rafmacalaba/data-use-mentions-extended huggingface dataset:
g., a male wage premium of 15% and a higher education premium of 75%.

We estimate Equation (2) using the Ecuador worker database and show the results in Appendix Figure E1 and Table E3. The negative coefficients in Panel A of Appendix Figure E1 show that Ecuadorian workers initially employed by harder-hit firms experience a persistent reduction in months worked until 2017, relative to workers in less hard-hit firms. The effects on worker months worked become more negative over time, although the hypothesis that all effects are similar across years cannot be rejected. The point estimates imply that an Ecuadorian worker whose initial firm faced a 10 percentage-point worse shock, worked in the . formal sector between 2009 and 2017 for a total of 6.1 fewer total months (7% fewer months).<sup>48</sup> But GFC firm shocks do not significantly affect workers’ real wages in Ecuador. These baseline effects for workers in Ecuador are robust to a series of checks shown in Appendix Table E4.

Allowing for heterogeneity across worker types in the adjustments to GFC firm shocks through the estimation of Equation (5), we find a strong negative adjustment of employment

> 48This calculation uses the point estimate for 2017 in the months worked regression of -0.068 and the average number of months worked of 9.2 from Appendix Table E1

56
Currently, in many countries, the existing bankruptcy regimes do not perform very well (Djankov et al., 2008). A survey on debt enforcement of practitioners from 88 countries indicates that bankruptcy procedures are time-consuming, costly and inefficient (i.e., unable to preserve the business as a going concern). In only 36 percent of countries, the business is preserved as a going concern, and an average of 48 percent of the business’ value is lost in debt enforcement. In a well – functioning bankruptcy system, the regime would ensure that the highest total value is achieved for the distressed firm. In other words, whether the firm should be closed down, liquidated piecemeal, sold as a going concern, or reorganized should depend on which option creates the most value for its creditors and shareholders. Some portion of firm value should be preserved for shareholders, even in bankruptcy. Otherwise, shareholders may do anything to prevent bankruptcy, including undertaking high-risk projects when the corporation is under distress (Hart, 2000). Shareholders that don’t exercise caution can lead to bankruptcies with high administrative costs and long delays, and the sales of viable businesses.

At the same time, ensuring the right incentive structure is critical. During this most recent crisis, for example, Germany revisited its long-standing rule _requiring_ company management to file for bankruptcy in certain situations, or face imprisonment. While this rule was originally instituted to ensure a level of debtor discipline and creditor confidence, the financial crisis prompted a fear amongst some policy-makers that declining asset values would create widespread _de facto_ balance-sheet insolvencies and prompt managers to put otherwise viable companies into insolvency proceedings. As a result, the filing requirement was amended to be less stringent (see Table I).

While there is no blueprint for optimal bankruptcy systems, there are several important principles that underlie the design of a good bankruptcy regime (Hart, 2000; UNCITRAL, 2005): 6
economies might trickle down and support the population. In particular, the results emphasize the ambiguous impacts that resource-led growth has had on poverty in other settings (e.g., Savoia and Sen 2021); these results build on the analysis in Pandey and Howes (2022) demonstrating how access to health has changed over the course of the resource boom in PNG, and extend the well-being indicators analyzed beyond health indicators over a portion of the resource boom; and the results comparing well-being in PNG to much of the rest of the world extend previous work that has shown similar disparities for either a subset of indicators or comparisons to a subset of the countries analyzed here (e.g., Howes and Mambon 2021; UNDP and OPHI 2022; Howes forthcoming; etc.).

The paper is structured as follows. Section 2 provides background information on the resource boom in PNG and some of the other significant changes that have occurred since the last official poverty estimate; Section 3 describes the data used in the analysis and the construction of the monetary and multidimensional poverty measures; Section 4 presents estimates of how well-being indicators have changed between 2009 and 20162018 for PNG and other comparison regions; Section 5 illustrates the disparity between economic activity and well-being in PNG relative to the rest of the world; and section 6 concludes.

# **Section 2. Background on Resource Growth and Welfare Measurement in Papua New Guinea**

As discussed in the Introduction, the resource sector is one of the largest contributors to the PNG economy. Although many economic statistics are imprecisely measured given the severe data deprivation in the country,<sup>4</sup> the biggest sectors of the economy have significant exports to other countries, and exports are much more precisely measured than many other economic statistics (e.g., Howes et al. 2022). Based on this information, the key contributors to overall economic activity include mining for gold, copper, cobalt, and zinc; drilling for natural gas and oil; and the
Policy Research Working Paper 11058

# **Abstract**

Accurate and comprehensive measurement of household livelihoods is critical for monitoring progress toward poverty alleviation and targeting social assistance programs for those who most need it. However, the high cost of traditional data collection has historically made comprehensive measurement a difficult task. This paper evaluates alternative satellite-based deep learning approaches using detailed household census extracts from four African countries to accelerate progress toward comprehensive, fine-scale, and dynamic measurement of asset wealth at scale. The results indicate that transformer architectures solve multiple open measurement problems, by providing the most accurate measurement of local-level variation in household asset wealth across countries and cities, as well as changes in household asset wealth over time. Experiments that artificially restrict data availability show the model’s ability to achieve high performance with limited data. The proposed approach demonstrates the promise of combining satellite imagery, publicly available geo-features, and new deep learning architectures for hyperlocal and dynamic measurement of wealth in data-scarce environments.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at dnewhouse@worldbank.org and tkilic@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
(relative to no letter at all) maintained more of their targeted employees on the payroll but without increasing the average wage of these (expanded upon below).

Figure 3. Dynamic treatment effects on employment report by the firm <!-- Start of picture text --> Soft letter Strong letter<br>Filing period Filing period<br>.06 .06<br>.05 .05<br>.04 .04<br>.03 .03<br>.02 .02<br>.01 .01<br>0 0<br>-.01 -.01<br>-.02 -.02<br>-.03<br>-.03<br>Marginal effect on employment (w.r.t control) Marginal effect on employment (w.r.t control)<br>-.04<br>-.04<br>-.05<br>-.05<br>Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec<br><!-- End of picture text --> Source: Authors' calculations using historical tax declaration records. Coefficients from random effects model plotted.

14
household and area models, when one defines bias with respect to both sample design (in our case simple random sampling within AGEB) and the model parameters, the design-model bias vanishes. This occurs because the sample mean of the predictor variables is an unbiased estimator of their population means, under a simple random sample design, and follows from the concept of design-model bias discussed in Valliant et al. (2000, p. 120).

In many applications, it is difficult or even impossible to apply a nested error regression model with household-level auxiliary variables, either because household-level auxiliary variables are not available for the entire finite population or due to the presence of measurement error in the household-level auxiliary variables. In these situations, the unit-context model would be a reasonable option. Like a nested error regression model with household-level auxiliary variables, a unit-context model is very flexible in producing estimates of a wide range of linear and nonlinear parameters (e.g., different FGT indices) using a single model. This is in sharp contrast with an area or sub-area level model, which requires separate models for each estimated parameter. The unit-context model also permits incorporation of uncertainty incurred due to estimation of different model parameters, such as the variance of the sampling error.

# 3. Data

We evaluate the ability of data derived completely from satellite imagery to predict municipalitylevel poverty in Mexico. In this section, we discuss the different data sources used to construct the requisite variables as well as the data used to validate the performance of the models.

## 3.1. Household data

We conduct the main analysis using monetary poverty estimates at the household level from the 2014 MCS-ENIGH Household Survey data. The survey is conducted every two years by the Mexican National Institute of Statistics and Geography (INEGI) to understand the income, spending, socio-demographic, and employment situation of the country. It is a nationally representative, cross-sectional household survey. The 2014 MCS-ENIGH survey covers 58,125 households, of which approximately 75% are urban and 25% are
Table A.6: Robustness: alternative measure of TFPR

|A. Dep. variable: log TFPR (ACF)|(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|Post _×_ Exports share (log)|0.041**<br>(0.018)|||||||
|Post _×_ Output share (log)||0.022<br>(0.025)||||||
|Post _×_ initial capital stock (log)|||0.022<br>(0.020)|||||
|Post _×_ initial capital per worker (log)||||0.024<br>(0.054)||||
|Post _×_ initial average wages (log)|||||0.041**<br>(0.019)|||
|Post _×_ RCA relative to Poland||||||0.080*<br>(0.040)||
|Post _×_ RCA relative to the world|||||||0.116**<br>(0.051)|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|N (obs.)|134174|134174|134174|134174|134174|134174|134174|
|N (frms)|18355|18355|18355|18355|18355|18355|18355|

Notes: Authors’ calculations based on survey data
important aspect of the quantitative analysis is the fact that the measurement of labor inputs is done at a worker level. As such, the differences in labor inputs across sectors does not account for differences in hours worker, human capital or talent across sectors. Given the evidence of differences in these factors across sectors14, the estimates of distortions are somewhat upward biased. This would lead to even lower gains in productivity in the second counterfactual exercise. To the extent that these differences across sectors remained stable over time, they would not affect the findings obtained in the first counterfactual in any significant way.

# **4.3 Contribution of Changing Distortions to Growth**

A number of studies have found large gaps in productivity across sectors and have interpreted these gaps as suggestive of misallocation of resources across sectors. Based on this interpretation, these studies quantify the impact of closing of gaps on aggregate productivity. However, these findings are highly sensitive to model misspecification and measurement errors. Therefore, it is important to ascertain whether measured gaps in productivity truly imply misallocation. The goal of this paper is to examine the role of distortions in a historical perspective. Over the last few decades countries have seen sizable changes in GDP per capita over time. For instance, the median growth in GDP per capita over two decades has been 40% for the sample of countries for which estimates of distortions are available. The country at the 90<sup>th</sup> percentile has realized a doubling of its GDP per capita over the same period. A fundamental issue of interest here is to discern whether changes in distortions over time have been important drivers of growth or not.

Using data from WDI and GTAP, I have obtained consistent estimates of sectoral gaps and have estimated resulting distortions for a large number of countries for relatively long periods. In the first exercise, I ask how much of the economic growth experienced by countries over long stretches of time can be accounted for by observed changes in distortions. For example, what fraction of
value for 2002 is 3.95, very close to the realized 3.87. This statistic suggests an almost unchanged dependency ratio until 2015, but a strong rise in old age dependency thereafter. As shown in Table 1, the cohort currently aged 45 to 64 is smaller than that between 15 and 44. This is clearly what drives the labor-supply-friendly demographic projection until 2015.

**Table 1.  Age Distribution of the Latvian Population, January 2003**

|Age|0-14|15-24|25-34|35-44|45-54|55-64|65+|Total|
|---|---|---|---|---|---|---|---|---|
|%pop.|16.3|14.9|13.8|14.6|13.2|11.6|15.7|100.0|

_Source_ : Demographic yearbook 2003.

5
the 2021 in-person data. Each row is its own dependent variable.

- Overall Housing Quality is defined as a normalized housing quality index that includes indicators for quality floors, roofs, and walls, indicators for access to grid electricity and piped water, and the number of people per room.

- Total Monthly Housing Expenditures is the sum of rent paid, mortgage, and housing upgrade costs paid in the last month.

- Total Consumption is log of the sum of food consumed in last seven days (Cereals and cereal products, Live animals, meat, and other parts of slaughtered land animals; Fish and other seafood; Milk, other dairy products, and eggs; Oils and fats; Fruits and nuts; Vegetables, tubers, pulses; Sugar and desserts; Ready-made food and other food products (baby food, spices)) multiplied buy number of months consumed in last 12 months, home-produced goods multiplied by months produced, annualized food received as gifts and annualized non-food purchases (utilities; water; infant needs; household and hygiene items; linens; clothing and footwear; basic household items; and school costs).

- Center for Epidemiologic Studies Depression Scale (CES-D) is a measure for depressive symptoms and includes 10 items asking about the past week. It is defined as a mean-effect index of 10 items scored 0-3 in the past week (I was bothered by things that usually don’t bother me, I had a problem in concentration on what I was doing, I felt depressed and troubled in my mind, I felt that everything that I did took up all my energy, I felt hopeful about the future (reversed), I felt afraid, I had difficulty in sleeping peacefully, I was happy (reversed), I felt lonely, I lacked the motivation to do anything). It is then normalized by subtracting the control group’s mean and dividing by its standard deviation.

- Strengths and Difficulties Questionnaire (SDQ) is a screening questionnaire administered to a randomly selected child aged three to
technological upgrading and productivity growth by opening opportunities and stimulating supplies of capital and skills. China‘s establishment of special economic zones, followed by a range of support by national and local governments for further industrial deepening in its three major urban/industrial agglomerations (the Pearl River Delta centered on Shenzhen, Dongguan and Foshan, the Yangtze River region around the Shanghai-Suzhou axis, and the Bohai region in the vicinity of Beijing and Tianjin) and in a number of inland cities (including the footwear cluster in Chengdu and the Wuhan opto-electronics cluster) highlights how a mix of instruments can be employed together, including support to science parks and extension services, encouragement of local universities to deepen industrial linkages, attracting a major local or foreign anchor firm that can trigger the in-migration of suppliers and imitators, and above all dense transport and communication connectivity infrastructure (Yusuf, Nabeshima and Yamashita 2008).

# **4  OUTSTANDING ISSUES**

Looking forward, there remains an important policy research agenda to better understand both evolving patterns of green innovation and the relative effectiveness of appropriate mixes of policies in different technological and institutional contexts.

Regarding patterns of green innovation, there is need for better data collection on green innovation. In particular, there is no direct evidence on adoption and adaptation of green technologies. There are almost no data measuring BoP green innovations to meet the needs of poor consumers. Firm-level surveys could be a good source to better understand what is taking place on the ground, with the recent EU Community Innovation green module a good starting point for more systematic inquiry across developing countries. More generally, it would be useful to better monitor public and private inputs being devoted to green innovation, starting with a more detailed breakdown of green R&D expenditures (as in Frischtak (2011) for Brazil), and then going beyond R&D investments to measure the broader range of green investments in intangible capital, including other expenditures on innovative property (such as green architectural and engineering designs), on green-related software and databases, and
control firms for treated and placebo firms.<sup>40</sup> Larger donor pools and the volatility of the outcome variables substantially slowed the underlying SCM fitting procedure.

We have very large potential donor pools made up of all the Serbian firms that imported between 2010 and 2013. Consistent with other applications of the SCM we limit the donor pools to firms that share some similarities. In particular, firms that are potential donors and/or placebos are limited to being within the same decile of 2010 total import value as the treated firm. The treated firms for which there are continuous imports are all within the 4<sup>th</sup> decile or higher in terms of their 2010 import value.

As noted in Section 3.2 we do not draw placebo firms from the donor pool used to construct the synthetic control firm for the estimated treatment effect of the IHC. Rather, for each treated firm we first randomly allocate untreated firms in the 2010 import value decile to placebo or donor status so that placebo firms are not eligible to be components of the synthetic control firm for the treated firm (nor can they be components of the synthetic control firm for other placebos). The synthetic control units for both treated and placebo firms are constructed by drawing from the same (large) pool of potential donors. For our benchmark (and preferred) specification, with three pretreatment values of the outcome variable as conditioning variables, as well as for the majority of other specifications a potential donor pool of 125 firms was large enough to allow synthetic control firms to be constructed for treated and placebo firms for all outcome variables.<sup>41</sup>

# **5. Results**

## **5.1 Difference-in-Differences Results**

Before presenting our main results from the pooled SCM, as a benchmark we provide results from DiD regressions to estimate the effect of IHC adoption on the five firm-level outcomes discussed in Section 4.1.<sup>42</sup>
15 other than poverty and inequality may affect growth (these factors may include, for example, positive externalities of education and infrastructure, or a negative impact from trade distortions).

# V **Sectoral Growth Patterns and Future Poverty Reduction**

# _VI Frameworkfor the simulations_

The findings of the previous sections can be used to inform policy. By combining the regional panel estimates of the net elasticity of poverty to growth with a consistent macroeconomic model for Bangladesh, we can simulate future trends in poverty. Denote GDP by Y, the average propensity to consume by c, population by N, and the net elasticity of poverty to growth in average consumption by X (as before). Then, the impact of GDP value added growth on poverty in any of the two sectors (urban or rural) depends on four parameters: the rate of sectoral GDP growth, the rate of sectoral population growth, the change over time in the share of sectoral GDP used for consumption by households in the sector, and the sectoral elasticity of poverty to sectoral consumption growth. For each sector: The term within brackets in (13) is the growth in per capita consumption, that is the sum of the growth in the share of income which is consumed and the growth of income per capita. The percentage change in poverty is obtained by multiplying the percentage change in per capita consumption by the elasticity of poverty to consumption growth. Using changing sectoral population shares over time to reflect the differentiated growth patterns by sector, we can then simulate changes in national poverty following changes in sectoral growth, consumption, and population. This is done below with three simulations: a base case scenario, a higher growth scenario through additional growth in urban areas, and a higher growth scenario with more growth in rural areas. The simulations are not intended to be precise
account, and it is inconsistent with most other accounts of why ethnic Indians are placing work into India (e.g., productivity advantages). From these and prior results, we conclude that taste-based preferences among oDesk actors in the originating countries is the key driver of the ethnic bias observed in outsourced contract placement to India.

# **7. Link to Broader Diaspora Connections**

To this point, our analysis has only exploited data derived from oDesk itself, and yet at multiple points we have framed our analysis in terms of the general diaspora literature. There remains a 20