rafmacalaba/data-use-mentions-extended huggingface dataset:
behavior mainly in the long term. The second group of variables used to describe savings during working life and dissavings during retirement are financial variables such as interest rates, inflation rates, available financial instruments, and initial wealth levels. These variables are significant as they dynamically affect the intertemporal consumption decisions of households.

The life-cycle model of household savings behavior, grounded in expected utility theory, is generally associated with Modigliani<sup>5</sup> and Friedman (1957). Modigliani and Friedman made their literary contributions by translating the abstract notion of an optimal consumption profile into a model that could be estimated econometrically (King, 1985). This demonstrated the empirical relevance of the separation of the consumption and income profiles, which is the principal implication of the life-cycle model. Modigliani stresses the role of wealth in the consumption function, Friedman’s concept of “permanent income,” and how both are based on the idea that current consumption is not determined primarily by current income.

Nevertheless, empirical tests of these two major hypotheses focused on cross-country studies that resulted in many drawbacks (Muradoglu and Taskin, 1996). The key challenge is the comparability of samples that include countries at different levels of development; this questions the reliability of these studies as far as interpreting differences across country groups.<sup>6</sup> Moreover, SchmidtHebbel, Webb, and Corsetti (1992) emphasized another great challenge that cast doubt upon using national aggregate savings data, assuming that the private savings account was a predominant part of total savings. They emphasized that aggregation of savings is inconsistent across countries, which resulted in a number of cross-country inconsistencies due to the difference in the methodology employed in their derivation. The following section provides a brief review of recent empirical literature on modeling savings behavior.

# **i) Recent Developments in Literature on Savings Behavior in Emerging and Developing Countries**

Kwakwa (2013) examines the key determinants of national savings using the Johansen cointegration technique as well as the error correction model for Ghana
production process, and global value chains that are more decentralized in nature, with individual producers incurring costs to set up links upstream and downstream from them (see Bernard, Moxnes, and Ulltveit-Moe, 2018, Antràs and de Gortari, 2019).

# 3.1 Measurement

Firm-level datasets containing information on the import and export transactions of firms can be fruitfully used to construct measures of GVC participation similar in nature to those discussed above based on the country- industry information in global Input-Output tables. More specifically, transaction-level customs datasets of the type available from the World Bank’s Export Dynamics Database can be used to identify the set of firms in a country that participate in trade, further distinguishing firms that export, firms that import and firms that both export and import. When a given firm in a given country both imports and exports, it is natural to conclude that this firm participates in GVCs.

To map this definition more precisely to the definition of backward GVC participation developed in country-industry studies, one would ideally also resort to product-level information to verify that the goods imported by an exporting firm are indeed intermediate inputs (rather than final goods), so that one can more comfortably conclude that this firm is indeed using foreign value-added in their production destined for exports. Without linking customs-level data across countries, it is much harder to come up with analogous firm-level measures of forward GVC participation. The 15
Relevant covariates_ Women were also interviewed for the household socioeconomic characteristics of infant and neonates who died, birth order and maternal age during the time of delivery. Some sociodemographic variables based on a priori causal knowledge were included as covariates in this study, such as educational status of both parents, rural or urban residence of households and asset wealth indices calculated by principle component analysis. Other relevant information collected from each BDHS household survey were primary source of drinking water, sanitation, geographical division and river basins for the BDHS clusters.

# _(iii) Salinity and drinking water_

Groundwater EC point data were collected from two sources. First, we collated groundwater EC data from a monitoring network of 461 boreholes under a regional-scale hydrogeology study conducted in 19 coastal areas by the Bangladesh Water Development Board (BWBD) (Zahid et al., 2013; Zahid et al., 2016). Secondly, we digitized and georeferenced the contoured map of groundwater EC (Rahman and Ravenscroft, 2003) for shallow aquifers (<150 m bgl) and extracted EC point data at 102 locations predominantly in the northern part of Bangladesh. We then interpolated the point EC data (n=563) for the entire country using the Inverse Distance Weighting (IDW) algorithm in the ArcGIS environment, after 7
The sectoral labor shares are taken from the GTAP database (Center for Global Trade Analysis, 2015) and are discussed in more detail previously.

# **5.2   Aggregate effects of changes in wedges over time**

Before quantifying the aggregate implications of wedges, I briefly show how they have evolved over the years. Have they increased, decreased, or been cyclical? Note that the estimates of take both positive and negative values, both of which imply inefficiency. Therefore, in principle, I need to jjppaa ττ transform the labor market wedges to make them monotonic with inefficiency. However, the 17
Providing convincing tests of the theory is challenging due to the difficulty of measuring morality, and to a lesser extent ability, across countries. A key variable in the model is the correlation between morality and ability among workers in a society, so it is necessary to find survey data covering numerous countries in a reasonably comparable way. The World Values Survey (WVS) project is the source that best fulfills these criteria. We identify plausible proxies for ability and morality in the WVS, compute country-level variables from them, and include them in an income-levels regression based on the specification of Rodrik, Subramanian and Trebbi (2004). Results are generally consistent with the main predictions of the theory.

The World Values Surveys have been conducted in more than 80 countries beginning in 1981, in five survey “waves” through 2008. Samples are designed to be nationally representative, with the number of respondent households ranging from several hundred in some cases to several thousand in others. The survey questionnaire was originally designed in part to measure differences across countries and over time in materialist and “post-materialist” values as societies modernized and became wealthier. The data have been used in hundreds of academic publications, mostly in other disciplines but also in economics (e.g. Knack and Keefer, 1997; Alesina and La Ferrara 2005).

The best available proxy for ability in the WVS is educational attainment. The Data Appendix lists the eight attainment categories from lowest to highest. We make one change in the WVS ordering, collapsing categories 4 and 5 because it is not clear whether respondents who have completed secondary education with a vocational emphasis have “more” (or less) education than those with an incomplete secondary education with a college preparatory emphasis.

Educational attainment is obviously an imperfect measure of ability. Some famously successful entrepreneurs even in wealthy countries do not have university degrees. For several reasons it 15
, farmers are further asked whether the crops they harvested had any damage on them and what the cause of damage was. We define disaster crop losses as any loss in crop area or any damage on the crops harvested due to climatological (drought, irregular rain, hail, wildfire), hydrological (flood) or biological (insect infestation, disease) reasons. This does not include losses due conflict, unavailability of inputs or other household or socio-economic events. We denote a plot with a disaster loss as any plot for which at least one crop on the plot had a disaster-induced loss.

To calculate the size of disaster losses, we use farmers’ reports of the share of the planted area lost due to disasters and, where available, the percent of damage on crops that were harvested. To determine the potential harvest that could have been achieved in the absence of disaster losses, we employ the methodology proposed by the Food and Agriculture Organization (FAO).<sup>54,55</sup> This means determining the realized harvest for each crop on the plot, as described in the preceding section, and scaling it up in proportion to the planted area lost and degree of damage on the crop that is attributed to disasters. The quantity lost equates to the difference between the potential harvest without disaster losses and the realized harvest. To aggregate losses across crops, we value loss quantity of each crop on each plot using a constant set of prices, which we then convert to 2020 US dollars. In order to correct for outliers in the self-reported data, we winsorize these loss values at the 99<sup>th</sup> percentile.

Equation (1) formalizes the construction of the plot-level loss aggregate.

12
# **4. Variables and Data Sources**

Our dataset has a panel structure (repeated observations for each country); ‘geo’ and ‘period’ are the variables that identify country and time, respectively. The former is an encoded variable: it ranges from 2 (Albania) to 208 (Zimbabwe) but STATA displays country names instead of numbers. Our dataset additionally contains a ‘country’ and ‘NAMES_STD’ variable<sup>13</sup> and their ‘iso3’ code as a string variable. Additionally, ‘region_un’ classifies each country’s region, based on UN standards.

The ‘period’ variable indicates 5-year average periods, where period 1 encompasses the 19701974 data, while period 10 contains the 2015-2019 data. Note that the inclusion of those timefixed effects accounts for all global factors that affect all countries equally (in proportionate terms, as the dependent variable is logged). They could reflect global commodity prices or the global interest rate environment, or global advances in technology.<sup>14</sup>

## **4.1 Income Level**

The dependent variable we aim to explain is _lrgdpna_pc_ , the real (inflation-corrected) **income level per capita** , in logarithmic terms. The variable is based on PWT10.0 and constructed by dividing the series _rgdpna_ by _pop_ . The natural log is taken so that changes in the explanatory variables can be interpreted as percent changes in per capita income levels.

Although national income data are converted into (constant 2017) US$, note that this variable does not allow for proper comparison of welfare across countries because it does not account for differences in price levels across countries. If goods are half the price in one country compared to others, half the income level (in US$) is needed for the same consumption welfare. Such comparisons are facilitated through PPP adjustments, as in the PWT series _rgdpo_ or _rgdpe_ (Feenstra et al., 2015), which are also
but not significant. Using Sri Lanka’s household survey, we can also employ an alternative measure of working conditions (table 5, columns 5–6): the share of bonuses and allowances over cash earnings and the share of food stamps over cash pay. Favorable working conditions correspond to having a higher-than-average performance pay ratio and a lower-than-average food stamps ratio. Our dependent variable represents an average between those two variables. The analysis shows that this index of working conditions in textile and garment industry compared to other industries worsened in the post-MFA period by 3 percent. However, the working conditions improved for women in the post-MFA period by 8.5 percent relative to men and compared to other industries (see table 5, column 6).

# **5. Conclusions**

Despite gloomy post-MFA expectations, the apparel industries of both Cambodia and Sri Lanka managed to increase export volumes, maintain world market shares, and grow at a healthy rate, outcomes that may eventually have poverty-reducing implications. The resilience of the industries in these two countries can be explained by several factors. The United States and the EU established safeguard quotas against imports from China from 2005 until the end of 2008, which mitigated the impact of the MFA phase-out. Other factors such as proactive policies, industry ownership, and background were also important.

Under the assumption that women are more apparel-specific labor input than men and that the apparel sector is a female-labor intensive sector, the simple theoretical model predicts that a negative price shock will translate into a relative decline in apparel wages compared to other industries in the short run and a relative decrease in female wages compared to male wages across all industries in the long run.

The increased competition in apparel exports after the MFA phase-out drove down the unit values of apparel both in Cambodia and Sri Lanka. The empirical results of the post-MFA changes support the theoretical predictions. Workers, the majority of whom are female, in the apparel industry were receiving a premium comparable to that ofan average worker. This premium decreased in
analysis and presents descriptive statistics on the GEP, disaggregated by region, time period, and other salient dimensions. Section 3 uses a regression framework to test, conditional on differences in initial conditions, whether there are regional disparities in the extent to which economic growth translates into poverty reduction and whether elasticities have changed over time. This section also tests whether the extent to which economic growth translates into improved household welfare is mediated by certain policy variables. The final section concludes.

# 2. Data description

We use data from the Poverty and Inequality Platform (World Bank, 2022b) which replaced PovcalNet, the World Bank’s database for monitoring of global poverty (see World Bank, 2022c for a description of data sources and methods used).<sup>4</sup> The database contains income or consumption distributions from nationally representative household surveys typically carried out or supervised by national statistical offices or international agencies, used for national and international poverty monitoring. Following the traditional approach of the research on the GEP, we create a database of “spells” by combining each pair of consecutive surveys within every country. By construction, only countries with at least two comparable household surveys (and hence one spell) are included in the database of spells. This results in a dataset of 1,604 spells based on 1,870 nationally representative household surveys for 139 countries between 1969 and 2021. Countries presented in the database accounted for 95.1 percent of global population in 2019 (and 95.4 percent of population in the “developing world<sup>5</sup> ”).

The distribution of consecutive spells by regions is shown in Table 1. The majority of spells are from two regions with frequent (mostly annual) poverty surveys: Europe and Central Asia and Latin America and

> 4 The Poverty and Inequality Platform replaced PovcalNet in March 2022. 5 The world excluding high-income countries.

3
# _<mark>(b) Measuring characteristics of crop mix</mark>_

<mark>A movement of resources from low-value to high-value agriculture, including export agriculture, has been considered as a channel through which smallholders may raise agricultural productivity and income (Otsuka, Nakano and Takahashi 2016; Ton et al. 2018; Wang, Wang and Delgado 2014). Farmers’ crop choice may be gendered, partly reflecting gender norms associated with different crops (Carr 2008). The majority of the previous literature finds that male farmers have more access to production and marketing of cash and export crops than their female counterparts (Ali et al. 2016; Croppenstedt et al. 2013; Hill and Vigneri 2014;</mark> Kilic, <mark>Palacios-Lopez and Goldstein</mark> 2015 <mark>; Maertens and Swinen 2012; and</mark> Mahaja 2019 <mark>). A meta-analysis by Ola and Menapace (2020) finds that in Africa, male farmers are more likely than female farmers to participate in high-value markets, but it does not find such a systematic effect in Asia and South America.</mark> <mark>A simple</mark> Export Orientation Index is constructed to capture the relative importance of exportoriented and domestically oriented crops, which tend to be high- and low-valued crops respectively in a farmer’s portfolio. Specifically, the index is calculated as follows: where X is the Export Orientation Index for farmer _i_ , and is the export intensity of crop _k_ , i.e., the share of exports of the overall production quantity at the national level taken ii kk XXDD SShXX from FAOSTAT (2014-2017 average).<sup>7</sup> is the share of cultivated area for agricultural item _k_ in individual _i_ ’s total cultivated area.<sup>8</sup> iikk Figure 4 shows a positive relationship between SShSS productivity and the Export Orientation Index. Reflecting the difference in crop mix between men and
record immigrants when they achieve their resident status rather than when they ...rst enter the country. Finally, de...nition of migrants - by nationality or birth - vary across countries. Most importantly for our purposes, those data are not available by education level. The ‡ows of immigrants to a country can only be recovered by measuring the stock of foreign born people in a destination country (from a certain origin country) at di¤erent points in time and then taking the di¤erence. The other advantage of starting with data on stocks of migrants is that they are usually from national censuses which tend to be more representative and complete than other data sources. Plus censuses often account for (i) undocumented immigrants at least in some countries like the US, (ii) they categorize immigrants by place of birth, rather than nationality which can change over time and across countries due to naturalization laws and (iii) report their education levels.

Our database is described in Docquier et al. (2010) who construct bilateral measures of immigrant and emigrant stocks for 195 countries in 1990 and 2000. The starting point for the new data is Docquier and Marfouk (2005) which collected the stock of foreign-born in OECD destination countries in 1990 and 2000, by country of origin and level of schooling (primary, secondary and tertiary). These data are supplemented with original data from the censuses of a large number of non-OECD countries. Finally, for many destination countries with no data on immigration, bilateral migrant stocks were predicted using a gravity framework as described in greater detail in Docquier et al. (2010). Their own census data would su¢ ce to measure immigration into OECD countries. However, evaluation of emigration also requires data from all the possible destination countries, at least the most relevant ones. In other words, emigrant stocks from a certain origin can only be measured by aggregating all migrants recorded in the censuses of all destination countries. As some important destination countries (such
89|61|
|1992|260|65|80|167|158|51|
|1993|149|157|49|140|166|46|
|1994|138|91|60|96|133|42|
|**WTO**|||||||
|1995|78|79|50|71|86|45|
|1996|79|147|35|95|131|42|
|1997|127|119|52|125|121|51|
|1998|90|176|34|135|131|51|
|1999|176|182|49|154|204|43|
|2000|138|160|46|130|168|44|
|2001|179|193|48|145|227|39|
|2002|109|206|35|127|188|40|
|2003|86|148|37|90|144|38|
|2004|113|107|51|61|159|28|
|2005|62|139|31|44|157|22|
|2006|71|133|35|49|155|24|
|2007|61|104|37|38|127|23|
|2008|75|138|35|48|165|23|
|2009|64|145|31|48|161|23|
|2010|38|133|22|66|105|39|
|2011|63|92|41|44|111|28|

Sources: GATT and WTO antidumping database.

Table 7 shows the number of anti-dumping, countervailing and safeguard measures taken by the 50 countries in our sample cumulatively over the period 1995-2011 as well the number products affected by these actions. The last four columns show the number of trade remedy 21
> /YY the sale of industrial and commercial goods and services. They include government-owned and/or government-controlled companies. The government’s monitoring and reporting are focused on major NFPCs, which have government ownership of more than 50 percent of total equity, minimum annual sales of at least MYR100 million and/or significant impact to the economy (Ministry of Finance Malaysia 2018). 8 In a list of 21 countries with the highest shares of SOEs among their top 10 firms and which also have at least 10 firms on the Forbes Global 2000 list (Kowalski et al. 2013), Malaysia is ranked fifth. Among the top 10 countries on this list (China, United Arab Emirates, the Russian Federation, India, Malaysia, Saudi Arabia, Indonesia, Brazil, Norway and Thailand), we calculate the median value for average over 2006-2015 as 7 percent. Our calculations also indicate that the baseline projection of 6 percent for Malaysia’s is slightly below the 75<sup>th</sup> percentile value for average over 2006-2015 (6.8 percent) of high-income fuel-based economies (fuel exports/merchandise exports >15 percent). II<sup>GG</sup> /YY II<sup>GG</sup> /YY 9 II<sup>GG</sup> /YY
# **References**

1. Abdi, H. (2007). The Kendall rank correlation coefficient. _Encyclopedia of Measurement and Statistics. Sage, Thousand Oaks, CA_ , pages 508–510.

2. Alatas, V., Banerjee, A., Hanna, R., Olken, B. A., and Tobias, J. (2012). Targeting the poor: evidence from an experiment in Indonesia. _American Economic Review_ , 102(4):1206–40.

3. Alix-Garcia, J.M., Sims, K.R.E., and Costica, L. (2019). Better to be indirect? Testing the accuracy and cost-savings of community leader surveys for poverty targeting. Working paper, University of California Davis.

4. Baird, Sarah, Craig McIntosh and Berk Ozler (2013) The Regressive Demands of Demand-Driven Development, Journal of Public Economics, Vol. 106, pp. 27-41.

5. Basurto, M. P., Dupas, P., & Robinson, J. (2019). Decentralization and efficiency of subsidy targeting: Evidence from chiefs in rural Malawi. _Journal of Public Economics_ , 4047.

6. Brown, C., Ravallion, M., and Van de Walle, D. (2016). A poor means test? Econometric targeting in Africa. Technical report, The World Bank, Washington, DC.

7. Cameron, L. and Shah, M. (2014). Mistargeting of cash transfers, social capital destruction, and crime in Indonesia. _Economic Development and Cultural Change_ , 62(2):381– 415.

8. Conning, J. and Kevane, M. (2002). Community-based targeting mechanisms for social safety nets: A critical review. _World development_ , 30(3):375–394.

9. Friedman, J., Hastie, T., and Tibshirani, R. (2001). _The elements of statistical learning_ , Volume 1
**|
||(0.008)|(50.925)|(0.003)|(32.899)|(0.008)|(65.304)|
|Mean in control|0.068|237.230|0.013|74.133|0.068|163.096|
|Adjusted R-squared|0.026|0.012|0.010|0.008|0.025|0.007|
|Observations|13464|13464|13464|13464|13464|13464|
||**Panel**|**C: Loans o**|**utside the**|**village**|||
|Treatment|-0.011|-289.012*|0.031***|362.863***|-0.016|-675.220***|
||(0.015)|(163.245)|(0.004)|(52.772)|(0.014)|(203.455)|
|Mean in control|0.179|892.901|0.018|150.147|0.177|742.753|
|Adjusted R-squared|0.081|0.026|0.013|0.008|0.077|0.009|
|Observations|13464|13464|13464|13464|13464|13464|

**Source:** Author’s analysis based on original survey data. **Notes:** The table reports ITT estimates based on specification (1) estimated with OLS. The sample includes observations from ultra-poor households surveyed at the midline and endline surveys. All specifications control for the baseline level of the outcome, survey wave and subdistrict (strata) fixed effects. Standard errors are clustered at the BRAC branch office level (unit of randomization)