rafmacalaba/data-use-mentions-extended huggingface dataset:
, the analysis assumes that the benefit<br>received is equal to the amount spent per student or person belonging to the health care system.<br>FIGURE V-9: PROGRESSIVITY AND REDISTRIBUTIVE EFFECT OF EXPENDITURE ON EDUCATION AND HEALTH IN LATIN AMERICA<br>Panel A: Education Expenditure Panel B: Healthcare Expenditure<br>0.8 0.055 0.8 0.04<br>0.05<br>0.7 0.7 0.035<br>0.045<br>0.6 0.04 0.6 0.03<br>0.5 0.035 0.5 0.025<br>0.03<br>0.4 0.4 0.02<br>0.025<br>0.3 0.02 0.3 0.015<br>0.2 0.015 0.2 0.01<br>0.01<br>0.1 0.1 0.005<br>0.005<br>0 0 0 0<br>Kakwani Coefficient Marginal contribution Kakwani Coefficient Marginal contribution<br>Source: The Impact of Taxes and Social Spending on Income Distribution and Poverty in Latin America. An Application of the<br>Commitment to Equity Methodology (CEQ), 2017. Colombia World Group staff estimates using the 2017 ENPH<br>Kakwani Coefficient<br>Kakwani Coefficient Redistributive effect<br>Redistributive effect<br>Regressive <---------> Progressive Unequalizing <---------> Equalizing Regressive <---------> Progressive Unequalizing <---------> Equalizing<br>Peru, 2009 Uruguay, 2009 Costa Rica, 2010 Ecuador, 2011 Chile, 2013 Mexico, 2010 Colombia, 2017 Brazil, 2009 El Salvador, 2011 Bolivia, 2009 Brazil, 2009 Colombia,
and 2017 school years in both CLRW treatment and control schools. Monitors attended reading instruction classes and assessed whether specific teaching practices and techniques promoted by the CLRW intervention were used by the teacher. Practices used by teachers during teaching and observed by the monitors were divided into 6 domains: phonemic awareness, phonics, reading of words, writing words and letters, forming of sentences and reading comprehension. For each of the practices within these domains, the observer rated whether the practice was fully, partially or not implemented. Classroom observations were conducted for all teachers in both 1st and 2nd grade of treatment and control schools. Estimates of effects described below account for unequal selection probabilities of schools and the small population of schools through a finite population correction.

The second source of data, the Tongan Early Grade Reading Assessment (TEGRA) was an adaptation of the Early Grade Reading Assessments initiated by the Research Triangle Institute (RTI International 2009) and adapted and applied to more than 100 languages in 65 countries (Dubeck & Gove 2015). The assessment was adapted to the Tongan language prior to its first implementation in 2009 and updated for subsequent rounds in 2014, 2016 and 2017. The assessment measured reading skills across 9 domains. Data were also collected on several measures of home support for reading.

The 2016 and 2017 rounds of TEGRA were used in this analysis to measure the impact of CLRW on students’ acquisition of reading skills during the school year. The survey was conducted in February and October of 2016, corresponding to the beginning and end of the 2016 school year, and in March and October 2017, corresponding to the beginning and end of the 2017 school year. All treatment and control schools were sampled. Within each school, all first and second grade classes were selected within which a random sample of students was drawn, stratified by gender and, for first grade, whether they had attended a CPBA previously. For the October 2017 round, third grade classes were also selected. For this analysis, standard errors were estimated to
WPS3774

# **Innovation and Development around the World, 1960-2000**

Daniel Lederman � Laura Saenz Office of the Chief Economist Latin America and the Caribbean The World Bank

## _Abstract_

This paper presents a database of indicators of innovative activity around the world since the early 1960s. The data include measures of innovation outcomes as well as variables related to innovation effort. The main indicator of innovation outputs is patents. The main variables related to innovation inputs are investment in research and development (R&D) and technical personnel (engineers, scientists) working in R&D activities. The sources of these data are publicly available (OECD, UNESCO, etc.), yet there have been few attempts at double checking the consistency of these data and digitizing observations dating back to the 1960s. After discussing the sources and definitions of the data, the paper examines trends and patterns of innovation outputs and inputs by looking at the over-time behavior of the relevant series and comparing the performance of developing and high-income countries. The authors also provide cross-regional comparisons and a detailed examination of trends in selected countries. In turn, the authors provide estimates of the impact of innovation on long-run development by following an emerging empirical literature on the determinants of levels of GDP per capita. The econometric results suggest that innovation might indeed have strong positive effects on long-run development, which might be stronger than the direct effects of institutions. The analysis pays close attention to issues related to the potential endogeneity of innovation (and institutions) with respect to the level of development.

World Bank Policy Research Working Paper 3774, November 2005 _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
presenting policy-makers with robust confirmation of the theory-based assertion that it is the institutional environment in general, and rule credibility, policy credibility, and resource adequacy and predictability in particular, which drive performance. Third, they provide an opportunity to inform in-country reform discussions with current data.

Fourth, the surveys also allow country-specific hypotheses to be tested. Many widely held views on public officials are often repeated but without substantive evidence - more akin to "urban myths" than to empirical observations. The surveys allow such assertions to be tested and supported or refuted.

Finally, the surveys allow some assessment of _which_ aspects of institutional environment are impacting on performance. It suggests those reform actions that seem most promising for higher performance, given the country's existing public sector conditions.

The surveys and analyses were financed under the Bank-Netherlands Partnership Program (BNPP). Surveys of public officials funded by the BNPP have been completed in Albania, Argentina, Bangladesh, Bolivia, East Caribbean States, Guyana and Indonesia. Surveys are in progress or are being initiated in Bulgaria, Cameroon, India, Kenya, Macedonia, and Moldova. This program has provided funds for data analysis of a separate survey of public officials in Armenia, and another data analysis exercise is also planned for an ongoing survey in Benin. The survey instruments were based on a model designed in collaboration with Professor Bert Rockman of the University of Pittsburgh. World Bank staff working on a particular country tailored this basic questionnaire according to country-specific background and issues in public sector reform.

This paper summarizes the premises on which the surveys were designed and analyzed, and the analytical framework. In explaining that analytical framework, examples have been used whenever it was considered their use would help make ideas clear. The examples have been drawn from survey experience during 1999 and 2000, and country data in Albania, Armenia, Bolivia, Guyana, Macedonia, and five countries in the East Caribbean region. The selection of examples was guided by their usefulness in illustrating the steps in the analytical framework, and not
countries. In 24 countries in the HIT data set tariff protection is pro-rich (and liberalization, pro-poor).

One simple way to assess how much we can gain by exploiting the heterogeneity in the household surveys is to compute the gains from trade using aggregate price changes and aggregate income and budget shares. To do this, we aggregate the staple agriculture income and budget shares from the 4-digit classification up to staple and non-staple agriculture. We then compute the welfare gains from the elimination of aggregate tariffs, as before. The

> 9Note that the HIT data have limited heterogeneity in expenditures on manufacturing and services. This may also play a role in our results.

> 10In the online Appendix we show that our results are very robust to using alternative cutoffs. In addition, we show that both average gains and the pro-poor bias are correlated with GDP per capita. The pro-poor bias is also larger in countries with higher levels of poverty.

10
for example, stabilize the KHR to USD exchange rate in response to a US policy rate increase by the Fed. Therefore, we can expect a decrease in international reserves when there is a positive shock in the US federal fund rate. International reserves are included in the estimation as a share of GDP ( _intresvgdp_ ).

# **4. Data and estimation results**

# **4.1 Data**

Quarterly series between the fourth quarter of 1994 and the fourth quarter of 2016 for the variables mentioned above are obtained from various sources.<sup>9</sup> The data for US federal fund rate, KHR to USD exchange rate, lending interest rate, and CPI are obtained from the International Financial Statistics (IFS) published by the International Monetary Fund (IMF). Information on exports and imports is obtained from the Direction of Trade Statistics (DOTS) published by the IMF. Since quarterly GDP data for Cambodia is not available, it is generated by interpolation from the annual GDP series obtained from the World Development Indicators (WDI) published by the World Bank (WB). Broad money (M2) is the sum of money in circulation, demand deposits, and time and saving deposits of both KHR and foreign currencies. Data is obtained from the Economic and Monetary Statistics published by the National Bank of Cambodia (NBC). Data on crude oil price is retrieved from the Primary Commodity Prices published by the IMF. International reserve levels are obtained from the NBC. The dummy variable is 1 for 1997Q3-1998Q4, 2003Q3-2003Q4, 2008Q3-2009Q4, and 2013Q3-2013Q4, and zero elsewhere. Potential GDP in real values required for the calculation of output gap data is generated applying the Hodrick-Prescott filter on the real GDP series.

Since our estimation uses quarterly data, adjustments are conducted to correct for seasonal fluctuations, except for the US federal fund rate and domestic lending interest rate. CPI is used to convert nominal GDP into real GDP.

# **4.2 Unit root tests**

As mentioned
,691|6,691|6,690|6,693|
|R-squared|0.021|0.007|0.011|0.137|0.133|0.150|
|F-test|1.40|1.23|1.23|0.57|0.32|0.82|

_Source_ : Data for the study comes from a survey of farmers in northern Uganda. The survey was conducted by Innovations for Poverty Action.

_Notes_ : Team (language group) fixed effects and a constant are included in all regressions but not shown. Robust standard errors in parenthesis are clustered at the enumerator level.

26
|51|
|**Agriculture**|21|25|
|**Industrial Processes**|15|15|
|**Instrumentation and**<br>**Devices**|6|6|
|**Environment**|4|1|
|**Bioinformatics, food**<br>**biotechnology and others**|5|2|
|Total|100|100|

Source: DBT SBIRI database.

The composition of approved relative to proposed SBIRI projects across biotechnology application areas is provided in Table 2. Of the approved 86 projects, 51% have been in healthcare. Agriculture projects are second, with 25% of approved projects. Applications of biotechnology to instrumentation and devices cut across traditional disciplines, so they have

> 18 To put these sales figures into perspective, again based on the pharma subset of all biotech industries, the top 10 Indian pharmaceutical companies by world-wide sales in 2009 ranged from $1.52 billion (Dr. Reddy‘s) to $498 million (Glenmark Pharma). See Bruche (2012), Table 1. According to the 2011 BioSpectrum–ABLE biotech industry survey, the size of the Indian biotech industry by revenues stood at roughly $3.5 billion (with bio-pharma accounting including diagnostics and devices accounting for 62%, bio-agriculture 14%, bio-industry 3.6%, bio-informatics 1.4%, and bio-services 19%). See http://biotechnews.co.in/April2011/toTheReaders.pdf.

23
- 32 -

2.41 In most Asian countries, increased consumption of rice is positively related to incomes at lower levels of income, whereas there is reduced demand for rice at the highest levels of income. A country's per capita GDP also influences rice consumption. Japan has reached a level nf income at which all urban consumers reflected in national statistics eat about the same amount of rice. In Indohesia and rural India. countries with lower national incomes, per capita consumption of rice is positively related to income. Different countries thus have different income elasticities of demand for rice, and these elasticities change over time as income increases.

2.42 From a nutritional standpoint, wheat and corn historically have been less expensive sources of energy and protein than rice. The lower cost of wheat and corn is one reason that food-deficit countries in Asia tend to import wheat to meet the needs of low-income consuimers for a basic food staple. It should be noted, however, that the preparation costs (milling and baking) of wheat are higher than those for rice, an important consideration since the gap between rice and wheat pricee has narrowed. While governments can save foreign exchange by importing the less-expensive wheat, the price consumers pay for the final product might be quite similar.

2.43 In aggregate terms, rice consumption in Asia has been increasing rapidly as a result of population growth and rising incomes. On a per capita basis, however, consumption has risen in some countries, remained stable in a few, and even declined in others. There is little evidence to suggest that increases in rice production have done much more than keep pace with population growth and achieve modest improvements in per capita consumption. Analyzing food self-sufficiency in terms of aggregate data on production and consumption has little relevance if a significant proportion of the population cannot afford an adequate diet. There is a sizable amount of latent consumption potential that would be realized if incomes rise. Although the income elasticity of demand for wheat is slightly higher than that for rice,
between the two periods. While for Costa Rica and El Salvador the additional emissions seem to come mainly from dirty industries, both type of industries played a role in the expansion of emissions for Nicaragua and Honduras. A more detailed analysis is necessary to assess the extent to which the variation in pollution relates to changes in composition and to control for underlying trends in emissions.

**Figure 1. Average pollution per year, before and after DR-CAFTA negotiation (pounds)**

<!-- Start of picture text --> CRI SVL<br>40000000 80000000<br>35000000 70000000<br>30000000 60000000<br>25000000 50000000<br>20000000 40000000<br>15000000 30000000<br>10000000 20000000<br>5000000 10000000<br>0 0<br>before after before after before after before after before after before after<br>air water total air water total<br>clean dirty clean dirty<br>NIC HND<br>35000000 18000000<br>16000000<br>30000000<br>14000000<br>25000000<br>12000000<br>20000000 10000000<br>15000000 8000000<br>6000000<br>10000000<br>4000000<br>5000000<br>2000000<br>0 0<br>before after before after before after before after before after before after<br>air water total air water total<br>clean dirty clean dirty<br><!-- End of picture text --> _Source_ : IPPS and authors calculations Figure 2 presents the results of the decomposition analysis under the baseline scenario for total emissions<sup>11</sup> (results for air, water and metal pollution can be found in ~~the appendixA~~ <u>nnex 2). The first</u> striking fact of the analysis is the importance of scale effects. More than 90 percent of pollution variation results from a scaling up of production. This is consistent across all countries
No|Yes|No|No|Yes|No|
|Firm*Year FE<br>Notes: The table shows estimates of OLS r|No<br>egressions on our loan|No<br>-level data for th|Yes<br>e sample of firms|No<br>that at any tim|No<br>e in our sampl|Yes<br>e period begin|

Notes: The table shows estimates of OLS regressions on our loan-level data for the sample of firms that at any time in our sample period begin an earmarked credit relationship with a bank. The dependent variables correspond to the interest rate spread (columns 1 to 3) and log volume (columns 4 to 6) of working capital loans. _Earmarkfbt_ is an indicator variable that equals one for all consecutive periods after a firm receives its first earmarked loan from a bank, and zero otherwise. Other controls include the length of the relationship duration of each firm-bank pair and an indicator variable that equals one for firms headquartered in locations above the median banking concentration (measured by the Herfindahl Index) and zero otherwise. Columns 1 to 3 control for maturity, volume and a dummy variable indicating if the loan was collateralized at the time of issuance. Columns 4 to 6 control for maturity, interest rate spread and a dummy variable indicating if the loan was collateralized at the time of issuance. All regressions include fixed effects for the ten loan rating categories established by regulators, as well as fixed effects for the type of loan (whether the loan has a fixed rate or a rate indexed by Brazil's CDI). Standard errors are doubled clustered at the bank and time level.

20
holds self-selecting, where only those in very dire circumstances might be claiming food assistance. However, it is further important to note that the increase in assistance was persistent and continued for at least the entire year following displacement. Thus, the large amount of food assistance that is reaching displaced households is likely contributing to the quick post-displacement recovery of the worst-off households. Importantly, as food prices continue to rise and as repeated currency crises continue to translate into rapidly rising food prices, this assistance is significantly increasing in value over time.

However, the increase in assistance only reached approximately half the displaced households in the sample. Since better-off households that did not receive additional food assistance also experienced a recovery, there are likely other factors contributing to their surprisingly quick recovery. For example, it is possible that some of the better-off households had more resources (e.g., networks) to rely on after settling in the migration district or that they were generally better able to assimilate. Regardless, even with these potential advantages over relatively worse-off households, other settings have inferred much more persistent adverse outcomes following displacement using different estimation strategies, such as comparing post-displacement outcomes of displaced to host populations (e.g., IPC 2017; etc.) or analyzing the well-being of displaced households long after the conflict has ended (e.g., Ibanez and Velez 2008).

# **Section 6. Conclusion**

Using rare food security and migration data from the ongoing conflict in the Republic of Yemen, we find a sharp, immediate decline in food access after displacement and a return to pre-displacement levels within months, on average. A portion of this rapid improvement is driven by increased food assistance, but there was also a surprising improvement among a subset of the sample that did not receive additional food assistance.

Combined, these results illustrate the consequences of forced displacement during active conflict. However, there are several caveats to this analysis. First, given data constraints, this analysis focuses on food access and not other
+ / '|0 / *|0 / 0|+++ / **|++ / **|
|Website dummy|0 / 0|0 / '|0 / 0|+ / 0|++ / **|+ / **|0 / 0|+ / *|+ / '|
|Budget Dummy|0 / '|0 / 0|0 / *|0 / 0|+ / *|0 / 0|+ / 0|+ / 0|0 / 0|
|Port Dummy|- / **|0 / '|0 / 0|- / **|+ / 0|0 / 0|+ / *|0 / **|- / *|
|Capital Dummy|0 / 0|0 / 0|0 / 0|0 / 0|- / 0|- / 0|+ / ***|- / *|0 / 0|
|**Country Variables**||||||||||
|Country Population|0 / 0|0 / 0|0 / 0|0 / 0|0 / 0|0 / 0|++ / **|0 / 0|- / *|
|Urbanization (%)|+ / '|0 / '|0 / 0|+ / '|+ / 0|+ / 0|0 / *|0 / '|+ / '|
|Kearney Globalization Index|0 / '|0 / 0|0 / 0|+ / 0|+ / *|+ / *|+ / *|+++ / ***|+++ / ***|
|Control of Corruption|+ / **|0 / *|+ / '|+ / **|++++ / ***|++ / **|+ / 0
15 “selected” individuals (respectively 17 and 22) were sampled out of localities in T1 (respectively T2 and T3). Finally, 10 “non-selected” individuals were sampled in each (rural) locality to study spillovers. When it comes to estimating spillovers, the sample size for the non-selected was determined to detect minimum effects between 0.18 standard deviations (for the pooled treatment) and 0.26 standard deviations (for comparisons across arms), for a power level of 80% and significance level of 5% and using baseline data for the intra-cluster correlation (ICC) parameter. Power was recalculated ex-post using the follow-up control group data to compute the mean, standard deviation and intra-cluster correlations for key outcomes. The minimum detectable effects remain between 0.15 and 0.22 standard deviations for the main outcomes. This means that the study is powered to detect a 20% impact (+5,300 CFA) on profits in independent activities, a 37% impact (+5,800 CFA) on starting capital or a 23% impact (+5,800 CFA) on the value of assets in the main activity.

> 29The sample includes 3,237 “selected” individuals, and the pooled specification with a common support cut-off contains 2,936 observations.

> 30The spillover sample includes 1,983 “non-selected” individuals, and the pooled specification with a common support cut-off contains 1,201 observations.

> 31In the sample of “selected” individuals, attrition is 10.56%, 11.30% and 10.44% for T1, T2 respectively T3 (using the common support cut-off). In the sample of “non-selected” individuals, attrition is 7.92%, 8.22% and 8.17% across T1, T2 respectively T3 (using the common support cut-off). In the total sample of selected individuals (not using the common support cut-off), attrition is on average 10.8% for “selected individuals” and
)|(0.00594)|(0.0142)|(0.0181)|(0.0232)|(0.0204)|
|country fixed effect|No|Yes|No|No|No|No|
|Observations|505|505|505|505|505|505|
|R-squared|0.198|0.444|0.249|0.267|0.250|0.268|

Notes: Table shows results of a regression of the annualized log change in survey mean consumption or income per capita on the annualized log change in private household expenditure per capita as measured from national accounts. Sample consists of all consecutive spells between 1981 and 2021, dropping spells with baseline and endline poverty rates lower than 2 percent. Sample drops from 575 to 505 because for some countries data on private household expenditures from national accounts are missing. Robust standard errors in brackets. ***: Statistically significant at 1%; **: Statistically significant at 5%; *: Statistically significant at 10%.

25