rafmacalaba/datause-encoder-data huggingface dataset:
the effect of VC, relative to R&D, on the number of granted patents. They address concerns about causality by exploiting variations across countries and over time in private equity fund-raising and in the structure of private equity funds. They found that the effect of VC is significant only in the subsample of high-VC countries, where the ratio VC/R&D has averaged around 3.9 percent between 1991 and 2005 and VC has accounted for 10.2 percent of industrial innovation during that period. They also found that VC is relatively more successful in fostering innovation in countries with lower barriers to entrepreneurship, with a tax and regulatory environment that promotes venture capital investment, and with lower taxes on capital gains. Faria and Barbosa (2014) also found robust empirical support for a positive impact of VC on innovation on a sample of 17 European countries.

The thrust of the foregoing review is therefore that more recent empirical studies, which address a range of econometric problems that plagued the earlier literature (inadequate controls, endogeneity bias, direction of causality, sampling issues, and so on) tend to a large extent to support the existence of significant negative effects of financial barriers and lack of finance on the ability of firms–especially the smaller ones–to engage in innovation. This is especially so for studies that use direct indicators based on firm’s own assessments. We now turn to a formal analysis of the links between finance, innovation and growth, and study how the existence of finance constraints can be associated with multiple equilibria.

# 3 The Model

The economy that we consider is populated by individuals with different innate abilities, who live for two periods, adulthood and old age. Population is constant. Each individual is endowed with one unit of time in the first period of life, and zero unit in old age. Wages are the only source of income in adulthood.

There are three production sectors in the economy: the first produces a final good (manufacturing, for simplicity), the second intermediate inputs (which depreciate fully after use), and the third designs. As in Romer (1990), each new design involves the production of a new intermediate input, using a technology similar to the one used to 10
**Table 7. Summary of results of trends in income related MNCH inequalities, (20002014)**

||Rate Dif|ferences<br>|Rate|Ratio<br>||Test of do<br>concentrat<br>|minance:<br>ion curves<br>|
|---|---|---|---|---|---|---|---|
|||||||||
||Bottom<br>40% vs.<br>Top60%|oorest<br>(q1) vs.<br>Richest<br>(q5)|Bottom<br>40% vs.<br>Top60%|oorest<br>(q1) vs.<br>Richest<br>(q5)|CI|mca rue|iup rue|
|**_Panel A: Health_**|**_Status_**|||||||
|Stunting|Worsened|NS|Worsened|Worsened|Worsened|NS|NS|
|Wasting|NS|NS|NS|NS|Worsened|Worsened|NS|
|Underweight|Worsened|NS|Worsened|NS|Worsened|Worsened|NS|
|NMR*|NA|NA|NA|NA|Improved|NA|NA|
|IMR*|NA|NA|NA|NA|Improved|NA|NA|
|U5MR*|NA|NA|NA|NA|Improved|NA|NA|
|**_Panel B: Health_**|**_Services_**|||||||
|Measles<br>Vaccination|Improved|Improved|Improved|Improved|Improved|Improved|NS|
|Full|NS|NS|NS|NS|Improved|NS|NS|
|Immunization||||||||
|Contraceptive|NS|NS|NS|Improved|Improved|Improved|NS|
|ANC4+|NS|NS|NS|NS|Improved|Improved|NS|
|SBA|NS|NS|Improved|Improved|Improved|Improved|Improved|

Note: Table summarizes results of different approaches presented in Tables 3 thru 5 and the test of dominance. The test of dominance is based on O’Donnell (2006) and O’Donnell et al. (2008). The number of evenly spaced quintile points is 19 (from 5% to 95%) and the significance level is 5%. The dominance test rules, mca and iup, respectively denote multiple comparison approach and intersection union principle. NA is test not applicable. NS is no significant change from the earlier survey. * The improvement for mortality indicators is a progression of the distribution from a more pro-poor towards the line of equality.

There are four key messages of this paper. First, broadly, socioeconomic inequalities in maternal health services (Contraception, ANC4+, and SBA) narrowed over time. The result is robust to changes in method of analysis and measures of socioeconomic characteristics; the inequality narrowed by wealth group and also place of residence. Expansions in measles vaccination have also been pro-poor and pro-rural.

35
rates in developing countries appear to be larger than in OECD countries. This is likely to be explained by the subjective nature of some of the key innovation concepts. Respondents in different countries may have different understanding of what is considered as innovation, particularly when they are asked whether the firm has introduced a “new or significantly improved” product or process. In countries with higher prevalence of very incremental innovation, such as developing countries, it becomes even more subjective to judge how substantial or significant these innovation efforts are.

Third and more importantly, implementing innovation surveys is costly, especially in developing countries where the use of self‐administered or web‐based questionnaires is limited and data collection at the firm level heavily relies on face‐to‐face interviews. Comparability and reliability of the data collected are two key elements to consider when implementing innovation surveys. Measuring innovation is costly and difficult, and identifying the best instrument to collect innovation data is key from a cost‐benefit perspective.

These challenges motivated the Enterprise Analysis Unit of the World Bank Group to conduct an experiment to test two different approaches to measure innovation in developing countries. The first approach, more economical, uses a short questionnaire embedded in a general firm‐level survey, namely the Enterprise Surveys (ES). The second approach uses a standalone, longer questionnaire and it is significantly more expensive, although it provides more information.<sup>2</sup> The hypothesis to test is whether a cheaper approach could provide reliable higher frequency innovation data for key innovation inputs and outputs indicators by embedding a few questions in general firm‐level surveys.

This paper summarizes the results of the experiment. Using a rich data set of 11,150 firms in 15 countries we quantify differences in innovation rates as measured by the short and long questionnaire and, more importantly, based on insights from the subjective measurement literature, we provide some empirical evidence on what determines these differences.

First we compare self‐reported innovation rates in the short and long questionnaire across different types of innovation outputs (product, process, and marketing) and one measure of innovation input (R&D). Second, focusing on product and process innovation, we look at the description of the innovation introduced at the firm level to establish to which extent self‐reported innovations are actual innovations. We then compare verified and self‐reported innovation to determine whether the short or the long questionnaire is correct. Finally, as one of the main challenges of collecting data on firm‐level innovation

> 2 The standalone questionnaire was implemented as a follow up of the ES thereby reducing the need to collect data on other firm level variables that are helpful to understand the role and effect of innovation.

3
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weapons, which are used for international war rather than internal instability or for controlling dissatisfied civilians.26 Overall, the results suggest that a major determinant of levels of military spending is external threats as measured by NMES. These results suggest that for individual countries, military spending is rationally motivated by foreign threats. Such a conclusion is what an economist should expect, given that economics is built on the assumption that people are rational with regard to their expenditures. However, much of the literature assumes military expenditure in the developing countries is not rationally motivated. The threat response nature of military expenditure also implies that while a country can lower its military expenditures ifits neighbors do so as well, regional conciliation and disarmament might be important preconditions for reductions in military spending.

# VI. CONCLUSIONS

The purpose of our research was to analyze the economic impact of military expenditures. This entailed reviewing trends in military spending across regions and over time, analyzing the impact of military expenditures on economic growth as well as the channels through which this impact takes place, identifying the impact of military expenditures on other key economic variables, and analyzing the determinants of military spending. The key conclusions are summarized below.

* Levels of Military Spending. The share of military spending in GNP has been generally falling, including in the high spending areas of the Middle East and North Africa. The regions with the most serious econcmic problems - Sub-Saharan Africa and Latin America - spend relatively low shares of GNP on the military.

* The Impact of Military Expenditures on Economic Growth. For the full sample of 71 countries, our hypothesis of a quadratic milex-growth relationship was supported. As
|P128938|2012|2017|6/18/2017|11 Closed|Kiribati|
|---|---|---|---|---|---|
|P128938|2012|2018|11/30/2017|12 Closed|Kiribati|
|P128938|2012|2018|6/11/2018|13 Closed|Kiribati|
|P128938|2012|2019|9/19/2018|14 Closed|Kiribati|
|P128938|2012|2019|3/20/2019|15 Closed|Kiribati|
|P128938|2012|2019|6/1/2019|16 Closed|Kiribati|
|P128939|2012|2012|3/17/2012|1 Closed|Tonga|
|P128939|2012|2013|12/8/2012|2 Closed|Tonga|
|P128939|2012|2013|5/29/2013|3 Closed|Tonga|
|P128939|2012|2014|12/31/2013|4 Closed|Tonga|
|P128939|2012|2014|6/13/2014|5 Closed|Tonga|
|P128939|2012|2015|12/22/2014|6 Closed|Tonga|
|P128939|2012|2015|6/29/2015|7 Closed|Tonga|
|P128939|2012|2016|12/30/2015|8 Closed|Tonga|
|P128939|2012|2016|6/29/2016|9 Closed|Tonga|
|P128939|2012|2017|12/20/2016|10 Closed|Tonga|
|P128939|2012|2017|6/23/2017|11 Closed|Tonga|
|P128939|2012|2018|11/30/2017|12 Closed|Tonga|
|P128939|2012|2018|6/22/2018|13 Closed|Tonga|
|P128939|2012|2019|12/21/2018|14 Closed|Tonga|
|P128939|2012|2019|6/20/2019|15 Closed|Tonga|
|P128940|2012|2012|3/17/2012|1 Active|Tuvalu|
|P128940|2012|2013|11/24/2012|2 Active|Tuvalu|
|P128940|2012|2013|5/27/2013|3 Active|Tuvalu|
|P128940|2012|2014|12/30/2013|4 Active|Tuvalu|
|P128940|2012|2014|6/15/2014|5 Active|Tuvalu|
|P128940|2012|2015|12/24/2014|6 Active|Tuvalu|
|P128940|2012|2015|6/6/2015|7 Active|Tuvalu|
|P128940|2012|2016|10/30/2015<br>|8 Active|Tuvalu|
|P128940|2012|2016|4/15/2016|9 Active|Tuvalu|
|P128940|2012|2017|11/2/2016|10 Active|Tuvalu|
|P128940|2012|2017|5/18/2017|11 Active|Tuvalu|
|P128940|2012|2018|11/19/2017|12 Active|Tuvalu|
|P128940|2012|2018|5/10/2018|13 Active|Tuvalu|
|P128940|2012|2019|11/15/2018|14 Active|Tuvalu|
|P128940|2012|2019|5/20/2019|15 Active|Tuvalu|
|P128940|2012|2020|12/12/2019|16 Active|Tuvalu|
|P128940|2012|2020|6/12/2020|17 Active|Tuvalu|
|P128950|2013|2013|1/28/2013|1 Active|Bosnia and Herzegovin|
|P128950|2013|2014|7/30/2013|2 Active|Bosnia and Herzegovin|
|P128950|2013|2014|1/22/2014|3 Active|Bosnia and Herzegovin|
|P128950|2013|2014|6/21/2014|4 Active|Bosnia and Herzegovin|
|P128950|2013|2015|12/22/2014|5 Active|Bosnia and Herzegovin|
|P128950|2013|2015|6/17/2015|6 Active|Bosnia and Herzegovin|
|P128950|2013|2016|1/7/2016|7 Active|Bosnia and Herzegovin|
|P128950|2013|2016|6/28/2016|8 Active|Bosnia and Herzegovin|
|P128950|2013|2017|12/20/2016|9 Active|Bosnia and Herzegovin|
# **Introduction**

As the financial crisis emanating from the US spills over to the developing world in 2008/09, many people are asking whether there will be a reversal of the recent progress against poverty. This will depend in part on the distribution of the impacts of the crisis, and it should not be presumed that the poorest will be affected most; ironically, the same things that have kept many people poor in the first place—geographic isolation and poor connectivity with national and global markets—will help protect them from this crisis.<sup>2</sup> The prospects for reversing past progress will also depend on the distribution of past gains, in so far as this will determine how many people might be vulnerable to even small income losses. Consider Figure 1, which plots three hypothetical cumulative distribution functions (CDFs). The initial distribution is marked “A”. If all incomes increase by a similar proportion then the distribution shifts to “B”. There will be a fall in poverty across all possible poverty lines and a wide range of measures.<sup>3</sup> Alternatively (and there are other alternatives), the gains may be larger at lower poverty lines, with little or no reduction in poverty at high lines, as in distribution “C.” Poverty is also unambiguously lower for C than A, but C has a bigger bulge in the middle of the distribution, in that the density is appreciably higher at the median than for B and this holds in a wide interval around the median. In terms of the prospects of falling below relatively low poverty lines, distribution C has more people vulnerable to an aggregate economic contraction than B.

So it is important to know whether the developing world has moved toward a distribution more like C than B. While there have been a number of empirical studies of global poverty and inequality,<sup>4</sup> this issue has been neglected.

This paper aims to fill this gap in knowledge. The following section discusses the generic issues that arise in measuring the size of the “middle class” in developing countries and proposes a new definition. The paper then presents empirical results on the size of the middle class, how it has changed over time, and the proximate causes of the observed cross-country differences in the pace at which the middle class has grown. Some observations are also made on the possible

> 2   For evidence on this point in the context of the longer-term impacts of the Indonesian financial crisis in 1998 see Ravallion and Lokshin (2007).

> 3   As shown by Atkinson (1987), if the CDFs for two distributions do not intersect up to some maximum poverty line, then the ordinal comparison is robust to both the poverty line and the precise poverty measure, within a broad class of additive measures.

> 4   Contributions include Bourguignon and Morrison (2002), Chen and Ravallion (2004, 2008), Milanovic (2005) and Sala-i-Martin (2006); for a recent survey of this literature see Ferreira and Ravallion (2009).

2
**Table 5.** Cross-section (OLS) regression estimates of gender wage gap by skill of occupation (standard errors in parentheses)

||**1**<br>**Low and lower**<br>**middle income**<br>**countries**|**2**<br>**High and higher**<br>**middle income**<br>**countries**|
|---|---|---|
|Low skill occupation|-0.08|-0.02|
|(omitted: high skill occupation)|(0.01)|(0.01)|
|Year dummies|Yes|Yes|
|Dummies for Cyprus, Japan and Korea|Yes|Yes|
|Dummy for (ex) Communist|Yes|Yes|
|Observations|2257|7666|
|Adjusted R<sup>2</sup>|0.03|0.40|

Note: standard errors are corrected for clustering within the same country/occupation observations.

40
Diamond, C. and T. Fayed. 1998. “Evidence on Substitutability of Adult and Child Labor” _Journal of Development Studies_ . 34(3): 62-70.

Duflo, E., R. Glennerster, and M. Kremer.2008, “Using Randomization in Development Economics Research: A Toolkit” in T. P. Schultz and J. A. Strauss (eds.) _Handbook of Development Economics_ , Vol. 4: Elsevier: 3895-3962.

Dwyer, J. 1995. “The School Nutrition Dietary Assessment Surveys” _American Journal of Clinical Nutrition_ . 61: 173S-177S.

Edmonds, E. V. 2008. “Child Labor” in T. P. Schultz and J. A. Strauss (eds.) _Handbook of Development Economics_ , Vol. 4: Elsevier: 3607-3709.

Gahvari, F. and E. Mattos. 2007. “Conditional Cash Transfers, Public Provision of Private Goods, and Income Redistribution” American Economic Review, Vol. 97, No. 1, pp. 491-502, March.

General Accounting Office. 2002 “Foreign Assistance: Global Food for Education Initiative Faces Challenges for Successful Implementation,” Report to Congressional Requesters 02-328, United States General Accounting Office, Washington, DC.

Glewwe, P. and H. G. Jacoby. 1995 “An Economic Analysis of Delayed Primary School Enrollment in a Low Income Country: The Role of Early Childhood Nutrition” _Review of Economics and Statistics_ , 77(1): 156-69.

Grantham-McGregor, S. M., S. Chang, and S. P. Walker. 1998. “Evaluation of School Feeding Programs: Some Jamaican Examples” _American Journal of Clinical Nutrition_ , 67S: 785S-789S.

Institut National de la Statistique et de la Démographie and ORC Macro. 2004. “Enquête Démographique et de Santé du Burkina Faso 2003,” final report, INSD et ORC Macro, Calverton, Maryland, USA.

Jacoby, H. G. 2002. “Is There an Intrahousehold “Flypaper” Effect? Evidence From a School Feeding Programme,” _Economic Journal_ ,, 112 (476): 196-221.

21
TEGRA provided data on nine reading domains; a list of these is included in the annex. An average reading score is used to measure the overall effect of the CLRW intervention. It was defined as the average of the standardized scores of the nine reading domains. The reading domains were standardized using the mean and standard deviation of the baseline sample for each particular grade and round of survey, as some reading domains were not comparable between rounds. An index of home support was also used in this analysis and defined as the average for the 7 measures of home support. These measures of home support were whether a student receives help with his or her homework, whether parents are interested in his or her school day, whether he or she reads aloud at home, whether someone reads to the student at home, whether the child has been absent in the past two weeks of school, and whether he or she attended preschool and a CPBA. Note that the student is the respondent to these questions and is typically 6- or 7-year-old; it is not clear how reliable their responses were.

Three samples were used to estimate the effects of the CLRW intervention. The first sample was used to measure the average effect of one year of exposure to the intervention. This consisted of 1<sup>st</sup> grade students in 2016 and 2017. Both cohorts were weighted equally to provide an average effect. 1<sup>st</sup> grade students in 2015 were excluded from this because no data were collected at the beginning or end of the 2015 school year. The second sample was used to measure the effect of two years of exposure. This consisted of students who started 1<sup>st</sup> grade in 2016. The third sample was used to measure the persistence of the CLRW intervention’s effect on reading skills. This consisted of students who were in 1<sup>st</sup> grade in 2016 but compared their test scores at the end of the 2016 school year (at the end of two years of exposure to the intervention) with those at the end of the 2017 school year, providing an estimate of the effect of being out of the intervention for one year.

# _Descriptive statistics and baseline balance_

Table 6 presents baseline means and balance of three variables: average TEGRA score, whether a child was female, and the index of home support. Figure 2 plots the density of average TEGRA score and Annex Figure 1 plots the densities for all 9 reading domains. The sample 23
countries, with data on micro-economic reforms drawn from the World Bank’s Doing Business project.

Our data are not subject to the criticisms on other measures of economic institutions, which suffer from significant endogeneity issues, and measure outcomes rather than institutions (for example, Glaeser et al 2004). The Doing Business data are focused on specific regulatory or legal reforms, and as such they are a better indicator of underlying institutions. At the same time, they are sufficiently micro-measured so as to avoid the possibility of such reforms affecting the level of democracy.

We find robust evidence for the link between democracy and growth-enhancing reforms. The estimates imply that a move from below-average to above-average level of democracy increases the probability of reform by 20.4 percentage points, a large effect given that the mean value of the dependent variable is 55.1%. Higher-end estimates imply an increase of 28.6 percentage points in the probability of a reform in moving from below- to above-average level of democracy.

Using a continuous measure of democracy, we find that moving from the lowest to the highest value on the democracy scale increases the probability of reform by 17 to 35 percentage points. Finally, using an alternative measure of democracy, we find that democracy increases the likelihood of growth-enhancing reform by a high of 40 percentage points and a low of 16 percentage points. These results are robust to the inclusion of various proxies for the initial institutional environment, controlling for per capita income, and restricting the sample to developing countries.

4
measures remain, and even strengthen, when controlling for these factors. This suggests that in assessing agricultural relationships using control variables will not address measurement error. The implications of this study mirror those articulated by Escobal and Laszlo (2008) in their assessment of self-reported access to market or public infrastructure; namely, those who use self-reported land areas in an analysis of agricultural relationships need to address the likelihood that responses are reported with error and that this error is correlated with other household characteristics.

Exploring the implications of these results on the analysis of the inverse productivity relationship, we find that using self-reported land areas to assess the inverse productivity relationship tends to lead to an over-estimate of the relationship. While GPS land area measures still find the inverse productivity relationship exists, in our particular analysis it is not found to be as strong as when self-reported areas are used with estimated coefficients for the two measures being statistically significantly different. Further, the use of control variables in assessing the relationship creates a greater divergence between the GPS and self-reported estimates indicating a level of endogeneity of the self-reported areas. It might be argued that the results are driven by under reporting of land areas using GPS measures, particularly on plots smaller than 0.5 hectares where some studies have shown issues with GPS measures (Schoning et al., 2005; Keita and Carfagna, 2009). However, sensitivity analysis suggests that even for larger land sizes, where the confidence of GPS measure is greater, self-reported land areas still overestimate the inverse productivity relationship.

While the results indicate the inverse productivity relationship is overestimated, the results do not appear to consistently hold across all countries. This suggests that the deviation between selfreported and GPS land areas is context specific depending on circumstances within a country. This makes self-reported areas even more problematic as it becomes unclear what exactly it is measuring and what omitted factors the self-reported measure might be capturing. As such, there will always be the potential for biased estimates of agricultural relationship given the endogeneity of selfreporting. Even with potential errors in GPS measures, it should remain exogenous to household characteristics and a preferred option for analyzing agricultural relationships. The final conclusion then reinforces the arguments made by Gibson and McKenzie (2007) that GPS information should become a routine part of survey data collection since they tend to provide better measure and help to identify causal relationships.

13