rafmacalaba/datause-encoder-data huggingface dataset:
# **S2 Conceptual Framework**

## **S2.1 The Model**

I consider the nutritional level of the child ( _Ntc_ ) to be a choice that results from the household’s efforts to maximize its utility (Becker 1965; Strauss and Thomas 1998; Currie 2000). The household is constrained financially, in time available, as well as by the child’s nutritional production function, which is defined as follows: Maximizing household welfare subject to financial and time constraints implies that the child’s _n_ 35 nutritional status will also depend on the price for nutritional inputs ( _pt_ ) , as well as on the household wealth level<sup>36</sup> . Household wealth was chosen for this study instead of individual income since income data is not available in the Young Lives survey, which I relied upon. Since consumption is a function of wealth, this can be seen as a net cumulative indicator of economic well-being. The survey has a wealth indicator based on a series of household characteristics

> 35 The price chosen is the price of rice, the main staple in Peruvian diets. Some of the rice prices were missing, so they were imputed using the province averages to replace the missing values.

> 36 I am not able to directly control for income hence the solution to this maximization problem yields a conditional demand function for nutritional outcomes (Pollack 1971).

10
# **1 Introduction**

The diffusion of knowledge is central to endogenous growth theories (Romer (1986, 1990); Grossman and Helpman (1991b,a); Rivera-Batiz and Romer (1991)). Due to its public good nature, new knowledge can be generated from all existing knowledge or R&D investments. Despite their appeal, endogenous growth theories rely on a broad set of assumptions that remain untested. For instance, there could be differential effects of knowledge spillovers, depending on the types of innovations and the distance (geographical or technological) between the innovator and the source of existing knowledge. Simply put, it is not obvious that a poor country can easily adopt knowledge from technologically or geographically faraway economies; it is not clear that low-productivity firms can easily learn from firms operating at the technological frontier.

This paper explores the impact of knowledge spillovers on the generation of new knowledge using two types of data: patents granted by the United States Patent and Trademark Office (USPTO) and firm data on product-quality upgrading and the introduction of new goods from the World Bank’s Enterprise Surveys. Although patents might be appropriate to study innovation patterns and externalities in developed countries, they might be ill suited for analyzing innovation patterns in developing countries. Hence both approaches might be more revealing than focusing only on one type of innovation.

In a first set of econometric estimations, following Rivera-Batiz and Romer (1991), we test whether the so-called “cumulative knowledge” or the “R&D investment” function explains patenting activity at the country level. If knowledge spillovers are international, then patenting by one country should be influenced not only by the stock of patents received by nationals, but also by the stock of patents worldwide. More specifically, we estimate specifications of patent functions with elements of the cumulative-knowledge and R&D-investments models, and allow for countries’ patenting activity to be influenced by the accumulated domestic and foreign knowledge. In a second set of estimations with firm data we explore the determinants of product-quality upgrading and the introduction of new products, with a focus on the role of within-country and within-industry spillovers across firms. That is, the empirical strategy on patents exploits the time dimension of the data, and the analysis of quality upgrading and new products by firms exploits the industrial sector classification of the data but not a time dimension since we do not have panel data on firms (from throughout the world). We would have liked to use sectoral data on patents however there is no unique correspondence between patent classes and industrial sectors.

The literature on spillovers has theoretical foundations on models of endogenous growth. These models were first introduced in Romer (1990, 1986), and then subsequently studied by Grossman and Helpman (1991a,b) and Aghion and Howitt (1992) among others. In Romer (1990) the aggregate stock of designs for intermediate inputs changes as a function of the existing stock of designs 1
22

**Table 6. Robustness II: adding financial openness variables**

||Manufacturing|Agriculture|Natural Res.|
|---|---|---|---|
|Ln(Initial Quality)|-14.9***|-14.4***|-15.1***|
||(0.13)|(0.19)|(0.40)|
|Ln(Initial GDP p.c.)|0.3400***|0.3540***|-0.1080|
||(0.0378)|(0.1030)|(0.1940)|
|Initial Institutional Quality|0.0049***|0.0059**|0.0036|
||(0.0011)|(0.0028)|(0.0051)|
|Initial Human Capital|0.0107***|0.0040|0.0033|
||(0.0021)|(0.0058)|(0.0106)|
|Initial FDI inflows|0.0087***|-0.0082|0.1270***|
||(0.0319)|(0.0079)|(0.0173)|
|Initial Trade Lib.|0.6870***|1.0300***|0.5530|
||(0.0818)|(0.2290)|(0.4240)|
|Initial Agric. Lib.||-0.0479<br>(0.1740)||
|Initial Dom. Financial Lib.|-0.2510**|0.2640|-0.3630|
||(0.1020)|(0.2960)|(0.5240)|
|Initial Ext. Capital Account Lib|-0.1270***<br>(0.0489)|0.1230<br>(0.1430)|-0.2030<br>(0.2570)|
|Observations|80,076|25,501|6,802|
|R-squared|0.858|0.846|0.835|

Notes: All equations estimated using observations averaged of 10-year non-overlapping periods and include country-product and product-time fixed effects. The dependent variables is the annualized growth rate of product quality. *, **, and *** denote statistical significance at the 10 percent, 5 percent and 1 percent level, respectively. All coefficients and standard errors are multiplied by 100 for presentation purposes.
# **Table** **<u>8: ITT tested children  + baseline score</u>**

|||||**ITT**<br>|<br>|||||**TO**<br>|**T**<br>|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||C vs.|HBP|C vs.|CBP|CBP vs.|HBP|C vs. H|BP|C vs.|CBP|CBP vs.|HBP|
|Dependent variable|N|Difference|P-value|Difference|P-value|Difference|P-value|Difference|P-value|Difference|P-value|Difference|P-value|
|**TVIP**||||||||||||||
|Raw score|2565|0.012|.867|0.001|.994|-0.007|.93|0,164|0,86|0,039|0,883|-0,035|0,86|
|Age equivalent|2148|-0.012|.874|-0.029|.696|-0.001|.984|-0,038|0,967|-0,045|0,861|-0,017|0,967|
|Age equivalent - age|2148|-0.019|.821|-0.045|.596|0.005|.956|-0,137|0,897|-0,092|0,755|0,075|0,897|
|**ASQ**||||||||||||||
|ASQ communication|2547|0.079|.314|-0.048|.566|0.127|.112|0,86|0,377|-0,176|0,545|1,661|0,377|
|ASQ gross motor|2548|-0.005|.897|0.02|.576|-0.019|.504|-0,122|0,775|0,061|0,613|-0,285|0,775|
|ASQ fine motor|2556|0.037|.471|0.103**|.04|-0.061|.242|0,443|0,495|0.335*|0,078|-0,845|0,495|
|ASQ problem solving|2556|0.047|.454|0.032|.552|0.013|.809|0,573|0,45|0,058|0,747|0,12|0,45|
|**WJ**||||||||||||||
|Raw score|2564|0.028|.613|0.003|.954|0.025|.639|0,184|0,779|0,013|0,948|0,273|0,779|
|% answers correct|2564|0.021|.693|0.027|.624|-0.008|.881|0,092|0,887|0,097|0,602|-0,181|0,887|
|**Anthropometrics**||||||||||||||
|hfa(z score)|2543|0.055|.208|0.023|.603|0.009|.807|0,31|0,496|0,068|0,65|0,1|0,496|

Note: "Impact" column presents results of the regression of the dependent variable on the treatment status (either control, HBP, or CBP); C vs. HBP, results of the HBP treatment; C vs. HBP, results of the CBP treatment; and CBP vs. HBP, results of the HBP treatment versus the CBP treatment. Each regression is robust to the heteroskedasticity and accounts for intra-village correlation (109 clusters in total). “ITT” is intent to treat, “TOT” the treatment on the treated. All results are controlled for sex, age dummies, sex and age dummies interaction, and the baseline test score of each dependent variable. Since baseline test scores are available only for children aged above 36 months at baseline, only 2/3 of the oldest children are included in these estimations. ** 5% significance.

20
# **Appendix Table 5.4: Practice by Practice Impacts on Digital Marketing Practices in Round 2**

||Firm<br>website|Search<br>engine tools|Uses<br> e-commerce|Business <br> Facebook|Business <br>Twitter|Business <br>Instagram|Business <br> Snapchat|Business <br>Tumblr|Business <br>Whatsapp|Business <br> Youtube|Business<br>Pinterest|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Assigned to Insourcing|0.128**|0.096*|0.072|0.233***|0.139**|0.122**|0.015|-0.000|-0.003|0.028|0.029*|
||(0.057)|(0.051)|(0.058)|(0.059)|(0.054)|(0.060)|(0.015)|(0.001)|(0.061)|(0.029)|(0.018)|
|Assigned to Outsourcing|0.173***|0.082|0.087|0.203***|0.115**|0.122**|0.001|0.015|0.039|0.043|0.023|
||(0.059)|(0.050)|(0.058)|(0.059)|(0.053)|(0.061)|(0.011)|(0.011)|(0.061)|(0.031)|(0.017)|
|Assigned to Training|0.011|-0.023|-0.052|0.094|0.017|0.046|0.007|0.000|-0.100*|0.015|0.015|
||(0.055)|(0.045)|(0.055)|(0.060)|(0.050)|(0.061)|(0.013)|(0.001)|(0.060)|(0.028)|(0.015)|
|Assigned to Consulting|0.095*|0.036|0.010|0.161***|0.084|0.041|-0.007|0.007|-0.033|0.028|0.007|
||(0.056)|(0.048)|(0.056)|(0.060)|(0.052)|(0.060)|(0.007)|(0.007)|(0.060)|(0.029)|(0.013)|
|Mean of Control Group|0.280|0.182|0.311|0.473|0.198|0.427|0.008|0.000|0.489|0.046|0.008|
|Sample Size|678|678|678|675|675|675|675|675|675|675|675|
|P-value: all treatments zero|0.011|0.076|0.083|0.001|0.036|0.169|0.151|0.557|0.189|0.674|0.395|
|P-value: all treatments equal|0.040|0.061|0.047|0.094|0.118|0.333|0.125|0.392|0.119|0.861|0.638|
|P-value: In=Out=Consult|0.423|0.468|0.343|0.466|0.620|0.298|0.156|0.224|0.475|0.881|0.428|
|P-value: Insource = Outsource|0.465|0.794|0.787|0.599|0.673|1.000|0.361|0.163|0.488|0.656|0.755|

Notes: Regressions control for stratification batch fixed effects and baseline value of digital marketing practices index. Robust standard errors in parentheses. *, **, *** denote significance at the 10, 5, and 1 percent levels. Practices are all dummy variables, and estimation is for second follow-up survey. **Firm website** denotes the firm having a website; **Search engine tools** denotes using search engine marketing tools to improve customer traffic; **Uses e-commerce** denotes using an e-commerce platform to sell products or services; **Business Facebook, Business Twitter, Business Instagram, Business Snapchat, Business Tumblr, Business Whatsapp, Business Youtube,** and **Business Pinterest** denote having a business profile or page on these different social media platforms.

69
# **4. Conclusions**

This analysis of the relation between age and well-being in Europe and Central Asia (ECA) takes advantage of panel and cross-sectional data from three large surveys: the Integrated Values Survey (IVS), the Life in Transition Survey (LiTS), and the Russia Longitudinal Monitoring Survey (RLMS). By focusing on the ECA region, the analysis makes two important contributions. First, by expanding the current research focus on high-income countries, it is better able to assess the universality of the U-shaped relation identified for Western countries. Second, it is able to leverage the disparate impacts of recent political and economic transformation in most ECA countries on the well-being of different cohorts to address the methodological problem of distinguishing between age and cohort effects. As Frijters and Beatton (2012) point out, the ideal would be to ‘follow representative individuals throughout their whole life, starting at birth’ (p. 529), but such data are not available. We overcome this limitation by examining well-being across the lifespan in a synthetic panel data set for ECA countries and a true panel data set for the Russian Federation.<sup>15</sup> We hypothesize that if a U- shape is observable in societies that have witnessed dramatic economic and social changes in past decades then, once cohort effects are corrected for (albeit imperfectly), they are most probably not the explanation for any observed U-shaped relation. In the aggregate, our findings mostly support the existence of a U-shape in individual well-being in the ECA up until the 60s and 70s but point to an apparent decrease in well-being among the oldest old, even after we control for self-reported health. This pattern remains robust for most of our cross-sectional and panel analyses, although within the ECA, there is significant heterogeneity in the pattern of well-being across the lifespan.

> 15 In the absence of large-scale panels that follow the same individuals across their lifespan, the most promising way to address the cohort issue is most probably by using less subjective measures of well-being such as in Weiss _et al._ ’s (2012) ape study.

19
_ANDR EE_<sup>_ ́_</sup> _DECEMBER - 2021_ _24_

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**Table 6. Prices and Sexual Activities by Client Type**

||(1)<br>Dependent Variabl|(2)<br>e: Price Paid|(3)<br>_p_-value for<br>interaction term|
|---|---|---|---|
|Vaginal Sex|24.21|7.58||
||(25.10)|(27.59)||
|Vaginal Sex * Regular||57.44<br>|0.18|
|||(42.50)||
|Anal Sex|77.19|72.34||
||(20.93)***|(21.65)***||
|Anal Sex * Regular||16.77<br>|0.52|
|||(26.04)||
|Oral Sex|23.68|6.20||
||(15.06)|(15.14)||
|Oral Sex * Regular||52.32<br>|0.1*|
|||(31.40)*||
|Manual Stimulation|48.60|39.60||
||(15.79)***|(14.95)***||
|Manual Stimulation * Regular||28.39<br>|0.27|
|||(25.43)||
|Massage|61.27|74.12||
||(14.18)***|(16.27)***||
|Massage * Regular||-38.98<br>(26.52)|0.14|
|Kissing|51.92|52.10||
||(12.06)***|(11.88)***||
|Kissing * Regular||0.92<br>(22.54)|0.97|
|Company|72.07|73.43||
||(12.94)***|(14.58)***||
|Company * Regular||-5.05|0.81|
|||(21.52)||
|Stripping|39.78|29.50||
||(11.22)***|(13.74)**||
|Stripping * Regular||27.54<br>(22.84)|0.23|
|Sex in Thighs|34.87<br>|48.24<br>||
||(15.68)**|(18.49)***||
|Sex in Thighs * Regular||-40.74|0.17|
|||(29.27)||
|"Other" Activities|58.58<br>|18.80<br>||
||(33.41)*|(27.08)||
|"Other" Activities * Regular||92.37|0.08*|
|||(52.08)*||
|Unprotected Sex|42.33<br>|33.60<br>||
||(16.45)**|(19.98)*||
|Unprotected Sex * Regular||23.72|0.43|
|||(30.19)||
|Regular Client|-16.64|-89.75||
||(14.69)|(49.32)*||
|_p_-value for F-test that "regular"||0.20||
|<br>coefficient and all interaction||||
|terms are jointly insignificant||||
|Mean of Dependent Variable|453.40|453.40||
|Observations<br>|18824<br>|18824<br>||
|Number of women|192|192||
|R-squared|0.04|0.05||

Note: All regressions are fixed effects regressions with controls for the month and the day of the week. Clustered standard errors (at the individual level) in parentheses. All regressions include controls for the round of data collection. The dependent variable is Kenyan shillings. The exchange rate was approximately 70 Kenyan shillings to $1 US during the data collection period.

* significant at 10%; ** significant at 5%; *** significant at 1%
income countries. They focus on the effects that trade liberalization could have on the environment and use a cross‐section of urban area pollution and country per capita GDP. Shafik and Bandyopadhyay (1992) find similar results while focusing on the decision nations make to offset the negative environmental effects associated with growth and how that decision may be affected by the lack of incentives (for example, when the burden of pollution is experienced by the poor or by other nations).

Since then, many studies have focused on this empirical phenomenon, looking at different measures of pollution and their relationship with growth. Stern (2004) and Dinda (2004) review some of these studies focusing on the theoretical and empirical approach. They find that despite the fact that many studies look at different pollutants, they all emphasize that the environment deteriorates with economic growth until a certain point beyond which the effect changes and the environmental quality starts improving. However, they do not agree on the level of per capita GDP at which pollution starts declining, partly because some studies look at emissions and others look at concentration levels. The turning point ranges from approximately $3,000 up to almost $23,000 per capita GDP (see Stern, 2004 for a list of these studies and the corresponding turning points).

More recently, studies have also tested the existence of an EKC for particular regions<sup>10</sup> or within particular countries.<sup>11</sup> However, although most studies have used urban pollution measures, they usually use national or regional per capita GDP to measure growth. The poor availability of city‐level GDP data had resulted in only a few studies that look at the EKC at the city level.<sup>12</sup> Global city‐level per capita GDP data allow us to start exploring if there is evidence of an EKC at the city level and try filling this gap in the literature.<sup>13</sup> We test if the EKC found at the country level also holds at the city level. Following the standard approach, we regress a measure of pollution, in our case, the city annual mean concentration level of PM 2.5, against the natural logarithm of the per capita city GDP. Since we have a panel data set, between 2006 and 2015, we include fixed effects by year to account for unobserved time shocks. We use a cubic specification (as

> 10 For example, some studies have focused on Asian countries (Apergis & Ozturk, 2015) or OECD countries (Jebli, Youssef, & Ozturk, 2016).

> 11 For example, China (Hu, Hernandez del Valle, & Martinez‐Garcia, 2017), Vietnam (Al‐Mulali, Saboori, & Ozturk, 2015), Cambodia (Ozturk & Al‐Mulali, 2015).

> 12 Some studies have used pollution and city‐level GDP data for Chinese cities. See for example Brajer et al. (2011). 13 Our goal with this approach is to contribute to the discussion on the relationship between pollution and development at the city level and quickly test the existence of an EKC. We do not intend to thoroughly analyze this topic.

16
Policy Research Working Paper 9902

# **Abstract**

This paper examines the short-term implications of the COVID-19 pandemic for inequality in developing countries. The analysis takes advantage of high-frequency phone survey data collected by the World Bank to assess the distributional impacts of the pandemic through the channels of job and income losses, food insecurity, and children’s education in the early days of the pandemic and subsequent period of economic recovery leading up to early 2021. It also introduces a methodology for estimating changes in income inequality due to the pandemic by combining data from phone surveys, pre-pandemic household surveys, and macroeconomic projections of sectoral growth rates. The paper finds that the pandemic had dis-equalizing impacts both across and within countries. Even under the assumption of distribution-neutral impacts within countries, the projected income losses are estimated to be higher in the bottom half of the global income distribution. Within countries, disadvantaged groups were more likely to have experienced work and income losses initially and are recovering more slowly. Inequality simulations suggest an increase in the Gini index for 29 of 34 countries in the sample, with an average increase of about 1 percent. Although these shortterm impacts on inequality appear to be small, they suggest that projections of global poverty and inequality impacts of COVID-19 under the assumption of distribution-neutral changes within countries are likely to underestimate actual impacts. Finally, the paper argues that the overall inequality impacts of COVID-19 could be larger over the mediumto-long term on account of a slow and uneven recovery in many developing countries, and disparities in learning losses during pandemic-related school closures, which will likely have long-lasting effects on inequality of opportunity and social mobility.

This paper is a product of the Poverty and Equity Global Practice and the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// www.worldbank.org/prwp. The authors may be contacted at anarayan@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team