rafmacalaba/data-use-mentions-extended huggingface dataset:
stable, affecting on average 43 percent of the workforce. There is, however, an important regime shift underway in Brazil, and this change is related to the price index to which nominal wages are indexed. During the first years of the sample, up to 1999, the price index to which wages are indexed is the change in the minimum wage. Subsequently, and coincident with the introduction of inflation targeting in 1999, the price index that represents the focal point of wage negotiations moves away from the minimum wage and becomes closely related to the evolution inflation. Interestingly, the variance of this price index across agents also increases after 1999 -a feature that is consistent with the index being an indicator of inflation expectations.

The results in this paper are related to two recent macro studies. In the context of price setting, Benati (2008) shows that the rate of inflation persistence in different countries and time periods varies with the monetary policy regime, while Hofmann et al. (2010) show similar time variation in US wage dynamics. These two papers offer compelling time series evidence that challenges the notion that the persistence of inflation, or wage indexation to past inflation, are intrinsic to the deep structure of the economy, and invariant to changes in the monetary regime. Our work is a first attempt to examine the stability of downward nominal and real wage rigidities in an environment subject to policy changes and rapid macroeconomic stabilization. To this end, relying on matched establishment-worker data constitutes a great advantage. A general problem with the aggregation in macro series is that it assumes that the evolution of wage rigidities is the same for all establishments or groups of individuals. However, agents with different characteristics are likely to react differently to macro and policy changes. Some of these are subtle changes. For instance, our results for Brazil show that after the introduction of inflation targeting some agents appear to start negotiating wage changes as a function of expected inflation, while others remain pegged to changes in the minimum wage. Understanding the evolution of
Enforcement score.|
|DB Get Electricity|DB|Country-level Getting Electricity score.|
|DB Get Credit|DB|Country-level Getting Credit score.|
|DB Construction Permit|DB|Country-level Dealing with Construction Permits score.|
|WB HLO|Angrist et al.,<br>(2019)|Country-level Harmonized Learning Outcome score. It is one of the factors used to<br>construct the World Bank Human Capital Index.|

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24 in government authority. For example, Friedman, et al. (2000) show empirically that countries with more corruption have a higher proportion of activities in the unofficial economy. The private sector depends on effective legal regulation. Torgler and Schneider (2007, 2009) show that that improving governance and institutional quality and tax morale helps lessen a possible incentive to go underground. The results are quite robust when using more than 25 proxies of governance and institutional quality, testing for endogeneity and running a broad variety of specifications. Hanousek and Palda (2004) work with survey data from the Czech and Slovak Republics, Hungary, and Poland, and find strong support for the hypothesis that there is a positive correlation between tax evasion and the perceived government services based on the taxes paid. A government based on a well-functioning democracy produces more trust than a dictatorship. A lack of public trust undermines state revenues and thus the government‟s ability to perform its function. Trust in public officials might tend to increase taxpayers‟ positive attitudes and commitment to the tax system and tax-payment, which finally exerts a positive influence upon tax compliance. Institutions that taxpayers perceive as fair and efficient might have a positive effect on tax morale, conditional on whether society views taxes as a price paid for government‟s positive actions. Thus, if taxpayers trust their public officials, they are more willing to be honest. If the government acts trustworthily, taxpayers might be more willing to comply with the taxes. Similar to the tax administration, the relationship between taxpayers and government can be seen as a relational contract or psychological contract, which involves strong emotional ties and loyalties. Such a psychological tax contract can be maintained by positive actions, based on trust. If the government tries to generate trust with well-functioning institutions, co-operation can be initiated or increased. Furthermore, when taxpayers are satisfied with the way they are treated, the co-operation is enhanced. If the outcome received from the government is judged to be fair in relation to the taxes paid, no
representative sample of the unorganized manufacturing sector. A manufacturing establishment is considered part of the unorganized sector if it has fewer than ten employees and uses electricity. If the establishment does not use electricity, the threshold is 20 workers. As of 2005, the unorganized sector accounts for approximately 99% of Indian manufacturing establishments, 80% of Indian manufacturing employment, and 16% of Indian manufacturing output.

Establishments are surveyed by the NSSO with state and four-digit National Industry Classification (NIC) stratification. Using the provided sample weights, we construct populationlevel estimates of establishments and employment, and we aggregate the cross-sectional microdata to the state and three-digit NIC level. This allows observation of state-industry cells at three periods during which political reservations were implemented differentially by state. This time variation provides for our differences-in-differences identification strategy.

The outcome measures of business ownership and creation (as well as the control variables) are based on the number of observations in each state-industry cell multiplied by the sampling weight. In the estimations which use an employment-based measure of entrepreneurship, we consider the total employment at an establishment to consist of working owners plus any paid and unpaid employees.

We detect the gender of the business owner based on a survey field which captures details regarding the ownership of the establishment. The NSSO surveys since 1994 include questions which classify the ownership of each establishment among several types (1994 titles): proprietary (male), proprietary (female), partnership with members of the same household, partnership between members not of the same household, cooperative society, public sector, limited company (outside public sector), or other/unknown. We focus primarily on the establishments listed as either male proprietary or female proprietary, which constitute 98% of establishments in the unorganized manufacturing sector. The information captured in this field is an outcome of the survey and not a factor in the stratification design.

In our later analysis, we also investigate specifically household-based establishments by employing a survey question which captures details regarding premises of the establishment. The establishment premises can be
0.0 0% 0.0 0%<br>New links Old links Share of active links<br>USD, trillions USD, trillions<br>Share of active links Share of active links<br>2001 2003 2005 2007 2009 2011 2013 2015 2017 2001 2003 2005 2007 2009 2011 2013 2015 2017<br>USD, trillions USD, trillions<br>Share of active links Share of active links<br>2001 2003 2005 2007 2009 2011 2013 2015 2017 2001 2003 2005 2007 2009 2011 2013 2015 2017<br><!-- End of picture text --> Note: This figure shows the trends in the value of international investment positions and share of active links by block for bank loans and deposits. The value of positions distinguishes old and new links. Old links are those that have a positive value in 2001. New links are those that have a zero or missing value in 2001. By definition, there are no new links in 2001. Values are expressed in trillions of 2011 U.S. dollars (USD). The share of active links is the number of country pairs that have a positive value in each year over the number of country pairs with a positive or zero value in the same year. Country pairs with a missing value are not considered. Bilateral data are aggregated for all countries within a source region to all countries within a destination region.
the OLS estimates are biased downwards.

In the analysis so far, we have examined the effects of exporting on the share of workers who completed training in that same year. In light of the theoretical mechanisms discussed in the introduction, firms might be also expected to increase the share of workers who received technical training in prior years (and are therefore already equipped with the skills required by exporting firms). While our training data do not contain information on training activity before 2009, we can use the 2009-2012 data to redefine the dependent 12
the baseline survey must include a rich set of covariates to estimate a (first-stage) selection regression. One of the advantages of RCTs, especially in developing countries, is that they are less demanding in terms of data; however, even with randomization, firm-level covariates can be useful in verifying that the treatment and control groups are comparable in their observable characteristics. This is especially important for small samples. The availability of a rich set of covariates allows for the analysis of heterogeneity in the effects of the program. Moreover, a deep knowledge of the objectives of the program as well as its administrative and institutional details can be important for the design of surveys that collect the right type of information to control for the selection process (Ravallion, 2008).<sup>37</sup>

> 37 Qualitative information, collected from surveys or focus groups, can complement quantitative survey data though it cannot be the basis for credible impact evaluation by itself.

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study team and any peers who accompanied them to the clinic were not provided a voucher unless they had their own voucher.

At the end of the 10-month intervention period, 625 women (93.2 percent of the baseline sample) were re-interviewed either in person or via phone for the endline survey.<sup>21</sup> Figure 1 provides details on the reasons for attrition. Table A.2 shows that there are no significant differences between attritors and non-attritors in terms of treatment status and along a range of baseline characteristics that are relevant for the study. Although the joint F-statistic is also insignificant, at baseline, non-attritors were significantly more likely to report that their mothers-in-law were opposed to FP and were more likely to use an FP method than nonattritors.<sup>22</sup> The imbalance by mother-in-law opposition is less worrisome because it biases us against finding a positive effect on FP access and use. However, it is unclear whether the imbalance by baseline FP is likely to bias our estimates upward or downward.<sup>23</sup>

> 21We conducted 18 percent of the endline surveys by phone mainly because some women could not be contacted at their recorded locations from baseline, either because they had moved or were visiting someone outside their homes. As per our protocol, we made up to three attempts to contact each woman at her home, after which we attempted to contact her by phone if she was not available. As Table A.3 shows, there are no observable differences in the baseline characteristics and treatment status between women who were surveyed in person or by phone at endline.

> 22Although our attrition rate is relatively low, we estimated Lee bounds for the treatment impacts and find that our main results are not driven by selective attrition. These results are available upon request.

> 23For instance, women who had previously used an FP method at baseline may be more likely to use our vouchers to switch from other providers to the ADC. However, such women may
4. Product prices increase in the winning county on average following the introduction of a new million dollar plant.

The effect on the total number of new varieties consumed in the local market is somewhat more ambiguous. From the household budget constraint in Equation (2), an introduction of a new plant increases the total household income (i) directly through an increase in the local wage rate _wz_ and (ii) indirectly through increased household labor supply _nz_ . If prices of existing products did not increase, then our model directly implies new variety introduction in the local retail market. However, local prices also increase in tandem, entailing a higher cost for the existing market basket. Thus, whether households consume new varieties depends on the relative dominance of the income and price terms. If the increase in household income dominates the increase in total expenditure on existing products, we should observe an increased consumption of new varieties of goods. Thus, our final prediction pertains to the introduction of new products in the local retail market following an MDP introduction.

5. Product varieties increase/ decrease in the winning county following the introduction of a new million dollar plant depending on whether the increase in income dominates/ is dominated by the increase in total expenditure on existing products.

We proceed to test our predictions empirically in the next section.

# **3 Data Description**

Our empirical analysis combines a number of data sets. This section provides an overview of the data and the construction of our key measures used in the empirical analysis.

## **3.1 Million Dollar Plants**

We use data on large plant openings in the United States between 2001 and 2011 from Bloom et al. (2019). The authors follow the traditional approach in this literature in extracting information about the new plant locations of corporations from the real estate journal _The_ 17
countries. I then drop 12 countries for which there are remaining gaps in the government spending data. This results in a final sample of 29 countries reported in Table 1. Given that I am focusing on the most aid‐dependent countries in the world, it is not very surprising that the majority of countries in my sample are located in Sub‐Saharan Africa. The only four exceptions are Bolivia, Morocco, Tunisia, and Jordan. For these 29 countries, World Bank spending is large not only as a share of GDP, but also as a share of total government spending. The time‐averaged share of World Bank‐financed spending in total spending ranges from a low of 3.3 percent in Jordan to a high of 18.5 percent in Uganda, and averages 9.3 percent.

Merely restricting attention to countries that are large recipients of World Bank loans on average is however not enough to ensure that fluctuations in disbursements on World Bank loans are large – it could for example be the case that countries receive a large but steady flow of project approvals, leading to a large but stable flow of disbursements. Fortunately for my purposes, this is not the case, as disbursements on World Bank loans fluctuate significantly over time. Table 2 documents summary statistics on the magnitude of fluctuations in the four measures of disbursements on World Bank projects, ∆��� , as well as total government spending, ∆�� and output growth, ∆�� , pooling all country‐year observations in my sample. Fluctuations in total disbursements are quite substantial, with a standard deviation of 1.4 percent of GDP, and of 1.1 percent of GDP for disbursements on previously‐ approved projects. By way of comparison, the standard deviation of GDP growth rates is 3.9 percent in this sample, and the standard deviation of changes in total government spending is 2.9 percent.

# **4.  Estimates of the Government Spending Multiplier**

# **4.1
is obtained from the IMF’s Balance of Payments Statistics. Finally, external shocks are added to the regression as controls. We proxy terms of trade shocks as the annual percentage change in the terms of trade index, and the data is collected from WDI. The world real interest rate is proxied by the money market rate of the reference country as in Di Giovanni and Shambaugh (2008) and this indicator is gathered from IFS.

# **4. Empirical Evidence**

This section aims to set up the empirical assessment on the determinants of sudden stops and presents the evidence obtained from our regression analysis. We use an _effective_ sample of 82

> 10 The health of the financial system can be captured more accurately by another comprehensive set of indicators such as ratio of liquid assets to deposits and short-term funding, bank capital to assets, non-performing loans (NPLs) to total gross loans, the ratio of provision to NPLs relative to total gross loans, and regulatory capital to riskweighted assets among others. However, there are serious constraints on time series availability for these series (e.g. since 2000 at best) that prevents us for using them in our regression analysis.

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**Figure 20: Measure of climate policy environment**

<!-- Start of picture text --> Czech Republic<br>Lithuania<br>Romania<br>Poland<br>Hungary<br>Bulgaria<br>Slovak Republic<br>Ukraine<br>Estonia<br>Macedonia<br>Uzbekistan<br>Moldova<br>Georgia<br>Armenia ECA with projects<br>Albania<br>Serbia<br>Russian Federation<br>Azerbaijan<br>Slovenia<br>Latvia<br>Croatia<br>Belarus<br>Kazakhstan<br>Kyrgyzstan<br>ECA without projects<br>Tajikistan<br>Montenegro<br>Turkmenistan<br>Bosnia and Herzegovina<br>0.0 0.2 0.4 0.6<br>Climate laws, institutions and measures index<br><!-- End of picture text --> Source: Teytelboym and Steves (forthcoming) 73
Figure B.1: **Density of the Assignment Variable by Survey Status – Sample Frame and Surveyed Households** <!-- Start of picture text --> −.5 −.4 −.3 −.2 −.1 0 .1 .2 .3 .4 .5<br>Hectares from acre or hectare cutoff<br>Sample frame Surveyed households<br>.2<br>.15<br>.1<br>Density<br>.05<br>0<br><!-- End of picture text --> Notes: The figure plots the density of the forcing variable (bank-recorded landholdings) for (a) all households in the sample frame and (b) the sample of surveyed households, in bins of 0.1 hectares within<sup><u>+</u></sup> _−_<sup>.5 hectares around the eligibility cutoff.</sup> Figure B.2: **Density of the Assignment Variable by Bank Type – Commercial Banks and Credit Cooperatives** <!-- Start of picture text --> −.5 −.4 −.3 −.2 −.1 0 .1 .2 .3 .4 .5<br>Hectares from acre or hectare cutoff<br>Bank accounts Cooperative accounts<br>.2<br>.15<br>.1<br>Density<br>.05<br>0<br><!-- End of picture text --> Notes: The figure plots the density of the forcing variable (bank-recorded landholdings) for all surveyed households and separately for (a) accounts at commercial banks (b) accounts at credit cooperatives, in bins of 0.1 hectares and a band of<sup><u>+</u></sup> _−_ .5 hectares around the program
# **1 Introduction**

Governments deploy bank bailouts to mitigate risks of financial instability and reduce the likelihood or severity of recessions that systemic bank distresses and failures often trigger. Bailouts may mitigate financial disintermediation shocks, uphold credit supply, and maintain economic conditions by reducing corporate and personal bankruptcies, and thus unemployment. But they have social and fiscal costs. Financial support to distressed banks may impose large redistribution costs on society when taxpayers are not adequately compensated for the risks the government takes or for the funds that bank owners never return (Dell’Ariccia et al. 2022).<sup>b</sup> Government intervention can help bankers socialize losses and privatize profits. Past bailouts also raise the expectations of future bailouts and generate fiscal contingent liabilities. They can feed the moral hazard incentives of banks, reduce market discipline, and impact the competitive landscape in banking (Calderon and Schaeck, 2016; Gropp et al., 2011). Ultimately, rescuing distressed banks can distort the allocation of savings/capital in the economy because they could be partially allocated based on the political and regulatory connections of banks. Bailouts can also decrease confidence in banks and be politically unpopular, including because they are perceived by the citizens as unfair (Berger and Roman, 2017), and can sometimes lead to sovereign debt problems or even civil unrest (Berger and Roman, 2020).

This paper studies the link between bailouts and bank size, ownership, and financial soundness, using bank-level data from Kazakhstan from 2009 to 2021. Kazakhstan is a particularly interesting country for this type of analysis because of repeated bouts of financial instability and the staggered bank bailouts involving several types of public interventions. The country has experienced two waves of financial crises since 2008 because of commodity price shocks, economic concentration in extractive industries, and weak governance, among other challenges. The economic crisis triggered by COVID-19 also renewed pressure on banks and the banking system. From 2009 to 2021, the banking system comprised between 26 and 40 operating banks and witnessed
. It is positive for 39% of households, making up between 2% and 80% of overall reported spending.

Non-food expenditures are categorized in spending on housing and appliances, transportation and communication, and clothing. The latter can be split up further in spending on men’s, women’s and children’s clothing. Housing expenditure accounts on average for 8% of total spending in Kilimanjaro and 5% in Ruvuma, with spending on the other two categories between one third and one half of that. Finally, three more“social”expenditure categories are considered: health expenditures, education expenditures (in effect an investment expenditure) and gifts given.

To study spending behaviors in rural China, the annual 2000-2004 household panel

> 18 Non-farm income in kind and in cash have been recorded separately in the questionnaire.  Crops and livestock sold make up cash farm income, with the remainder classified as in kind income (for auto-consumption and seeds).

> 19 Staple food includes bananas, grains, potatoes and cassava. Non-staple food includes expenditures on meat, dairy, and fruits and vegetables.