rafmacalaba/data-use-mentions-extended huggingface dataset:
**Figure 1: Electricity supply reliability improves with income and electrification.**

<!-- Start of picture text --> 10000<br>1000<br>100<br>10<br>1<br>0<br>0<br>0 20000 40000 60000 80000 100000 120000 140000<br>GDP per capita, ppp (USD)<br>10000<br>1000<br>100<br>10<br>1<br>0<br>0<br>0 10 20 30 40 50 60 70 80 90 100<br>Access to electricity (% of population)<br>(SAIDI)<br>Hours of outage per year per customer<br>(SAIDI)<br>Hours of outage per year per customer<br><!-- End of picture text --> _Source: authors based on IEA, Doing Business and World Bank data._ There is consistent evidence showing that the quality of electricity provision matters for household income. “The provision of reliable electricity affects economic transformation through at least three pathways: lowering the cost of doing business, thereby increasing business entry; increasing the performance of existing firms through higher productivity and revenue; and increasing the welfare and quality of life of households, thereby enhancing the offer of productive labor services.” (World Bank, 2019). Chakravorty, Pelli, & Marchand (2014) find that better quality electricity measured in higher daily supply is associated with higher income among rural households in India. Results from their analysis using a representative panel of more than 10,000 households show that during the study period (1994-2005), access to electricity led to a 9% income increase among rural non-agricultural households, while being connected to the grid and having access to better quality electricity with fewer disruptions increased 5
<!-- Start of picture text --> 35<br>-*—Census of India<br>33<br>Se -—eBenchmark specification<br>2 31 ——Population size<br>; a<br>5<br>29<br>p= Population density<br>= —=—Population size + population density<br>2oO 27 -*Built-up share<br>a<br>95 -<Lit-up share<br>-©-Built-up share + lit-up share<br>23 —Random forests method<br>21<br>2001 2011<br><!-- End of picture text -->
of the household welfare results for households within each decile provides the results at the decile level labeled ―unreconciled sequential approach‖ in table 4.2. We find that the richer households are estimated to gain significantly more in the unreconciled sequential approach, since the return to capital increases more in the unreconciled approach (this is not a general result). On the other hand, we find that the bias from ignoring the quantity feedback effects on prices is very small if the data are reconciled first between the household and representative consumer models. See Rutherford, Tarr and Shepotylo (2005) for further details.<sup>**53**</sup> Based on these results, we believe that a sequential approach to assess household impacts of trade policy changes is likely to be a good approximation to the results of a more difficult to implement integrated approach, provided three conditions are satisfied: (1) the household data are reconciled with national accounts data; (2) the factor and consumption shares of the representative agent in the sequential approach are equal to factor and consumption shares that are the sum over all households; and (3) the representative agent and the households exhibit the same optimizing behavior. The latter implies that the first order effects are the same.

# **Alternate Reconciliation of the National Account and Household Budget Survey Data**

As opposed to our central model, in this ―national accounts‖ based model we allow the household factor shares to adjust while keeping the factor shares from the national accounts fixed. Taking a weighted average of the 55,098 household welfare gains, we get an estimated 7.2 percent of consumption; this is only trivially different than the 7.3 percent gain in our central model (and coincides with our representative agent model which is based on the national income accounts data).

> 53 We have also shown that if we are only interested in results at the decile level (as opposed to household results), running a model with thousands of
average annual frequency of significant landslides during the period from 1980 until 2018 from the World Bank; and (v) _pollution_ – annual average levels of PM 2.5 in 2019 from NASA.

For infrastructure, we rely on data from the Harmonized Global Critical Infrastructure index <u>(Nirandjan et al., 2022), capturing access to (i)</u> _energy infrastructure_ , including cable, line, minor line, power tower, power pole, plant, and substation; (ii) _water infrastructure_ , including water towers, water wells, reservoirs, and water works; (iii) _waste management facilities_ for solid and water waste; (iv) _transportation infrastructure_ , including roads, railways, and airports; (v) _education infrastructure_ , including kindergartens, schools, colleges, universities, and libraries; and (vi) _health infrastructure_ , including ambulatory and non-ambulatory medical facilities.

The last line in the top panel of <u>Table 4 reports the</u> **χ**<sup>**2**</sup> -test of joint significance of the administrative risk and resilience variables, based on the regression in which the 11 risk and resilience variables are added to the baseline specification; the last line in the bottom panel reports the marginal effect of age in specifications that include the administrative data on environmental risk and resilience. The risk and resilience variables are jointly significantly correlated with climate beliefs, but they have no significant effect on the magnitude of the marginal effect of age on climate beliefs.

In <u>Table 5,</u> we undertake a similar exercise for the two climate action variables related to government priorities and willingness to pay higher taxes to support the climate agenda. One key difference is that when examining the determinants of climate action, we also include the two dependent variables from <u>Table 4</u> related to perceived risks of climate impacts as explanatory variables. Their inclusion is motivated by the notion
While none of these issues are new, our approach improves on previous literature by using a larger sample and applying panel data econometric techniques. A number of contributions (Hatting et al. 1998, Savadogo et al. 1995, etc.) have demonstrated that- farm level data can provide useful insights to examine these issues. However, these studies are characterized by small samples (122 and 150 households, respectively), a restricted set of variables (e.g. no information on household education, non-agricultural assets, or soil quality is available), and reliance on cross-sectional analysis. This limits not only the ability to generalize from the results obtained and make broader inferences but, more importantly, may also bias the results significantly. The reason is that there is likely to be a strong correlation between the large number of unobserved household and farm characteristics and input use. As, to take just one example, more educated farmers would tend to use inputs more intensively, the coefficients on observed inputs in a traditional production function will be biased upwards. Thus, the impact of purchased inputs would be overstated and cross-sectional evidence alone would appear to suggest that farmers "'underuse" these inputs.

To overcome these limitations of cross-sectional analysis, we apply panel data techniques. Under the condition that farmers' unobserved characteristics are time-invariant, use of panel data techniques allows to overcome these limitations that have plagued the earlier literature and are likely to have led to an upward bias on the coefficients for purchased inputs. Instead of making it appear these inputs are more effective and farmers inefficient in not applying the optimal quantities, they allow to retrieve structural parameters regarding the impact of specific inputs in the production function. Furthermore, use of instrumental variable estimators allows us to estimate the impact of time invariant productivity shifters such as producers' endowments of physical assets, human capital, and their access to public services. Both the structural parameters for specific inputs and the productivity shifters enable us to derive more precise policy recommendations.

Instead of relying on a
When using the PME, our study is focused on the MSCI EM Index<sup>7</sup> as a benchmark because it more accurately reflects the countries where IFC invests and provides a more relevant point of comparison. When available, country-specific MSCI indices are used to estimate PME returns. For investments in emerging markets without a country-specific MSCI index, the MSCI EM index is used.

**Total Value to Paid-In Capital (TVPI):** The TVPI is a performance metric used in private equity to assess the value created by an investment relative to the capital invested. It measures the total value generated by a private equity investment to the capital that has been invested (paidin capital). It helps investors understand the overall return on their investment, combining both realized returns (cash distributions) and unrealized returns (the current value of remaining investments).

TVPI reflects the total return on investment. It considers both the interim cash flows (distributions) and the remaining value of the asset (the residual), providing a holistic view of the investment’s performance.

The TVPI is computed as: Anything above 1 means an investment grew in nominal value. Anything below 1 means the investment shrank in nominal value.

In addition to the TVPI, we calculate an annualized version of this metric: For ease of interpretation, we subtract 1 so that annualized returns above (below) zero indicate growth (or loss) in nominal value. “Years” denotes the number of years that any equity investment has been in IFC’s portfolio. Annualizing the TVPI allows us to discount investments by their respective duration, changing the distribution of this series compared to the standard TVPI.

The frequency chart of equity returns in EMDEs (Figure 2), as measured by the PME over the (country-specific) total return MSCI EM index, exhibits a highly skewed distribution, which indicates significant deviations from a normal distribution. The mean return of 1.16 suggests that, on average, equity investments in these markets
inference we draw from these findings is not that global indices have no place; rather, it suggests the need, from the outset, for curating a broad suite of data when anticipating and responding to complex policy challenges, the better to promote real-time learning. Doubling down on what can be readily measured limits the space for eliciting those ‘unobserved’ (and perhaps unobservable) factors that may turn out to be deeply consequential.

3. An emphasis on the easy to quantify inhibits understanding of the foundational problem <mark>An indiscriminate emphasis on aggregated, quantitative data can erode important nuances about the root causes of the problems we want to fix, thereby hampering our ability to craft appropriate solutions and undermining the longer-term problem-solving capabilities of an organization. All too often the designation of indicators and targets has the effect of causing people to become highly simplistic about the problems they are trying to address. In such circumstances, what should be organizational meetings held to promote learning and reflection on what is working and what is not become instead efforts in accounting and compliance. Reporting, rather than learning, is incentivized and management increasingly focuses on meeting the target numbers rather than solving the problem. Our concern here is that, over time, this tendency progressively erodes an organization’s problem-solving capabilities.</mark> <mark>The education sector is perhaps the best illustration of this; time and again practitioners have sought to codify “learning” and time and again this has resulted in an obfuscation of the actual causes underlying the problem.</mark> In a well-intentioned effort to raise academic performance, “the standards movement” in education promoted efforts hinged on quantitative measurement, as reported in the league tables of the Program for International Student Assessment (PISA).<sup>23</sup> PISA runs tests in mathematics, reading, and science every three years with groups of fifteenyear-olds in countries around the world. Testing on such scale requires a level of simplicity and “standardization”, thus the emphasis is on
estimates show a positive 30-year elasticity of city population with respect to market access. These are average effects. The importance of heterogeneous effects have been examined by recent papers, examined in the survey of the literature by Berg et al. (2017), but most papers have looked at the spatial heterogeneous impacts. They find that transportation infrastructure increases the access of rural regions to cities, with the agglomeration effects of cities potentially causing productive capital and skilled labor to move from rural regions. Faber (2014) finds that NTHS highway connections in China have led to a reduction in local GDP growth and industrial output among peripheral counties on the way between targeted metropolitan centers relative to non-connected peripheral counties. A few papers have looked at the heterogeneous impacts across industries. Using historical U.S. data, Chandra and Thompson (2000) find that connections to highways have heterogeneous effects across industries. Ghani et al. (2016) find different impacts from the upgrades to India’s Golden Quadrilateral highway network on the organization and efficiency of manufacturing activity across districts, depending on their distance to the Golden Quadrilateral roads. Our paper contributes to this literature by looking at the heterogeneous impacts of roads on outcomes at the household level.

There is a lack of consensus on the benefits of electrification. While many studies find substantial benefits from electrification on income, labor supply and education outcomes (Dinkelman, 2011; Van de Walle et al., 2017; Zhang, 2018), others suggest that these benefits may be overstated (Burlig and Preonas, 2016; Aklin et al., 2017). However, a recent paper by Fried and Lagakos (2020) quantifies the long-run, general equilibrium effects of eliminating power outages on firm and aggregate productivity in Nigeria, for which they find much larger estimates than previously estimated in the literature. In line with this paper, our work analyzes the impacts of better access to the national grid for multiple African countries.

4
_ The analysis of economic structure is based on national accounts and regional accounts data collected by the Central Bureau of Statistics in Jakarta and in the provinces. The regional data are less reliable in earlier years and even in later years only symptomatic of broad trends.<sup>4</sup> Employment and wage data are taken principally from the National Labour Force Surveys (SAKERNAS) conducted irregularly before 1986 and then on an annual basis since then. Comparisons of trends for the period 1971-1990 4 Central Bureau of Statistics staff suggest that there is considerable pressure for the regions to report data which are broadly consistent with national trends. The regional accounts data are likely to particularly unreliable for smaller sectors (personal communication, Jakarta, April 1996).
based in simultaneously held concepts in people’s minds, as evidenced by identical answers to standard questions; in reality, evidence of cohesion, consensus, and shared intersubjectivity is to be found in regularities of physical behavior, communicative style, and social organization. For instance, hierarchical understandings of a society may be more readily observed by looking at how people hold their bodies, when confronted with an authority figure, than by asking people to answer a survey.

> 16 We note that recent work deploying ‘anchoring vignettes’ (e.g., King et al 2004) within household surveys assessing cross-national phenomena goes some way to addressing this concern. The methodological role of such vignettes, however, is largely to enhance the construct validity of survey questions (i.e., the extent to which a given concept, such as ‘government effectiveness’, elicits comparable responses in different contexts). Our concern with meaning events is broader, including the extent to which survey respondents recognize surveys themselves as a legitimate means of determining, comparing and disseminating knowledge (and, in turn, shaping policy).

11
around Rwanda attended the bootcamp, even though for logistical reasons it was held in the South of the country following strict COVID-19 guidelines.

Our results indicate that the program facilitated an accelerated entry into the labor market in a period characterized by COVID-19-related disruptions. Using data from three online followup surveys—administered 4, 10 and 15 months after the training, each with very high response rates—we see increases in the probability of earning income, hours worked, and main job earnings. There is evidence of significant increases in job search in the short run and persistent increases in job networks, increasing interactions with other program participants even 15 months after the training. However, there is no evidence that the training facilitated youth transitioning into formal employment, with no effects on changes in access to formal jobs even in the short run. We validated the lack of effect on formal jobs with a snapshot from administrative data on Social Security records, which indicates no differences between treated and control youth in ever being registered with a formal employer 17 months after the training. The short-run labor

> 5All the statistics presented in this paragraph were obtained from authors’ calculations using the microdata of the Rwanda Labor Force Survey.

2
....................................................<br>31|
|Recent and less-recent behavior ....................................................<br>32|
|Within-question order effects ....................................................<br>33|
|Sensitive Questions and Truth-Revealing Mechanisms ........................................ 33|
|V.CONCLUSIONS<br>....................................................<br>35|

# **I. INTRODUCTION**

Industrialized nations make rigorous data-collection the foundation of their policy analysis. The paucity of data on firms in developing countries, however, makes policy analysis in those countries more difficult, The World Bank has increasingly focused on firmn-level surveys to help build a data foundation in developing countries and transition economies. The most extensive firn surveys implemented by the World Bank include the Regional Program on Enterprise Development (RPED) survey in 8 African countries, the Industrial Competitiveness
proportion of household members that are illiterate, while Pakistan has the highest and Bangladesh has the second highest. In South Asia, Pakistan scores the worst when it comes to education among the international extreme poor. Although Bhutan has the second lowest international extreme poverty rate in the region, its international extreme poor are worse off in many categories when they are compared with the Indian international extreme poor. For example, only 19 percent of the heads of household who are international extreme poor in Bhutan are literate, compared to 50 percent in India and 29 percent in Bangladesh.

Bangladesh scores much worse in many of the non-monetary dimensions than India. India has the highest proportion of international extreme poor in the region, while Bangladesh had the second highest proportion of international extreme poor. But the Bangladeshi extreme poor have lower human capital and lower levels of physical assets compared to their Indian counterparts. Bangladeshi households that are in international extreme poverty have lower educational attainment, and have lower ownership rates of household assets, even though both Bangladeshi and Indian international extreme poor have the same proportion of households engaged in agriculture.

**Table 14. Summary statistics of indicators of non-monetary well-being for the extreme poor** **<u>identified using the standardized consumption aggregates</u>**

|**Variable**|**Bangladesh**|**Bhutan**|**India**|**Maldives**|**Nepal**|**Pakistan**|**Sri Lanka**|
|---|---|---|---|---|---|---|---|
|_Household Composition_||||||||
|Household size|5.68|8.18|5.60|7.58|7.20|9.68|5.68|
|Dependency Ratio|0.47|0.40|0.95|
lack proper access to a bank line of credit - a major obstacle for firm growth. In addition, other financing sources such as equity markets are underdeveloped.

This paper summarizes the status of financial inclusion in Africa. We first provide a brief overview of the African financial sector. We then use a novel data set - the Global Findex - to

> 1 See, for example, King and Levine (1993); Beck, Demirguc-Kunt, and Levine (2007); Beck, Levine, and Loayza (2000); Demirguc-Kunt and Levine (2009); Klapper, Laeven, and Rajan (2006); and World Bank (2008). 2 The reference to the Global Findex data is Demirguc-Kunt and Klapper (2012), ―Measuring Financial Inclusion: The Global Findex Database‖, World Bank Policy Research Working Paper 6025.

2
Policy Research Working Paper 10458

# **Abstract**

This paper studies the effect of bank ownership on product innovation by borrowing firms, highlighting the role of the state, foreign, and combined foreign-state bank ownership. It uses Enterprise Survey data for more than 22,000 firms in 49 countries from 2016 to 2020, linked to Fitchconnect data on banks: their ownership, soundness indicators, and legal origins. The paper confirms that a firm’s access to bank credit is associated with a greater probability of product innovation, even when adjusting for possible reverse causality. If the credit is provided by a state-owned bank, the probability that the borrowing firm will innovate increases. The analysis does not find a similarly positive effect for foreign bank ownership. But when considering the combined effect of foreign state ownership, the results are most statistically and economically significant. Although the results may not be extendable to research and development spending (a key input to innovation), the findings show that foreign state banks can serve as an additional financing vehicle to stimulate radical innovation alongside equity financiers.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice and the International Finance Corporation. 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 fdenicola@worldbank.org; mmelecky@worldbank.org; and miootty@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
# **Migration and Asset Accumulation in South India: Comparing Gains to Internal and International Migration from Kerala**

Ganesh Seshan<sup>1</sup> World Bank

**Keywords:** Internal migration, international migration, household welfare, assets, India, Kerala **JEL Codes:** F22, F24, O12, O15, R23

> 1 Email: gseshan@worldbank.org. I thank Dr. Irudaya Rajan and the Center for Development Studies, Kerala for kindly providing the KMS datasets. I am grateful to Arjun Bedi, Daniel Westbrook, and Sulagna Mookerjee for valuable comments and suggestions on an earlier draft.