rafmacalaba/data-use-mentions-extended huggingface dataset:
|**Program**|**Country's**|**Quality of**|**Governance**|<br>**Comments**|
|---|---|---|---|---|
||**Weak**|**Fair**|**Good**||
|Reducing Wage<br>Compression|Negligible|Negl.|Negligible<br>__________|More relevant as an incentive mechanism<br>for career development. May increase<br>corruption if the public sector viewed as<br>lucrative career option by greedy elements<br>of society.|
|Merit based civil<br>service|Low|Medium|High|May be derailed by bureaucratic processes<br>in highly corru pt societies.|
|Public Opinion<br>Surveys|Low|Medium|Medium|Public opinion surveys have served as a<br>useful tool in articulating citizens'<br>concerns (e.g. Bangalore scorecard).|
|Financial<br>accountability|Low|Low|Medium<br>I_________|Appropriate when democratic<br>accountability and a substantial<br>accounting/bookkeeping infrastructure<br>with some integrity are in place.|
|Parliamentary<br>oversight<br>_________________|Low<br>___|Medium<br>_________|Medium<br>__________|Parliamentary oversight can be helpful but<br>parliamentary micro-management not an<br>effective form of governance.|
|Reducing Public<br>Employment|Medium|Low|Low|May reduce opportunities for corruption.|
|Decentralization|Medium|Low|Low|May improve accountability and may<br>increase sense of social purpose for public<br>officials.|
|Client-based civil<br>service / Bureaucratic<br>culture _____|Medium|Medium|Low|Success depends
Gille 2 areas, of which 72 percent depends on agriculture (Census of India, 2011). Agriculture accounts for 17 percent of GDP and employs 51 percent of the total labor force (World Bank, 2010).<sup>2</sup> This paper studies the productivity effects of the mean education of neighbors in the agricultural sector, which can take place through learning spillovers. First, farmers can learn from their neighbors about agricultural technology. The fact that there are learning spillovers when there is a technological change has been widely asserted in the literature (see for example in the case of agriculture in developing countries Foster and Rosenzweig [1995]; Munshi [2004]; Conley and Udry [2010]). Second, neighbors’ education can influence the _adoption_ of technologies, through their own adoption of technology. It can also have a positive impact on the _use_ of technologies, or more generally, neighbors’ education can increase efficiency in the use of inputs (Weir and Knight 2007). It can be the case if all farmers are not on the production frontier, which would be a signal that certain inputs, such as fertilizers, manure or grains are over- or underused. Finally, neighbors can have an _allocative effect_ : their education level would have an impact on farmers through learning about which input or output to choose, given their relative prices (Kumbhakar and Lovell 2003).

The existing empirical literature mainly focuses on education externalities in cities or in firms in developed countries (see for example Acemoglu and Angrist 1999; Moretti 2004a, b). Very few papers study education externalities in agriculture in developing countries, with a majority subject to limitations inherent to the study of peer effects, namely the problem of definition of peers, and the identification challenge due to group-level unobservables. Appleton and Balihuta (1996) introduce neighbors’ average level of education in the production function of farmers in rural Uganda. They find that the proportion of farmers with primary schooling is positively and significantly related to farmers
# 8 QUANTIFICATION AND COUNTERFACTUALS

In this section, we use both data from our production network and our newly estimated elasticities to quantify the role of these elasticities in the propagation of shocks. To do this, we need to write down the Leontief matrix in standard form. Given the production structure of our economy, we need four submatrices: (i) firm purchases of 4-digit HS products, (ii) firm sales of 4-digit HS products, (iii) labor employed by each firm, and (iv) final sales by each firm. The first two submatrices are directly constructed from the firm-to-firm trade data from the pre-Covid-19 period of March 2019 to February 2020. Labor employed and final sales by firms are obtained by merging in firm-level data from _IndiaMART_<sup>29</sup> , which contains information on firm-level employment and final sales. For more details on this, see Appendix C.

The economy is comprised of _N_ = 93 _,_ 260 firms, _I_ = 1 _,_ 293 different HS-4 products, labor, and a composite final good. The average firm buys 10 distinct products as a buyer and sells 5 distinct products as a seller. The most connected buyer and seller buys and sells over 500 distinct products. We use this 94 _,_ 555 _×_ 94 _,_ 555 input-output matrix to understand how complementarities at the firm level affect the propagation of shocks through production networks.

While recent work also quantifies the effect of firm-level shocks on aggregate GDP up to the second order, they mostly rely on changes in firm-level final sales rather than the full production network. Instead, we identify the most connected firms, deriving the Leontief inverse from the entire production network. Without relying on any approximation, we use the full network to quantify the importance of firm connectivity separately from firm size. This exponentially increases computational complexity from the order of ( _N_ + _I_ + 2) to ( _N_ + _I_ + 2)<sup>2</sup> . As such
study.

# **3. Empirical literature on teacher training and student learning**

The quality of teachers and their effect on students' learning is a central theme of educational policy. The international literature indicates that a student with a low performance teacher might end the school year accomplishing less than half of the curriculum planned for the grade, while students with an excellent teacher may accomplish 50 percent more than expected curriculum (Farr, 2010; Hanushek and Rivkin, 2010). Thus, exposure to low-quality teachers for years running can lead to insurmountable deficits of students' learning.

Despite the recent evidence confirming the central role of teacher quality in students’ learning, there is still no consensus on how much schooling and teacher training contribute to a greater or lesser degree, to raise the quality of education. The first works on the productivity of teachers were developed based on the educational production function estimates from cross-sectional data, and regression analysis of aggregated levels of student performance in teacher training measures and several other controls.

In studies conducted until the mid-1990s for public schools in the United States, Hanushek (2003) identifies 170 estimates of the effect of teacher training measures on the performance of students, of which only 14% reported statistically significant results, 9% of which showing positive effects. This percentage drops to zero when the author considers only high-quality studies (based on the estimates of the "value added" by a student of the same state). The author suggests that the difficulty in capturing the effect of teacher education may be in part due to low variability of the indicator between American schools. In another compilation, spanning 63 studies applied to developing countries, Hanushek (2003) reports that 56% of estimates of the effect of teacher's education school performance were positive and significant.

Wayne and Youngs (2003) conducted an extensive review of the existing empirical literature until the early 2000s, when most of the evaluation studies of the effects of the education of teacher passes using student-level longitudinal data and involve broader sets
of product _h_ from country _i_ . So as to facilitate a comparison of results across different policy instruments, we use simple indicators for application and removal of TTB policies in lieu of the applied tariff rates found in equation (2).<sup>26</sup>

> 25 There is some measurement error with the survey data as there were a few instances in which petitioners left the entry for the relevant 10-digit HTS code blank, or instead entered an incorrect product code that was not subject to the safeguard. These exclusions were omitted from the empirical analysis.

> 26 We have also estimated model specifications in which we use the different tariff rates that were associated with the 2002 safeguard across different products; our results are broadly robust to using the tariff rates instead. For the 267 10-digit HTS codes that faced a tariff in the first year after the safeguard, 185 (69 percent) received a 30 percent tariff, 60 (22 percent) received a 15 percent tariff, 15 (6 percent) received a 13 percent tariff, and 7 (3 percent) received an 8 percent tariff in the first year. In March 2003, the tariffs for each of the categories were reduced to 24 percent, 12 percent, 10 percent, and 7 percent, respectively. Use of ad valorem tariff rates for AD/CVD cases is a bit

16
Textiles*|China|15-Apr-93|18-Oct-94|501%|501%|
|7|Electrical machines and<br>equipment*|China|15-Apr-93|18-Nov-94|129%|129%|
|8|Bicycles|China|15-Apr-93|22-Sep-94|594%|279%|
|9|Pencils|China|09-Aug-93|18-Oct-94|451%|451%|
|10|Valves|China|01-Nov-93|18-Oct-94|0|105%|
|11|Pencil sharpeners|China|19-Nov-93|18-Oct-94|0|145%|
|12|Baggage and bags|China|29-Nov-93|25-Nov-94|0|0|

* Cases corresponding to the China Package. Source: Based on data from World Trade Atlas and several DOF.

The decision to initiate investigations and impose high antidumping duties against Chinese products was important from the political perspective. However, its impact from the commercial point of view had been carefully considered. Mexico did not breach any international trade agreement, since these investigations involved products from a non-WTO Member. Additionally, most antidumping duties were applied to consumer goods, thus avoiding any impact on Mexican exports’ competitiveness.

Table 8 below illustrates —through the multi-country/multi-product steel cases filed in the same year the China Package was published— some aspects of the ambivalent and apparently 26
creation of new formal businesses, nor by greater survival among formal rms. However, the authors argue that these interventions were not cost-eective, since the program led to net losses in tax revenues. Taken together, these results indicate that reducing the costs of entering the formal sector had very limited or no formalization eects in Brazil, while reducing the ongoing costs of formality was more eective, but the eects were not large and the policies do not seem to be cost eective.<sup>34</sup> This raises the question of why reducing the costs of formality have such limited eects.

As discussed previously, Ulyssea (2018) nds that only 9.3 percent of informal rms in Brazil are potentially productive rms hindered by high regulatory costs. The informal

> 32Monteiro (2016) shows that SIMPLES is not cost-eective. Even in the retail sector the increase in revenues that results from formalization does not compensate for the revenue loss of already formal micro and small rms that have a reduction in their tax burden.

> 33Corseuil and Moura (2016) evaluated the eect of SIMPLES on several rm performance indicators in the manufacturing sector for several years, including the SIMPLES Nacional in 2007. They found no eect of SIMPLES on rm performance.

> 34See Ulyssea (2020b) for a discussion of the evidence on formalization policies in Brazil and other countries.

30
MDAT score, we see that children in several of the enhanced arms have significantly higher social skills than children in the cash-only arm. Once again, a comparison with the corresponding section of Appendix Table 3 shows that the magnitude of the effect from the enhancements is almost as large (0.11-0.13 vs 0.14) as the effect on social skills from the provision of cash. Again, adding the behavioral enhancements leads to almost as much of a bump in social development as providing cash. Similarly, children in the arms where cash was augmented by Mother Leader groups as well as the arm where this was further augmented by the Planning tool display significantly higher language learning than children in the cash-only group. The differences engendered by the addition of the Affirmation nudge are generally positive but not large enough to be statistically significant. As seen in Appendix Table 3, the provision of cash does not make any statistically significant difference to language learning at all – so this is an area of child development where we see significant improvements only once cash is supplemented with the behavioral enhancements we test.

Finally, we see no statistically significant evidence of improved fine motor skills from any of the behaviorally enhanced arms – but, as Appendix Table 3 shows, cash also does not lead to significant effects on this component of child development.

# **6.4:  Dosage Analysis**

Although our interventions were designed to be delivered with the same intensity to all participants in the enhanced arms logistical hurdles led to a slightly delayed rollout in one district, which together with the timing of the midline survey created some (albeit not exogenously assigned) variation in intensity of treatment. This meant that while the median village in the relevant arms received 5 nudge sessions, a quarter of villages received 4.5 or less, while a quarter had received between 6 and 7 by the time of the midline survey. This variation, albeit not experimentally induced, allows for an analysis (albeit a cursory one) of the effects of intervention intensity
countries on the African continent for the years 1960–2004. In the original data set, the categories type 3 and type 4 are not mutually exclusive. Table 1, however, presents the data in categories that are mutually exclusive and exhaustive so that all the information in any cell (country-year) can be represented by only one symbol. For countries with two rows of codes, in the top row (row 1) our coding is as follows: An X denotes conflicts that are type 3 (“internal”) at intensity 3 (“war”) if PRIO/Uppsala does not also designate a type 4 conflict at intensity 3 in that year. An I denotes a type 4 (“internationalized internal”) conflict at intensity 3 in which the “location” of the conflict was within the country if PRIO/Uppsala does not also designate a type 3 conflict at intensity 3 in that year. An O denotes years that PRIO/Uppsala designates as both type 3 and type 4 (and the location of the type 4 conflict is within the country), each at intensity 3 (i.e., there is overlap according to the PRIO/Uppsala data). An F denotes type 4 conflicts of intensity 3 in which a country was involved but for which the location was outside that country. There are no countries that have years in which PRIO/Uppsala designates a type 3 conflict and a type 4 conflict with a location outside the country. A blank denotes a year of no conflict of either type 3 or type 4 at intensity 3, in effect a year of peace in the coding used by the econometric studies if they use intensity 3 data. In the bottom row (or the only row for countries with only one row; row 2), X, I, O, F, and blank have the same meanings as just discussed except that the intensity is 1–3 (“minor,” “intermediate,” or “war”) rather than intensity
Figure A.6: Event study: the effect of foreign acquisition on firm-level employment (common treatment) <!-- Start of picture text --> (a) French ownership (b) British’s ownership<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>0 0<br>Coefficient Coefficient<br>-.1 -.1<br>-.2 -.2<br>-.3 -.3<br>-.4 -.4<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br><!-- End of picture text --> Notes: Authors’ calculation based on the Orbis database. The plot is created by regressing the log of employment on a full set of event time indicators interacted with the a dummy variable indicating whether a firm was acquired by a Foreign (either French or British) shareholder after 2004 and controlling for firm and year fixed effects. The estimation includes only firms established before 2004. The omitted baseline is the last pre-treatment year, 2003.

Figure A.7: Event study: effect of German acquisition on employment based on doubly robust estimator <!-- Start of picture text -->
-4 -3 -2 -1 0 1 2 3 4<br>Years from aquisition<br>.2<br>.1<br>ATT<br>0<br>-.1<br>-.2<br><!-- End of picture text -->

Notes: Authors’ calculation based on the Orbis database. The plot is obtained by implementing the doubly robust estimator of Sant’Anna and Zhao (2020). The estimation includes only firms established before 2004. The
) provide a good synthesis of the available literature, they focus on developed countries. We use three measures of city-level urban costs: congestion (hours lost due to traffic), pollution (PM2.5 emissions) and crime (homicide rate).

Congestion information is sourced from TomTom, which provides real time traffic statistics for over 600 million users around the world. Congestion reflects the average annual additional hours spent driving in rush hours in 2018 and is available for 337 cities, of which 69 cities are in developing countries. Pollution, measured using PM2.5 emission levels, is sourced from the World Health Organization and is available from 2008 to 2015. We use pollution data for 2014, the most recent year covering a sample of 298 cities (with 78 in developing countries). We exclude China from the pollution analysis because of potential data reporting concerns highlighted in Greenstone et al. (2020). Crime is measured using the homicide rate obtained from United Nations Office on Drugs and Crime (UNODC) global study on homicide (2019) and is available from 2003 to 2017. We utilize crime data for 2015, the latest year comprising 124 cities (with 63 in developing countries). Summary statistics of these variables are provided in Table 2. Figure 4 shows how pollution, congestion and crime vary across levels of economic development.

City-level population density is calculated as the population per square kilometer of _built up density_ using 2015 data from the Global Health Settlement (GHS). Unfortunately, this data is only available at irregular intervals (1975, 1990, 2000 and 2015), which combined with limited time horizon of our urban cost measures, limits us to a cross-section analysis. We also examine robustness to population density from country census data that are available for approximately half the cities in our baseline sample.

> 5 An alternative approach of demeaning citations using the mean citations of publications in that year is not possible, due to few observations in some publication years.

> 6 The analysis is robust to defining country
16 The impact on both outcome variables during the rainy season is similarly more pronounced after controlling for SGRY. In Panel A, the coefficient for _NREGAd,t × Rainy_ with all controls and SGRY is 0.35, which is significantly different from zero at 1% level). The coefficient for _NREGAd,t × Rainy_ in Panel B is more negative in column 6 (relative to column 3), but the estimate is not significantly different from zero. All estimates of column 6 are statistically different from the corresponding estimates in column 3 at the 1% level. Tables S9 and S10 in the online appendix show table 3 separately for men and women, respectively. The results continue to be more pronounced after controlling for SGRY for the sub-samples split by gender.

# Impact on human capital outcomes

Finally, I estimate the impact of human capital outcomes in terms of school enrollments and child labor. It has been established in the literature that income effects result in higher investment in education when returns to education are higher (Bharadwaj et al. 2020; Jacoby and Skoufias 1997; Jensen 2000; Maccini and Yang 2009). On the other hand, there is increasing evidence that increases in low-skill wages can decrease investment in human capital as adolescents substitute education with work to benefit from higher wages (Atkin 2016; Cascio and Narayan ming; Charles et al. 2018; Shah and Steinberg 2017). The direction and magnitude of this effect are relevant for policy as public workfares are a popular means of redistribution in developing economies.

A recent and growing literature on NREGA has examined how NREGA has affected human capital outcomes. Using Young Lives data from Andhra Pradesh (one state), Afridi et al. (2016) and Mani et al. (2020) find NREGA to increase human capital investment. On the other hand, Islam and Sivasankaran 2015; Li and Sekhri 2020 and Shah and Steinberg 2021 conclude, using nationally representative data sets, that school enrollment numbers declined while child labor increased due to the spillover effects of
- 16 -

5.02 The enumerator then asked the respondent whether he or she would like to be connected to the New Onitsha Water Scheme and have a meter if the price of water were N 1 per drum.9' Figure 4 presents in schematic formlVe way the the "bidding game" proceeded in response to this question., For example, if the respondent answered "Yes" to a price of N 1 per drum, then the enumerator raised the price to N 2 per drum, and again asked whether the respondent would like to have a metered connection. If the respondent answered "No" to a price of N 2 per drum, the enumerator lowered the price to N 1.50 per drum, and again asked the respondent whether he would like to have a metered connection. After this question was answered the enumerator stopped the bidding game. Similarly, in the first instance if a respondent said "No" to water at N 1 per drun., the price was lowered in increments to N 0.12.

5.03 Using this bidding game procedure, it was possible to classify each household into one of the following seven groups based on how much the respondent indicated the household was willing to pay per drum: _N_ 0.00 - 0.11 per drum _N_ 0.12 - 0.24 " " _N_ 0.25 - 0.49 " " N 0.50 - 0.99 " " _N_ 1.00 - 1.50 " " N 1.50 - 1.99 " " > N 2.00

- 9/ In most situations it would be impossible to carry out a willingness-to-pay survey in which the enumerator raised or lowered the commodity price in this manner because individuals would simply not know how much water they consumed in terms of a standardized unit, such as a drum. However, because water vending is so widespread in Onitsha, not only is almost everyone used to thinking in terms of using standardized volumes of water,