rafmacalaba/data-use-mentions-extended huggingface dataset:
their employers from the Longitudinal Research Database (LRD). Since they have plantlevel information on costs, they can estimate plant-level earnings equations, which represent the aggregation of individual-level wage equations over workers employed in a plant. It is worth pointing out that Hellerstein, Neumark, and Troske (1999) estimate the wage regression at the worker and firm levels to test the robustness of the firm-level estimations and do not find significant differences. They proposed an innovative model discussed in Section IV to estimate firm level equations since productivity is available at the firm level only. The model, based on traditional economic theory, includes an extended Cobb-Douglas production function and a standard wage determination model. By simultaneously estimating production functions and wage equations at the plant level, they compare relative marginal products to relative wages of different types of workers, leading to new evidence on productivity-based and nonproductivity-based explanations of the determination of wages. This represents an improvement over Verner (1999) since it allows for variation in human capital characteristics to have a differentiated impact in worker productivity. They find that the higher wages of medium-aged and older workers is justified by their higher relative productivity. On the other hand, even though women earn lower wages, they are not significantly less productive, indicating the existence of discrimination.<sup>2</sup> This paper follows Hellerstein, Neumark and Troske’s (1999) approach to the study of wage regressions by linking them to productivity functions. This paper uses a unique Mexican data set that combines data on manufacturing firms and their workers to estimate relative marginal products and compare them with relative wages. The paper is organized as follows: section II introduces the data, section III presents the variables used in this work along with some descriptive statistics, section IV presents the framework and explains the methodology, section V analyzes the results, in general, by firm size and examines training by gender, and section VI offers conclusions.

> 2 Hellerstein and Neumark (1999) use the same methodology to test for gender discrimination in
**Table 9: long-term impacts of the 4P program on empowerment - decision-making - ITT**

||Fraction of<br>domains for<br>which<br>respondent<br>makes the<br>decision|Fraction of<br>domains in which<br>respondent's<br>opinion is heard<br>|Fraction of<br>domains in<br>which<br>respondent<br>cares about<br>making the<br>decision<br>|Dummy for<br>dictator<br>decision<br>making<br>Wom|Dummy for<br>contributor<br>decision making<br>en|Dummy for<br>separate<br>spheres<br>decision<br>making|Dummy for<br>norms-based<br>decision<br>making|Dummy for<br>most-<br>knowledge<br>decision making|
|---|---|---|---|---|---|---|---|---|
|ITT|0.019|0.036*|0.041**|-0.034|-0.002|0.024|-0.051|0.011|
||(0.45)|(0.08)|(0.03)|(0.35)|(0.96)|(0.65)|(0.18)|(0.78)|
|Observations|437|437|437|437|437|437|437|437|
|Adj. R2|0.05|0.02|0.04|0.01|0.01|-0.00|0.05|0.06|
|Control mean|0.627|0.
not do so in year _t −_ 1, and zero otherwise<sup>14</sup> , and (iii) a firm exit dummy variable which takes the value of 1 if firm _i_ in the country _c_ did not export product _p_ to destination _d_ in year _t_ but did so in year _t −_ 1, and zero otherwise.<sup>15</sup> Finally, we define the intensive margin as the FOB export value in USD of firm _i_ in country _c_ of product _p_ to destination _d_ in year _t_ .

# **3.3** **_Auxiliary data: Bilateral tariffs_**

While NTMs are proliferating in scope and stringency, tariffs remain the classical market access tool. We access applied bilateral tariff data from the World Integrated Trade Solution (WITS). In cases where applied tariffs are preferential, we use the latter. Where both applied or preferential rates are missing we use the most-favored-nation or the bound rates.

> 14We omit firms that have exported to a product-destination market every year from the entry probability analysis. Also, we exclude the initial year of the sample from this analysis because we cannot ascertain whether this is the first time the firms are entering the market or not.

> 15We do not include firms that exported solely to a product-destination market in the last year of the sample in our market exit probability analysis.

10
$525 billion or 91 percent of total identified investments is expected to benefit IBRD or IDA-eligible countries.<sup>3</sup> Only US$66 billion in completed projects has been identified as of end-2016; most BRI investment is still in the construction or planning phase. The analysis largely focuses on 50 developing countries, which have been identified as lying on the original BRI transport and connectivity corridors (De Soyres et al., 2018) and for which debt and macroeconomic data are available. This approach implies that, for example, only two Sub-Saharan African countries, namely Kenya and Tanzania, are included in the database, despite significant Chinese investment in Sub-Saharan Africa, some of which has been recently rebranded as BRI related.<sup>4</sup> Several countries have already scaled back on BRI investments or requested debt relief from China. In October 2018, Malaysia decided to suspend and renegotiate US$20 billion worth of projects fearing not to be able to repay such debt and Pakistan cut down the cost of a large railway project. China had to provide debt relief to several borrowing countries in recent years<sup>5</sup> ; in some rare cases, difficulties in repaying debt has resulted in China taking possession of key infrastructure in lieu of repayment in Sri

> 2 See, for example, “How Big Is China’s Belt and Road?”, April 3, 2018, Center of Strategic and International Studies. 3 US$ 10 billion of financing directed to Latin American Countries was not considered in the analysis.

> 4 In October 2018, the Forum on China-Africa Cooperation announced US$60 billion financing to African Countries and this financing has been re-branded as part of the BRI (see https://thediplomat.com/2018/09/focac-2018rebranding-china-in-africa/).

> 5 Hurley _et al._ (2018) provide a comprehensive list.

2
reularl|[0.00908]***<br>-00291|[0.00818]***<br>-00273|[0.00817]***<br>-00273|[0.0183]***<br>-00288|[0.00820]***<br>-00273|[0.0185]***<br>-00294|[0.0183]***<br>-00282|
|p gy<br>Household Home & WASH index|.<br>[0.00398]***<br>-2.12E-05|.<br>[0.00334]***<br>0.000316|.<br>[0.00335]***<br>0.000317|.<br>[0.00668]***<br>0.000518|.<br>[0.00334]***<br>0.000302|.<br>[0.00682]***<br>0.000721|.<br>[0.00646]***<br>0.00137|
||[000408]|[000279]|[000279]|[000501]|[000279]|[000487]|[000469]|
|Urban|.<br>0.0408<br>|.<br>0.021<br>|.<br>0.021<br>|.<br>0.0409<br>|.<br>0.0212<br>|.<br>0.038<br>|.<br>0.0363<br>|
|Use internet in the last 3 months|[0.00838]***<br>0.015|[0.00518
the former may lead to additional steps for “course correction” in the context of an ongoing survey, the latter would be useful for decisions regarding future surveys.

In the literature, the term “interviewer effect” has been used to reference interviewer contributions to variations in interview outcomes. Interviewers can contribute to the variability of respondents’ answers, non-response/survey participation, measurement error or bias, and interview length. West and Blom (2017) summarizes findings concerning interviewer effects, focusing on high-income countries. In low- and middle-income countries, interviewer characteristics such as gender (FloresMacias et al. 2008; Vollmer et al. 2021) and ethnicity (Adida et al. 2016 <mark>) have been found to</mark> affect responses. Research has also demonstrated that interviewer characteristics can affect answers to sensitive questions, including those on political preference in Uganda (Maio and Fiala 2020), domestic violence in India (Singh et al. 2022), and abortion (in the context of Demographic and Health Surveys) (Leone et al. 2021).

The paradata-driven analyses of interviewer effects on survey duration have been limited in lowand middle-income countries until now - in contrast with the numerous applications that have been focused on high-income contexts. The latter category of research has typically relied on multilevel models, which are typically used for analyzing data with a hierarchical structure or when the data is nested within a larger group (such as responses within a group of interviewers). These models have been used to decompose the contributions of interviewer effects, enumeration area effects, and respondent effects towards the variance in survey duration. In high-income countries, the effects of these variables have overall been quite modest (Couper and Kreuter, 2013).<sup>10</sup> With the increasing availability of CAPI/CATI paradata, there is now an opportunity to undertake similar analyses in low- and middle-income countries.

In what follows, we use the CAPI survey paradata at our disposal and obtain multi-level model estimations to discern
Metals Wood Metals<br>(a) (b)<br>2 6<br>4<br>1.5<br>Average Markup Average Markup 2<br>1 0<br><!-- End of picture text --> Figure 2: Trend in Markups Level, By Sector

**_Source:_** Author’s analysis based on data from the World Bank Regional Project on Enterprise Development (RPED) and Ghana Manufacturing Survey (GMES) from 1992 to 2003.The surveys were conducted by the Centre for the Study of African Economies (CSAE) at the University of Oxford, University of Ghana, and Ghana Statistical Service. **_Note:_** Markups level aggregated at 2-digit International Standard Industrial Classification (ISIC) level. Panel (a) shows markups computed on material input, while Panel (b) shows markups computed on labor.

Panel (b) of fig. 2 displays average markups computed on labor input over time. The dynamics generally appear to be the same for all sectors. However, the average markup computed on labor tends to increase over the years. The food, textiles, wood, and metal sectors grew by 43 percent, 25 percent, 32 percent, and 92 percent respectively, from their 14
Yet, despite this upward trajectory in economic growth, Ghana’s private sector employment remains largely informal. According to the World Bank Ghana Enterprise Surveys the size of the formal private sector remains small: registered firms account for a tiny share of total employment in Ghana, an estimate near 2 percent (estimates based on the GLSS6, World Bank 2016). To these, add concerns that highly concentrated production in commodity-based industries leave the Ghanaian economy at risk of adverse shocks and requiring further diversification.

Together these trends beg the question: has this growth been jobless? The link between economic growth and job creation depends on the extent to which growth generates employment, while the povertyreducing effects of employment generation depend on the type of jobs that respond to growth: notably this depends on the extent to which poor workers benefit from new jobs. Indeed, since 2005 job creation has more than kept up with the growth of the working-age population **.** Annual job creation reached 4.0 percent between 2005 and 2012, while the annual growth in the working-age population was about 2.6 percent, based on the Ghana Living Standard Survey (rounds 5 and 6). However, relative to its economic growth, the Ghanaian economy has created relatively few jobs. During the same period, average economic growth increased 8 percent per year (much more than before 2005), meaning that every 1 percent increase in economic growth was associated with 0.5 percent increase in job growth. The employment-growth elasticity (the percentage change in employment given a 1 percentage point change in growth) dropped to 0.5 from an average of 0.7 in the 15 preceding years.<sup>1</sup> This suggests a marginal slowdown in job creation in response to economic growth that was driven by sectors that generate low employment: mining and commercial oil production.

Most job creation in recent years has taken place in economic sectors with relatively low labor productivity, and in urban self-employment, rather than wage employment (World
. Thus, Ivus (2010) found that a group of developing countries (those that were not British or French colonies) that were obligated by membership in the WTO agreement to adopt stronger patent reforms than a similar group that did not have to reform as much, experienced significantly higher import growth in high-technology products. Delgado et al. (2013) studied a more comprehensive set of developing countries and found that, after implementation of TRIPS patent rules, there was a significant rise in imports of patent-intensive goods. Maskus and Yang (2018) found a significantly positive effect of changes in an index of national patent reforms in the TRIPS era on the growth of R&D-intensive sectoral exports in both emerging and developed 3
**<u>Table A.3. RDD regressions of employment of individuals using VHLSSs 2004 to 2008</u>**

||||Dependen<br>|t variables<br>|||
|---|---|---|---|---|---|---|
|Explanatory variables|Currently<br>working<br>(yes=1, no=0)|Self-<br>employed<br>farm work<br>(yes=1, no=0)|Self-<br>employed<br>non-farm<br>work (yes=1,<br>no=0)|Having wage<br>job (yes=1,<br>no=0)|Log of<br>monthly wage<br>(wage<br>workers)|Log of<br>monthly wage<br>(all workers)|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Program|0.0461***|0.1172***|-0.0340***|-0.0371*|0.1427*|-0.6372**|
||(0.0160)|(0.0314)|(0.0106)|(0.0201)|(0.0814)|(0.2508)|
|(Poverty rate – 50)|0.0008|-0.0081**|0.0035**|0.0054**|-0.0054|0.0548**|
||(0.0019)|(0.0038)|(0.0015)|(0.0022)|(0.0069)|(0.0254)
), near the top of the figure.

# **References**

- **Bazzi, Samuel, Martin Fiszbein, and Mesay Gebresilasse** , ““Rugged individualism” and collective (In) action during the COVID-19 pandemic,” _Journal of Public Economics_ , 2021, _195_ , 104357.

- **Chen, Xi and William D. Nordhaus** , “Using Luminosity Data as a Proxy for Economic Statistics,” _Proceedings of the National Academy of Sciences_ , 2011, _108_ (21), 8589–8594.

- **and** , “VIIRS Nighttime Lights in the Estimation of Cross-Sectional and Time-Series

- GDP,” _Remote Sensing_ , 2019, _11_ (9), 1057.

- **Elvidge, Christopher D., Feng-Chi Hsu, Kimberly E. Baugh, and Tilottama Ghosh** , “National Trends in Satellite-Observed Lighting,” _Global Urban Monitoring and Assessment Through Earth Observation_ , 2014, _23_ , 97–118.

- **, Kimberly Baugh, Mikhail Zhizhin, Feng Chi Hsu, and Tilottama Ghosh** , “VIIRS Night-Time Lights,” _International Journal of Remote Sensing_ , 2017, _38_ (21), 5860–5879.

- **Geddes, Jeffrey A, Randall V Martin, Brian L Boys, and Aaron van Donkelaar** , “Long-term trends worldwide in ambient NO2 concentrations inferred from satellite observations,” _Environmental health perspectives_ , 2016, _124_ (3), 281–289.

S16
the month-of-agespecific standard deviation. PPVT scores are available in the second and third rounds of the YL survey, i.e., when children were 4-6 and 7-8 years.

# **_C. Measures of maternal mental health_**

The explanatory variable is constructed using information on maternal common mental disorders from the Self Reporting Questionnaire 20 items (SRQ20), a screening (case-finding) tool included in the YL survey. This tool is recommended by the World Health Organization and has acceptable levels of reliability and validity in developing countries. The SRQ20 consists of 20 yes/no questions with a reference period of the previous 30 days. It is not diagnostic and cannot separate out anxiety from depression, but to the extent that these conditions are closely related and both

> 9 Within each household, YL surveyed an index child who was born in 2000-2001 and is followed from infancy until they reach their mid-teens. More details of this survey are presented in Bendini and Dinarte (2020). 10 A detailed analysis of the attrition bias between rounds 1 and 2 of the Young Lives sample strongly indicates that attrition is highly unlikely to bias research inferences. (Outes-Leon and Dercon, 2008)

> 11 Children do not need to name the objects or actions or be able to read or write them. It is just an object identification or association process.

7
scopes included under the EU CBAM is expected to broaden over time.

**The EU CS3D regulation is set to impact GVC metal exports to the EU, both directly and indirectly.** Figure 6 highlights Armenia’s metal GVC sector as the most affected, with over half its domestic metal products output destined directly or indirectly for the EU. Macedonia and Bosnia and Herzegovina follow closely. Other countries may also be exposed indirectly if they supply metal inputs to firms serving the EU market.

> 20 Note that the underlying trade data for the relative CBAM exposure index is from 2019, prior to Russia’s invasion of Ukraine. Using 2022 data, the CBAM exposure index value for Ukraine is likely to be higher due to its heightened export concentration towards the EU.

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This version: 15 Feb 2024 25

**Table 3** : Focus Group Level Responses on Improvements in Living Standards

|**Theme from qualitative coding**|**%**<br>**of**<br>**FGDs**<br>**improvement**|**that**|**mentioned**|
|---|---|---|---|
||Peninsular Malay|sia|Sabah|
|Improvements in education|70.7||46.6|
|Improvements in poverty, income, and<br>standards of living|43.9||60|
|Improvements in infrastructure|45.2||66.6|
|Source: MLSQS 2019 data.||||

# **Improvements in poverty levels**

Respondents across ethnicities and regions noted a mobility out of poverty as jobs moved from agriculture to industries, requiring less physical labor and paying more.

_Twenty-five years ago, we moved from rubber plantations to industries. The money came from these opportunities._ [Bumiputera, Selangor] An increase in the availability of professional jobs led to improved incomes.

_In my parents’ generation, jobs were not professional. They lacked technical knowledge, they only did business. There were less middle-income groups. Now there are more professionals and more middle-income groups._ [Chinese, Selangor] Respondents have witnessed their community members moving from poverty to middle-income to even upper middle-income categories.

25
Ho Chi Minh City, the country’s economic powerhouse, extreme poverty is nearly eradicated – and yet more people than anywhere else in the country live in flood hazard zones. In other words, high-income provinces and districts tend to be more exposed to flood risks. However, a different study finds that in Ho Chi Minh City, the poor are disproportionally exposed to flooding (Narloch and Bangalore 2018). How do these results fit together?

Indeed, at the local level the relationship between poverty and flood exposure can be inversed. Flood hazards tend to be highly localized, with some neighborhoods being at risk from frequent inundation, while other nearby neighborhoods may benefit from higher elevation or better drainage systems. Hallegatte et al. (2017) review the literature and conclude that land and housing markets often push poorer people to settle in these riskier areas, especially where land is scarce. Indeed, a meta-analysis of the literature suggests that a 1 percentage point increase in the yearly probability of flooding is associated with a 0.6 percent decrease in housing prices (Daniel, Florax, and Rietveld 2009). In Ho Chi Minh City, for instance, qualitative surveys suggest flood-prone areas can be much cheaper than non-flood-prone areas for the same quality of accommodation (World Bank and Australian AID 2014). Using a household survey in Dar es Salaam, Tanzania, Erman et al. (2019) find that flood-prone dwellings are valued at 30 percent less on average than safe ones.

Reduced housing prices, then, make it possible for poor people to access housing opportunities that could be out of reach in the absence of risk (Husby et al. 2015). In developing countries with informal markets, land scarcity can be particularly acute and land markets function poorly (Durand-Lasserve, Selod, and Durand-Lasserve 2013). In these places, it may not be the prices that push poor people into risky places but simply the availability of land with appropriate access to jobs and services. Informal settlements are often located in hazard-prone locations such as
, 2022; Heger & Neumayer, 2019; Miranda Montero et al., 2017) and, more specifically, floods (Kocornik-Mina et al., 2020) _._ Most analyses using nightlight data usually demonstrate a negative impact of disaster and climate shocks on nightlights but with effects resorbing within the year following the event (Bertinelli & Strobl, 2013; Elliott et al., 2015; Gillespie et al., 2014). Schippers & Botzen (2023) find that for a severe disaster such as Hurricane Katrina, the effect can be longer lasting.

However, there is a debate about the accuracy of nightlights as a proxy for economic activity. Critics argue that nightlight intensity may not capture economic activity accurately in all contexts, such as highly rural areas, where changes in lighting efficiency could affect the amount of light observed without necessarily reflecting changes in economic activity. Possibly other cultural and social factors or government policies on lighting could also influence the amount of nightlight observed.

2
._663|0_._673|0_._626|0_._553|0_._665|0_._674|0_._630|
|Nr. of countries|16|16|16|16|16|16|16|16|
|Nr. of hh|134548|134548|134548|75505|134548|134548|134548|75505|

Source: International Urban Poverty Database. Note: *** p < 0.01, ** p < 0.05, *** p < 0.1. Robust standard errors in parentheses. WorldPop 250m is used for the DOU method. The dependent variable is the log of per capita consumption expenditures, expressed in PPP and not spatially deflated. Only urban households (“Urban center” or “Urban cluster”) are included in specifications (4) and (8). The baseline category is “Rural” in all specifications, except (4) and (8), where it is “Urban cluster”. Demographic control variables include household size and household head’s age, sex, and marital status. Education is a categorical control variable that summarizes the education of the household head in seven categories. Employment is a categorical control variable that summarizes the household head’s labor status and employment sector.

with high-density areas (urban centers and urban cores) having a higher premium. The density effects are particularly large in some countries, including Malawi, Niger, Uganda, and Angola. In these countries, urban centers and urban cores have a substantially higher premium than low-density urban areas. In the case of Tanzania, there is no premium observed in low-density urban areas. As regards the DB approach, towns, which are classified as rural, are poorer than cores and suburbs for most countries, and even poorer than fully rural places in Niger, Angola, and Tanzania.

# **5.3 Spatial distribution of poverty**

## _Where is the mass of poverty concentrated?_

In
is reduced by one, until all components are estimated to have positive variance. See assumption 2 for details on the positive variance constraint (and why positive variance is not necessarily guaranteed). Negative variance estimates are only obtained for urban samples during 2018.

Once we have an estimate of the conditional distribution, we obtain 50 random draws of NSS consumption for each household in the CPHS sample (conditional on each household’s CPHS consumption value). For each of the 50 realizations of NSS consumption data, we evaluate the corresponding poverty headcount rates and selected measures of inequality. The point estimates of poverty and inequality are obtained by averaging over the 50 different realizations.

When a new NSS household consumption survey becomes available, both NSSconsumption and CPHS-consumption can be observed for the same year (albeit in different surveys with their own sample of households). Accordingly, one could estimate

> 23To illustrate, we report the empirical goodness of fit for the mixed normal distributions for the years 2015 and 2019 in Figure 8 of Appendix 3.1

37