 1% increase in US producer prices in the same sector raises US import prices by 0.9% (significant at the 1% level). The coefficient is much larger than pass-through of foreign competitors’ prices or GI2011’s measure of import competitors’ prices (both around 0.6). Controlling for the sectoral PPI substantially reduces pass-through of the bilateral RER (from 0.23 → 0.16) and also reduces PT of competitors’ RERs (0.35 → 0.10), which becomes insignificant.

Table
Access figures from Demographic and Health Surveys (1996 and 2006) and census (2002). _Note_ : A country is considered non-water-scarce if its renewable water resource per capita is greater than 3,000 cubic meter per year. — = data not available.

These improvements represent progress toward the Millennium Development Goals for 2015 in both water supply and sanitation. Access to improved water increased from around 57 to 67 percent of the population between 1996 and 2006. Access to improved sanitation rose from 18 percent in 1996 to 30 23
Figure 8: Percentage of Workers with “Good” Quality Jobs, by Characteristics <!-- Start of picture text --> Education Firm Size Formality<br>100 77.2 78.1 91.4 79.3<br>80 61.7<br>60<br>40 30.5 21.9 20.3<br>20<br>0<br>< HS College HS Large Public Small Formal Informal<br>Gender Location Migrant<br>100<br>80<br>60 44.8 47.3 54.6<br>36.2 35.7<br>40 22.8<br>20<br>0<br>Female Male Rural Urban No Yes<br>Union<br>100 89.0<br>80<br>60 38.5<br>40<br>20<br>0<br>No Yes<br>Percent<br><!-- End of picture text --> _Notes_ : Authors’ calculations using SEDLAC data. Countries included in each sub-figure differ based on availability. Individuals are counted equally across countries.

24
(Bruns & Luque, 2014; Leyva et al., 2015), elements of what happens in the classroom are inevitably lost as neither tool captures quantity and quality. This problem is even more acute in low- and middle-income countries, which are often characterized by high absence rates and low instructional time (Bold et al., 2017; World Development Report, 2018).

The _Teach_ classroom observation tool addresses this shortfall, as it measures the quantity _and_ quality of teaching. Although the tool is primarily high inference, the Time on Task component includes a simplified version of the Stallings tool to capture whether teachers provide a learning activity and students are on task.

Research indicates effective teachers maximize the amount of time students spend on learning (Wharton-McDonald et al., 1998; Stronge, 2018). In fact, time lost in classroom instruction is associated with behavior issues and poorer student academic outcomes (Bruns & Luque, 2014; Dobbie & Fryer, 2013; Lavy, 2010; 2015). Using data from seven Latin American countries, Bruns and Luque (2014) showed that teachers from schools ranked in the top 25th percentile of student learning spent, on average, 80 percent of their time on task, as compared to teachers in the bottom 75th percentile, who, on average, spent only 30 percent of their time on task.

Like time on learning, student engagement is another important predictor of learning (Castillo, 2017). In the same study, Bruns and Luque (2014) found when students are on task and engaged, they learn significantly more than when they are distracted or off task.

# 3.1.2. What Are Effective Teaching Practices?

This subsection provides the evidence and ‘content validity’ for the teaching practices captured in _Teach._<sup>3</sup> The Quality of Teaching Practices component is organized into three areas: _Classroom Culture, Instruction,_ and _Socioemotional Skills_ . These areas have nine corresponding elements that point to 28 behaviors. The
|Work is harmful or<br>dangerous||0.490***||0.190*||-0.0636|||
|---|---|---|---|---|---|---|---|---|
|||(0.156)||(0.106)||(0.125)|||
|Concern about job loss|-0.174*|-0.232**|0.130*|0.160**|0.102|0.129|||
||(0.103)|(0.110)|(0.0694)|(0.0734)|(0.0788)|(0.0836)|||
|Any part of wages|||||||||
|transferred off the record?|0.162|0.201|0.152|0.231*|0.352***|0.371***|||
||(0.149)|(0.168)|(0.106)|(0.124)|(0.114)|(0.132)|||
|Constant|1.464*|0.850|7.529***|7.941***|1.937***|2.202***|-3.893***|-4.567***|
||(0.813)|(0.884)|(0.848)|(0.934)|(0.727)|(0.820)|(0.672)|(0.592)|
|Observations|4624|4104|4616|4098
|12.3|...|28.2|28.2|
|Universitv or Above|14.6|9.8|8.1|14.8|24.0|24.0|
|All|19.8|14.1|13.3|20.2|29.8|29.8|

_Source:_ Household Expenditure and Income Date for Transition Economies Data Set (HEIDE). _NVotes:_

- I/ For Estonia, secondary education and vocational-technical education are combined and shown in the category labeled "Secondary." Definitional problems in the Estonian dataset precluded a separation of these two kinds of education.

- 2J/ The poverty gap is the poor's average shortfall in expenditures from the poverty line, expressed as a percentage of the poverty line (this measure is also known as the expenditure gap ratio).

34
Policy Research Working Paper 7872

# **Abstract**

This paper analyzes the effects of a multilateral debt relief program on child health. The International Monetary Fund and the World Bank launched the Heavily Indebted Poor Countries Initiative in the late 1990s to reduce the debt burdens of poor countries, and explicitly linked the initiative to the aim of poverty reduction and social targets. As a result, debt-servicing costs have gone down by an average 1.8 percentage points of gross domestic product in Heavily Indebted Poor Countries. However, the social effects of debt relief are not well known. The paper employs micro data on infant mortality from 56 country-specific Demographic and Health Surveys to investigate the effects of the Heavily Indebted Poor Countries Initiative on child health. The retrospective fertility structure of the data allows for analysis using the within-mother variation in the probability of survival of babies before and after different stages of the initiative. The results suggest that after a debt-ridden country enters the program, which is conditional on reform and pro-development policies, and receives interim debt relief, the probability of infant mortality goes down by about 0.5 percentage point. This translates into about 3,000 fewer infant deaths in an average Heavily Indebted Poor Country. The findings are particularly strong for infants born to poor mothers and mothers living in rural areas, and are driven by access to vaccines early in life and during pregnancy. There are no child health effects from graduating from the program and receiving full debt relief.

This paper is a product of the Office of the Chief Economist, Africa Region. 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://econ.worldbank.org. The author may be contacted at anna. welander@nek.lu.se.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues.
D. J., Brück, T., Hanmer, L. C. & Klugman, J. (2021). Introduction to the Special Issue on 'Gendered Dimensions of Forced Displacement'. Under review.

- Baliki, G. (2017). Empirical Advances in the Measurement and Analysis of Violent Conflict. Doctoral dissertation, Humboldt University Berlin.

- Bastia, T. (2014). Intersectionality, migration and development. Progress in Development Studies, 14(3), 237-248.

- Bello, A. R. S., & Baig, M. B. (2014). Coping strategies of Darfurians displaced women in Khartoum. Journal of Agricultural Extension and Rural Development, 6(5), 168-174.

- Brück, T., & Stojetz, W. (2020). Data Options for Assessing Gendered Dimensions of Forced Displacement, Background Note for the World Bank's Gendered Dimensions of Forced Displacement Program. Berlin: ISDC - International Security and Development Center.

- Brück, T., Justino, P., Verwimp, P., Avdeenko, A., & Tedesco, A. (2016). Measuring violent conflict in micro-level surveys: Current practices and methodological challenges. The World Bank Research Observer, 31(1), 29-58.

- Charmes, J. (2012). The Informal Economy Worldwide: Trends and Characteristics. Margin: The Journal of Applied Economic Research, 6(2), 103–32.

- Crenshaw, K. (1990). Mapping the margins: Intersectionality, identity politics, and violence against women of color. Stan. L. Rev., 43, 1241.

- Davies, A., & Jenner, A. (2020). A Tale of Two Cities-IDPs Plan for Their Durable Solutions in Darfur. Refugee Survey Quarterly, 39(4), 507-513.

- Doss, C. R., & Quisumbing, A. R. (2020). Understanding rural household behavior: Beyond Boserup
severance is similar across firm-ownership status in Sakernas (2008). Second, neither survey shows a consistently positive association between eligibility and the level of wages across the full range of wage rates.

3.3 _Compliance with current severance pay regulations_

> 9 Question TK25A3 of the IFLS (2007) survey asks individuals to identify a set of benefits offered by the employer.  One of these benefits is severance payment eligibility.

> 10 Table 2 display details of severance eligibility and receipt of severance pay based on the Sakernas (2008) survey.  The IFLS (2007) findings are consistent.
**Figure 1.1: Per capita income trends in a multi-polar world - emergence** **<u>of Brazil, China and India</u>**

<!-- Start of picture text --> $35,000<br>$30,000<br>$25,000<br>$20,000<br>$15,000<br>$10,000<br>$5,000<br>$0<br>1950 1965 1970 1975 1980 1985 1990 1995 2000 2005 2008<br>South Korea Brazil China<br>India United States Japan<br>Singapore Taiwan SSA-Madagascar<br><!-- End of picture text --> _Source: Angus Maddison Data Base (http://www.ggdc.net/maddison/)_ Among the set of developing countries, most raced to close the income gap with the developed countries under the leadership of their respective governments and support of international development agencies in the second half of the twentieth century but only a handful were successful in transforming their aspirations into reality. Among them were Japan and the East Asian Tigers (Hong Kong SAR, China; the Republic of Korea; Singapore; and Taiwan, China) (Figure 1.1).

The growth poles in the past were all high-income countries such as England, US, Japan and Germany. In the last quarter of the twentieth century this changed and for the first time, large developing countries such as China, India, and Brazil become growth poles. If the new growth poles continue to maintain their dynamic growth trends, their industries will upgrade to more capital intensive ones and leave a large space of labor-intensive industries for other low-income countries to enter. This paper tries to explore the implication of this new phenomenon for other developing countries, which still face the daunting challenges of achieving dynamic growth to reduce poverty.

# **_Multi-polarity and its potential dividends_**

Since the
# **Table 7. The Effect of Characteristics of Land Title on Investment**

The sample consists of all nonfinancial listed firms in the CSMAR database from 2008 to 2017.This table presents parameter estimates from OLS regression of investment on Titling Firm dummy variable and two land title characteristics, whether the previous tenure has expired and whether the land’s user rights is in contention. The independent variable is the averaged investment (capital expenditure divided by PP&E) between 2010 and 2014 net of investment between 2010 and 2012. _Titling Firm_ is a dummy variable that is 1 if the firm has allocated land before the land title scheme starting from 2012 and 0 if otherwise. _Tenure Expired_ is a dummy variable that is 1 if the tenure of land controlled by the firm has expired and 0 if otherwise. _Rights contended_ is a dummy variable that is 1 if the user rights of the land were fragmented in terms of land was being leased to other users or subject to rights contention from multiple claims and 0 if otherwise. All variables are winsorised at the upper and lower 1-percentile. Industry FE denotes whether the industry of the listed firms (categorized according to industry categorization from CSMAR database) is considered in the specification. Exchange FE denotes whether the effect of exchange (Shanghai or Shenzhen Exchange) in which the firms are listed is considered in the specification. The _t-statistics_ are computed using standard errors that are White-corrected. The *,**,*** indicates significance at the 1%, 5% and 10% level respectively.

|||∆(Investm<br>|ent/PP&E)<br>||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Titling Firm|0.046|0.013|0.100|0.081|
||(0.305)|(0.
# Annex E: Sampling

## The processes employed to select the wards, households, and respondents was as follows:

<u>Ward (cluster) selection: We used the list of wards and its population figures from the 2021 Nepal Census.</u> The sampling frame consisted of the list of all wards within each selected province. Each province comprises of districts and within each district are municipalities (urban and rural municipalities) which are further broken down into wards – the smallest administrative units. Districts were stratified by urban and rural areas to ensure greater statistical power for detecting differences between the 2 localities. The stratification by urban-rural was done to be proportionate to the population share of each group within a province, resulting in a self-weighted sample, allowing for analysis of data at province level and further at locality level within each province. To select the wards, a random start point was generated to negate any bias in the list and to provide an independent chance of selection from the list. The probability proportionate to size (PPS), gives an independent chance of selection to each wards as per its population size, i.e., a higher chance of selection to wards with higher population size.<sup>37</sup> As a first step of random selection of wards, the cumulative frequency (CF) of population of households in a ward was calculated. Since the unit of analysis for our study purpose was households having certain criteria and we expected the main outcome variables (social norms) to vary at household levels (as opposed to at an individual level), the household population figures served as the basis for sampling purpose (as opposed to population size of individuals for a ward). Applying PPS, in the first step, the required number of wards were selected for Categories 1 and 2 households (households with working and non-working females respectively). Following this, the clusters allocated for Category 3 (households with migrant population) households were taken as a subset of the
# V. Drivers of agricultural income growth

The results from the first model are presented in Table 6. As expected, per capita crop income is significantly higher among those who farm more land and apply more labor, fertilizer and pesticides. Households that used fertilizer and pesticides had crop incomes that were 12 and 19 percent higher than households that did not. The correlation between the use of these inputs and crop income is higher for the households that were in the bottom 40 percent: for these households crop incomes were 22 percent higher for those that used fertilizer and 14 percent higher for those that used pesticides. Crop income is not significantly higher among those using improved seeds. Although there was some increase in input use (at the extensive margin) among panel households during this period, the increase was relatively marginal. The proportion of households using fertilizer increased from 17

> 12 Self-reported by the household.

> 13 An alternate specification was also run using the value of purchased inputs rather than a series of indicator variables in an attempt to capture the quantity of inputs used. The results are qualitatively unchanged.

> 14 A variation of specification (1) and (2) where a measure of whether the household was affected (i.e. was within a 5 km radius) by a flood episode identified between November and December 2011, and between August and September 2012 was also included in E, but results are not statistically significant from zero. Flood episodes were calculated by digitizing flood imagery downloaded for flood events recorded in the Emergency Database (EM-DAT). These data are available from 2011 onwards, so they only cover the last wave of the UNPS used in our analysis (2012), which might explain the insignificant results obtained.

16