rafmacalaba/datause-encoder-data huggingface dataset:
Figure 3: Distribution of employment elasticities to innovation sales <!-- Start of picture text --> 0 .5 1 1.5 2 2.5<br>kernel = epanechnikov, bandwidth = 0.0454<br>2.5<br>2<br>1.5<br>Density<br>1<br>.5<br>0<br><!-- End of picture text --> Note: Table 1: Overview

|||All|Africa|South Asia|ECA|MENA|
|---|---|---|---|---|---|---|
|Total||15033|1652|3322|6658|3401|
|Size|Small (<20)|0.54|0.73|0.36|0.61|0.49|
||Medium (20-99)|0.36|0.23|0.47|0.33|0.38|
||Large (100+)|0.10|0.04|0.17|0.06|0.12|
|Age|<5|0.05|0.13|0.04|0.05|0.01|
||5 to 9|0.22|0.30|0.19|0.24|0.16|
||10 to 14|0.21|0.21|0.19|0.24|0.18|
||15 to 19|0.19|0.12|0.17|0.23|0.18|
||20 +|0.32|0.22|0.41|0.23|0.46|
|Industry|Food|0.08|0.09|0.09|0.06|0.12|
||Textiles|0.04|0.02|0.07|0.02|0.04|
||Garments|0.06|0.04|0.07|0.05|0.10|
||Wood, Paper|0.03|0.03|0.04|0.03|0.02|
||Publishing, Printing|0.03|0.03|0.02|0.03|0.03|
||Chemicals|0.03|0.02|0.06|0.02|0.03|
||Plastics|0.03|0.01|0.07|0.03|0.02|
||Non metallic mineral products|0.03|0.02|0.04|0.04|0.01|
||Basic metals, products|0.07|0.07|0.10|0.05|0.07|
||Machinery|0.03|0.01|0.07|0.03|0.00|
||Electronics|0.04|0.01|0.10|0.03|0.01|
||Furniture|0.03|0.07|0.03|0.03|0.03|
||Construction|0.05|0.04|0.02|0.08|0.03|
||Motor vehicle services|0.03|0.05|0.02|0.03|0.02|
||Transportation|0.05|0.03|0.03|0.05|0.06|
||Wholesale|0.09|0.09|0.02|0.12|0.08|
||Retail|0.18|0.24|0.07|0.24|0.12|
||Hotels & Restaurants|0.06|0.12|0.05|0.04|0.06|
||IT,Professional services|0.02|0.01|0.02|0.02|0.01|

Note: Based on estimation sample covering 53 countries.

27
decline in poverty occurred in 1995, just after the Real Plan was introduced and the minimum wage was increased<sup>16</sup> . For Brazil, the Real Plan and the increased minimum wage caused a decline in poverty to a level lower than at any point during the 1980s, and the poverty index reveals a two percentage points reduction in the headcount in the 1981 to 1999 period (Figure 5). The Asian crisis had a small negative effect on the headcount ratios via the pressure on the currency and the higher interest rates (Figure 5 and Tables B5 to B7). For Brazil and the Northeast region, the headcount poverty ratio of household heads dropped by around 7.3 percent and 12.3 percent, respectively, in the 1993-99 period. In 1999, the incidence of poverty was 22.4 percent and 44.3 percent in Brazil and the Northeast region, respectively.

**Figure 5: Headcount Index: Indigence Line**

<!-- Start of picture text --> 70.0%<br>60.0%<br>50.0%<br>40.0%<br>30.0%<br>20.0%<br>10.0%<br>Brazil NE Maranhao Rio Grande do Norte Pernambuco<br>0.0%<br>1981 1985 1988 1990 1993 1995 1997 1998 1999<br><!-- End of picture text --> _Source:_ Authors’ calculations based on PNADs 1981 to 1999.

16 In May 1995, the minimum wages were increase by 43 percent while monthly inflation was around 2 percent.

13
but 64 buyers on average, with a standard deviation of 532. Finally, the sparsity of the network increases with the narrowness when defining a product and the frequency of the data.

To further understand the composition of economic activity of the Indian state of our analysis, in Table A4, we show the types of goods firms sell and buy and what fraction crosses state and country borders. In our state, firms are mostly in the business of selling vegetables, plastics, and minerals and buying machinery, metals, and vegetables. In terms of the type of trade, firms in our state are more likely to sell to other firms in the state. This contrasts with how firms in our state buy goods, where the share of purchases that come from within the state is almost the same as from other Indian states. Finally, international exports and imports represent a non-negligible but small share of sales and purchases.

Before using the lockdown variation to understand how firm-to-firm transactions are affected, we verify the stringency of these lockdowns in Figure A2 using Google mobility data. The data shows how the number of visitors to (or the time spent in) categorized places changes in comparison to baseline days. The baseline day is the median value from the 5-week period Jan 3 – Feb 6, 2020.<sup>17</sup> Until the beginning of March 2020, there were essentially no differences in mobility trends across _Red_ , _Orange_ , or _Green_ zones. But starting at the end of March 2020, we see that there is a substantial reduction in different types of activities (time spent in retail and recreation, grocery and pharmacy, parks, commuting, and workplaces) in _Red_ zones compared to _Green_ zones; with _Orange_ zones in between. People in _Red_ zones also spent more time at home than people in either _Orange_ or _Green_ zones. We notice that starting in August 2020, a few months after the centralized lockdown was over, these differences reduced, and by December 2020, these differences, especially in workplace mobility, become negligible.

# - 3 REDUCED FORM EVIDENCE

In this section, we outline a simple empirical specification to provide evidence showing the role of lockdown policies on key outcome variables for firm-to-firm trade. We show that the sudden Covid-19 lockdown policies between March and May 2020 led to a rise in unit values and a fall in the monthly number of transactions between firms.<sup>18</sup> In the next section, we exploit this variation to estimate firm-level elasticities of substitution across intermediate suppliers of the same product.

> 17 See the Google Covid-19 Community Mobility Reports.

> 18To see a similar application of this empirical strategy for domestic violence and economic activity in India, see Ravindran and Shah (2020) and Beyer et al. (2021).

9
|in (Total Other Africans)|||2.OE-4<br>(7.3)|||3M.ES<br>(7.7S)|
|---|---|---|---|---|---|---|
|In (Tota1 Non-Africans)|||-1.9E-S<br>(1.6E-4)|||-1.0E4<br>(1.6I 4)|
|In (Wage Bl of<br>Temporary Worers)|1.9EB4<br>(8.6E-5)|1.8E4!<br>(7.9E-5)|1.7E4<br>(8.3B-5)|22E-4*<br>(925)|1.6E-4*<br>(8.5E-5)|1.7E-4<br>(8.7E-5)|
|InK|0.479<br>(0.048)|0.510<br>(0.045)|_0.456_<br>(0.052)|0.572<br>(0.052)|0J<sup>7</sup>1<br>(0.048)|0.484<br>(0.055)|
|Time<br>Industry|40.048<br>(0.016)|4.0606<br>(0.016)|.0056<br>(0.018)|.0074<br>(0.017)|4.074<br>(0.017)|_4.06e_<br>(0.017)|
|Intercepts|yes|yes|yes|yes|yes|ye|
|R2|O83|0m82|0879|0.881|0.881|OBBO|
|N|387|387|387|376|376|376|

* - Si cant at the 5 percent sati test leveL + - Significant at the 10 percent statistical test leve. Source 80-firm sample

0-
particularly in a country like Argentina where unions have a strong voice in political decisions. But narrowly examined, the fairly small drop in union membership is not large enough to explain the increases in wage inequality that took place in the 1990s.

Changes in minimum wage also have the potential to influence wage inequality. The real value of the minimum wage greatly eroded during the period of hyperinflation of the late 1980s and early 1990s. Because it dropped to a point below the wages of essentially all workers, the minimum wage cannot have contributed to changes in inequality between the early and late 1980s. However, it is conceivable that the minimum wage during the 1990s did have some effect. The nominal wage was increased from 97 pesos to 200 pesos in 1993 and remained there until 2003. Between 1992 and the end of the fixed exchange rate in December 2001, the minimum wage remained essentially unchanged in real terms.

As a first order effect, the increase in the minimum wage that took place in 1992 would be expected to decrease wage inequality, at least among formal salaried workers. As Maloney and Nuñez (2003) point out, the minimum wage can have complex effects on the wage distribution, beyond those on formal salaried workers near the minimum wage. In many countries, the minimum wage has both “numeraire” and “lighthouse” effects that spill over to the informal sector. The numeraire effect is the bunching of wages at round multiples of the minimum wage, due to the fact that the statutory minimum wage is often used as the numeraire for wage negotiations. The lighthouse effect refers to the concentration of informal workers (for whom the minimum wage is not enforced) at the minimum. Using 1998 EPH data, Maloney and Nuñez find strong evidence of both effects in Argentina. Likewise, Khamis (2007) examines the effects of changes in the minimum wage 1993 and 2004 on wages and finds positive effects on both formal and informal wages, with a larger effect for informal wages.

Given these effects, it is difficult to determine with certainty what the wage distribution would have looked like with a lower minimum wage. Overall, however, it seems likely that both the direct effect of the minimum wage increase and the numeraire and lighthouse effects tended to raise the wages of those in the lower part of the distribution, reducing inequality even while overall wage inequality increased.

# **_Trade_**

Time trends at first glance suggest that widening inequality may be due to the trade liberalization that took place over the course of the 1990s, and a wide international literature 6
The estimated coefficient on the _size_ variable is positively and statistically significant, indicating that bigger banks are more costly to stabilize using public resources. The average marginal effect of size on the public expenditure for a bank bailout is 14.1 billion tenge (Table 5, Column 1). A one percent increase in the size of a troubled bank implies, on average, more than a 4-fold increase in bailout expenditures. This result is in line with evidence for the U.S. banks following the GFC where systemically important banks were more likely to receive larger government equity infusions (Bayazitova and Shivdasani, 2012). If the troubled bank is stateowned, the average marginal cost increases significantly by an additional 9.6 billion tenge.

If the bailout instrument is debt financing of the distressed bank, the additional cost relative to an equity injection reaches on average about 39 billion tenge. The costliest by far are bailouts using purchases of non-performing exposures (loans) in which cases the additional bailout expenditure compared with equity injections can reach, on average, 324 billion tenge.

The significance of the variables controlling for bank financial soundness—in particular, accumulated provisions and capitalization—suggests that banks with greater buffers against losses require much less public expenditure to stabilize (rehabilitate) and highlights the importance of independent and credible supervision of banks. The sign of the inverse of Mills ratio is estimated as negative, implying that unobservable factors that increase the probability of bank bailout may also decrease the cost of bailouts—however, the coefficient appears insignificant in the first Tobit regression (column 1).

# _6.4 Projecting expected contingent fiscal liabilities from bank bailouts for 2022-23_

We proceed with illustrative projections of fiscal contingent liabilities from bank bailouts by combining the estimates of the likelihood of bailing out a troubled bank with the average cost of a bank bailout depending on the applied bailout instrument. Specifically, we predict “annual” fiscal contingent liabilities from bank bailouts using the financial statement data for each bank from October 2022 published by the National Bank of Kazakhstan (NBK). We follow these steps:

- a) Obtain the October 2022 bank-level financial statement data,

- b) Compute the likelihood of a bailout for each bank by type of bailout instrument using estimated equation (2).

- c) Compute the average bailout expenditure for each bank using estimated equation (3).

- d) Use the bank financial data and the predicted bailout probabilities and expenditures obtained in the b), and c) steps. The expected annual contingent liability per bank is obtained by multiplying the predictions from (2) and (3) for each bank, and adjusting for the historical frequency with which each bailout instrument was used (equity 30%, debt 26% and NPE purchase 44% of the time on average during the studied sample). We then

22
performed in Section 2. I expect to find that only informal business owners from the formal business owner species are more likely to register their business after the implementation of SARE. In contrast, informal business owners from the wage worker species should be more likely to become wage workers after the reform since the reform also created more jobs.

Testing these hypotheses sheds light on the effect of SARE on firm formalization and transitioning to wage work for different types of informal business owners. At the same time, it provides a test of the classification of informal business owners into wage worker and formal business owner species. That is, if I find that informal business owners from the wage worker (formal business owner) species are more likely to become wage workers (formal business owners) when a reform makes it easier to do so, this confirms the validity of the species classification.

# **4. Identification Strategy and Transitions into Different Occupations**

This paper follows the identification strategy in Bruhn (2008 and 2011) to measure the effects of SARE on business registration and transitions to wage employment of informal business owners. In particular, I exploit the fact that the reform was implemented in different municipalities at different points in time to estimate the following equation where the outcome variable yict is a dummy variable indicating the occupation (e.g. formal business owner, wage worker) of individual i living in municipality c in quarter t. The regression includes municipality fixed effects, βc, and quarter fixed effects, γt. The variable SAREct is the reform dummy and, for each municipality, it is equal to one for the quarter in which the reform was implemented and for all following quarters. The vector EC1999 consists of control variables from the 1999 Economic Census interacted with a linear time trend, t. These variables are log GDP per capita, log number of economic establishments per 1000 capita, log fixed assets per capita, and log investment per capita. The standard errors of the regressions are clustered at the municipality level.

The coefficient δ measures the unbiased impact of SARE on outcomes yict under the assumption that the time trends of yict would have been parallel across municipalities in the 8
results, large parts of physical infrastructure were destroyed or rendered inoperable, and GDP in 1999 declined by about 40 percent (World Bank (2002a)). Soon after the violence ceased, Timor-Leste began rebuilding itself with the establishment of a United Nations transitional administration and support from the international donor community and NGOs. During 2000 and 2001, Timor-Leste made enormous progress in rehabilitating its economy, with GDP growing at an annual rate of 15 to 20 percent, reconstructing infrastructure, reintegrating refugees and building the key elements of a sustainable political process in an environment of internal peace. This transition has transformed people’s lives fundamentally and in ways that go far beyond purely material endowments. Although the fundamental achievement of gaining independence is overwhelmingly positive, the vast structural changes were accompanied by conflict, destruction, and displacement. This paper is an empirical study of changes in economic well-being (‘welfare’) and empowerment (‘power’) in Timor-Leste from the pre- to post-conflict period.* It draws on the only nationally representative household survey and community census of Timor-Leste to investigate the impact of the systemic changes between the violence of 1999 to end-2001, six months before independence. It demonstrates how subjective and objective measures, based on current and recall information, can be used to develop a differentiated profile of changes in well-being from the pre- to post-conflict period. ° However, in interpreting the findings, it is important to bear in mind one qualification. Recall data are not representative of the historic population in case of large population changes. This is likely to be the case in Timor-Leste, where parts of the population left the country for good - in particular Indonesians and supporters of the old regime. The results in this paper therefore reflect changes in well-being as perceived by the 2001 population rather than the population.

The rest of the paper is organized as follows. The next section describes the data. Section 3 takes a first look at the pattern of changes in self-rated welfare and power between 1999 and 2001. Section 4 investigates the determinants of welfare and power in 2001, and Section 5 analyzes the changes in welfare and power. The final section concludes.

4 The terminology is adopted from Lokshin and Ravallion (2002) who analyze subjective welfare and power in Russia, focusing on the differential impacts of individual income, household income and income inequality. Frey and Stutzer (2000) argue that direct democracy and federal structure raise self-reported well-being in Switzerland and ? finds that the quality of goverment matter for happiness. These two papers do not separate between welfare and power. > In the following, I will use ‘1999’ to refer to the date before the violence of September 1999, and ‘2001’ to end 2001, the time of the fielding of the TLSS.
A a BA PN dh )A o )A - v lt <!-- Start of picture text --> ''<br>0.008 ''<br>' ~~ -Median (57 days)<br>'<br>'<br>'<br>0.006 !<br>rd2 ''<br>zg '<br>2 0.004 H<br>'<br>'<br>'<br>'<br>'<br>0.002 '<br>'<br>'<br>'<br>'<br>'<br>'<br>0 t<br>50 150 250 350 450 550<br>Days between passage and implementation<br><!-- End of picture text --> <!-- Start of picture text --> 1<br>2<br>a 0<br>DS<br>£4x<br>23ic5v . .<br>(e)—&5 2 |<br>4<br>0 1 2 3 4 5 6 7 8<br>Quarter<br><!-- End of picture text -->

<!-- Start of picture text --> 1<br>2<br>a 0<br>SD<br>~2&xov a . -<br>35ic |.<br>— 2<br>[e)3 .<br>4<br>0 1 2 3 4 5 6 7 8<br>Quarter<br><!-- End of picture text -->
where _φ_ ( _·_ ) is the Standard Normal density function. The IMR measures the hazard of nonselection: the higher the IMR for an NTM, the less likely is an importing country to have imposed the NTM, based on the NTMs imposed by its neighbors. Intuitively, if the IMR is high but the importing country imposes an NTM anyway, endogeneity is more of a concern, and the regression should adjust for this correlation. Formally, we include the IMRs as controls in Equation (1).<sup>23</sup> The specification effectively controls for each market’s _likelihood_ of being treated, based on the policy behavior of neighboring countries.

Column 1 of Table 1 presents estimates for Equation (1) _without_ the control function for endogeneity of NTMs while column 2 adds the IMRs resulting from the control function estimation. Comparing these two columns reveals a key finding: failing to account for the endogeneity of NTMs biases their estimated trade impact toward zero. The estimated impact of TBTs and joint SPS-TBTs is biased downward. This suggests that quality regulation is more likely to be implemented in products where trade values are declining, e.g. because of lower-quality varieties flooding the market.<sup>24</sup> Importantly, the estimates show that SPS and TBT measures enhance trade on average, whether implemented independently or simultaneously. This is consistent with the hypothesis that SPS and TBT increase product quality and stimulate demand. In contrast, pre-shipment inspections and non-technical measures deter trade (weakly in the case of preshipment inspections), as expected given their often protectionist motives.<sup>25</sup> The estimated treatment effects are large, ranging from a 23.5% increase in imports following the introduction of an SPS measure, to a 20.7% decrease in imports following the introduction of a

> 23The control function approach is preferred to an instrumental variables approach given (1) the binary nature of the endogenous variables and (2) the use of a PPML estimator with high-dimensional fixed effects. There is currently no estimator that accommodates a Probit first stage and PPML second stage, both with high-dimensional fixed effects. IV regressions with a linear first stage and high-dimensional fixed effects yielded unreasonably large coefficients, indicating the importance of correctly specifying the Probit first stage. The control function approach yields consistent estimates as long as the IMR captures the dependence between the PPML error term and the endogenous NTM variable Wooldridge (2015).

> 24OLS estimates of Equation (1) using the probability of trade at the exporting country-importing countryproduct-year level as an outcome confirm that some markets indeed disappear following new regulations. Estimates are shown in Appendix Table A3.

> 25Reassuringly, the unreported coefficients on tariffs are also negative. While this indicates that higher tariffs are associated with lower imports, we do not interpret this effect as causal since we do not estimate a control function for tariffs.

15
# **III. Data and Methodology**

All our data are taken from the World Bank’s World Development Indicators (2005). We start with using data averaged over all observations in the period 1980-2003 to diminish the effects of business cycle. We do, however, also use instrumental variables and panel data regressions as robustness tests. As we are not only interested in the effects of financial sector development on undernourishment, but also in the channels through which it happens, we conduct several analyses. First, we study the link between financial development and the prevalence of undernourishment. Second, we decompose this causal link by studying the link between agricultural productivity and undernourishment. Third, we study the link from financial development to overall agricultural productivity per worker and other specific agricultural productivity indicators. And fourth we decompose these links further by investigating the effects of financial sector development on use of productivity enhancing inputs requiring upfront layouts. Fifth, to investigate the importance of (direct) access to financial services, we study the effects of outreach of financial services on undernourishment, agricultural productivity, productivity enhancing inputs, and prices. To establish causation, we use an instrumental variables approach when necessary.

# _A. Variables and descriptive statistics_

Table 1 presents the description of all the variables we use. Table 2 presents descriptive statistics of the variables which are central to our analysis: undernourishment, private credit to GDP, agricultural productivity, and the cereal yield.

# <u>Main variables</u>

We have three main variables: undernourishment, private credit to GDP, and agricultural productivity. Our main LHS variable is the undernourishment, defined as the prevalence of undernourished people as a percentage of the total population. Undernourishment is only documented for the period 1980-2003, with most countries only having three observations in the 1990s: 1992, 1995, and 1998. In the basic sample, undernourishment is on average 19 percent, but varies widely over the world, ranging from 2.5 percent in 9