rafmacalaba/data-use-mentions-extended huggingface dataset:
informality trap) is to create samples of formal and informal firms that are “similar” and compare the performance variables across these otherwise similar samples. The propensity score inverse probability weighting (IPW) estimator is used to assess the degree to which the performance gaps may be due to informality itself. The procedure first estimates a probit model where covariates are used to predict informality and the predicted probability (the propensity score) is used to weight observations to make them similar.<sup>14</sup> Then, the weighted samples of formal and informal firms are used to calculate the mean difference in the performance variables.

The covariates used in the probit model are similar to the ones used in the discussion of correlates of informality below (see section 4.2). Log productivity is both a key performance variable as well as a key driver of selection into formality, as emphasized by Ulyssea. The use of an outcome variable in constructing a propensity score is clearly problematic in cross section data. Since the purpose here is exploratory, two models are used to predict propensity scores. In the first model, log productivity is not

> deviation of 1, which facilitates comparison across different explanatory variables. As such, they measure how many standard deviations the left-hand side variable changes in response to a standard deviation change in a right-hand side variable. 13 The magnitude of the reduction is assessed in the following way:  The coefficient on the informality variable is equal to the difference in the means of log productivity of formal and informal firms. The exponentiated value of the coefficient in turn is equal to the ratio of the geometric means of productivity in formal and informal firms. This means that without any controls, the (unconditional) ratio of the geometric mean of productivity among formal firms to that of informal firms is 3.63 (exp (1.289)= 3.63). Once controls A are introduced, the (conditional) ratio of geometric means for the two groups is reduced to 1.65
113 producers who reported comprehensive agricultural production, sales, and time use data.<sup>10</sup>

> 9The main survey respondent was the household head, with specific questions on the receipt and use of oxen addressed to the producer in instances where the two diverged. Moreover, we randomly selected and surveyed one girl and one boy in the household and administered age-appropriate numeracy and literacy assessments using the Uwezo instrument (accessible at `http: //www.uwezo.net/wp-content/uploads/2012/08/KE_2012_Tests.pdf` ).

> 10Of the 201 cotton producers surveyed at endline but dropped from the study, 136 reported partial agricultural output or sales data, 41 were secondary producers in multi-producer households, and 24 reported incomplete time use data.

8
a model of perfect intersectoral labor mobility from one of segmented markets.

# **3.8 Robustness: NREGA**

India’s National Employment Rural Guarantee Act (NREGA), passed in 2005, provides every rural household with 100 days of manual labor at a state-level minimum wage, which is typically above the market wage. Phased in nationally beginning in 2006, NREGA, by official counts, provided employment to 53 million households in 2010-11 over and above existing public works programs. Imbert and Papp (2012), using NSS-EUS data and exploiting the gradual phase-in of the program, find that NREGA increased overall public works employment while (modestly) raising private-sector wages in rural India. Since these 20
maximum measure captures large, non- or semi-anthropogenic sources of pollution that may spike in certain months (e.g. seasonal forest burning).

We also examine whether our results are sensitive to alternative data on wealth, using data from DHS clusters, or pollution, using ground based monitor data. In both cases the sample we are able to analyze using the alternative datasets is smaller than our global sample because of a lack of comprehensive data covering all 103 countries in our sample.

When we estimate the country-specific correlation using DHS wealth data and our global pollution data set we find the same general pattern of correlation with roughly half the countries exhibiting a positive relationship between wealth and pollution (Figure SI-7). Nigeria and Nepal remain the countries with the most negative and most positive correlations respectively. There are some countries where the sign of the correlation flips when using the DHS data relative to RWI but in general there is broad agreement in sign and magnitude across the two data sets (Figure SI-8).

We also examine the trend in pollution within the seven countries for which we have pollution data from ground monitors that represent at least 10 distinct RWI grid-cells. Figure SI-9 shows that in most of these countries, including all of those for which we have the largest number of ground monitors, the relationship between pollution and wealth is qualitatively similar when calculated using satellite measures of pollution or ground monitors. In the same figure we highlight which ground monitors are in urban or rural settings to underline the point that restricting analysis to those places with ground monitors will often result in the omission of most rural locations and so will fail to capture the urban-rural gradient that appears to be at the heart of our primary results. In several of the countries included in this sample there is only data from one or two rural ground monitors for the entire country. There is also often substantial temporal gaps in ground monitor data, on average ground monitors only report measurements in 45% of the months over which we have satellite measures of pollution. This can
vaccine uptake, is less challenging to implement from a survey design perspective, and is applicable to multi-topic surveys that require the selection of a broadly knowledgeable respondent.

In this sense, our findings also underscore the complementarity between phone and face-to-face surveys. The latter remain indispensable to provide a recent sampling frame for households and individuals which our results suggest are some of the mainstays of reliable estimates. At the same time, phone survey data can complement face-to-face data collection. For example, the usefulness of survey data for policy has been limited by its low frequency (every 2-3+ years) and the difficulty of conducting surveys in hard-to-access or conflict-affected areas (Cutts et al. 2016, DanovaroHoliday et al. 2021, Burton et al. 2009). Phone surveys offer the opportunity to improve on these limitations.

As phone surveys have been proposed as flexible vehicles for (health) data collection (Gourlay et al. 2021, Zezza et al. 2022, Glazerman et al. 2023) our findings may find direct application in future (vaccine) data collection efforts. Such efforts may include campaigns to catch up on (foregone) child immunization which saw the largest sustained decline in 30 years during the COVID-19 pandemic (WHO 2022, The Lancet Child & Adolescent Health 2021, Causey et al. 2021) or a large-scale rollout of the first Malaria vaccine (Wilyard 2022).

31
# **Box 2. Lessons from FDI Grievance Management Pilot Projects at the WBG**

<mark>The WBG has piloted an FDI conflict management mechanism in eight countries, producing evidence that low-cost solutions exist to promote investment retention and expansion. The WBG developed an institutional conflict management mechanism, called SIRM (Systematic Investor Response Mechanism), to respond to the needs of governments by setting up a minimum institutional infrastructure to coordinate state-wide responses to investor grievances. In pilot projects, WBG teams have supported: (i) the composition and positioning of the Lead Agency as a coordinator; (ii) the design and deployment of the ICT tracking tool to register; (iii) the coordination protocols to ensure inter-agency coordination and collaboration in resolving grievances; and (iv) following-up and measuring the economic impacts of</mark> resolved grievances.

<mark>Although most pilots are still being implemented, 39 grievances from eight pilots have been identified which are serious enough to place investments at risk. Three FDI projects were verified according to the WBG M&E methodology, resulting in US$200 million in investment retained, US$20 million in</mark> reinvestments, and a conservative estimate of US$10 million in public cost savings.

<mark>The most common issues identified by the SIRM pilots are sudden/arbitrary regulatory changes (60%), followed by contract breaches (22%) and expropriation (18%), confirming the findings of the 2009-13 MIGA/EIU and 2017 GIC surveys. SIRM pilots confirm the critical role that specialized and sub-national regulatory agencies play in generating most investment conflicts. Among the 39 contentious cases identified by the pilots, specialized agencies were involved in 64% (25) of instances and sub-national agencies for 15% (6) of them. Pilots also show that while grievances arise in all sectors, service sector cases predominate with 46% of the total, followed by primary/extractive industries (36%) and</mark> manufacturing (18%
be other factors leading to higher wages even while there is still rural underemployment. And temporary reversals might be expected, notably if the current construction boom does not continue.<sup>21</sup> With the backdrop of this history of recent economic change in India, this paper revisits the implications for poverty of both the higher rate of growth and the pattern of growth. The sectoral imbalance in India’s post-reform growth would be a concern for poverty reduction if the model linking poverty to growth had remained the same, notably with the rural and agricultural sector contributing most to poverty reduction. However, previous research by Datt and Ravallion

> 18 Hnatkovska and Lahiri (2013) also show that the narrowing of the wage gap persists when one controls for education and occupation.

> 19 These are our estimates based on the NSS 50th and 68th employment-unemployment survey rounds.

> 20 The share of agriculture, forestry and fishing in total (usual status) employment declined from 76% in 1993-94 to 49% in 2011-12 (Jacoby and Dasgupta, 2015).

> 21 At the time of writing (late 2015) lifted by public infrastructure investment, the construction sector has continued to expand overall, despite a slow-down in the residential building construction sector (Wilkes and Kumar, 2015). However, wage growth for construction workers has been lower than in other occupations.

5
[0.311]|
|First Stage F-Statistic|9.43|9.71|9.71|9.71|10.73|6.02|11.87|
|N|10,152|10,022|10,022|10,022|10,068|18,562|18,562|

_Source_ : Author’s analysis based on data from the Ethiopian Demographic and Health Survey (in years 2005, 2011, and 2016). _Note_ : MTI = mother tongue instruction; FPE = free primary education. The dependent variable is described at the top of each of the five columns. In columns 1–4 it is an indicator that equals 1 if true, and in columns 5–7 it is the ideal number of children. Skilled/Professional jobs include professional, clerical, and skilled manual job groups; the other categories exactly describe the occupation groups included. Ideal number of children is censored at 20; no women in the Demographic and Health Survey report having more than 18 children, and non-numerical responses are assigned the maximum value. In columns � 1–5, Years of Schooling _izy_ is the predicted level of schooling instrumented with the FPE intensity measure _Izy_<sup>FPE</sup> ; in column 6 additional instruments include two MTI intensity measures _Izy_<sup>MTI-T</sup> and _Izy_<sup>MTI</sup> , which denote the measures for MTI regions without and with script change, respectively, and the interactions for regions in which two interventions occurred; in column 7 the joint intensity measure ∆ _Izy_ is used. All sample and specification definitions can be found in Section D.2 of this supplementary online appendix. Unless otherwise noted, all regressions include birth year and zone fixed effects, zone-specific linear trends, and a cubic for age. The first five columns include only observations from non-MTI regions; all regions are included in the final two columns. Each estimate is from a unique
# **_References:_**

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8. **National Bureau of Statistics.** China Statistical Yearbooks. Beijing : China Statistics Press, Various Years.

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11. **Centre for Health Statistics and Information, Ministry
potentially eligible localities were grouped based on geographical proximity, and relatively isolated communities were excluded from the selection process. Third, eligible households were selected using information on covariates of poverty obtained from a field census conducted in each locality before its incorporation into the program.<sup>5</sup> The program started in 1997 in 6,300 localities with about 300,000 beneficiary households and expanded rapidly during the following years. In 1998, it was delivered to 34,400 localities (1.6 million households), and, in 1999, coverage increased to 48,700 localities (2.3 million households). The expansion of the program continued in subsequent years both in rural and urban areas.

An experimental evaluation of the program was conducted during this phase of geographical expansion in rural areas. A sample of 506 villages was randomly drawn from a set of localities that had been selected to be incorporated into the program, which were located in seven central states of Mexico (Guerrero, Hidalgo, Queretaro, Michoacan, Puebla, San Luis de Potosi, and Veracruz) after stratification by geographic region (which coincide roughly with the States) and population size. The randomness of the evaluation sample is corroborated in section S1 of the supplemental appendix. We document in particular that evaluation localities do not have different observable characteristics compared to non-evaluation localities located in the same neighborhoods. Also, the characteristics of evaluation localities and their population are not statistically significantly associated with the number of evaluation localities once the number of non-evaluation localities in their neighborhood are controlled for. Of those villages, 320 localities were randomly assigned to the treatment group and started receiving the program’s benefits in March and April 1998; the remaining 186 formed the control group and were thus prevented from receiving the program benefits until November 1999.

# _Program Take-up_

5. A proxy-mean index was computed as a weighted average of household income (excluding children), household size, durables, land and livestock, education, and other physical characteristics of the dwelling
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and Venables, 1999; Javorcik, 2004). It should be borne in mind that a significant portion of FDI flows to large multinational corporations. These companies are less likely to take an interest in the DBIs, since the DBIs specifically focus on SMEs which may face different regulatory hurdles than large companies. For example, large companies often have a legal team in place that can readily deal with legal issues, such as incorporation of subsidiaries or court representation to enforce contracts – SMEs would have to hire lawyers and the associated expenses can be considerable. Larger companies will thus be more interested in a sister-project to the DB reports, the World Banks Group’s ‘Indicators of Foreign Direct Investment Regulation.’ <mark>However, SMEs still play an important role in attracting FDI, in particular through joint ventures (Fujita 1994). Improving the investment climate can thus open opportunities for new business initiatives, as well as portfolio investment into existing companies expanding their operations. In this light, the DBIs constitute an important source of information on business opportunities to international investors, potentially resulting in more foreign investment and accelerated growth.</mark> Secondly, the DB studies can be used as reform benchmarks that the incumbent government’s performance can be evaluated against. Since the DBI’s more or less objectively measure the regulatory environment, governments can legitimately claim credit for an improvement on individual DB components.<sup>3</sup> The DBIs are thus popular amongst development partners and are frequently used as benchmarks for financial aid, such as the International Monetary Fund’s Extended Credit Facility or the European Union’s General Budget support. But, perhaps more importantly, the DB ranking can also underpin electoral promises which may eventually help governments at the polls – and, conversely, these objective benchmarks may force governments to make structural reforms if there is popular demand for improving the investment climate. The DB ranking can thus serve both as a carrot and stick for driving the regulatory reform process.

The DBs rankings
32 students dropping out of school altogether. However, we suspect that this would not be a quantitatively important factor. To see this, note that data from UNESCO (2020a) suggest that in LICs (MICs) 0 _._ 7% (0 _._ 3%) of primary students and 1 _._ 8% (1 _._ 4%) of secondary students were “at risk” of dropping out due to the pandemic (see Web Appx. Table A.5 for the full details). Suppose 2% of students drop out of school due to the pandemic and, to be conservative, assume that they drop out randomly. Average schooling in the LICs (MICs) is 6 (8) years, so random dropouts imply an additional 6 _×_ 0 _._ 02 = 12% (8 _×_ 0 _._ 02 = 16%) of schooling lost, or 6% (8%) if we spread it over the 2 years of the pandemic. For LICs, this moves the magnitude of the schooling shock from 0 _._ 28 to 0 _._ 34. Figure 5(c) shows that this would increase the welfare cost of the COVID pandemic in the LIC from 1 _._ 5 to 1 _._ 6 percent, still far from the 2 _._ 5 percent in the HICs and the 2 _._ 2 percent in the MIC. For MICs, the schooling shock would increase from 0 _._ 33 to 0 _._ 41 and the welfare cost would only increase from 2 _._ 2 to 2 _._ 3 percent (Figure 5(b)).

Likewise, the data from UNESCO (Web Appx. Table A.5) suggest that dropouts are likely to be similar between males and females across the three income groups.

Given that the number of at-risk students exceeds the number of likely dropouts, and that at-risk students were likely not
median annual employment growth rate of just under 1.5 percent.

> 8 A few studies have used data from other developed countries. Bentolila, Jansen, Jimenez, and Ruano (2013) use matched bank-firm data from the Spanish Credit Register and show that during the Great Recession, firms with credit relationships with weak banks had substantially lower employment levels than firms borrowing from nonaffected banks. Popov and Rocholl (2015) use German banks’ heterogeneous exposure to the US subprime mortgage market to show that firms with credit relationships with affected banks experienced significantly larger declines in employment and labor compensation relative to firms whose credit relationships were with healthy banks. Boeri, Garibaldi, and Moen (2012) use data from 11 OECD countries and find that highly leveraged sectors are characterized by higher job destruction rates than low-leveraged sectors during banking crises.

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complement financial deregulation with accelerated reform in the real sectors of the economy to reduce tensions that arise from the slower pace of adjustment in the latter sectors.46/ RespODses to false signals and economic instability The huge daily flow of information that influences financial decisions adds to the potential for broader economic destabilization. Among that wealth of data it is often difficult to discern signals that denote changes in economic fundamentals from those that do not. Thus, financial markets are constantly exposed to the danger of precipitating chain reactions to "false" signals or to surrogate key indices--such as US monthly trade figures--that have acquired a major importance, yet are often inaccurate.

More generally, the financial markets' ability to respond and adjust to constantly changing data induces interest-rate volatility. It also contributes to misalignment of exchange rates to the extent exchange-rate determination is incerasingly the outcome of financial agents' responses to spurious signals. This poses the danger that attempts to correct marketinduced exchange-rate misalignments through official intervention become an overriding priority that constrains other objectives of monetary policy. Greater difficulty in the conduct of monetary policy Short-term financial assets, such as shares in money-market funds, are now widely held by households. High liquidity--money market funds are instantly cashable--low risk profile and minimal transaction costs give these assets many of the characteristics of morey, as reserves of purchasing power. This has made the demand for money unpredictable and lowered the velocity of circulation of money. The scope for monetary targeting is thereby diluted and 46/ World Economic Outlook, IMF, April 1978, p.6.