rafmacalaba/data-use-mentions-extended huggingface dataset:
Next, I measure R&D offshoring using patent applications in which at least one applicant is a resident from country j (developed) and simultaneously at least one inventor is a resident from country i (developing/emerging/transition) —similar R&D offshoring measures in the context of internationalization of inventive activity are used in Guellec and van Pottelsberghe de la Potterie (2001), Harhoff et al. (2013), and Thomson et al. (2013). Again, when inventors come from various countries, a single copatent for each bilateral i-j pair is computed.

It is worth mentioning that previous studies on the determinants of international co-patenting use information from single patent offices only, with few exceptions (Martínez and Rama 2012; Picci 2010). This practice is likely to deliver biased estimates due to the “home bias effect,” which may emerge when using patent data from one single office for cross-country analysis. Since patents at the USPTO, EPO, or JPO, for instance, protect innovation within their respective geographical areas, they are preferred by domestic firms and thus their innovative capability is overestimated with respect to foreign firms. Using data from the PCT mitigates this effect.<sup>13</sup>

# **Control Variables**

Control variables include geographical, linguistic, cultural, and historical barriers to cross-country collaborations. In particular, the great circle distance between the most populated cities of countries (measured in km) is included, as well as a dummy variable indicating whether two countries share a common border, a dummy variable valued 1 if and ranking in a patent is an interesting avenue of research, which goes beyond the scope of the present paper.

> 13. Other biases inherent to the existence of multiple jurisdictions and patent offices are discussed in de

> Rassenfosse et al. (2014)—such as the nonrandom choice of patent office. Again, the use of PCT

applications should mitigate these biases.

17
, all these data-related opportunities crucially depend on the continuation of existing panels and the launch of new ones, which should go in parallel with efforts towards improving questionnaire design and interoperability with other data sources.

## **_A. Look at (im)mobility in the broader framework of adaptation to climate stress_**

While the issue of small sample sizes of households with migrants, stemming from the ‘rare event’ nature of migration, is an intrinsic shortcoming of general-purpose multi-topic representative surveys, a change of perspective on the issue can help in thinking about new research directions. In particular, our review has emphasized that the potential immobility traps of climate-related hazards are at least as important as the mobility effects triggered by such events. In this respect, household surveys could be employed not 30
the relationship between gender, growth, and development, there have been few attempts to rigorously model the role of inter- and intragenerational (or vertical and horizontal) health externalities, and the impact of access to infrastructure on women’s time allocation in this context. In this section we present a threeperiod, gender-based overlapping generations (OLG) model of economic growth with public capital that captures both of these aspects.<sup>16</sup> 15In developed countries, a number of studies have focused on how greater access to consumer durables (made possible by improved technologies and access to electricity) helped to “liberate” women from domestic production activities and led to dramatic increases in married female labor-force participation. Studies for the United States include Greenwood and Seshadri (2005), Greenwood et al. (2005), and Coen-Pirani et al. (2010); Cavalcanti and Tavares (2008) provide a cross-country analysis. Coen-Pirani et al., in particular, estimated the effect of household appliance ownership on the labor force participation rate of married women using microlevel data from the 1960 and 1970 US Censuses. The results show that the diffusion of household appliances contributed significantly to the increase in married women’s labor force participation rates during the 1960s. It may also be an explanatory factor for the subsequent increase—today, 80 percent of college-educated women are in the labor force, compared to 62 percent in 1963; as of July 2010, women accounted for almost 47 percent of the work force and 51 percent of professional workers. However, the spread of contraception methods may have played an equally (if not more) important role in the latter expansion, by allowing women to postpone marriage and child bearing, and increasing their incentives to invest time in the acquisition of advanced skills.

16The model both simplifies—by excluding human capital aspects—and integrates the models in Agénor (2009) and Agénor and Agénor (2009), while focusing on different issues. A brief summary of the literature on gender-based OLG
.08|0.09|0.07|0.39|0.54|
|Std Dev|0.45|0.45|0.40|0.40|0.28|0.28|0.49|0.49|
|Sample|Techn.|Gener.|Techn.|Gener.|Techn.|Gener.|Techn.|Gener.|

_Notes:_ Source: IMSS administrative data for formal employment and endline survey data for informal employment, June 2019-May 2021. Observations are at the person-month level. Standard errors, clustered at individual level, are reported in parentheses. We control for demographic characteristics, socioeconomic status, work experience, time and school fixed effects.<sup>_∗_</sup> _p <_ 0 _._ 10,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 and 24 months, respectively (Col. 3). The share with temporary contracts is not affected (Col. 5). Results are very similar in magnitude and significance when we do not control for any covariates (Appendix Table A2). By contrast, we do not observe a change in formal employment or switching between employment types for graduates from general schools (Col. 2, 4, 6) or those planning to continue their education. The fact that the wage incentive does not change decisions to enter the labor market for this group assuages concerns about unintended consequences of offering short-term monetary incentives for formal work.

Our results further suggest that slightly less than half of these average formal employment gains come from a reduction in informal work (Panel A and B, Col. 7). One potential concern with the analysis of informal employment is that we rely on self-reported data from our endline survey. To test the reliability of this data, we take
area; the estimates are population characteristics for the country as a whole, as well as by province, and for urban and rural areas.<sup>39</sup> As in many developing countries, poverty in Afghanistan is more prevalent in rural areas.<sup>40</sup> Rural areas often lack basic infrastructure, like roads, electricity and communications; and households often have limited access to basic services, like healthcare and education. Given the dependence on agriculture, rural households are often more susceptible to the impact of natural shocks, like drought. In this context, it is not surprising that total monthly per capita consumption in rural areas is 44% lower than that in urban areas, and that rural households spend a larger share (63%) of total consumption on food than urban households (44%). Rural and urban households also differ in their levels of food security. Although their per capita calorie intake is similar, rural households have lower levels of dietary diversity. These statistics suggest that while calorie intake may be similar across areas, micronutrient intake is not.

Following guidelines from the World Food Programme (WFP, 2008), we create three food consumption profile groups using the recommended cutoffs (of 28 and 42 for populations whose daily diets include sugar and oil) for the food consumption score (FCS). Approximately 20% of all Afghan households have poor or borderline diets, with a disproportionate number in rural areas.

Further differences between rural and urban households can be observed in the calorie and expenditure shares devoted to various food groups. (Table 2) The patterns are consistent with those seen in other low-income countries. Grain, including wheat, breads and other cereals, represent 48% of food expenditure and 70% of calorie intake for the national sample. On average, urban households devote significantly fewer resources to grains and dairy and significantly more resources to the other food groups than rural households; household calorie shares follow a similar pattern. We also present the consumption
participant firms may differ in terms of the alternative uses they could give to their funds.<sup>44</sup> A simple approach to address the heterogeneity of the effects when differences are observable is to add interaction effects with the treatment dummy variable in a regression framework that estimates the average treatment on the treated effect. Treatment effects can also vary with the distribution of outcomes themselves. Volpe Martincus and Carballo (2010) examine the impacts of export promotion activities across quantiles of the distribution of Chilean firms’ growth rates of exports using quantile treatment effects estimation. They find stronger effects at the lower end of the distribution, which are combined with data on firms’ export histories to show that smaller and relatively inexperienced firms as measured by their total exports benefit more from export promotion.

> 43 See Abbring and Heckman (2007) and Ravallion (2011) on the heterogeneity of impacts in evaluation studies.

> 44 While these opportunity costs may not be observable, they are likely to vary with observable characteristics such as the conditions in the local market or in the sector of activity.

34
2007), the banking system has experienced a substantial credit growth, reaching an annual growth rate of 39 percent at the end of 2007 (or an average annual growth rate for the three years of 27 percent). Even though these growth rates were mainly a result of the low starting base of the banks’ lending activities in the previous years, the intensive growth was close to crossing from a sustainable credit growth to a credit boom (Chart 4). The intensive credit growth was particularly notable in the banks’ lending to households.<sup>23</sup> The loans to this sector experienced considerably higher annual growth rates (58.8 percent at the end of the first quarter of 2008) compared to the growth of loans to enterprises, which had reached 33.5 percent at the end of the same period (Chart 6). The more dynamic growth was a compensation for the low level of lending activities typical for the period of transition, when banks generally restrained from granting loans to households. In 2004–2007, the

> 19 According to the decision on credit risk management, an ―A‖ or ―B‖ client is a client that pays its loan obligations as they fall due, or with insignificant delays, and that has solid credit history.

> 20 The NBRM Credit Registry is a public-type credit registry that collects data from the domestic banks and saving houses on credit exposures higher than:

> - For banks: denar 300,000 (almost euro 5,000) for legal entities and denar 5,000 (euro 82) for natural persons

> - For saving houses: denar 50,000 (euro 820) for legal entities and denar 5.000 (euro 82) for natural persons. 21 During the analyzed period, the first-rated collateral included cash and cash-equivalents; guaranties and securities issued by the country, the NBRM, central banks, and the governments of EU member-states, the United States, Switzerland, Japan, Canada, Australia and Norway;
PO.TicY RE.SFARCH WORKING PAPER 21 _15_

# Summary findinigs

Firm-lcvel data for the Czech Republic (1992-96) suggest that foreign investment had a positive ilmpact on recipient firms' total factor productivity (TFP) growth, This result is robust to corrections for the sampleselection bias that orevails because foreign investment tends to go to firms with above-average productivity perfornmance.

This result is not surprising, given the presumption that foreign investors transfer new technologies and knowiedge to partner fir;ns. With some lag, this is likely to be reflected in greater TFP growth.

Foreign direct investment appears to have a greater impact on TFP growth than joint ventures, suggesting that parent firms are transferring more know-how (soft or hard) to affiliates than joint venture firms get from their partners.

Joint ventures and foreign direct investment together appear to have a negative spillover effect on firms that do not have foreign partnerships. This effect is relatively large and statistically significant. But if the focus is restricted to the impact of foreign-owned affiliates (foreign direct investrment) on all other firms in an industry, the magnitude of the negative effect becomes much smaller and loses statistical significance.

Thlis result, together with the fact that joint ventures and foreign direct investment together account for significant shares of total output in many industries, suggests that more research is needed to determine how much knowledge diffuses frorn firms with strong links to foreign firms to firms that do no, have such links. Especially important is the extent of spillovers amnong joint venture firms and benteen foreign affiliates and firms with joint ventures.

Insofar as joint venture firms invest more in technological capacity (as suggested by their training efforts), those firms could be expected to be better able to absorb and benefit from the diffusion of know-how. The absence of such capacity nmay underlie the observed negative spillover effect on other firms in the industry. Longer time series and collection of data on variables that measure firms' in-house technological effort would help identify the magnitude and determinants of technological spillovers.

- - This paper a product of
found similar effects in the 2014 follow-up survey that suggest measurement error.<sup>14</sup>

# **3 IMCI Classification and Illness Spells**

Throughout the paper, we identify “overuse” and “underuse” of care _relative to the IMCI guidelines_ proposed by the WHO. The C-IMCI is part of the community and family arm of the IMCI itself, the WHO’s and UNICEF’s primary children’s health program, which has been adopted by over 80 countries (Bryce et al. (2004)). The C-IMCI goes through a set of simple checks in order to spot danger signs and classify symptoms into gastrointestinal illness, respiratory illness, malaria, and so on. It targets the causes of about 70% of child deaths.<sup>15</sup> All diagnostic tests can be operationalized in the field. The effectiveness of these checks in identifying the underlying disease was rigorously tested (see Gove (1997) and the studies therein). Based on the duration and severity of symptoms, the C-IMCI algorithms make recommendations for when formal medical care should be sought (see below). The C-IMCI is explicitly tailored to a developing-country context, and we assume that its careseeking recommendations take into account factors like the expected quality of care and resource constraints.<sup>16</sup> What justifies the use of the IMCI to assess need for care? Ultimately, we are interested in assessing the allocation of acute care. However, the optimal allocation is unobserved.

> 14In 2014, we prompted parents to report visits from any NGO workers who measured the child’s weight or height in the last 2 weeks, and we asked whether the child had a card entitling them to receive free health care at the clinic (still avoiding direct mention of Action for Health). We found that 12% more CHW visits were reported in the full program group than in the CHW-only group, but we also found that 10% of families reported visits in the
Policy Research Working Paper 9491

# **Abstract**

Total factor productivity is a key element of economic growth and an important performance metric for policy makers. This note describes the methodology for measuring firm-level total factor productivity using the World Bank’s Enterprise Surveys cross-country data. It also presents some estimates recovered from the production function. Two versions of the production function are estimated: one Cobb-Douglas, the other a more flexible translog specification. Both estimations are at the two-digit industry level pooling all the Enterprise Surveys data across economies. Evidence is found against using a Cobb-Douglas specification, which is more parsimonious, and in favor of using the flexible translog specification. The resulting firm-level estimates are all published in the Enterprise Surveys database with a unique firm identifier to link to the rest of the Enterprise Surveys data; because the estimates are reliant on new data, they are updated periodically as new Enterprise Surveys data become available. The results show that: (i) median firms operate close to constant returns to scale; (ii) gross-output and value-added production functions provide similar ranking of sectors in terms of output elasticities, capital intensity, and returns to scale; (iii) there is large, firm-level heterogeneity in output elasticities; and (iv) gross-output-based total factor productivity measures are less dispersed than the value-added ones.

This paper is a product of the Global Indicators 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 hmaemir@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
4,707**|**1,559**|**9,287**|
|Telephone mainlines|696|1,464|259|2,419|
|Mobile lines|2,325|3,243|1,300|6,868|
|**Electricity**|**9,894**|**18,607**|**17,602**|**46,103**|
|Electricity generation|8,214|16,467|17,139|41,820|
|ElectricityAccess|1,680|2,140|463|4,283|
|**Water and Sanitation**|**1,764**|**2,497**|**647**|**4,908**|
|Water|745|1,040|190|1,975|
|Sanitation|1,019|1,458|457|2,934|
|**TOTAL**|**23,254**|**48,303**|**34,261**|**105,818**|
|_Share by country group_|_22%_|_46%_|_32%_|_100%_|
|**Investment**|**10,261**|**20,739**|**15,786**|**46,786**|
|**Maintenance**|**12,992**|**27,564**|**18,475**|**59,032**|

_Source_ : Authors’ calculations. Note: Total financing is estimated based on IMF/IFS investment data and World Bank Private Participation Infrastructure database.

7
# **Figures and Tables**

**Figure 1: Evolution of the premium on industry-level Suharto family connectedness – individual competition indicators**

<!-- Start of picture text --> Entry Exit<br>'93'94'95'96 '00'01'02'03'04'05'06'07'08'09 '93'94'95'96 '00'01'02'03'04'05'06'07'08'09<br>Suharto era Post Suharto era Suharto era Post Suharto era<br>PCM PE<br>'93'94'95'96 '00'01'02'03'04'05'06'07'08'09 '93'94'95'96 '00'01'02'03'04'05'06'07'08'09<br>Suharto era Post Suharto era Suharto era Post Suharto era<br>Herfindahl MS4<br>'93'94'95'96 '00'01'02'03'04'05'06'07'08'09 '93'94'95'96 '00'01'02'03'04'05'06'07'08'09<br>Suharto era Post Suharto era Suharto era Post Suharto era<br>lnN lnP<br>'93'94'95'96 '00'01'02'03'04'05'06'07'08'09 '93'94'95'96 '00'01'02'03'04'05'06'07'08'09<br>Suharto era Post Suharto era Suharto era Post Suharto era<br>.4 .2<br>.2 .1<br>0<br>0<br>PC family PC family<br>β β -.1<br>
Comparable Area (MCA) concept as implemented by IPEA researchers. MCAs group municípios in each of the four census years so that their boundaries do not change during the study period. All data have then been aggregated to match these MCAs. The resulting data set represents 123 urban agglomerations that consist of a total of 447 MCAs.

The sources for the majority of data employed in this paper are the Brazilian Bureau of Statistics (IBGE) Population and Housing Censuses of 1970, 1980, 1991 and 2000. We used the full Brazilian census counts to get information about total population and housing conditions (urbanization rate). Other data were collected only for a sample of households. We used this census sample information for income, industrial composition, education, piped water provision, and electricity availability. The sample sizes varied across census years (1970: _25_ percent; 1980: _25_ ; 1991: _12.5_ ; 2000: _5_ )., but all are representative at the município level, and thus are also reliable at the MCA level employed in this study. Table 15 reports the main variables, their source and the years available.

**Table 15: Variable definitions and data sources**

|**Data**|**Source**|**Years**|
|---|---|---|
|Population|Population Censuses|1970, 1980, 1991, 2000|
|Urbanization rate|Population Censuses|1970, 1980, 1991, 2000|
|Income per capita (monthly deflated to 2000<br>values)|Population Censuses (sample)|1970, 1980, 1991, 2000|
|Industrial composition (percentage of labor|Population Censuses (sample)|1970, 1980, 1991, 2000|
|force employed in an economic activity sector)|||
|Years of schooling (average)|Population Censuses (sample)|1970, 1980, 1991, 2000|
|Percent of households with access to piped<br>
Despite growing evidence on the effectiveness of parenting programs evaluated in small-scale trials, key questions remain relating to how to make these programs scalable, and how to sustain their impacts long-term. Individual home visits provide an opportunity to tailor activities to individual circumstances and personal barriers to behavioral change, but they are expensive to implement at scale.<sup>3</sup> Group parenting programs are less costly to implement at scale and favor the creating of support networks, but they offer less individualized attention and guided practice. The evidence of these programs, which is the type of program we examine here, is somewhat mixed.<sup>4</sup> Group-based interventions have been shown to shift parental knowledge about child development 3 A home visiting model in Jamaica that provides individualized support to parents to help them improve their child development found large effects on children’s developmental outcomes, sustained until adulthood (S. M. GranthamMcGregor et al. 1991, Gertler et al. 2014). Cross country replication of the Jamaican home visiting curriculum to other settings (such as Bangladesh, India and Colombia) has so far found impacts on child development in the short-term (O. P. Attanasio et al. 2014; Hamadani et al. 2006; Nahar et al. 2012; Vazir et al. 2013).

4 The systematic evidence in the LANCET series on Early Childhood Development suggests that interventions that directly engage the child in play experiences and guide caregivers in providing stimulating interactions, as well as give parents the opportunity to practice with their children and receive feedback, can be effective in remediating early disparities in child development (Black et al. 2017; Britto et al. 2016; Engle et al. 2007, 2011). A recent systematic meta-analysis, on the other hand, finds mean effect sizes in children’s language and cognitive outcomes of 0.32 SD for home-based studies, and 0.59 SD for group visit studies (Aboud and Yousafzai 2015).

3
able to collect data on the volume of SME financing from 50 countries, which enables us to assess cross-country variation in the levels of SME financing.

# **2.1 SME definitions**

One of the main challenges in performing a cross-country analysis of SME data is the absence of a universal definition of what constitutes an SME. A number of efforts aim to streamline and harmonize SME definitions (OECD, 2004), although the heterogeneity of SMEs themselves and the nature of the economy they operate in might mean that establishing a global definition is not 7