rafmacalaba/data-use-mentions-extended huggingface dataset:
Asset data collection is relatively easy and cost-effective. Many household surveys, including DHSs and MICS, collect asset and housing conditions through appropriate modules. Using assets minimizes measurement problems associated with income and consumption-based indicators, such as recall bias and seasonality.<sup>16</sup> Estimation of a wealth index using the PCA method is rather straightforward, and there are several ready-to-use commands in statistical and econometric software to implement the method.

Despite its simplicity, important limitations in using asset indexes to measure and track well-being over time should be considered. Several papers have demonstrated that correlation between asset indexes and consumption poverty is low (for instance, Filmer and Scot, 2008). The discrepancy may be stronger in the poorest areas where households spend larger shares of their budgets on food. Nevertheless, many still consider asset indexes useful for welfare analysis when consumption data are not available.

Using asset indexes across heterogenous settings and over time has concerns (Harttgen et al., 2013). First, preferences for some assets, such as mobile phones and flat-screen TVs, may increase over time and become part of “normal” living conditions; this may cause incomparability between asset indexes over time. Second, asset indexes do not consider age or quality of assets; some households may accumulate old assets, therefore overestimating a rise in assets. Third, in many countries, access to some assets, such as electricity or clean water, is a result of public policies and does not represent a reliable proxy measure for actual household consumption.

Constructing asset indexes using MICS or DHSs could represent an important alternative to monetary poverty in MENA countries where existing consumption surveys are very old. However, as shown in the previous section, countries do not collect MICS or DHSs frequently in the region and the problem of outdated data remains. In addition, correlation between assets and monetary poverty could be poor in the region, especially in conflict-affected countries where asset ownership may not highly correlate with poverty.

_Food security and subjective well-being_ This section focuses on two
# Figure 6. Regional potential growth

## A. Potential growth in EMDE regions

## B. Potential growth in EMDE regions

<!-- Start of picture text --> Percent Potential growth<br>10<br>Actual growth<br>8<br>2000-2021 potential growth<br>6<br>4<br>2<br>0<br>EAP ECA LAC<br>2000-10 2011-21 2022-30 2000-10 2011-21 2022-30 2000-10 2011-21 2022-30<br><!-- End of picture text --> <!-- Start of picture text --> Percent Potential growth<br>Actual growth<br>8<br>2000-2021 potential growth<br>6<br>4<br>2<br>0<br>MNA SAR SSA<br>2000-10 2011-21 2022-30 2000-10 2011-21 2022-30 2000-10 2011-21 2022-30<br><!-- End of picture text --> C. Share of countries and GDP with potential growth below the previous decade average in EMDE regions D. Share of countries and GDP with potential growth below the previous decade average in EMDE regions <!-- Start of picture text --> Percent<br>100 Share of countries Share of GDP<br>80<br>60<br>40<br>20<br>0<br>2011-21 2022-30 2011-21 2022-30 2011-21 2022-30<br>EAP ECA LAC<br>E. Contributions to regional potential<br>growth<br>Percentage point<br>10 TFP Capital<br>8 Labor Potential growth<br>6<br>4<br>2<br>0<br>EAP ECA LAC<br>2000-10 2011-21 2022-30 2000-10 2011-21 2022-30 2000-10 2011-21 2022-30<br><!-- End of picture text --> Percent <!-- Start of picture text --> Share of countries Share of GDP<br>100<br>80<br>
forecasts may lead policy makers and the private sector astray. Most relevant is the challenge of low data capacity and transparency, a long-standing issue in many countries, particularly developing economies. These data problems impede sound analyses and policy making. This paper focuses on growth forecasts. The paper seeks to unpack the relationship between growth forecast errors and their determinants, with special attention given to the role of data systems.

To study the determinants of growth forecast errors, we rely on time series of growth forecasts published by the World Bank, the IMF, and the private sector between 2010 and 2020. With the benefit of hindsight, we can compute forecast errors by comparing the World Bank and IMF forecasts for a given year with the realized growth rates covering a large number of developing countries. In turn, we estimate regression models where the key explanatory variable is the World Bank’s Statistical Capacity Index (SCI). As far as we know, this paper is the first to assess the empirical relationship between growth forecast errors and data systems.

In sum, this paper presents five empirical contributions to the existing literature on macroeconomic forecasting. First, same-year forecast error—the difference between forecast growth and realized growth—is sizable. Globally, between 2010 and 2020, the average same-year forecast error is 1.3 percentage points for the January World Bank’s forecasts and 1.5 percentage points for the January IMF’s forecasts. Second, the Middle East and North Africa region (MENA) has the largest forecast errors among world regions. Third, on average, improvements in a country’s Statistical Capacity Index (SCI), a measure

> 2 Using the IMF’s WEO data, Beaudry and Willems (2022) found that overestimating annual growth by 1 percentage point over three years reduces real GDP growth three years after that by about 1 percentage point on average.

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# **3 Data**

The goal of the paper is to assess the direct and indirect effects of export expansion on local labor market outcomes in Viet Nam while accounting for supply chain linkages. To do this, we exploit variation in export expansion across provinces and industries between 2010 and 2019 and combine export data from the United Nations Commodity Trade Statistics (UNCOMTRADE) data, inputoutput coefficient matrix from Global Trade Analysis Project (GTAP) data, and information on local labor market outcomes from Viet Nam’s Labor Force Survey (LFS) data. Details on each dataset and cleaning techniques are described below.

## **3.1 Labor Force Data**

Our main source of labor market data is the LFS provided by General Statistics office of Viet Nam (GSO) between 2010 and 2019, a period during which it was implemented every year. The LFS observations collect information in a host of areas including key labor market, household, and individual demographic characteristics.

Our analysis looks at two main sets of outcome variables: wage outcomes and employment outcomes. The wage outcome data sets include real annual wages, real annual income, college degree wage premium and gender wage premium. The employment outcome sets include employment rate, inactive rate, informality status, and female labor force participation. All of the outcomes are constructed from survey questionnaires, of which the wage outcomes are calculated at the province x sector level, while the employment outcomes are aggregated at the province level because we do not have sector information for those who are not employed. Over the period 2010 to 2019, several changes were introduced in the Viet Namese LFS, together with updates in concepts and definitions. These have been standardized to make key labor market outcomes, administrative geographies, as well as industry classifications, comparable over time.

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account for about half of the labor intensive items in the wood group while products like hand saws, metal barrels, structural metal products, wire springs, or sheet metal work in the fabricated metal sector are produced by labor intensive methods. However, Table 6 suggests that the labor intensive products, in which developing countries have a comparative advantage, are distributed throughout the industry sectors with a few exceptions like petroleum refining and chemicals.

Given the previous (1965-1986) developing country export expansion of products identified as labor intensive in 1965, Table 7 examines the implications of the updated 1980s product list using U.S. employment and shipment information. Specifically, the table shows the 1982 value and share of industry employment and shipments that originates in labor intensive products. The latter is further disaggregated into three component product groups (very high, high or moderate labor intensity).

As was the case with previous analyses, Table 7 demonstrates the "vulnerability" of the textile and clothing sectors to exports from developing countries. Over 80 per cent of textile mill (SIC 22) shipments and employment originate in labor intensive products while the corresponding shares for apparel (SIC 23) exceed 95 per cent. The table also shows that employment and shipments in both sectors are highly concentrated in the "very high" labor intensive group which would appear to be most suitable for developing countries. High "exposure" ratios (i.e., industries where labor intensive products account for at least 50 per cent of total employment or shipments) occur for wood products (100 per cent of shipments and employment), furniture and fixtures, rubber and plastic products, leather products (100 per cent coverage) and miscellaneous manufactures. Overall, 43 per cent of 1982 United
The main coefficient of interest in equation (1) is β . If β̂ > 0 , then subsequent to country _j_ liberalizing preferentially toward _k_ , country _j_ also reduced its levels of import protection toward countries _-k_ in industry _i,_ and then preferential liberalization is interpreted as a _building block_ for trade liberalization toward PTA outsiders. Conversely, β̂ < 0 would indicate that the country increased its levels of import protection toward PTA outsiders following an episode of preferential liberalization, and would thus be evidence of a _stumbling block_ effect of preferential liberalization.

The literature thus far, and in particular the Estevadeordal et al. (2008) approach from which we build, has defined ∆τi−k to be the change in the MFN tariff that country _j_ applies toward imports from PTA non-partners, and it has defined L.∆Prefik to be the lagged change in the preferential tariff that country _j_ applies toward imports from PTA partner _k_ . The first contribution of our paper is to redefine each of these measures of import protection so that they include not only MFN and bilateral applied tariffs, but they also reflect country _j_ ’s potential application of temporary trade barriers – i.e., antidumping and safeguards – on imports in industry _i_ .

The decision to examine the impact of more expansive measures of import protection is again motivated by the data for Argentina and Brazil that we reported in Section 2. At the same time that each was reducing its tariffs toward one another bilaterally and implementing applied MFN tariff changes toward MERCOSUR non-partner countries, each was also independently implementing its own new import protection through the application of TTBs.

Ideally, countries would apply their TTBs as ad valorem tariffs; if this were the case then we could simply redefine each of ∆τi−k and L.∆Prefik to reflect the sum of the applied ad valorem tariff plus the ad valorem temporary trade barrier. Unfortunately, many (if not most) of the TTBs that Argentina and Brazil applied during this period were not in ad valorem form;
from forestland into agricultural production which might be reflected in lower observable increases in agricultural production and agricultural trade, relative to agreements that do not include such provisions. But these environmental provisions may also support the process of agricultural intensification, by creating incentives to invest in agricultural capital and technology as they limit opportunities for agricultural land expansion. The overall impact of RTAs with environmental provisions on agricultural output and exports might thus reflect a combination of these two effects.

We measure agricultural activity using data from UNFAO that includes total agricultural exports, the total land area harvested, and the total weight of agricultural output. The land area harvested will capture land use change at the extensive margin while the total weight of agricultural output will reflect changes in both the extensive and intensive production margins. As above, our trade measures aggregate bilateral trade flows across all trading partners which we likewise argue is the relevant measure to consider in our setting.

In Table 2 we report estimates of our triple-difference model on agricultural and forest product outcomes. We find strong evidence that environmental provisions limit agricultural extensification. In Column (1) we find that land area harvested increases approximately 6% following entry into force of an RTA without environmental provisions. The inclusion of such provisions offsets these increases, with 6% lower area harvested that is statistically significant at the 5% level. For RTAs that include these provisions, entry into force does not lead to any discernible changes in agricultural harvest area. Likewise, entry into force of RTAs without environmental provisions leads to an approximately 6% increase in the weight of agricultural output, however inclusion of environmental provisions does not totally offset this increase and the effect is not statistically differentiable from zero. Taken together, we interpret these findings as evidence that environmental provisions mitigate agricultural land expansion at the extensive margin while apparently still leading to output increases at the intensive margin–e.g. through increased yields. Agricultural exports exhibit similar patterns, with an increase of approximately 14% following entry into force for agreements without environmental provisions that are partially
(see Figure 2). In comparison, the domestic content share of China’s non-processing exports stood at a much higher 88.7%, meaning that imported inputs only represented 11.3% of the export value.

# **[Figure 2 about here]**

In this section, we are primarily interested in the geographic characteristics of China’s processing trade regime. To analyze the countries of origin of processing imports and the destination countries of processing exports, an important data issue that needs to be addressed is that 90% of China’s trade with its largest trading partner, Hong Kong SAR, China, are re-exported elsewhere (Feenstra, Hai, Woo and Yao, 1999; Feenstra, Hanson and Lin, 2004; Ferrantino and Wang, 2007). This can significantly affect the analysis since it biases the _true_ source country of processing imports and the _true_ destination country of processing exports that are shipped through Hong Kong SAR, China. To account for these re-exports, we follow Ma, Van Assche and Hong (2009) by linking the processing trade data from China’s Customs Statistics to a data set from Hong Kong SAR, China’s Census and Statistical Office on its re-exports. This allows us to estimate the country of origin of processing imports re-exported through Hong Kong SAR, China and the destination country of processing exports re-exported through Hong Kong SAR, China. A comparison of columns 1-2 and 3-4 in Table 1 illustrates the impact of adjusting for re-exports through Hong Kong SAR, China on China’s processing trade with its major trading partners. It almost doubles the share of processing imports originating from China’s other major trading partners and increases by a quarter the share of processing exports destined to these same economies.

# **[Table 1 about here]**

China’s processing trade regime heavily relies on East Asian inputs. As it is shown in Figure 3, China heavily sources its inputs from neighboring East Asian economies, with 75.1%
above to being below the expected share of labor in exports, and in 2011 it was also below most comparator countries. In minerals on the other hand, South Africa’s labor intensity of exports has markedly increased and by 2011 it had the highest share of labor in exports not only among the comparator countries but vis-a-vis most other countries in the world. This change was due entirely to an increase in the intensity of labor directly supporting minerals exports, rather than labor supporting exports via their backward linkages. There was a significant increase in the direct LVAX share, more than doubling from $13 to $30 per $100 of gross exports, while the indirect LVAX share declined by almost half, from $22 to $12 per $100 of gross exports.

This finding is consistent with micro evidence based on annual reports from listed mining firms, which suggests that between 2001 and 2011, wages as a percent of total costs in South Africa’s mining sector almost doubled, from 20% to 35% (Gwatidzo and Benhura, 2013). The mining sector also experienced increases in the number of jobs by over 25% between 2001 and 2011 according to official statistics. South Africa’s top minerals exports in 2011 were unwrought gold (HS 710812, accounting for 10% of total goods exports) and unwrought platinum (HS 711011, accounting for 7% of total goods exports). South African gold and platinum production is highly unionized, which helped drive these wage increases.

**Figure 15:  Total labor content per $100 of gross exports across sectors and GDP per capita 2001 vs. 2011**

<!-- Start of picture text --> Machinery and Equipment nec, 2001 Machinery and Equipment nec, 2011<br>ZAF PER<br>COL BRARUS<br>RUSBRA CHNZAF<br>CHN CHL COL CHL<br>TUR IND<br>IND<br>TUR<br>THA<br>THA<br>PER<br>4
shares from the 1950 data on expellees and post-1950 data on resettlers. The numbers of expellees and resettlers were then added to the existing totals.

# _APPENDIX 4. PROPENSITY MEASURES_

This appendix presents the propensity measures used to disaggregate the 236 aggregate origin regions/countries identified in the censuses. Let _Mo,d,t_ denote the number of migrants from origin country _o_ in destination country _d_ in year _t_ . These are the entries in the bilateral matrices that need to be completed. Now, instead of Mo,d,t , suppose a census in country _d_ gives the number of migrants originating from region _R_ (which includes country _o_ ), denoted as _MR,d,t_ . The problem is to find an allocation rule (  _o,d,t_ ) for estimating the bilateral stock from this aggregate amount. The allocation rule can be written as _Mo,d,t_ =  _o,d,t MR,d,t_ .

One type of aggregation problem occurs in the case of migrants from Czechoslovakia, the Soviet Union, and Yugoslavia and their successor states. For example, in many cases, migrants are recorded from Czech Republic, Slovakia, and Czechoslovakia in the same year. Belgium‘s 2001 reports 308 migrants from Czechoslovakia, 554 from the Czech Republic, and 412 from Slovakia. Presumably, migrants who left before the partition reported Czechoslovakia as their origin country, whereas most postpartition migrants reported the successor countries. In such cases, it is assumed that the distribution of migrants from these two countries was the same before and after the break-up of Czechoslovakia. Of the 308 migrants recorded as originating from Czechoslovakia, 177 migrants (308*[554/966]) were assigned to the Czech Republic and 131 (308*[412/966]) to Slovakia.

In other cases of aggregated migrant stock data, migrant data from other decades were used as the basis for disaggregation. Migrants were allocated according to a relative propensity, which is averaged over time. This can be formally
700–729.

- Geniaux, G. and Napoleone, C. (2008). Semi-parametric tools for spatial hedonic models: An introduction to mixed geographically weighted regression and geoadditive models. In Baranzini, A., Ramirez, J., Schaerer, C., and Thalmann, P., editors, _Hedonic methods in housing markets: Pricing environmental amenities and segregation_ , pages 101–127. Springer Science+Business Media, LLC, New York.

- Government of India (2013). Census of India 2011. Technical report.

- Government of Maharashtra (1971). Maharashtra Slum Areas (Improvement, Clearance and Redevelopment) Act.

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but not yet implemented, seeks to facilitate the unrestricted cross-border movement of cargo, passenger, and personal vehicles between BBIN countries. Under the agreement, trucks carrying export-import or transit cargo can move inside the territories of other countries without transshipping to local trucks at border land ports.

This paper explores the aggregate and regional effects of changing current transport and trade barriers between Bangladesh and India on aggregate real income and on the distribution of population and real income across regions within both countries. It uses a spatial general equilibrium model calibrated to these two economies, along with road network travel time calculated using GPS data, to measure changes in economic outcomes given changes in trade costs across regions.

The paper examines three scenarios. The counterfactuals explore the potential benefits 2
through reference person contact information ranged from 7 percent in Burkina Faso to 20 percent in Malawi. In all cases, the share of respondents reached through reference contacts was higher for rural and agricultural households with a substantial 22 and 23 percent of agricultural households reached through reference contacts in Ethiopia and Malawi, respectively.

Another strategy to increase coverage and response rates is by providing free phones to respondents. This strategy was used by the World Bank for its Listening to Africa (L2A) series of evaluations as well as by other researchers for individual experiments or impact evaluations (Dabalen et al., 2016; Dillon, 2012; Gaddis et al., 2021; McCullough et al., 2020). Beyond the cost implications of this strategy, providing mobile phones may meaningfully affect respondent behavior which the survey is seeking to measure and therefore interfere with the survey objectives or experimental design. Survey designers and researchers should carefully consider the implications of providing phones to respondents. An approach to limit this effect is to hand out phones to certain reference contacts in each community which can then connect selected respondents with interviewers.

One method to improve the chances of successful contact and interview with a respondent is to arrange some automated pre-contact to the sample units, typically and perhaps easiest through SMS message. This pre-contact can help to inform respondents of the survey as well as when they can expect a followup call for the interview. With this information, respondents may be more willing to answer a call from an interviewer and more closely monitor their phone if the call is expected. Pre-contact SMS messages have been shown to reduce nonresponse and enhance cooperation among phone survey respondents (Dal Grande et al., 2016).

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# **6 Variation by Sex**

COVID-19 mortality rates are higher for men than women. Of the 26 countries considered in the analysis in this paper, mortality data by both age group and sex were located for 21. The basic pattern of steeper age-mortality curves in high-income as compared to middle-income countries holds for men and women separately, as shown in Figure 14. Further figures with separate results for men and women are found in the Appendix.

**Figure 14: Average Rates of Increase per Year of Age in COVID-19 Mortality by Sex**

<!-- Start of picture text --> 30-39 40-49 50-59 60-69 70-79 80+ 30-39 40-49 50-59 60-69 70-79 80+<br>Males Females<br>High income Low and middle income<br>.8 .8<br>.7 .7<br>.6 .6<br>.5 .5<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br><!-- End of picture text --> Note: See Appendix for data sources.

# **7 Conclusions**

Among the 26 countries considered in this analysis, the observed age-mortality curves are notably flatter in middle-income countries as compared to high-income countries. This reflects some combination of differences in attribution of death to COVID-19, varying patterns of infection by age, and varying mortality rates by age among those infected. The findings from the analysis in this paper suggest the experience with COVID-19 in high-income countries may not be generalizable to middle- and low-income countries. Distinguishing between the various explanations should be a topic for further research.

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