rafmacalaba/data-use-mentions-extended huggingface dataset:
5. _ELLc_ : under the same model as in 2. In cases where we use a transformation different from the natural logarithm, random location effects and household residuals are drawn from their empirical distribution.

# **Unit-context models:**

1. _UC − CEBa_ : Unit-context model originally proposed by Nguyen (2012) but with EB, similar to Masaki et al. (2020). Model fit is done using Henderson’s Method III (with sampling weights) and estimates are obtained using CensusEB as noted in CMN (2020). The fitted model follows:

- _yach_ = _zacα_ + _taω_ + _gsλ_ + _ηa_ + _εach_ .

2. _UC − CEBac_ : Unit-context model fit is done using REML and estimates are obtained using CensusEB as noted in Marhuenda et al. (2017). The fitted model is:

- _yach_ = _zacα_ + _taω_ + _gsλ_ + _ηa_ + _ηac_ + _εach_ ;

3. _UC − CEBsa_ : Similar to model 8, however random effects are specified at the municipal and state levels. Just like 5, the goal here is to borrow strength from the state for municipalities that are not in the sample.

4. _UC − ELLc_ : ELL estimates under model in _UC − CEBa_ but random effects specified at the PSU level, as was originally proposed by Nguyen (2012) and then by Lange et al. (2018). In cases where we use a transformation different from the natural logarithm, random location effects and household residuals are drawn from their empirical distribution.

- _yach_ = _zacα_ + _taω_ + _gsλ_ + _ηac_ + _εach_ .

The chosen measures to evaluate performance of the considered predictors are bias, MSE, and root MSE obtained as: where _j_ stands for one of the methods: _CEBa, CEBc, direct, CEBac, CEBsa, ELLc, UC − CEBa, UC − CEBac, UC − CEBsa,_ and _UC − ELLc_ ; _τa_ denotes the true population parameter 25
mainland regions of Tanzania to conform to the price analysis reported in section 3. The LSMS‐ ISA surveys covered 4,232 mainland Tanzanian households in 2008‐09, 4,241 households in 2010‐11 and 4,215 households in 2013‐14.

Interviews took each time place over a 14 month period within the two year survey window, with the majority of households surveyed within the 12 month period of that year. Very few interviews took place in either the first month (September 2008) or the final month (October 2009) of the initial wave. The second two waves had full initial months but a relatively small number of interviews in the final one or two months (October and November 2011 and November 2013). Pearson χ<sup>2</sup> tests reject randomness of the interview months over regions.<sup>17</sup> This is remedied through the inclusion of household fixed effects and inclusion of time varying household characteristics, including the asset level of the household (as a proxy for wealth), household size (persons, not adult equivalents), the sex and education level of the identified head of household, a rural‐urban dummy and dummy variables for region of residence and survey wave.<sup>18</sup> Households were not generally interviewed in the same month in each wave. This is partly due to inter‐regional movements and partly due to changes in the interview months for households that remained in the same region. 5.3 percent of households crossed regional boundaries between the first two waves and 6.8 percent did so between the second and third waves. 55.2 percent of households that remained in the same region were interviewed in the same calendar month in the 2008‐09 and 2010‐11 rounds and 40.3 percent were interviewed in the same month in the 2010‐11 and 2012‐13 rounds. Only 20.5 percent of households that remained in the same region for all three waves
# **How Useful Is CPI Price Data for Spatial Price Adjustment in Poverty Measurement? A Case from Ghana**

Xiaomeng Chen, World Bank Rose Mungai, World Bank Shohei Nakamura, World Bank<sup>1</sup> Thomas Pearson, Boston University Ayago Esmubancha Wambile, World Bank Nobuo Yoshida, World Bank

## **JEL Classification:** D12, E31, O15

**Keywords:** price indexes, household welfare, purchasing power parity, housing, Ghana

> 1 Corresponding author (snakamura2@worldbank.org). We would like to thank the Ghana Statistical Services (GSS) for allowing us to use the CPI price data for the analysis in this paper. We also thank Giovanni Vecchi and Nicola Amendola for their useful comments on the earlier version of the paper. 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 (IBRD) or the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
The first component is correlated with firm productivity, _ait_ , with the degree of correlation governed by _γ_ . If _γ <_ 0 , the distortion discourages (encourages) investment by firms with higher (lower) productivity, the empirically relevant case. The opposite is true if _γ >_ 0 . The remaining components of _τit_ are both uncorrelated with _ait_ , but differ in their time-series properties. The term _χi_ is permanent while _εit_ is iid over time. Thus, the severity of factors 2 other than adjustment/information frictions is summarized by three parameters: _γ_ , _σχ_ , which 2 is the cross-sectional variation in the fixed component, and _σε_ , which is the volatility of the iid shocks to the wedge.

These five distinct forces all contribute to dispersion in _arpk_ : adjustment costs, uncertainty and the three components of the wedge, parameterized by _ξ_ , V , _γ_ , _σ_ 2 and _χ σ_ 2. The main _ε_ contribution of David and Venkateswaran (2017) is an empirical strategy to estimate these five parameters from firm-level data on revenues and capital. Specifically, they use a simulated method-of-moments estimator targeting the following five moments from Table 1: the variability and serial correlation of investment growth, the correlation of investment growth with lagged changes in _a_ , the correlation of _arpk_ with _a_ and the cross-sectional dispersion in _arpk_ . We follow their approach and report the resulting parameter estimates in Table 2.<sup>14</sup> The implications for _arpk_ dispersion and aggregate productivity are presented in Table 3 and discussed in detail there.

Table 2: Parameter Estimates

||||O<br>|ther Factors<br>|<br>|
|---|---|---|---|---|---|
||Adjustment Costs|Uncertainty|Correlated|Permanent|Transitory|
||_ξ_|_μ_<br>V/_σ_<br>2|_γ_|_σ_<br>2<br>_χ_|_σ_
areas may lack the institutional frameworks necessary for coordinated conservation activities, leading to unchecked exploitation of natural resources and increased threats to biodiversity. In some cases, these regions may be rich in biodiversity, but their legal ambiguity complicates the establishment of conservation policies and enforcement mechanisms.

Fragile and Conflict-Affected Situations (FCSs) are also included due to the significant impact that political instability, civil unrest, and war can have on environmental protection. Biodiversity conservation in FCSs is particularly challenging, as the breakdown of governance structures often leads to deforestation, poaching, illegal mining, and other forms of environmental degradation. Moreover, these regions frequently lack the resources to prioritize environmental protection amid immediate humanitarian and security concerns, further exacerbating threats to biodiversity.

Additional elements for our analysis are provided by terrestrial and marine water bodies that cross national boundaries. Their ecosystems are critical for biodiversity, but are often excluded from shared arrangements for water resource management. Even in cases where ecosystems are included, their protection depends upon monitoring and enforcement provisions that require sustained support.

Our terrestrial analysis focuses on transboundary river basins, which are particularly vulnerable to unsustainable water management practices, pollution, and habitat loss. Managing transboundary river basins requires international cooperation to ensure the long-term health of their ecosystems. Conflicting national interests, such as competing demands for water, can complicate conservation efforts, making these regions particularly important to study.

Our marine analysis focuses on transboundary overlaps in Exclusive Economic Zones (EEZs), which are formally managed by marine joint regimes with specific arrangements for the exploration and exploitation of natural marine resources (Flanders Marine Institute, 2019 and sourced from the United Nations' <u>DOALOS repository). Like transboundary river basin</u> arrangements, these regimes can be inconsistent in their attention to biodiversity management, particularly for migratory marine species. Cooperative governance in these joint regime areas is critical for ensuring that marine resources are used sustainably while safeguarding the biodiversity that exists in shared marine ecosystems.

By overlaying these critical geopolitical boundaries onto our species occurrence maps, we
>17</sup> Removing such exemptions has the 17In a survey of 197 businesses in Cameroon, Gauthier & Gersovitz (1997) report that 4 were legally exempt from sales tax, while 30 were legally exempt from the business profits tax. In Gauthier & Reinikka (2001) a similar survey of 158 businesses in Uganda reports 17 exemptions from sales tax and 41 exemptions from the corporate income tax. Both 22
30%<br><!-- End of picture text --> <!-- Start of picture text --> 40%<br><!-- End of picture text --> <!-- Start of picture text --> 50%<br><!-- End of picture text --> <!-- Start of picture text --> 60%<br><!-- End of picture text --> <!-- Start of picture text --> 70%<br><!-- End of picture text --> <!-- Start of picture text --> 80%<br><!-- End of picture text --> <!-- Start of picture text --> 90%<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br><!-- End of picture text --> <!-- Start of picture text --> 0%<br><!-- End of picture text --> We use these data to expand the numerator, by taking the ratio of internal to external financing of working capital as constant over the period of extreme economic distress. In reality, financing may dry up if banks are unwilling to lend. Alternatively, government-sponsored

> 4 The data on the statutory corporate income tax rate are taken from the PWC’s _Worldwide Tax Summaries_ .

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studies to build on as more sophisticated survey methodologies are developed to capture the informal sector.

Our findings are relevant for policy makers in terms of how they could increase the productivity for female-owned informal businesses. We ascertain a gender labor productivity gap in the informal sector and point to specific factors that have contributed to it. Thus, it follows that labor productivity of female-owned firms can be improved and the gap with male-owned firms reduced by providing women with more education (secondary or higher), opportunities to acquire experience in running businesses, facilitating purchase of capital equipment, providing more security, and enabling contract-based transactions. While our findings are novel, we do caution that they have their limitations. We are unable to explain a sizeable portion of the gender labor productivity gap uncovered in the informal sector. Furthermore, the location seems to have a significant contribution to the gap that may be related to location-specific or country-level policies. Last, our sample is not necessarily representative of the informal economies in the country or even the cities. Thus, it remains to be established if our results hold outside the sample used.

There is of course an additional policy-relevant question – why are these informal businesses not formalizing? It is unclear if increases in productivity are sufficient to trigger formalization. While the analysis presented in this study is silent on the transitions into formality, the data does provide some self-reported information on plausible reasons.<sup>9</sup> The information is presented in tables A4 and A5 for countries for which data are available. Most firms in the sample would like to register (56 percent). About 52 percent of female-owned businesses would like to register, in contrast to 57 percent of male-owned informal businesses. In three countries in the

> 9 The informal enterprise survey data includes a section where respondents indicate their desire for registration, the reasons they remain unregistered, and the potential benefits of registering. Not all this information is available for all the countries or harmonized.

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the districts that form the study are in some general sense representative, the sample of villages was selected based on an endogenous criterion: being at a specific stage in the VG application process. Within the 100 study villages, we interviewed every household using a short listing survey, providing a census of 61,611 households. Each household was then sorted into one of the following strata:

- **Village elites** (Village Executive Officer, or VEO, and the village chairman, or the VC),

- **Ineligible households** (i.e. households with no vulnerable individuals as defined by TASAF and described above in Section 1),

> 10 Co-party dummy always equals one when the non-CCM co-party equals one, so this dummy tests for a differential effect of non-CCM co-parties versus CCM co-parties.

9
interest in learning more about the business training. These included emails to the existing client database of JBDC; advertisements via social media, newspaper and radio; and messages from other MSME support organizations. The goal was to reach 2,000 MSMEs, with a target of having 1,000 of them attend a demonstration workshop, where the content of the course and the methodology to determine the price would be explained and willingness to pay for the course would be elicited. A short baseline survey was conducted on the phone or online upon enrollment in the study to provide basic background on the firm, including characteristics that can be used to examine what factors help predict willingness to pay.

This process launched in late June 2017 and was extended until May 2018. Overall, 1,823 entrepreneurs showed interest in the training and answered the baseline survey. We dropped 8 respondents who reported not having a business, and one respondent who did not provide any contact details. We also removed from the sample 20 outliers in terms of monthly 7
degree. To the best of our knowledge, this is the first paper to analyze the causal impact of the Covid shock on labor market dynamics in a developing economy, over a fairly long (16 months) time horizon since the end of the initial lockdowns in the summer of 2020. The findings are also likely to have relevance for other developing countries whose labor markets share some similarities with India.

Our paper also relates to the broader literature on the impact of economic crises in general on labor outcomes, the longer-term impacts of which can be particularly harmful for inequality and socioeconomic mobility. There is evidence to suggest that loss of jobs can leave lasting impacts on vulnerable workers, and particularly on young entrants to the labor market. For example, Guvenen et al. (2017) use data from the Social Security Administration in the United States to show that individuals who go through a long period of non-employment suffer large and long-term earnings losses compared to individuals with similar age and previous earnings histories. Because of this effect, a cohort that enters the labor market during a recession is likely to face long-term disadvantages that lead to lower lifetime earnings. Moreover, employment shocks that are unexpected in nature can be particularly harmful – studies have shown workers who are displaced by unexpected firm closures to experience significant and long-lasting reductions in earnings. A crisis also tends to affect workers unequally because of differences in their socioeconomic background, widening existing inequalities. Youth from disadvantaged backgrounds may be forced to enter the labor market at a time when few economic opportunities are available, compared with youth in households that are more well off or enjoy better access to credit, who are more likely to postpone labor market entry, accumulate more schooling or unpaid work experience, and improve their prospects of upward mobility.

This paper does not attempt a comprehensive analysis of the policy response by the Indian government but only focuses on the extensive vaccination campaign and its role in helping labor markets return to normal. For a review of India’s relief package - the Prandhan Mantri Garib
a much more in-depth examination of the gender impact of trade policy.

2.2 Female Labor Force Participation and the Gender Wage Gap in South Asia This section examines the differentials in female labor force participation rates as well as the extent of the gender wage gap (women’s wages relative to men’s wages) across different sectors/economic activities for the economies of South Asia.

The Global Gender Gap Report (2020)<sup>8</sup> has ranked South Asia as the second-lowest scoring region with an average remaining gender gap of 34 percent, ahead only of the Middle East and North Africa (MENA) region with the lowest performance (having an average remaining gender gap of 39 percent). Bangladesh is the best performer in the region and only one of the seven South Asian countries studied to feature in the top 100 of the Global Gender Gap Index, ahead of Nepal (101st) and Sri Lanka (102nd), and some 60 places ahead of India (112th). Furthermore, UN Women<sup>9</sup> reports that South Asia has the world’s

> 8 World Economic Forum. Global Gender Gap Report 2020, Geneva, 2019 available at

> - - - - <u>http://reports.weforum.org/global gender gap report 2020/dataexplorer.</u> 9 United Nations Entity for Gender Equality and the Empowerment of Women (UN Women). Progress of the World’s Women 2015-2016: Transforming economies, realizing rights, 2016.

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presented in previous sections can have a relatively small impact at the macro level.

# **7.2 Risk Sharing between Banks and the Government**

Banks and the government share, absorb, and mitigate the risks that the credit program entails by dealing with the expected default risk from the loans granted, unexpected credit risk, and funding needs for the banks. To understand the risks involved, it helps to measure the magnitude of the program allocation across groups of firms. We focus here on firms by risk category.<sup>26</sup> Micro data indicate that by the end of 2020, banks provide guaranteed loans worth US$11,467 million, or 4.6% of 2019 GDP, including loans to formal firms and natural persons

> 24 We use Equations (5) and (6) to partition firms into credit program users and non-users and measure the contribution of these groups to overall indebtedness.

> 25In unreported results, we decompose indebtedness into two additional margins: banking status and size. Across the different partitions of firms, large increases in firm leverage within groups happen for firms with a relatively small weight at the aggregate level.

> 26As an alternative, Appendix Table 6 reports the results of partitioning firms by size. This is useful because the public guarantee varies by size (from 85% for small firms to 60% for large firms, as explained in Section 4), effectively determining how firms and the government share risk. Furthermore, the size categorization allows us to cover all firms in our sample, permitting a better mapping between the debt levels computed from micro and macro data.

22
<br>fiber.|World Gallup Poll<br>(Global Diet Quality<br>Project)|2021-2022|N=55<br>countries|An indicator of dietary factors<br>protective against NCDs<br>The score ranges from zero to<br>nine expressed as an average<br>score.|
|**NCD-Risk score**|Score based on consumption during the<br>previous day or night of eight food<br>groups that are negatively associated with<br>meeting WHOrecommendations on free|World Gallup Poll<br>(Global Diet Quality<br>Project)|2021-2022|N=55<br>countries|An indicator of dietary risk<br>factors for NCDs|

41
firms (and governments) at the same time and for different purposes. Recent theoretical research shows that this may lead firms to respect data privacy less than is socially desirable, and they may also have incentives to inefficiently hoard data.<sup>104</sup> In short, when firms own data, they may over‐use and under‐share it while not adequately respecting consumer privacy. Policy priorities for making digital data work for development include: 97 Blumenstock, Joshua, Gabriel Cadamuro, Robert On. 2015. “Predicting Poverty and Wealth from Mobile Phone Metadata.” _Science_ 350 (6264): 1073‐1076. _._ 98 Burke, Marshall and David A. Lobell. 2017. “Satellite‐Based Assessment of Yield Variation and Its Determinants in Smallholder African Systems.” _Proceedings of the National Academy of Sciences_ (USA) 114(9): 2189‐2194. Osgood‐ Zimmerman, Aaron, et al. 2018. “Mapping Child Growth Failure in Africa Between 2000 And 2015.” _Nature_ 555 (41): 41‐47.

99 Blumenstock, Joshua, Nathan Eagle, and Marcel Fafchamps, 2016. “Airtime Transfers and Mobile Communications: Evidence in the Aftermath of Natural Disasters.” _Journal of Development Economics_ 120: 157‐ 181.

100 Lu, Xin, Linus Bengtsson, and Petter Holme, 2012. “Predictability of Population Displacement after the 2010 Haiti Earthquake.” _Proceedings of the National Academy of Sciences_ (USA) 109: 11576‐11581.

101 Wesolowski, Amy, et al., 2015. “Impact of Human Mobility on the Emergence of Dengue Epidemics in Pakistan.” _Proceedings of the National Academy of Sciences_ (USA) 112: 11887‐11892.

102 Evan Sheehan et al. 2019. “Predicting Economic Development Using Geolocated Wikipedia Articles,” Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2698‐ 2706.

103 Ramya Parthasarathy, Vijayendra Rao and Nethra Palaniswamy “Deliberative Democracy in an
Worldscope adjusts the reporting in the income statement and balance sheets of corporations to make it closer to international accounting standards. While this is generally straightforward for income statement items, the assets and liabilities numbers include different categories across the nine East Asian countries. In Indonesia, Japan, Korea, and Thailand, companies are not required to report intra-group lending, which distorts the leverage ratios. Similarly, different tax systems distort the reporting of profits. We hence use only stock market data, sales data net of excise taxes, and capital investment data. The latter two are flow figures and are not affected by the differences in accounting standards across the nine sample countries.

The database shows consolidated company account data when it is disclosed. Information on whether the accounts are consolidated is given by Worldscope in a field, which contains standardized text such as "All subsidiaries are consolidated," "Consolidation for significant subsidiaries, others are on equity basis," and "No consolidation, cost basis." If a company changes its consolidation practice, this change is recorded in the data. Of the 2,658 companies used in the empirical analysis, 505 companies accounting for 19% of the observations do not report consolidated statements. Indonesia and Thailand have the most such cases as a percentage of the respective country samples. To test the robustness of our main results, we rerun all regressions while excluding firms with unconsolidated statements.

Following on La Porta, Lopez-de-Silanes, and Shleifer (1999b), we analyze the control pattern of companies by studying ultimate shareholdings. In the majority of cases, the principal shareholders are themselves corporate entities, not-for-profit foundations, or financial institutions. We then identify their owners, the owners of their owners, etc. We do not distinguish among individual family members and use the family group as a unit of analysis. We divide corporations into widely-held and corporations with ultimate owners. A widely-held 10