rafmacalaba/data-use-mentions-extended huggingface dataset:
and poverty-reducing patterns of economic growth in rural India. This overview paper provides the overall framework for a more detailed study of the factors that constrain access to land in selected states, and considers potential policy instruments and mechanisms to reduce those constraints. Four main sections follow this introduction. Section 2 presents the analytical framework, followed in section 3 by a description of the context within which land relations in India are played out. Policy approaches to improving access to land are likely vary from state to state according to differences in agrarian structure, conditioned in turn by history and political economy. In light of shortcomings in available data, preliminary evidence is presented on the distribution of rural land, and on the relationship between farm size and productivity.

Section 4 outlines five key issues that are suggested to be among the most important constraints on access to land for the rural poor and socially excluded, and which warrant further analysis in selected states. These issues are: restrictions on land-lease markets, fragmentation of holdings, the widespread failure to translate women's legal rights into practice, access to and encroachment on commons, and transaction costs associated with land transfers.

The final section summarizes the main policy options considered here for enhancing access to land by the rural poor and other socially excluded groups. Since land is a state subject in India, it is not possible to suggest specific policy options except with reference to particular states. A companion paper presents an exploratory, state-level analysis of social exclusion and land administration in the state of Orissa (Mearns and Sinha 1998). This pilot study aimed to apply the overall framework developed in the present paper in a specific context, and to field-test the methodology so that it may be used as a 'template' for further studies in selected states of India. In order to produce trustworthy and meaningful findings relating to the issues identified here as priorities, careful triangulation is required between existing survey data, village studies, and primary fieldwork adopting methods of institutional and stakeholder analysis. The intention is that on the
(2.099)|(2.121)|(2.358)|(2.157)|(1.249)|(2.124)|
|Observations|89|89|78|89|79|89|89|89|78|89|79|89|
|R-squared|0.390|0.422|0.513|0.478|0.478|0.429|0.357|0.389|0.485|0.436|0.455|0.389|

- _1/ IMF 2020 Growth Forecast Changes (WEO October 2020 vs. WEO October 2019). 2/ WBG 2020 Growth Forecast Changes (MPO October 2020 vs. MPO October 2019). Standard errors in parentheses.  *** p<0.01, ** p<0.05, * p<0.1_

18
Policy Research Working Paper 7827

# **Abstract**

Globalization is creating many new trade and growth opportunities, with services trade increasingly becoming an issue for export-oriented economies. Services are important to country trade strategies, because they represent activities in which countries may have a comparative advantage, and they are drivers of competitiveness for the whole economy. This paper uses data from the World Development Indicators, two new databases (the Export in Value-Added database from the Global Trade Analysis Project, and Trade in Services data), and firm-level data. The paper employs a wide range of indicators to analyze the trade competitiveness of the services sector in the Russian Federation. Since service exports are less than would be expected considering Russia’s level of development, the study finds that the contribution of services to export diversification could be heightened significantly. The scale of Russian business services exports is relatively low, although exports of traditional services, like transport and travel, are performing well. Despite the relatively minor importance of exports of modern services, the category of other business services has in recent years been growing fast, and business services have strengthened their revealed comparative advantages. Yet Russia still has much potential for expanding trade in modern services. There is also potential to diversify services exports to other markets, such as France, Germany, Japan, and elsewhere in Asia, which today seems underexploited. Finally, although exports of direct services are low, services such as transport, distribution, finance, and other business services are making major contributions to other exports, in particular energy.

This paper is a product of the Trade and Competitiveness Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at ssaez@worldbank.org and erik.vandermarel@ecipe.org.

_The Policy Research Working
was the main source of growth.

# **8. Conclusion**

Human capital has a highly significant and positive impact on long-run growth in Guatemala. The importance of human capital is substantial. An increase by 1 percentage point of average years of schooling would raise output by about 0.33 percent. The effect is of similar magnitude to that in micro studies. A disaggregated analysis by level of education reveals that primary schooling is most important for productivity growth, followed by secondary schooling. Over the past decades, it appears that general education and basic technical skills have been the main

> 31 This finding is in agreement with other recent growth accounting studies for Guatemala, which are presented in Box 3.

> 32 Roldós (1997) examines the growth experience for Chile, and adjusts for changes in the quality of labor _and_ capital. These results are in the Appendix.

> 33 For example, Loayza et al. (2002) rely on the Barro and Lee (2001) data set on educational attainment.

-59-
the Gilbert and Ellice Islands, the former Yugoslavia, Czechoslovakia, Ruanda-Urundi, the Trust Territory of the Pacific Islands, and the Soviet Union. Except for the Soviet Union, the census data for these countries or territories are disaggregated and distributed among the destination countries currently in existence on the basis of more recent migration figures.<sup>13</sup> All of these assignments are made according to the distribution of immigrants of the successor countries in later years.

The Soviet Union is a unique challenge. As mentioned, the enforcement of new borders and the creation of new nation states typically create new migrants overnight. According to the foreign-born definition, people who cross new borders that are created with the break-up of a country are considered migrants, even if they moved before the break-up while the country was still unified. This is particularly problematic in the case of the Soviet Union because 15 new sovereign nations were created overnight, there have historically been large numbers of internal migrants, and migrants have traditionally been recorded using a definition based on ethnicity. Failing to make any adjustment for the Soviet Union, therefore, would result in a large artificial jump in the number of migrants at the time of break-up (see appendix 3).

13 For example, the 1988 census data for the Trust Territory of the Pacific Islands were disaggregated and distributed among the Marshall Islands, the Federated States of Micronesia, the Commonwealth of the Northern Mariana Islands, and the Republic of Palau. However, in years when a country conducted its own census but was also included in the census of a more aggregated region, the country‘s own census is prioritized.

9
million reactions on Facebook and Instagram across 22 Indian states),<sup>3</sup> our study focuses on its offline behavioral impacts: “Did the campaign work?” and “Did it work for those most at risk?” We adopt a multi-stage methodology. We start by recruiting survey respondents stratifying across 80 districts in the three study states: Uttar Pradesh, Jharkhand, and Chhattisgarh. Specifically, we use ads on Facebook and Instagram, where we offer mobile credit as an incentive to fill out a baseline survey using a chatbot integrated with Facebook Messenger. During the initial phase of recruitment, we conduct formative research to identify households most at risk of contracting malaria and ensure their inclusion in the study sample. After considering various demographic and socio-economic characteristics, we find that dwelling type is the most robust household-level predictor of malaria incidence.

Respondents living in solid houses (i.e., dwellings made of stones, bricks, cement, and concrete) are 43% less likely to report recent malaria cases compared to those living in semi-solid or non-solid dwellings (i.e., made of mud, straw, and tin).<sup>4</sup> Beyond being a strong predictor of malaria risk, dwelling type also provides a convenient mapping into administrative data on malaria incidence, which are disaggregated across urban and rural health facilities. In India, non-solid and semi-solid dwellings make up approximately 15% of housing in urban areas and around 50% in rural areas (India IIPS, 2022).

During the initial recruitment phase, respondents living in solid dwellings were more likely to join our survey. To correct for this imbalance, we stratify our recruitment by dwelling type. Since Facebook does not offer the option to micro-target ads based on housing characteristics, we use the Facebook Lookalike Audience tool to target individuals living in non-solid dwellings. We show that this technique works to increase the number of respondents living in non-solid houses.

We apply this technique to recruit survey respondents into a longitudinal sample
margins. Moreover, these firms are often highly mobile, and more likely to compare taxes across alternative locations (Wells 1986). Hence, taxes can be an important part of their cost structure, and the firms can easily move to take advantage of more favorable tax regimes.

The impact and the nature of incentive schemes may also differ if they apply to new or existing companies. For example, Rolfe et al. (1993) shows, using a survey of managers of US firms, that start-up companies will prefer incentives that reduce their initial expenses (equipment and material exemption), while expanding firms will prefer tax incentives that target profit. He also reports that manufacturing industries will prefer incentives related to depreciable assets because they utilize more fixed assets than service industries.

In an interesting study, Coyne (1994) suggests that small investors are generally more responsive to tax incentives than large ones. Taxes may play a more important role in the cost structure of small companies because they do not have the financial and human capacity to developed sophisticated tax avoidance strategies. Large multinational companies are also more likely to receive special tax treatments, whatever the tax laws applied by the host country. Oman (2000) reports some evidence that large firms, especially in the automobile sector, are more likely to negotiate secret advance agreements on how much they will pay in both industrial and emerging countries.

There exist a few studies that estimate separate equations for FDI financed by retained earnings and external funds (equity plus debt) (Hartman (1984) and Boskin and Gale (1987)). They typically found that FDI financed by retained earnings is more strongly influenced by host country tax rates. However, they do not offer any clear explanation for this result, but it is possible that equity and debt financing are also influenced by the tax policy in the home country, thereby reducing the impact of the host country's tax regimes (see next section for more explanations).

Finally, there is growing evidence that low taxes might be
# **1.  Introduction**

Changes in bank ownership during the 1990s and early 2000s substantially altered the governance of the world’s banking organizations. The developed nations of North America and Western Europe witnessed tremendous numbers of domestic bank mergers and acquisitions (M&As) and foreign acquisitions in response to deregulation, technological advances, and the globalization of nonfinancial economic activity. Ownership changes in other regions were even more dramatic. In the transition nations of Eastern Europe, the crisis nations of East Asia, many Latin American nations, and elsewhere, large shares of the banking systems had their ownership change from government control to private control and from domestic control to foreign control. These changes occurred as governments privatized many of their state-owned institutions and reduced entry barriers to foreign organizations, often in response to financial crises or to revisions in political philosophy.

These changes in the corporate governance of banks raise important policy and research questions. How do these changes affect bank performance? Are the changes permanent or do the banks tend to return to their previous conditions? How do banks that undergo governance changes reallocate their portfolios? To what extent do the dynamic changes following governance changes correspond to predicted effects: e.g., Do privatized banks tend to move closer to behaving like other privately-owned institutions? We address these questions using an econometric methodology that builds on some of the literature on the performance effects of various types of bank ownership in developed nations, and apply it to a unique data set on Argentine banks in the 1990s.

We refer to the different types of bank ownership – domestic ownership, foreign ownership, and state ownership – as forms of “governance.” Studies of U.S. corporations typically use the governance term to refer to the methods shareholders use to reduce managerial agency costs, such as board composition, voting rules, or stakes held by managers. Studies of governance outside the U.S., particularly in developing nations, often focus on the role of ownership in reducing these agency problems because of
our tests is the possibility of systematic (non-random) reporting biases. If RD is a source of disutility then it may well influence the answers given to interviewers. Could this undermine our tests? It is instructive to consider a simple, yet seemingly plausible, model of that bias. Suppose that utility depends on own-economic welfare and comparison-group welfare and that RD matters, but to ‘save face’ the respondent hides the true RD from the interview. More precisely, assume that the responses are biased in amounts that are directly proportional to the difference between (true) own welfare and that of the comparison group; if the respondent feels poorer than her comparison group then she over-states her own economic welfare and/or under-states that of her comparison group. Evidently then the true parameters are positively-weighted linear combinations of the estimated parameters based on the reported data.<sup>25</sup> The key implication is that (even though we do not know the bias parameters) if we find that both own-welfare and comparison-group welfare have positive coefficients then this must also be the case for the true parameters; reporting biases cannot be hiding a true RD effect when no such effect is observed empirically.

# **4. Data and methods for Malawi**

We use the 2004 round of the Malawi Integrated Household Survey (MIHS). This is a comprehensive, nationally-representative survey of households administered between March 2004 and April 2005 by the Government of Malawi’s National Statistical Office with assistance from the World Bank, under the Living Standards Measurement Study. A two-stage stratified sample is based on the 1998 Malawi Population Census and is drawn from 564 enumerating

> 25 Suppose that _u_ = α _y_ + β _y n_ + ε for a measure of own-economic welfare _y_ and comparison-group welfare _y n_ .  The reported values are related to the true values according to: _y_ ˆ = _y_ −φ 1( _y_ − _y n_ ) and _y_ ˆ _n_ = _y n_ +φ 2 ( _y_ − _y n_ ) where φ _i_ ∈ [
endogeneity of input choice (Olley & Pakes 1996, Levinsohn & Petrin 2003, Ackerberg et al. 2015). Unlike TFP estimates, some of these ratios are also available for most non-manufacturing firms.

The rest of this paper is organized as follows. Section 2 briefly summarizes the ES data, including steps taken for comparability and regarding outlier observations. Section 3 discusses the estimation of revenue-based TFP, so-called TFPR. Estimates of output elasticities and their derived characteristics are presented in section 4.

# **2 Data**

The World Bank’s Enterprise Analysis unit has been conducting surveys using a methodology that allows cross-economy analysis since 2006. To date, over 168,000 face-to-face interviews with top managers and business owners in 144 economies have taken place under this “Global Methodology”. This note uses these data to estimate TFP. Surveys not using the Global Methodology are excluded, as are the surveys conducted earlier than 2006. The data from Zimbabwe 2011 are excluded from analysis due to the complications due to hyperinflation just prior to the data collection. An additional 25 surveys are dropped because at least one of the key variables used in the analysis was not collected in these surveys.<sup>1</sup> This leaves 267 surveys in 134 economies and more than 161,000 interviews with top managers and business owners of firms spanning more than 40 different industries (specified by two-digit ISIC

> 1These are: Bangladesh 2007, Benin 2009, Bhutan 2009, Cambodia 2013, Cabo Verde 2009, Central African Republic 2011, Chad 2009, Congo 2009, Eritrea 2009, Fiji 2009, Gabon 2009, Lesotho 2009, Liberia 2009, Malawi 2009, Micronesia 2009, Niger 2009, Pakistan 2007, Rwanda 2011, Samoa 2009, Sierra Leone 2009, Timor-Leste 2009, Togo 2009, Tonga 2009, Vanuatu 2009, and the Rep ́ublica Bolivariana de Venezuela 2006.

2
percent larger in 2019 than in 2000, more than double the increase recorded in Tashkent. This trend is driven by higher fertility rates in urban areas paired with very low rates of migration.

But despite these low rates of migration and urban population growth, labor markets are remarkably stronger in urban areas of Uzbekistan, and particularly in the city of Tashkent. In July 2018 when the L2CU baseline data were collected, the employment rate among people aged 15-65 stood at 41 percent in rural areas, at 46 percent in urban areas, and at 57 percent Tashkent city. The median wage in Tashkent city was 61% higher than the national average in 2018, and 88% higher than among those 17
4) satisfaction, (5) health service delivery, (6) clinic quality, (7) community development and political engagement, (8) contributions to clinics, (9) water and sanitation, and (10) economic outcomes. The subcomponents comprising each outcome family were specified in the analysis plan and are listed in Appendix D.2.

Each outcome family represents a set of variables aggregated using control group-standardized indices per Kling et al. (2007). To create an index of _K_ outcomes, we first reverse outcomes where necessary such _K_ that a higher value indicates better outcomes. We then compute _y_ � _i_ =<sup>1</sup> ∑ _<u>yik − μ</u>_ 0 _k_ , where _μ_ 0 _k_ and _σ_ 0 _k K_ � _σ_ 0 _k_ �

> 11 Our decision to aggregate cases to the section-level stems from two features of our geocoding procedure (see Section E.22). First, many villages in Sierra Leone do not have recorded names; when patients report their residences, they tend to name betterknown towns, rather than their village or hamlet. As relevant administrative units, sections typically feature a central or headquarter town. Using a larger administrative unit, we avoid measurement error that arises from attributing patients to larger towns that actually occur in the surrounding villages. Second, our geocoding procedure matches residences to lists of geolocated placenames. When we use smaller geographic units, these often contain few placenames that we can match patients’ residences to: 85 percent of census enumeration areas (which are just 7 square kilometers on average) contain one or zero placenames. By contrast, the average section contains 8 geolocated placenames; 94 percent of sections contain more than 1 placename.

> 12 AEARCTR-0002085: `https://www.socialscienceregistry.org/trials/2085` .

11
>2<br>1.5<br>1<br>.5<br>0<br>2<br>1.5<br>Density 1<br>.5<br>0<br>2<br>1.5<br>1<br>.5<br>0<br>2<br>1.5<br>1<br>.5<br>0<br><!-- End of picture text --> _Source: LiTS II, 2010._ There could be many factors explaining differences between households’ subjective views on their relative position in the economic ladder and their objective position as measured by their actual relative income or consumption. Aside from measurement error in our household consumption aggregate, we may have miscalculated “actual” relative income for example because we do not account for cost-of-living differences or because of errors in constructing 7
that has aimed to measure job quality. To construct our measure, we follow the approach of Del Carpio et al. (2017), and complement it using tetrachoric correlation matrices to account for the dichotomous nature of the indicators that compose the PCA. Furthermore, this represents the first job quality study using a PCA in Bolivia and adds to previous literature that uses different methodologies (Ya ̃nez (2018), Brummund et al. (2018), Hovhannisyan et al. (2022 _a_ )). As such, we measure job quality from the supply side of the labor market. Second, this paper uses a rich household dataset to assess the quality of jobs across time and in a country characterized by stringent labor market regulations and a high segmentation between formal and informal sectors. Our data allows us to assess the quality of jobs for non-wage workers in a developing country context, contributing to a literature that has, in many cases, been restricted to data availability only for the salaried cases (Hovhannisyan et al. 2022 _a_ ). Finally, our time period allows us to assess the consequences of the pandemic on job quality, contributing to the literature on the economic impacts of the COVID-19 shock.

Assessing job quality will provide policy makers with valuable insights into the state of labor markets, complementing traditional variables such as participation or unemployment rates that may mask labor market performance in developing countries with significant informal sectors. Firstly, it aids in identifying workers who experience poor job quality, taking into account not only sociodemographic characteristics but also analyzing the economic sector and employment structure. Secondly, recognizing that job quality holds welfare implications for workers and their families (Clark 2005 _b_ ), the study identifies specific dimensions of labor conditions that require improvement to enhance overall job quality, considering common factors such as social benefits, wages, and contracts, as well as additional factors like overqualification and child labor. Thirdly, it presents an easily replicable and evaluative approach for assessing labor conditions across different contexts. Finally, evaluating how different