rafmacalaba/data-use-mentions-extended huggingface dataset:
suggestion of a significant residual time trend in applications, though the coefficients were negative in the grants equations. Finally, the unemployment rate had no detectable effects on lagged patent applications or grants.<sup>21</sup> In Table 3 the regressions are broken down into total patent applications, university patent grants, and other patent grants, using the SUR technique. The coefficients for the patent applications equations are quite similar to those in Table 2, as expected, and require no further discussion. Of interest here is whether there are detectable differences in behavior between patent grants to universities and patent grants to non-university actors. The lagged patent stocks were significantly positive only in the university equations, with an elasticity estimate of around 0.44 to 0.57. This suggests that inherited knowledge has a more powerful influence on innovation by universities than by other organizations. It is also noteworthy that the lagged university patent stock was estimated to have a small positive spillover impact on non-university patent grants. In contrast, lagged R&D expenditures had strongly positive effects on patent grants by other institutions, but not by universities. Again, this is due partly to collinearity in the technical inputs data. The implementation of the Bayh-Dole Act appears to have induced significantly more patent grants to both university researchers and to those in other institutions.

Turning to human inputs, we find that the ratio of foreign graduate students to total graduate students (FORTGR) had a significant and positive effect in all six patenting equations. The elasticity of total patent applications to increases in foreign students is around 0.48 and between 0.60 and 0.99 for patents awarded. The impact was larger on future university patent awards than on other patenting in specifications (6) and

> 21 The equations were estimated also without the unemployment rate, with virtually no difference in results.

20
FOFM firms in each country against WVS trust score. Data shows share of FOFM firms in each country against enf contracts rank from WB DBS.<br>Figure III: Trust as a Proxy for Rule of Law and Enforcing Contracts<br>(a) Rule of Law (b) Enforcing Contracts<br>2 FIN NOR 150 ITA<br>GBR DEU CHE NLD 100 MKD BIH SRB<br>FRA BGRSVK CZE<br>1 SVN EST ESP CZE 50 TUR SVN POL ESPUKRGBR<br>LTUPOLLVA EST NLD<br>HUN 25<br>SVK LVA CHE<br>ITA LTU HUN<br>0 TUR BGR RUS<br>MKD BIH FIN<br>SRB FRA<br>5 DEU<br>UKRRUS NOR<br>−1<br>0.2 0.4 0.6 0.8 0.2 0.4 0.6 0.8<br>Trust Trust<br>Plots WGI rule of law score against WVS trust country average. Correlation is 0.60. Plots DB Contract Enforcement Rank against WVS trust country average. Correlation is −0.41.<br>Share of Family Firms Share of Family Firms<br>Share of Family Firms Share of Family Firms<br>Rule of Law Score<br>Enforcing Contracts Rank<br><!-- End of picture text --> 43
confounded with cohort effects and where the role of early skills on later development cannot be modeled. With few exceptions (e.g., [Kobrosly et al. 2011]) and the Young Lives cohorts, longitudinal panels have primarily been focused in the US and Europe.

This paper is one of the few examples of a longitudinal cohort study set in a very low-income country in Sub-Saharan Africa that has assessed children with direct measures of cognitive development at two points in time: early childhood (3–6 years) and school age (7–10 years). The cohort was also evaluated with early math and literacy measures at school age. Previous work demonstrated that these same children showed significant differences across socioeconomic status in standardized scores for a wide range of cognitive and language tests; the largest gaps in the SES gradient were evident in receptive language and sustained attention scores (Fernald et al. 2011).

The primary objective of this paper is to take advantage of these longitudinal data and examine how the magnitude and age profile of SES gradients change over time, modeling skill formation as a cumulative and dynamic process. Differences in child development are assessed across wealth groups, and the evolution of the SES gradient across early childhood and into school age is documented. While other papers based on cross-sectional evidence have already documented that the SES gradient widens at early ages and then flattens out after age six during the school-age window (Schady 2011), this study relies on both the cross-sectional and the longitudinal aspect of the data to document the critical timing of the emergence and evolution of the SES differences in child development and learning outcomes.

We further add to the literature by assessing the magnitude of the SES gradients across _multiple_ developmental domains. Much of the current focus on children living in resource-poor circumstances is on height, weight, or morbidity outcomes or on a single dimension of child development (such as receptive language). In this study, multiple domains of child development are examined, including measures of cognition, language, and executive
the average energy spending share observed in the HBS 2019 in Bulgaria is 13.1 percent. The largest energy spending component is on electricity (59.3 percent of all energy expenditure), followed by other solid fuels (27.5 percent) and heat energy (9.3 percent).

**There are several definitions of energy poverty from a monetary perspective.** One stream of literature defines the energy poor as all households that spend more than 10 percent of their total expenditure on energy (Boardman, 1991). Thema and Vondung (2020) explore two additional energy poverty indicators. The first one is based on the national median of the energy spending share (often

15
> 11 Here, we use the data of bilateral import volume of Bulgaria from EU15, other EU8 countries, Romania and Turkey in 2000 since the data in 2001 is not available. As argued above, the IT infrastructure development level of Estonia is better than the EU15 average, we neglect the IT infrastructure improvement in Estonia. That is to say, we suppose that no improvements of IT infrastructure occur in Estonia. When we simulate the trade gain of a EU8 or a candidate member country resulting from the EU8 or candidate member countries, we neglect the trade gain of the country in question. In other words, we simulate the trade gain on the other EU8 countries (or other candidate member countries) resulting from the improvement of trade facilitation in the new or candidate member country in question.

> 12 The reason for the higher import gains for partner countries stems from a higher elasticity of export to trade facilitation improvement than the corresponding elasticity for imports, as found by WMO (2004).  This implies that if a country improves its trade facilitation, its export volume increase will be higher than its import volume increase.

16
Section II as well as how these have also evolved over time and differ during Highly Synchronized Tightening periods (defined in Section IV.1.1)

# V.1. Methodology and Data

In order to estimate the role of our seven global and domestic shocks, we employ the following FAVAR model: R,global π,global where Zt consists of global interest rates (ft ), global inflation (ft ), global output growth (fty,global), oil price growth (∆op), domestic interest rates (Rdomestic), domestic inflation (π<sup>domestic</sup> ), and domestic output growth (y<sup>domestic</sup> ).

The εt is a vector of orthogonal structural innovations, which include the following four global shocks: (i) common changes in monetary policy across countries (“global monetary policy”); (ii) the global demand for goods and services (“global demand shock”); (iii) the global supply of goods and services (“global supply shock”); and (iv) oil prices (“oil price shock”). It also includes the following three domestic shocks: (v) domestic monetary policy (“domestic monetary policy”); (vi) the domestic supply of goods and services (“domestic supply shock”); and (vii) the domestic demand for goods and services (“domestic demand shock”).<sup>52</sup> The model assumes stochastic volatility of the structural shocks—the residuals represented by the time-varying residual covariance matrix ∑t. These shocks are independently (but not identically) distributed across time.<sup>53</sup> This FAVAR model is estimated using monthly data with four lags (based on the AIC and SIC information criteria). The Bayesian routine we employ to estimate the model first searches for 500 successful draws from at least 1,000 iterations with 500 burn-ins; the results are based
and transport reforms have an impact on other markets (such as the labor market and investment savings market) that matter to the poor. These feedback effects are potentially significant for poverty alleviation, and an economywide analysis is needed. This usually calls for a multiagent, multicommodity model. Computable general equilibrium (CGE) models are increasingly becoming a useful analytical response to these needs.

CGE models simulate economic and social impacts of reforms and are based on the socioeconomic structure of a social accounting matrix (SAM), with its multisectoral disaggregation. The basic idea behind a SAM is to identify the linkages in an economic system. The basic elements when constructing a SAM are input-output tables combined with government accounts and household surveys. The household surveys are crucial for performing impact analysis on welfare and poverty. How deep the analysis can go depends on data availability. The CGE literature on the effects of public infrastructure service reform is rather modest: Chisari and his colleagues (1999, 2003) and Navajas (2000) for Argentina, Andersen and Faris (2002) for natural gas in Bolivia, and Logren and others (1997) for rural Morocco. Their main contribution is to show the importance of infrastructure for achieving the MDGs and, maybe most important, to show that good regulation is redistributive and progressive.

These new results from theory and econometric analysis have generated intense international debates. In fact, analysis of poverty targeting to ensure access to affordable minimum infrastructure services for poor people and of associated regulatory needs is probably one of the two fastest growing “business lines” for international donors, think tanks, and NGOs working on

> 23 See Foster and others (2000a, 2000b, 2003, 2004) or Gomez-Lobo and Contreras (2003) for examples. 24 In addition, in developing countries the rules of thumb are that households should not spend more than 5 percent of their income on water and sanitation and more than 5–10 percent on energy (depending on the region). 25 Estache, Foster, and Wodon (2002)
recognizes that refugees are often victims of conflict (Onoma, 2013; Fisk, 2018; Böhmelt, Bove and Gleditsch, 2019; Savun and Gineste, 2019). Recent research suggests that conflict between host communities and refugees may be avoided if refugees’ presence attracts aid and economic activity that benefits both (Lehmann and Masterson, 2020). We complement this literature on the refugee–conflict nexus, which thus far has relied on cross-country analysis, by exploiting—following Zhou and Shaver (2021)—within-country variation in exposure to refugee settlements.

The third strand explores the welfare consequences of refugees’ presence on host communities in developing countries. However, without auxiliary data (such as survey data on policy preferences regarding refugee policies), these studies cannot tell us how the economic consequences of refugee hosting affect social cohesion (if at all). Moreover, almost all studies in this research domain strand of the literature concentrate on a single domain, such as labor market outcomes (Fallah, Krafft and

> 5See also Zhou (2018), which examines how the presence of refugees can change local citizens’ opposition to citizenship inclusion in sub-Saharan Africa.

6
initially estimated $2 a day poverty status).

2. They vary in how they do this:

- a. PMT uses a continuous regression with variable selection (stepwise in 2012, Lasso in 2017);

- b. VM uses a binary regression with variable selection (stepwise).

3. They vary in the number of geographical models:

- a. PMT produces regional models;

- b. VM produces a single model for all regions.

The CLCI revised the models using the 2018 Multi Cluster Needs Assessment (MCNA) survey, which resulted in the 2019 Socio-Economic Vulnerability Assessment Tool (SEVAT).<sup>7</sup> The new models are closer

> 7 https://www.humanitarianresponse.info/en/operations/iraq/document/iraq-mpca-vulnerability-model-review

6
the U.S. manufacturing producer price index. The results show persistent effects of shocks to world grain prices on cereal price inflation as illustrated by the positive and significant coefficients on the fourth and twelfth lags. The results also show a positive effect of a change in U.S. manufacturing producer price on non-food price as well as a positive effect of a change in world food price on overall CPI.

Looking more specifically at oil price impacts, an increase in the oil price affects non-food prices and overall CPI in the 11<sup>th</sup> month after it has taken place on international markets. This long delay may be explained by the fact that retail oil prices in Ethiopia are controlled by the government, and price increases may not be immediately transmitted to consumers. For example, during the COVID-19 crisis, retail fuel prices in Ethiopia were maintained at the same level even though international oil prices collapsed, with the gains accruing to the public stabilization fund. As prices started to rebound during the last months of 2020, authorities increased retail prices in January and February 2021 by about 20 percent. This mitigates the direct link between the domestic and international oil markets. Keeping the exchange rate constant, a 1 percent increase in the international price of oil leads to a 0.01 percent rise of the non-food price and a 0.02 percent increase of the all-items CPI.

Overall, the results from the inflation models including the fiscal sector confirm the existence of statistically significant long-run effects of disequilibria in the fiscal sector on all inflation measures except non-food prices. Moreover, government borrowing has inflationary short-run effects (except for non-food inflation). This implies that fiscal imbalances as well as the way fiscal deficits are financed need to be considered in any attempt to understand the drivers of inflation in Ethiopia.

In summary, the models specified in this study perform well in explaining short-run and long-run inflation dynamics in Ethiopia over the sample period. The key regression results hold when we substitute the monetary sector by the fiscal
positive spillover effects of FDI may seem counterintuitive but is a robust result and consistent with related literature. Lu et al. (2014) find that a Chinese firm with higher exporting intensity is less affected by FDI into its industry. We find similar patterns using cross-country firm-level data. The intuition for this would be that to integrate more in the GVC, the sector has already had to improve on various fronts to survive from the fierce competition, so there is less room for improvement once it inserts into the GVC. It is also possible that sectors with higher GVC participation generate less domestic competition since they compete more in the foreign markets. Moreover, sectors with higher GVC participation acquire and sell fewer intermediate inputs and products in the domestic market; it is harder for domestic competitors to imitate, and thus there are fewer FDI spillovers.

The finding that FDI generates domestic competition in the recipient sector, and that firms with higher export intensity engage less in R&D compared to those with low export intensity is similar to the result in Farole et.al. (2018), which finds that GVC participation increases labor demand,
program’s effects provided that explicit scoring thresholds are used, but the number of firms close enough to the scoring threshold to enable such an approach is likely to be small in most cases.

Randomization is also likely to be a more cost-effective option than other quasi-experimental methods. Propensity score matching, for instance, would require firstly developing a sampling frame of businesses (which in itself is likely to be a challenge in many African countries) and then collecting enough information from a large enough sample to be able to select the most appropriate matching group of businesses to serve as a counterfactual – increasing the costs, time and logistics required to prepare a baseline survey. In contrast, a randomized experiment would not require a sampling frame (since all participating businesses in the study would come from the actual applicant pool) and baseline data for all of these businesses could potentially be collected cheaply through an application form that captures key outcome variables and indicators on which the randomization is likely to stratify on.

# **3.3 What are the Key Initial Issues to Consider for an Impact Evaluation of these Projects?**

As outlined in McKenzie (2010), there are a number of typical challenges in conducting an impact evaluation of policy operating at a firm level. First, the number of SMEs in many African countries is relatively small. In our group of matching grant impact evaluations, the DTI’s BBSDP project – a matching grant for black-owned SMEs<sup>7</sup> in South Africa - is a clear example of this challenge. The government was interested in supporting firms of a certain size because it already had a number of incentives for micro enterprises and wanted to shift focus from survivalists to established firms with potential for growth. In that vein, when preparing the project, the government had selected a minimum annual turnover of Rand 1 million (approximately USD 125,000) for firms to be eligible to apply. However, based on a set of representative firm-level surveys in South Africa, it seemed
638|
|Status of Agriculture Marketing Society-Census 2011|0.428|0.428|0.015|
|Percentage of IrrigatedLandfrom Total Land-SECC2013|5.894|6.502|1.166|
|Percentage of Households having Source of Light: Electricity -<br>Census2011|42.517|42.777|0.211|
|Percentage of Households having Bicycle-Census 2011|39.491|38.618|0.867|
|Density of Primary Health Centers (per 100 Households) -<br>Census 2011|0.013|0.007|2.207**|
|Density of Primary Schools (per 100Households)-Census2011|0.152|0.136|2.539**|
|Log of Total Geographical Area (Hectares)-Census 2011|6.006|6.040|1.141|
|Log Distance from Village of Nearest Bank (Kms.)|2.105|2.265|6.717***|
|LogDistrictHead Quarter(Distancein km)|3.915|3.907|0.344|
|Population Density ofthe Village-Census2011|2.652|2.536|0.967|

26 Significance levels: *:10%, **:5%, ***:1% 32
and Evidence from Field Experiments.” _The Review of Economics and Statistics_ 102(4):648–663.

- Delavande, Adeline and Hans-Peter Kohler. 2016. “HIV/AIDS-related Expectations and Risky Sexual Behaviour in Malawi.” _The Review of Economic Studies_ 83(1):118–164. **URL:** _https://doi.org/10.1093/restud/rdv028_

- Dhar, Diva, Tarun Jain and Seema Jayachandran. forthcoming. “Reshaping Adolescents’ Gender Attitudes: Evidence from a School-Based Experiment in India.” _American Economic Review_ .

- DHS. 2016. Tanzania Demographic and Health Survey (DHS) and Malaria Indicator Survey (MIS) 20152016. Technical report Ministry of Health, Community Development, Gender, Elderly and Children (MoHCDGEC) and Tanzania Mainland, Ministry of Health (MoH) and Zanzibar, National Bureau of Statistics (NBS) and Tanzania, Office of Chief Government Statistician (OCGS) and Zanzibar, ICF. **URL:** _http://dhsprogram.com/pubs/pdf/FR321/FR321.pdf_

- Ditlmann, R. K. and C. Samii. 2016. “Can intergroup contact affect ingroup dynamics? Insights from a field study with Jewish and Arab-Palestinian youth in Israel.” _Journal of Peace Psychology_ 22(4):380– 392.

- Doepke, Matthias and Michele Tertilt. 2009. “Women’s Liberation: What’s in it for Men?” _The Quarterly Journal of Economics_ 124(4):1541–1591.

- Doran, George T. 1981. “There’s a S.M.A.R.T. way to write managements’s goals and objectives.” _Management Review_ 70(11):35–36.

- Doyle, Aoife M., David A. Ross, Kaballa Maganja, Kathy Baisley, Clemens Masesa, Aura Andreasen, Mary L. Plummer, Angela I. Obasi, Helen A. Weiss, Saidi
For the UK, Javorcik et al. (2019) show it covers just above 85 percent of all (online and offline) job vacancies as measured by the UK Vacancy Survey. Bastos et al. (2023), exploiting the same dataset across 35 countries, find a close relationship between total job vacancy rates as measured by official statistical agencies and each country’s ratio of Lightcast job postings to total employment.

We complement the above evidence by comparing Lightcast job postings as a share of each country’s working age population, shown in Appendix Table A1. We find that Belgium, Switzerland and Luxembourg have the highest ratios of vacancies to working age population, while New Zealand, Norway and Italy have much lower shares. Another limitation of the data on online job postings is that they only measure stated but not necessarily realized demand. We therefore note that these measures of high-tech job postings serve as a proxy to track technology diffusion in the labor market, but future work expanding this with measures relating to the hires, separations, wages and internal training might provide a more complete picture. At this point,however, such consistent data is not available for such a wide sample of countries over time.

# **2.1.1 Identification of new technologies**

We use the raw text of the job postings to search for the 305 technology keywords from the classification of 29 technologies identified by Bloom et al. (2021).<sup>3</sup> We use Google translate to translate these keywords into all of the official languages of the 17 countries. We search for the keywords in both English and all official languages of the country. We then classify each job posting as being related to a specific technology if it includes at least one keyword for that technology.

This classification by Bloom et al. (2021) represents one classification of influential innovations that have been discussed prominently by investors and executives of publicly listed companies in the last two decades. Given that 53% of utility