rafmacalaba/datause-encoder-data huggingface dataset:
words, on average the current set of aggregate WGI indicators exhibit standard errors which are less than one-half those of a typical individual indicator. This highlights the benefit of averaging data from many different sources when seeking to measure broad concepts of governance as we do. Moreover, the likelihood of encountering an extreme outlier in a country's aggregate governance score is commensurately lower than in any individual source.

# **3.1  Cross-Country Comparisons of Governance Using the WGI**

We use Figure 1 and Figure 2 to emphasize the importance of taking these standard errors into account when making comparisons of governance across countries and over time. In the two panels of Figure 1, we order countries in ascending order according to their point estimates of governance in 2008 on the horizontal axis, and on the vertical axis we plot the estimate of governance and the associated 90% confidence intervals.<sup>10</sup> These intervals indicate the range in which it is 90 percent likely that the true governance score falls.<sup>11</sup> We do this for two of the six governance indicators, Political Stability and Absence of Violence/Terrorism, and Control of Corruption. The size of these confidence intervals varies across countries, as different countries appear in different numbers of sources with different levels of precision. The resulting confidence intervals are substantial relative to the units in which governance is measured. From Figure 1 it should also be evident that many of the small differences in estimates of governance across countries are not likely to be statistically significant at reasonable confidence levels, since the associated 90 percent confidence intervals are likely to overlap. For many applications, instead of merely observing the point estimates, it is often more useful to focus on the _range_ of possible governance values for each country (as summarized in the 90% confidence intervals shown in Figure 1), recognizing that

> obviously (thanks to the aggregation) we also found that the WGI had much lower margins of error than any ‘objective’ measure.  This underscores the fact that all efforts to measure governance involve margins of error, often non-trivial.

> 10 Due to space constraints we cannot label all countries in Figure 1.  Instead we show country names for countries at or near the vintiles of the distribution of the governance indicator. 11 A x% confidence interval for governance can be obtained as the point estimate of governance plus or minus the standard error times the (100-x)/2<sup>th</sup> percentile of the standard normal distribution.  For example, the 90% confidence intervals we report throughout the paper are the point estimate plus or minus 1.64 times the standard error.

17
Table 9. Diffusion through supply chain linkages controlling for distance

||Dep|variable: No|rmalized sha|re of tech-re|lated job ad|verts|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Pioneer|4.496***|4.336***|4.440***|4.341***|3.800***|3.769***|
||(0.398)|(0.395)|(0.396)|(0.395)|(0.777)|(0.777)|
|Pioneer_×_years since emergence|-0.204***|-0.193***|-0.201***|-0.196***|-0.189***|-0.187***|
||(0.036)|(0.036)|(0.036)|(0.036)|(0.072)|(0.072)|
|Nearest distance from pioneer city, logs|-0.071***|-0.070***|-0.071***|-0.071***|-0.071***|-0.070***|
||(0.006)|(0.006)|(0.006)|(0.006)|(0.006)|(0.006)|
|**Linkages of pioneer frms**|||||||
|Buyer||1.869***||||1.480***|
|||(0.300)||||(0.319)|
|Supplier|||0.701***|||0.074|
||||(0.173)|||(0.170)|
|Innovation partner||||1.656***||1.056***|
|||||(0.275)||(0.291)|
|Affliate|||||0.783|0.557|
||||||(0.774)|(0.773)|
|**Linkages of pioneer frms over time**|||||||
|<br>Buyer_×_years since emergence||-0.053*||||-0.054*|
|||(0.029)||||(0.030)|
|Supplier_×_years since emergence|||-0.006|||0.024|
||||(0.018)|||(0.018)|
|Innovation partner_×_years since emergence||||-0.037||-0.018|
|||||(0.026)||(0.027)|
|Affliate_×_years since emergence|||||-0.014|-0.001|
||||||(0.072)|(0.072)|
|Technology FE|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>Year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Firms FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Adjusted R-squared|0.041|0.041|0.041|0.041|0.041|0.041|
|Observations|9,834,969|9,834,969|9,834,969|9,834,969|9,834,969|9,834,969|

Notes: Standard errors clustered at the firm level in parentheses.The normalized share is capped at the 99<sup>_th_</sup> percentile of non-zero observations. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1

# **4.2.4 Linkages within and outside firm boundaries**

We also examine whether the patterns of technology hiring documented in the previous section are mainly driven by linkages within or outside firm boundaries. In particular, we separate buyers, suppliers and innovation partners that belong to the same ultimate owner as the pioneer firms (defined as those within the firm boundary), from those whose ultimate owner differs from the pioneer’s (defined as those outside the firm boundary). Only under 1 percent of our linked firms are within the firm boundary, while the vast majority are outside the firm boundary. The results shown in Table 10 reveal that the role of firm-tofirm linkages is driven by effects for firms outside the firm boundary, while linkages within the firm boundary are not statistically significant.

26
the increased availability of financial services helped existing informal business owners to continue their operations instead of closing their business and transitioning to being the wage-earners or not employed. This is consistent with evidence suggesting that micro entrepreneurs have very high rates of return on capital, and hence would benefit from increased access to finance (See McKenzie and Woodruff, 2008, and De Mel, McKenzie, and Woodruff, 2008)<sup>4</sup> .

Perhaps most importantly, Banco Azteca opening led to a reduction in the proportion of not employed individuals and increased income for this segment of the population.<sup>5</sup> This is because some previously not employed individuals opened informal business and others were more likely to work as wage earners after Banco Azteca opening. These results on employment and income mirror Karlan and Zinman’s (2007) finding that increased availability of consumer credit has a range of positive outcomes on the household well-being, including an increased ability to retain employment and increased income. The Karlan and Zinman analysis includes only individuals that work as wage earners. Our study, however, shows that increased access to finance has positive effects on self-employment, as well as on wage work.

It is important to point out that our paper does not establish whether the new bank opening had a direct impact, which it might have had given its large scale of the operations, or an indirect one – i.e. via improvement in the competition in the local financial sector. Azteca was probably competing for clients with existing microfinance organizations and credit unions. Thus, the impact on the economic outcomes we observe could have stemmed from the access to credit and savings provided by Azteca, or by a number of other financial institutions<sup>6</sup> . Nevertheless, our evidence suggests that improving access to low income households has a significant impact on the labor market and income levels.

> 4 Our findings are also consistent with Banerjee and Duflo (2004) who use an exogenous change in regulation that made credit more available to some firms to show that this increase credit led to an increase in firms’ sales and profits. The firms in their sample are, however, larger firms, receiving credit from traditional banks. 5 Our results on income are not as strong as our results on employment choices because of the nature of the data. The dataset we use is designed to measure labor market participation rather then household income or consumption. This implies that the quality of the labor market participation data is likely to be higher than the quality of income data available in the survey.

> 6 We have not been able to examine the market response to Azteca opening by microfinance institutions and credit unions since we do not have data on these institutions at the municipality level.

4
2006 to an average of nearly 50 pesticides in 2011. On the other hand, the BRICS, on average, set close to seven standards per product in 2006 compared to eleven in 2011.

Each country not only sets the number of pesticides to restrict per product but also sets the “intensity” of the standards, or the permissible level for each pesticide (the MRL). The higher the MRL, the less restrictive is the standard. Continuing the example from above of oranges in 2011, the average MRL among the 506 pesticides the EU regulates is 0.59 ppm, the average in Brazil for the 102 MRLs is 1.47 ppm, and in Russia the average for the 16 MRLs is 0.17 ppm. Thus in terms of the levels of MRLs, Russia is more restrictive than the EU, and the EU more restrictive than Brazil.<sup>6</sup> Figure 3 displays the average MRL per product in BRICS and non-BRICS importers. On average, BRICS markets are less restrictive than non-BRICS as their allowable levels of pesticides are higher. BRICS however are becoming more restrictive, particularly between 2006 and 2008. Over the same period, non-BRICS became less restrictive, but since have become slightly more restrictive again.

There are several challenges when working with MRL data. First, there are two dimensions of restrictiveness that need to be considered: the number of regulations per product, and how strict those regulations are. The second challenge is that the heterogeneity of pesticides regulated across products and countries makes it difficult to compare how restrictive one country is compared to another. For example, the 16 pesticides regulated in Russia for oranges in 2011 might not be included among the 102 pesticides that are regulated in Brazil. Thus the question arises: how do we compare Russia’s and Brazil’s restrictiveness for oranges? A third challenge, closely related to the second, is how to interpret the missing values that originate from a pesticide being regulated in one country but not the other. We cannot replace these missing values with zeros, as is commonly done with missing trade values, because an MRL set to zero is equivalent to banning that pesticide entirely.

In our analysis we use the number of pesticides and the average MRL to assess the restrictiveness of standards for each country-product-year. Furthermore, we created an index of restrictiveness for each country _i_ , product _p_ , in year _t_ . It is defined as follows:

> 6 The averages are estimated ignoring missing values as well as whether the same pesticides are restricted in one country compared to the other.

6
duction of a new establishment is attributable to increasing local house prices. Increased housing demand following migrations of a sizable mass of non-incumbent, especially highskilled, workers in the wake of a new MDP may push up house prices in the winning county. This creates a wealth channel for incumbent residents in the winning county, as in Stroebel and Vavra (2019), which in turn drives up the retail prices. While this mechanism would also be consistent with less consumer price sensitivity, the underlying mechanism is different in that the price sensitivity will be induced by wealth effect and not by income effect. We rule out this explanation in figure 5. In panel A, we analyze whether the winning county experienced a different house price growth trajectory following a new plant introduction. We do not find any differential house price dynamics between the winning and losing counties, mitigating concerns of wealth effect driven increases in retail prices in the winning counties. Panel B refines this analysis by considering only high-skilled zip-codes.<sup>19</sup> We again observe no discernible difference in house price trends between high-skilled winning and losing zip-codes. Finally, in Panel C, we do a within-winner analysis of house price trends between high-skilled and low-skilled places. Again we observe no significant differences in price trends. Taken together, these graphs assuage concerns that the observed patterns in retail prices are driven by differential changes in house prices in winning counties.

# **5 Conclusion**

In this paper, using detailed barcode-level prices, we show that the entry of a Million Dollar Plant in a county increases the barcode-level prices across all types of products in the winning compared to the runner-up counties. However, only the wages of high-skilled workers increase, while the wages of low-skilled workers do not change. Therefore, with rising retail inflation, real wages of low-skilled workers unambiguously decrease. The rise in the number of expensive varieties and varieties mostly consumed by skilled workers is consistent with the fact that the rise in income dominates the price effect for skilled workers, thereby leading to rising real wages. The introduction of MDPs therefore can lead to increased local inequality through its effect on local inflation. We also introduce a theoretical model that is consis-

> 19Since skill is not directly observed at an aggregate zip-code level, we use IRS SOI tax returns as a proxy for skill. We define households with taxable income of $50,000 or more as high-skilled households, and those with smaller incomes as low-skilled households. Finally, zip-codes with at least half of high-skilled households are categorized as high-skilled.

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# Figure 4. The duration of defaults: 1800-2020

_Marginal frequency distribution_ <!-- Start of picture text --> 20<br>15<br>1945-2020<br>N=143<br>10<br>1800-2020<br>Dashed line<br>N=279<br>1800-1945<br>5<br>N=136<br>0<br>0 5 10 15 20 25 30 35 40 45 50 55 60<br>Duration in years<br>Cumulative frequency distribution<br>100<br>1946-2020<br>80 N=143 1800-2020<br>N=279<br>60<br>40 Dashed line<br>1800-1945<br>N=136<br>20<br>0<br>0 5 10 15 20 25 30 35 40 45 50 55 60<br>Duration in years<br>Percent<br>Percent<br><!-- End of picture text --> Source: Farah-Yacoub, Graf von Luckner, and Reinhart (2021).

**_Early shallow restructurings_** _(sovereign debt):_ These early attempts to restructure are often characterized by the provision of some cash flow relief without necessarily reducing the intertemporal debt burden. For debtors, these restructurings provide a short-lived reprieve; for creditors, it provides time to adjust their exposure and leverage to the distressed debtors more gradually.  Overoptimistic economic projections (see Ghosal and Miller 2017) also contribute to the observed undershooting in the magnitudes of debt relief. Figure 6, from Reinhart and

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39 TABLE 5 AVERAGE SHARE OF PRIVATE (ASPR) AND PUBLIC (ASPU)<sup>FIRMS</sup> IN TOTAL OUTPUT OF NON-TRADABLE INDUSTRIES

|OBS|YEAR|ASPR|ASPU|
|---|---|---|---|
|1|1976|0.664083|0.335917|
|2|1977|0.664565|0.335435|
|3|1978|0.688629|0.311371|
|4|1979|0.682873|0.317127|
|5|1980|0.692831|0.307169|
|6|1981|0.685197|0.314803|
|7|1982|0.696513|0.303487|
|8|1983|0.699822|0.300178|
|9|1984|0.698908|0.301092|
|10|1985|0.708396|0.291604|

TABLE 6 TRADE PENETRATION RATIOS 1976-85

|OBS|YEAR|AXSHARE|AMSHARE|
|---|---|---|---|
|1|1976|1.8941|15.4643|
|2|1977|1.5995|14.7307|
|3|1978|1.6387|10.9462|
|4|1979|1.6954|10.1034|
|5|1980|2.9405|12.4574|
|6|1981|6.8825|13.0226|
|7|1982|10.0012|13.6607|
|8|1983|9.1189|13.3348|
|9|1984|13.3050|17.0171|
|10|1985|19.0618|18.3276|

AXSHARE=SHARE OF EXPORTS IN TOTAL OUTPUT AMSHAREuSHARE OF IMPORTS IN TOTAL APPARENT DOMESTIC CONSUMPTION. DEFINED AS OUTPUT+IMPORTS-EXPORTS
# **Appendix I. Empirical Evaluation of Pro-Poor Fiscal Stance: Selected Studies**

As noted in the main text, academic studies have themselves consistently used social spending or spending on the education and health sectors as proxy for pro-poor spending. The pro-poor stance of the budget is then assessed based on the relative shares of public spending on education and health care in the overall government budget (e.g., Mooji and Dev, 2004). Using a similar measure, Schneider (2003) determines whether decentralization leads to higher pro-poor spending.

|Author|Coverage|Period|Method|Pro-Poor Measure|Empirical Findings|
|---|---|---|---|---|---|
|Schneider 2003|68 Countries|1996|Regression analysis|Social sector spending|Political decentralization has a negative impact<br>and adminstrative decentralization a positive<br>impact on propoor spending.|
|Gomanee and<br>Morrissey 2002|57 Countries|1980-98|Regression analysis (panel)|Expenditure on social services,<br>education and health|Public social expenditure mediates the positive<br>effects of aid flows on poverty levels.|
|Gomanee, Girma<br>and Morrissey 2003|38 Countries|1980-98|Regression analysis<br>(quantile)|Expenditure on sanitation and<br>housing services, education and<br>health services|Aid can affect welfare via public expenditure,<br>and this effect tends to be greater in countries<br>with lower welfare.|
|Mooij and Dev<br>2004|India|1990-02|Descriptive statistics|Social sector spending|Documents a shift in spending, within the<br>social sectors, from income and employment<br>programmes to human development<br>programmes. Concludes that there is an urgent<br>need to increase social spending.|
|Corbacho and<br>Schwartz 2001|Mexico|1980-02|Descriptive statistics,<br>benefit incidence analysis|Expenditure on education,<br>health, social security and other<br>social expenditure programs<br>(including targeted spending for<br>human capital devt, basic social<br>infrastructure, and productivity)|Primary education spending is progressive,<br>considerable access improvement in health, no<br>significant impact from social security,<br>promising results from some recent initiatives<br>that target specific groups.|
|Mosley and<br>Hudson 2001|57 Countries|1994-98|Regression analysis|Expenditure on education, social<br>services, and agriculture|<br>Aid raises pro-poor spending in poor<br>countries but not in middle-income countries.|

In some cases, simple first-stage cross-country regressions of the poverty headcount (or some other measure of poverty) on each of the major expenditure categories, including education and health care, are estimated, to determine which expenditures are “pro-poor” (e.g., Gomanee, Girma, and Morrissey 2003). Without taking into account the complexities discussed in the main text, these simple, bivariate regressions provide some evidence that education and health care spending are pro-poor. In the second-stage, the statistical relationship between, for example, aid flows and pro-poor expenditure items is evaluated.

28
dence.<sup>10</sup> Then, feeding these effects on the coefficient mapping real misallocation to aggregate productivity (table 8), we find that the proposed improvement in finance misallocation ends up increasing aggregate productivity by 5.2% on average and by 6.4% in industries with high external finance dependence.<sup>11</sup> To put the quantitative implications of our identification strategy in perspective, we contrast our results with those found in the macro-development literature. In Buera et al. (2011), Midrigan and Xu (2014), and Moll (2014), salient examples in the literature, the quantification is based on the postulation of specific financial friction (a collateral constraint) in the context of a general equilibrium model of firm dynamics and the quantification of the productivity gains resulting from the alleviation of the friction. Our results fall in the ballpark of their findings. Midrigan and Xu (2014), for instance, document productivity losses in the order of 5% to 10% arising from the misallocation of labor and capital across firms within a sector, including the 5.2% that we estimate in our study.<sup>12</sup> We interpret the proximity of our results to those emerging from alternative quantification strategies as providing reassurance to the validity of our empirical specification.

In short, in this section we constructed a bridge between the finance misallocation methodology proposed in Whited and Zhao (2021) and the real misallocation counterpart pioneered in Hsieh and Klenow (2009). We proposed an identification strategy to tease out the association of finance with real misallocation and ultimately aggregate productivity. We found that a plausible improvement in the degree of cap-

> 10The reduction in the standard deviation of marginal returns to real inputs in the average industry is computed using equation 26 as follows: 0.160*0.1674 = 0.0268. That is the computed change in the finance misallocation measure (0.160) multiplied by the estimated _β_<sup>ˆ</sup> 1 from equation 23 (0.1674; estimate in Table 7, Column 2). Similarly, the value for industries with high external finance dependence is computed using the coefficients _β_<sup>ˆ</sup> 1 and _β_<sup>ˆ</sup> 2 estimated through equation 23: (0.160*0.1674)+(0.160*0.0405)=0.0333.

> 110.0268*192.7697 = 5.2; 0.0333*192.7697 = 6.4.

> 12Midrigan and Xu (2014) and Buera et al. (2011) find larger productivity gains from reversing financial frictions when accounting for compounding forces, such as the effect of these frictions on limiting the adoption of better and more modern technologies. However, since our methodology is designed to capture the allocative channel only, it is not comparable to the magnitudes generated by these magnifying mechanisms.

32
facing foreign investors than in those facing local investors. To the extent corruptionrelated obstacles differ for those two sets of investors, the ICRG ratings can be expected to focus on those most pertinent to its paying subscribers. Corruption ratings produced by development agencies (including the World Bank’s CPIA, and similar ratings produced by the African Development Bank and Asian Development Bank) are also potentially influenced by their constituents. Because the CPIA ratings are important in determining IDA allocations for the World Bank’s lower-income countries, the Bank’s country teams could benefit from proposing higher-than-warranted ratings. Country teams may also find their working relations with country counterparts impaired if their assessments are unfavorable. However, statistical analysis finds no evidence that IDA countries are overrated relative to non-IDA countries.<sup>7</sup> Corruption indicators differ in conceptual breadth; some are more multidimensional than others. The ICRG, NIT, and CPIA each provide a single measure of corruption, but one intended to reflect a mix of various aspects of corruption.<sup>8</sup> The BEEPS and WEF surveys contain multiple questions pertaining to narrower aspects of corruption. For some purposes, broader measures may be preferred: a researcher testing the hypothesis that more women in parliament reduces corruption (Swamy et al., 2000), or that corruption slows economic growth (Mauro, 1995), may not be concerned about exactly how corruption is defined. Theory may provide little guidance as to which aspects of corruption are most harmful to growth. Similarly, a donor wanting to direct more aid to less-corrupt countries may have no particular view on which aspects of

> 7 Specifically, if the CPIA corruption ratings are regressed on other available corruption indicators and on an IDA dummy, the coefficient for the latter is negative, instead of positive as implied by the potential incentive bias.

> 8 Question 16 in the CPIA actually contains three sub-ratings, but each of these in turn assesses multiple aspects of corruption (or mechanisms intended to deter it).

12