rafmacalaba/data-use-mentions-extended huggingface dataset:
.884)|(0.848)|(0.934)|(0.727)|(0.820)|(0.672)|(0.592)|
|Observations|4624|4104|4616|4098|4425|3930<br>|4902|4389|

Source: Authors’ estimation using RLMS 2016.

Note: Odd number columns include all observations, even number columns exclude type-2 informals (i.e., who workers employed but not at an enterprise in their primary job). This table shows the estimated coefficients from logit regressions that estimate how informal and formal workers differ in their subjective work characteristics and the incidence of envelope wages, controlling for other factors. Using RLMS, the authors define informal employment as workers that are employed at an enterprise without a contract in their primary job and workers employed in firms with no more than one employee (i.e., self-employed). All dependent variables are defined as binary. All specifications include region dummies. Omitted groups are age group (25-34), education (below secondary), sector (agriculture), occupation (managers), firm size (1-10) and hours worked (<40 hours). Armed forces are dropped from the sample. All observations are weighted using the individual weight. *, ** and *** indicate significance levels at 10 percent, 5 percent and 1 percent, respectively.

50 Informal Employment and Worker’s Wellbeing in Russia / June 2019
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higher share of self-employed workers with no income in the administrative data (perhaps due to underreporting to avoid tax obligations).

Methodological survey research on measuring labor outcomes outside of high-income settings have been less common. The quality and regularity of administrative data is one reason, also due in large part to the prevalence of informal and irregularly-paid work in these countries. Recent validation studies have involved smaller, more focused experiments where concurrent household surveys contrast alternative methodological approaches, or modules testing for different approaches are randomized within the same survey. Bardasi et al. (2011), for example, randomized the implementation of a “short” and “detailed” module in the Survey of Household Welfare and Labour in Tanzania, a survey of 1,344 households across seven districts in the country, to examine how employment statistics are affected by (a) the level of detail of questions and (b) self-reporting as opposed to using proxies. Their findings have strong gender implications; proxy response significantly lowers male employment in agriculture, but no effect for women (the proxy effect is mitigated when the spouse is selected as the proxy and when the proxy has some education). A few additional screener questions to identify work mitigates over-reporting of employment among women engaged completely in domestic work, as well as higher average weekly hours conditional on working, especially for men. Dillon et al. (2012) use the same data and experiment to study the effects of proxy reporting and question detail on child labor, and in Serneels et al. (2017) to examine the returns to education. In both of these studies, proxy reporting did not have an effect, but screening questions (i.e. a detailed versus short module) did matter. Finally, the findings around respondent selection can vary by context and experimental design — in a separate experiment from Ethiopia of 1,200 Fairtrade coffee households (Galdo et al., 2019), survey respondents were randomly selected to test, across three different seasons, the effects of proxy reporting
# **1. Introduction**

During the recent global financial crisis, several countries were forced to nationalize failing private banks. Abn Amro in the Netherlands, for instance, is now fully owned by the Dutch government. As a result, the average share of government ownership of banks by bank assets has increased in high-income countries form 7.3% in 2007 to 10.8% in 2009, to fall back slightly to 9.9% in 2010. The increased prevalence of state banks is providing renewed impetus to the debate on the economic costs and benefits of state banking. While previous research has shown that state banks tend to perform badly, misallocate resources and lead to lower economic growth, relatively little is known about how state banks react to business cycle fluctuations. To fill this gap, this paper examines the lending behavior of state banks over the business cycle, and also fluctuations in the main types of bank funding that make this lending possible. In addition, this paper considers the relative accounting for non-performing loans by state banks, as differences in the reporting of bad loans over the business cycle by state and private banks are a potential mechanism to explain different capacities to provide new loans. Our analysis is based on a sample of 1,633 banks from 111 countries over the 1999-2010 period.

We find that lending by state banks is less procyclical than the lending by private banks, especially if the bank is located in a country with good governance. We capture good governance by an index of government effectiveness, which increases with perceptions of the quality of public services, the degree of independence from political pressures and the credibility of a government‟s commitment to its own effectiveness. Moreover, lending by state banks located in high-income countries is even countercyclical. State banks also expand their credit relatively more during banking crises, which points at a stabilizing influence of state banks at a time of financial instability. Among private banks, we find that foreign-owned banks‟ lending is especially procyclical, perhaps because these banks
literature. Contrary to the literature that is mostly focused on corn(maize)-soybean rotations, we find that continued soybean cultivation incurs only a limited yield penalty and that the magnitude of yield reductions from monocropping is often below that of unsuitable predecessor crops, mainly sunflower, that deplete the soil horizon of water or increase disease pressure.

To allay concerns about measurement error or replicability of this approach in settings where yield information is not at all or less regularly collected, we repeat the regressions using indices of vegetative development that can be derived directly from satellite imagery (EVI, LAI, FAPAR, and LSWI). Using crop masks for the crops considered here produces results that are very similar to those obtained for yields. As such indices can be constructed at levels of spatial and temporal resolution well beyond what is within reach of statistical systems, this offers tremendous potential for research.

2
of endowments not being important in explaining GVC participation is also strongly rejected. Similar to the results in Column (1) of Table 1, countries with larger endowments of land and/or natural resources exhibit signi...cantly lower backward GVC participation shares, while those with stronger abundance in capital exhibit signi...cantly higher GVC participation shares. Moreover, countries located far from GVC hubs or those with larger domestic industrial capacity exhibit signi...cantly lower backward GVC participation.

# 4.2.1 Issues with Weak IVs and Statistical Inference

A concern with the second-stage IV estimates is that they could be biased and the statistical inferences made above invalid if the IVs are weak. That is, if the IVs included cannot explain the two endogenous variables, tari¤s and FDI in‡ows, then the second-stage results are questionable. Unfortunately, the econometric theory of IV between estimators with two endogenous variables is not yet developed. The existing econometric theory focuses on cross-sectional IV estimation, with i.i.d. error terms as discussed in Andrews et al. (2019). To properly test for weak IVs, we collapse our cross-country decadal panel data set into a single-period cross-country data set, by taking for each variable’s average over time within each country. In addition to enabling to properly test for weak IVs, this crosssectional speci...cation allows for heteroskedasticity to be addressed. Columns (3) and (4) of Table 3 present the ...rst-stage cross-sectional regressions, with standard errors robust to heteroskedasticity. Comparing Column (3) to Column (1) and Column (4) to Column (2) in Table 3, it is clear that the ...rst-stage cross-sectional regressions are very similar to the ...rst-stage between regressions. The ...rst-stage F-statistics for tari¤s and FDI individually are strongly signi...cant. Jointly, the Kleibergen-Paap F-statistic for the ...rst-stage is 10.39, which
vary for one population to another. In fact, a given area might have a positive location effect in one generated population and a negative effect in another. Since the location effects have an expected value of zero, the ELL method is tracking the average zero effect, which is the same for all the areas, but it does not capture the area effects in a given population. What is even more concerning is that the CB-EB method applied in Nguyen et al. (2018) and PovMap described in Section 4 yields an MSE that is more than 3 times greater that of the traditional ELL. This happens because a large amount of the MSE is the squared bias, which has been shown to be substantial for the CB-EB estimator. The proposed MC method for Census EB estimation presented here represents a massive improvement over the traditional ELL and the CB-EB approaches under the setup of this simulation experiment based on a poor model. The next section describes results when increasing the prediction power of the model.

# **7.2 Simulation experiment with improved model**

This experiment addresses one of the concerns of the previous simulation experiment: the poor explanatory power of the model. Hence, in this section a more informed model is considered, yielding an adjusted _R_<sup>2</sup> of about 0.42, which is much closer to that of real-world applications of ELL. The model includes six covariates. The first two, _x_ 1 and _x_ 2, are generated exactly as in the previous section. Four additional covariates are included, with values generated as follows:

1. _x_ 3 is a binary variable, taking value 1 when a random uniform number between 0 and 1 is less than or equal to 0 _._ 1 + 0 _._ 2 80<sup>_<u>c</u>_.</sup>

2. _x_ 4 is a binary variable, taking value 1 when a random uniform value between 0 and 1 is less than or
.8|38.8|0.041**|1.21|
|**_Access to Finance_**|||||
|Finance from internal sources(%)|59.4|56.4|0.570|1.05|
|Finance from banks(%)|24.4|26.6|0.664|0.92|
|Finance from trade credit(%)|10.6|9.1|0.707|1.16|
|% of firms with line of credit or loans from financial institutions|46.7|39.2|0.043**|1.19|
|% of firms usingbanks to finance investments|32.7|37.4|0.383|0.87|
|% of firms usingbanks to finance expenses|40.9|31.2|0.026**|1.31|
|%of firms identifyingaccess to finance as a major constraint|41.6|26.0|0.000***|1.60|

Source: Own calculations. Data: Enterprise Survey Indicators. NB:<sup>+</sup> t-test of difference in means. p*<0.1, p**<0.05, p***<0.01.

The percentage of manufacturing firms in the core identifying business licensing and permits as major constraints is 45 percent higher. On average, senior management has to spend more time in dealing with requirements of government regulation and the average number of visits or required meetings with tax officials is also higher. The percentage of manufacturing firms identifying taxes and tax administration as a major constraint is more than 20 and 40 percent 11
Household Budget Surveys. _Note_ : Subsidized flour and sugar in Morocco have different prices depending on varieties and forms; kg = kilogram; PPP = purchasing power parity.

# A Distributional Analysis of Subsidies

As indicated in the introduction, we use the microsimulation model SUBSIM to provide a distributional analysis of subsidies and simulations of alternative subsidy reforms. The publicly available version of SUBSIM comes in two flavors, SUBSIM direct, which estimates direct effects using Household Budget Survey (HBS) data only, and SUBSIM indirect, which uses HBS data and input-output matrixes to estimate direct and indirect effects.

This paper uses a third version of SUBSIM, which is not yet publicly available and which is designed to provide comparative analyses of subsidies across countries. This version is similar to 7
An actuarial, experience based approach to design and ratemaking would use long term data and incorporate spatial dimensions of yields across India to increase the quality of risk classification. By comparison, the existing approach to design and ratemaking for the NAIS uses the three year moving average yield as the average yield estimate for some crops, despite over ten years of yield data being available. An actuarial approach would also allow for noticeable trends in historical yield data caused by changes over time to farming practices, technologies, or inputs.

The statistical subtleties of risk classification are important. For example, consider the case of the NAIS cotton product in Gujarat leading up to 2009. The rapid uptake of Bt cotton across Gujarat, reaching 66% of the cultivatable area under cotton in 2009, led to a substantial increase in the average cotton yield over the 2000s.<sup>7</sup> However, the NAIS farmer premium rate was calculated using a Normal Theory Method without detrending, and so this trend in yields was mistaken for uncertainty. The NAIS farmer premium rate for cotton in Gujarat rose from 11.9% in 2003 to 17.2% in 2008 without a commensurate increase in the threshold yield. This product was increasing considered to be poor value by farmers and over the period 2000 to 2008 insured acreage for cotton in Gujarat state fell by 96%, from 567,000 ha to 20,000 ha. Incorporating a detrending methodology led to a decrease in farmer premiums by up to 80% (see Table 3).

Table 3: Statistical investigation of aggregate linear trends for six NAIS cotton products

|State|Crop|Premium,<br>no<br>detrending|Best estimate<br>trend<br>(kg/ha/year)|P-<br>value|Indicative<br>premium,<br>with<br>detrending|Percentage<br>premium<br>reduction due<br>to detrending|
|---|---
How could the business cycle shape attitudes (particularly trust) towards public institutions? First, a positive relationship between income and trust can operate through changes in loss aversion. Individuals experiencing a positive (negative) shock in their standard of living may feel more (less) secure and then more (less) willing to trust others in general and institutions in general. Second, trust and positive attitudes can be understood as an assessment of the political system and its institutions. An individual observing the business cycle may perceive shocks as sufficient statistics of institutions’ performances. For instance, a good economic situation could indicate good institutional performance (regardless of whether the attribution and assessment are correct), thus boosting trust in (and satisfaction with) state and related institutions. Third, and connected to the previous point, income shocks can directly affect the resources needed for well-functioning institutions. Therefore, changes in attitudes towards institutions may reflect changes in institutional equilibrium due to initial changes on the supply side of institutions.

In this paper, I use multiple waves of two public opinion surveys (i.e., LAPOP and Latinobar ́ometro) to analyze how people’s perceptions and attitudes towards public institutions respond to the business cycle in El Salvador. To do so, I estimate several econometric models explaining respondents’ attitudes towards different institutions (e.g., trust in the president or satisfaction with democracy) as a function of municipality-specific income levels. Nonetheless, the uncovered robust statistical associations do not necessarily imply causality. Indeed, economic development within a country is not a random process in which income has been randomly assigned across municipalities over time. The process of economic development and the formation of trust and confidence towards institutions (as well as its evolution), and the factors underlying the emergence and persistence of these attitudes are complex phenomena. Furthermore, some of those factors are unobserved, which hinders the identification of the causal effect of income on the aforementioned attitudes. Nevertheless, I argue that it is unlikely that my OLS results
<br>decisions.|
||**Monitoring**|Examines whether<br>organizational performance is<br>tracked using meaningful<br>metrics and with appropriate<br>regularity|Measures tracked are not<br>appropriate or do not indicate<br>directly if overall objectives<br>are being met. Tracking is an<br>_ad hoc_ process and most<br>processes aren’t tracked at all.|<br>Performance indicators have<br>been specified but may not be<br>relevant to the organization’s<br>objectives. Some important<br>performance indicators are<br>tracked formally.|Performance is continuously<br>tracked, both formally and<br>informally, using appropriate<br>indicators.|
|||Examines whether<br>organizational performance is<br>meaningfully reviewed with<br>appropriate frequency and<br>communicated to staff|Performance is reviewed<br>infrequently or in an un-<br>meaningful way (e.g. only<br>success or failure is noted).<br>The right information for a<br>constructive discussion is<br>often not present.<br>Conversations are ‘one-way’,<br>focus overly on data that is not<br>meaningful, and exclude most<br>staff. The purpose of the<br>review is not explicitly stated.<br>Next steps are not clearly<br>defined.|<br>Performance is reviewed<br>periodically with appropriate<br>data, and both successes and<br>failures identified. Objectives<br>of meetings are clear to all<br>participating, but results are<br>communicated only to senior<br>staff and conversations often<br>do not drive to the root causes<br>of problems. Next steps are<br>mentioned but not
over the sub-set of countries. Because the group has been selected by the first-stage equation, the possibility of selection bias would be introduced if a standard regression were used at the second stage. This bias is related to the magnitude of the correlation between the errors (that include the omitted variables) in the selection and investment equations. Where there is no correlation between these errors, there is no selection bias. The direction of the bias depends on the sign of the inter-equation error correlation.

The condition for identification of the Heckman estimation procedure is that the selection equation contains a significant variable(s) not included in the investment equation. The significance of the Mills’ ratio in the second stage indicates whether there would have been selection bias in its absence. If the Mills’ ratio is insignificant, a simple regression of the quantity of investment on explanatory variables would be unbiased. The canonical Heckman model also assumes that the errors are jointly normal. If that assumption fails, the estimator is generally inconsistent and can provide misleading inference in small samples.

**Data used in this paper are composed of both micro and macro level data.** The micro, projectlevel data are from the Private Participation in Infrastructure (PPI)<sup>2</sup> database. The PPI database is managed by the World Bank and the Private-Public Infrastructure Advisory Facility (PPIAF). The data are collected at the project level. The following information of each project recorded in

> 1 The data on the dependent variable consist of observations related to the investment. This would be zero for countries that had not attracted PPPs, and positive for those countries that had decided to use it. This observation is split into two components: a zero or one variable indicating the lack of entry or entry of PPPs, and a variable measuring investment for the subset of countries that attracted PPPs.

> 2 <u>http://www.ppiaf.org/ppiaf/page/private-participation-infrastructure-database</u>

4
youth literacy rate (15 to 24 years old) increased by 55 percentage points, from 30 percent in 1981 to 85 percent in 2011. National Account indicators, in contrast, show much slower progress. The share of agricultural output in GDP declined to 38 percent but remains the 9<sup>th</sup> highest in the world and the highest outside of Sub-Saharan Africa. GNI per capita is still one of the lowest in the world and the second lowest in South Asia after Afghanistan.

The rest of this paper sheds light on the way this unlikely but remarkable success story unfolded in Nepal. Section 2 offers a historical perspective of poverty reduction in Nepal, followed by a detailed account of changes in living standards since 1995 (Section 3). Drivers of poverty reduction and the roles of remittances are discussed in Section 4. Section 5 poses some key challenges on the country’s future path to improving living standards and building prosperity. The final section concludes.

> 6 In all three rounds of the Nepal Living Standard Surveys, data collection was carried out for 12 months in order to capture a complete cycle of agricultural activities and seasonal variations. Since the data collection period always encompassed two calendar years, the NLSS are often referred to with respective calendar years such as 1995/96, 2003/04 and 2010/11 to be precise. For brevity, however, we refer to the NLSS and all statistics estimated from the NLSS as of the year in which data collection started such as 1995, 2003 and 2010.

> 7 The official poverty estimate was 25 percent in 2010, reflecting an upward revision to the previous poverty line set in 19995. Appendix I offers an overview of changes in poverty measurement methodology in Nepal and its implications on poverty monitoring efforts.

> 8 The revised poverty line is Rs.19,261 per person per year in 2010 prices (World Bank 2013).

> 9 Based on benchmarking methodology described in Newman, John L., João Pedro Azevedo (2013)