rafmacalaba/datause-encoder-data huggingface dataset:
productivity for a country adjusted for country and industry fixed effects.<sup>5</sup> The results are overall robust to these tests, with the one failure happening in the first stage of the weighted analysis that excludes five top patenting industries for each country. These exclusions tend to result in slightly higher θ estimates, but the most important conclusion is that the results are broadly robust to these exclusions of special cases.

# Extended IV Results:

This section concludes with an extended discussion of identification, consolidating some of the earlier results in the paper and presenting one new one. It is very important that the instrument not reflect reverse causality or omitted factors– that is, that technology transfer from the United States facilitated by past migration be promoting foreign productivity increases abroad and the concomitant exports, rather than foreign productivity advances leading directly to both exports and heightened migration to the United States that results in greater patenting. This concern finds its greatest expression when contemplating a foreign multinational like Sony or MercedesBenz exporting new products and sending their inventors to the United States to engage in R&D and patenting.

An empirical study can never rule out every possible aspect of these inter-relationships, but the progress here is substantial. The instrument design Mit<sup>koutlinedinthemaintextholds</sup> fixed the 1980 ancestry spatial distribution in the United States for countries and interacts this distribution with Anglo-Saxon-led technology trends by city-industry. Identification with this IV design and the modelled fixed effects in the first-differenced specification (3) of the main text thus limits reverse causality to being present in the 1980 distribution, in a way that anticipates future technology growth for a country’s booming industries by US city, or that reverse causality come through the technology trend itself.

For the first part about endogenous US city selection being present by 1980, it is important to recall that the city distribution does not itself provide identification given the first differencing and fixed effects included in the first-differenced specification. What matters instead is the differential exposure across industries and technologies that a country’s US spatial footprint generates. Thus, for a bias to exist, it must be that a country’s spatial placements by 1980 sought out specific city-industry combinations in the United States that would become

> 5The top industries for each country in Columns 7-8 are determined through USPTO patent filings made by inventor teams working in each country and industry during 1975-1984. For Columns 9-10, the procedure regresses country-industry labor productivity on a set of country, industry, and year fixed effects. For each country, the one or five industries with the highest average residual labor productivity are excluded.

14
<!-- Start of picture text --> Figure 14. Country correlations for commodity<br>exporters CE and law and order<br>Low Law & Order index High Law & Order index<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>-0.2 Low L&O index = 0.50***<br>High L&O index = 0.28***<br>-0.4<br>-0.6<br>-0.8<br>AzerbaijanYemen Côte d'Ivoire Ukraine<br>Oman Congo, Rep.NicaraguaEcuador UruguayGuyanaLiberia Argentina<br>Brazil Namibia NigerBolivia KenyaZimbabwe AngolaQatar<br>Sudan SenegalMali Burkina Faso<br>Ghana Saudi Arabia MozambiqueThe Gambia Colombia Tanzania LibyaGabon Iceland EthiopiaMongolia<br>AlgeriaUzbekistan MyanmarSouth Africa Paraguay<br>Iran, Rep. of Madagascar<br>UgandaCameroon Indonesia Honduras Suriname Togo<br>Russian Federation Papua New Guinea<br>Bahrain Congo, Dem. Rep.<br>NigeriaPeru Iraq<br>Sierra Leone Kazakhstan Guinea-Bissau Costa Rica Botswana Armenia<br>Guinea<br>Chile<br>Zambia<br>Kuwait<br>New Zealand<br>NorwayMalawi<br>Australia Guatemala<br>Brunei Darussalam United Arab Emirates<br>government spending and real GDP 1980-2020<br>Correlation between cyclical components of real<br>Canada<br><!-- End of picture text --> Notes: Each bar represents the correlation between the (HP) cyclical components of real GDP and real government spending for countries with high (low) law and order, defined as law and order above (below) the sample median. The difference between the two averages is significant at the 5 percent level. *, **, and *** denote significance at the 10, 5, and 1 percent level, respectively. Sources: Authors’ calculations based on International Country Risk Guide and WEO (IMF) data.
Similarly, the methodology used to classify the economic sector follows the International Standard Industrial Classification of All Economic Activities (ISIC) at the one-digit level. This paper used the classification of industries in every household survey to categorize the economic sector according to the ISIC into 17 groups: i) agriculture, hunting, and forestry; ii) fishing; iii) mining and quarrying; iv) manufacturing; v) electricity, gas, and water supply; vi) construction; vii) wholesale and retail trade; viii) hotels and restaurants; ix) transport, storage, and communication; x) financial intermediation; xi) real state, renting, and business activities; xii) public administration and defense; xiii) education; xiv) health and social work; xv) other community, social, and personal service activities; xvi) activities of private households as employers; and xvii) extraterritorial organizations and trade.

The wage data sample is restricted to all paid workers (employees and self-employed) with ages 18-65. Hence, employers and unpaid family workers are excluded. After dropping zero and negative wages, the 1st and 99th percentiles of hourly wages are trimmed in every countryyear to remove outliers.

47
Desai, S., and J. Devaki. 1994. “Maternal Employment and Changes in Family Dynamics: The Social Context of Women’s Work in Rural South India.” _Population and Development Review_ 115–36.

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- Rosenzweig, M.R., and O. Stark. 1989. “Consumption Smoothing, Migration, and Marriage: Evidence from Rural India.” _The Journal of Political Economy_ 905–26.

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23
sanitation for non-poor households that didn’t have access to any sanitation at baseline. This group also showed improvements in the disposal of children’s feces.<sup>21</sup>

# _9.2 Program Effects on Child Health Outcomes_

Table 17 reports the ITT effects for child health outcomes. Consistent with the data presented in Table 8b, we find a statistically significant reduction in diarrhea prevalence (seven- and two-day prevalence). Specifically, being in a treatment community is associated with diarrhea prevalence being lower by approximately 1.3 percentage points from a 4.6 percent base. This is the case across all specifications on the panel observations (columns 3 – 6) and for seven- and two-day prevalence. This is a large effect—about a 30 percent (44 percent) reduction in seven-day (twoday) diarrhea prevalence. The fact that the estimated program effect changes only slightly in magnitude as we add controls is unsurprising given that the TSSM survey provides a balanced sample.<sup>22</sup> Columns 7 and 8 show that this is being driven by decreases in diarrhea among households that did not have sanitation at baseline.

We also find some evidence that parasitic infection intensity for both _Ascaris_ and _Trichuris_ , as proxied by number of egg worms per milligram, is lower in treatment communities. However, this result is not statistically significant over the whole sample.<sup>23</sup> Having blood or mucus in the stool is also significantly lower in treatment communities by about 1 percentage point (57 percent) as well as refusal to eat for children in treatment communities.

> 21 Additional one-tailed tests of significance (not reported in Table 16) show a significant decrease at the 10 percent level in the practice of open defecation for the treated non-poor households with no sanitation at baseline.

> 22 The estimate in column 2 uses the entire endline sample (not restricted to panel households) and although the point estimate is negative, it is weakly significant for seven-day prevalence ( _p_ <0.1), while it is statistically insignificant for two-day prevalence. Note that the use of the full sample involves adding children under the age of two, whereas all children in the panel sample are over age two as they had to have been born at the time of the baseline. This is shown in Figure 7. If we re-estimate these regressions only on children under age two, we find no treatment effect on diarrhea prevalence. Hence, the weakly significant and insignificance of the full sample results may be being driven by the younger age of the children. It may be that the treatment has a reduced impact on diarrhea of very young children because children under age two are unlikely to use a new facilitation facility themselves. In contrast, children between ages two and five would be taking themselves to the toilet so may be more likely to directly benefit from new facilities.

> 23 We also estimated the model with the dependent variable being the prevalence of each of _Ascaris, Trichuris_ , and hookworm categorized using the WHO (2002) thresholds into low, moderate, and heavy infection. We find no significant treatment effect.

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Table 4: Country-pair correlation regressions

||(1)<br>CPI|(2)<br>CPI|
|---|---|---|
|log index dagexp fn|-0.141<sup>_∗∗∗_</sup><br>(0.050)|-0.070<br>(0.050)|
|log index exp int|0.140<sup>_∗∗∗_</sup><br>(0.052)||
|log index dagexp int||0.129<sup>_∗_</sup><br>(0.069)|
|log index GEREF||-0.076<br>(0.052)|
|third ex|-0.187|-0.177|
||(0.123)|(0.123)|
|third im|-0.407<sup>_∗∗∗_</sup>|-0.409<sup>_∗∗∗_</sup>|
||(0.151)|(0.151)|
|N|7992|7992|
|R2|0.607|0.606|
|Time windows|2|2|
|Country pairs|4767|4767|
|Time FE|YES|YES|
|CP FE|YES|YES|

Standard errors clustered by country-pair in parentheses Note: observations with non-overlapping 10-year windows. _∗ p < ._ 1, _∗∗ p < ._ 05, _∗∗∗ p < ._ 01

# **GVC and the level of**

Now we turn to the country-centric regressions to explore the link between GVC participation and the _level_ of inflation. In this case the GVC indices are created by aggregating the trade flows from the exporter perspective. That is to say, the link between each country and the rest of the World is what that country exports to all its trade partners. Such an analysis has been developed in Andrews et al. (2018). The basic argument is that integration into GVCs might contribute to lower inflation via downward pressures on labor costs by raising productivity and reducing wages in the importing country, especially when low-wage countries are integrated in supply chains.

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**Table 6.  Tests for changes in country sample size**

|VARIABLES|Business us|e of the internet: ldig_bus-pca||
|---|---|---|---|
||18|19|20|
|lgdppcppp|2.238***|1.645***|1.611***|
||(6.997)|(10.55)|(11.09)|
|lwdi_inflation|-0.271|-0.218|-0.214|
||(-1.186)|(-1.423)|(-1.493)|
|lopeness|0.371*|0.608***|0.605***|
||(1.800)|(2.678)|(2.932)|
|inf_index|0.316|0.754***|0.769***|
||(1.141)|(3.220)|(3.506)|
|lmwtova_pw|0.450*|-0.0920|-0.0211|
||(1.910)|(-0.565)|(-0.138)|
|lrest_oecd|-1.174***|||
||(-2.891)|||
|lrest_ilo||-0.226||
|||(-0.564)||
|lepl_db|||-<br>0.568***|
||||(-2.976)<br>-|
|Constant|-18.41***|-16.93***|15.61***|
||(-7.155)|(-11.44)|(-12.47)|
|Observations|39|68|68|
|R-squared|0.891|0.896|0.908|

t-statistics in parentheses *** p<0.01, ** p<0.05, * p<0.1 22
mary school in order to create positive synergies between the two. In fact, as part of the program’s design, the primary school principal was expected to support the ECD center and participate as a member of the Community Coordination Committee (CCC). Children and their parents may also have familiarized themselves with the primary school, facilitating primary school enrollment.

We provide suggestive evidence of the importance of the potential mechanism of close proximity between families’ residences, the preschool, and the primary school. In our qualitative survey on a random sample from treated communities, almost 70% of the parents interviewed reported walking less than 15 minutes from their homes to the preschool center. Since the primary school is also by design close, distance could help explain enrollment in both preschool and primary education.

Furthermore, we find evidence that the DICIPE program significantly improved children’s cognitive and social-emotional skills, as assessed using the MODEL instrument. We evaluated early literacy skills, early math skills, executive function, social-emotional development, and fine motor skills for writing, all of which are measures in SD units. Our results are shown in Figure 3 and columns (1) to (5) of Table A4, respectively. We estimate 0.17 SD ( _p <_ 0 _._ 05) and 0.11 SD ( _p <_ 0 _._ 1) improvements in early literacy and math skills, respectively, as well as 0.14 SD ( _p <_ 0 _._ 05) improvements in fine motor skills for writing. Furthermore, target children in treatment communities demonstrated a 0.1 SD ( _p <_ 0 _._ 1) higher interest in literacy.

We estimate 0.16 SD ITT impacts on a skills index constructed over these variables (Table A4, column (7)). The estimated impact on skills is of magnitude consistent with but smaller than other early childhood interventions in the literature. For example, Bos et al. (2024) study a large-scale home visiting intervention in Bangladesh and estimate impacts ranging from 0.12–0.23 SD on child cognition, language, and socio-emotional scores. Similarly, Berlinski et al. (2009) study a large expansion of universal pre-primary education in Argentina and estimate 0.23 SD impacts on third-grade test scores. In general, the intervention led to positive and sizeable impacts on post-preschool cognitive and socioemotional skills of the children who benefited from the program.

22
Statistical Organization to construct wholesale input deflators (this process being similarly used by [Nataraj (2011)] among others).

We make a few sample restrictions. As is typical we remove any ASI observations which are reported as closed. We also exclude any service-sector establishments captured by the NSS. We also drop a small number of observations that are extreme outliers in the measure of output per worker and/or total output and/or total employment, report negative values for output, employees, or materials, or report zero or null persons engaged. Overall these restrictions remove a small percentage of the total observations from the underlying sample, and unreported robustness checks do not indicate major sensitivity to these restrictions. To avoid further concern regarding outliers we top-code and bottom-code the 1% tails (“Winsorizing”) of cell-level variables prior to the analysis described below.

During this long period of study industrial classifications were changed several times. We create a concordance between these classifications and the 1987 National industrial Classification (NIC) definitions. This leaves us with 180 approximately 3-digit industries. Because of the nature of the backwards and forwards linkages we model using input-output relationships, we then crosswalk the 180 NIC industries to 64 industrial groups which comprise the manufacturing sector in the 1989-90 input-output tables. Similarly, state definitions are concorded to pre-2001 definitions, leaving 23 states for a total of 1472 state-industry cells in a given year. We aggregate the establishment-level data to the state-industry level using the provided sampling weights (inverse of sampling probability) to construct population-level estimates of the commonlycovered accounts listed above.

During the course of our analysis we differentiate between two segments of the unorganized sector – the Household segment and the Non-Household segment. A large number of plants in the unorganized sector in India are very small household enterprises which mainly produce to subsist and employ family as workers. These enterprises have very low productivity and bad technology and are unlikely to be the main suppliers of inputs to organized plants (these are the plants characterized as “traditional” by [Moreno-Monroy et al. (2012)]). The other segment consists of plants (which account for most of unorganized sector employment and output) that are professional profit-maximizing enterprises (which we refer to as Non-Household and are characterized as “modern” by [Moreno-Monroy et al. (2012)]). These are the unorganized sector 12
We provide estimates of the patterns and predictors of firm exit using a unified and harmonized data methodology for 47 economies. By relying on such survey data, we leverage a singular methodology, with a well-defined universe of inference and are able to provide crosseconomy comparisons, including estimates of the effects of macro-level variables, including the regulatory environment. We find evidence consistent with results well-established in the literature, but for a broader range of economies, suggesting that the use of comparatively small, representative surveys may provide a viable opportunity for further research. This approach appears to be widely applicable, particularly when compared to the feasibility of coordinating and scaling up census-based data collection efforts. However, while the use of ES data enables such comparisons, we adopt a necessarily conservative approach, due to the considerations of the difficulty of data collection by the collection of survey samples, in periodic waves. As such, we find estimates for firm exit and job loss that tend to fall below comparative estimates from census- and registry-based studies; this likely indicates that survey-based approaches to firm exit can be considered as lower bounds of firm exit.

One particular strength from our approach is the possibility of systematic cross-country research. We exploit measures for barriers to entry and exit as a logical first-effort: further crosscountry analysis utilizing additional topic areas will surely be needed. This agenda should also include further refinement of data measurement of firm exit, including building on ongoing, continuous data collection efforts such as the ES and other globally comparable data sets. Additional research will benefit from multi-wave panels, including the observations of several economies over more-frequent and heterogeneous external conditions, as well as further integration of exit measures with data entry.

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