rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 7934

# **Abstract**

This paper uses a regression discontinuity design to study the effect of Poland’s In-Tech program on innovation activities. The analysis focuses on a component of the program that provides grants to projects that are carried out by consortia of firms and research entities. Data from a 2016 follow-up survey of applicants to the 2012 and 2013 calls for proposals show that In-Tech largely funds projects that would not otherwise get funded by other agencies or by the consortia themselves, increasing the probability of a project being completed by almost 60 percentage points. The results also show that the program leads to more science-industry collaboration, and increases the probability of applying for a patent related to the proposed project, as well as the probability of publishing a research paper related to the project. The analysis also finds early effects on commercialization of products related to the proposed project, although these products currently still make up a small share of firm’s sales.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at mbruhn@worldbank.org and dmckenzie@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the
(26) **&H** = aCX 6TCY / (<sup>3</sup> XY Finally, the fraction of the variance in measured equivalent consumption that is due to measurement error is calculated as:

- (27) _Fraction measurement error in C_ = (&2C - **&H** ) / &C

The methodology to estimate the variance of changes in underlying living standards is analogous: (28) **&AH** = OAC,AX OAC,AY / **GAX,AY** where **6A,B** denotes the covariance between A and B.

The first instrument used is a measure of subjective living standards. In the Russian data, subjective living standards are measured by the question: _Please imagine a 9-step ladder where on the bottom, (thefirst step), stand the poorest people, and on the highest step, (the ninth), stand the rich. On which step are you_ today? Lokshin and Ravallion (1999) show that total household income is a significant predictor of the answer to the subjective welfare question, although its explanatory power is low. In the Polish data, living standards are measured by the question: _How would you rate the general material si uation of your household? Very good, good, average, rather bad or bad._ These answers are converted into a 5 point linear scale. By definition, this instrument should be related to underlying living standards. It seems unlikely that it is correlated with measurement error in equivalent consumption.

The second instrument is equivalent household income. While this instrument is related to underlying living standards, one may worry whether measurement error in equivalent income is independent of measurement error in equivalent consumption. For example, a misspecified equivalence scale would cause a correlation between these error terms. Similarly, households who willingly underreport income (e.g. due to forgetfulness or for fear of taxation), might also underreport consumption. Hence, this second instrument is
# **4 One Solution: Temporary Migration**

## **4.1 High Costs and Returns to Migration**

Temporary international migration is an expensive endeavour for which the BRMS provides a detailed breakdown for the first time. At their median values, the cost of migration exceeds (pre-departure) household income in the sample by a factor of 2.6. More than 10% of individuals require at least 10 years to raise the entire cost through their earned incomes.<sup>9</sup> Accordingly, credit access is critical for migration. The largest part of migration expenses, 56% is composed of the costs charged by the intermediary agencies that match workers with foreign employers.

In this section, we use the BRMS data to provide a cost-benefit analysis of temporary migration. Using a range of measures, we show that expected returns make migration a very profitable investment,<sup>10</sup> which enables returnees to start businesses for which local credit markets do not provide the necessary funds. Specifically, for these calculations, we use information on individuals’ earnings prior to migration, their wages while abroad, the duration of stay, the share of migration costs covered by loans, as well as the saving rates observed in different locations. Table 2 presents details of this information.

First, we compute a simple earnings-to-cost ratio, which we average across _N_ individuals _j_ in the sample: On average, the ratio of total earnings accumulated abroad over the cost of migration, _r_ 1, amounts to 9.3. Total foreign earnings are calculated as the time spent abroad multiplied by the average wage a migrant reports to have earned abroad. Foreign wages and migration _N_ costs imply an average break-even duration of _N_ <u>1</u> � _Cj/Wj_<sup>_a_=1</sup><sup>_._1years.Inourdata,the</sup> _j_ =1 median and the average duration of stay abroad are 4.
negative, global trade volume declined and remained suppressed for years thereafter, and global liquidity froze temporarily. Indian growth adjusted to a lower level. After the global financial crisis, India’s growth drifted down to about 7 percent, and some of the same drivers of growth that had played a prominent role during the pre-crisis boom were the ones that accounted for the slowdown. Slowdown has been most pronounced in investments and exports, both of which more than halved their contribution to growth.

**Figure 8: Three Phases of Growth**

8 A: Growth accelerated to an average rate of 8 B: Real per capita GDP growth followed the 5.4 percent during 1991–2003, followed by 8.8 GDP growth trajectory percent during 2004–08, and a slowdown thereafter <!-- Start of picture text --> 13 10<br>7.2<br>8.8 5.5<br>5.4 7.1 3.4<br>8 5 2.0<br>4.3<br>3 0<br>-2 -5<br>-7 -10<br>Real GDP Growth Average 1971-1990 Real per capita GDP Growth Average 1971-1990<br>Average 1991-2003 Average 2004-2008 Average 1991-2003 Average 2004-2008<br>Average 2009-2017 Average 2009-2017<br>1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br><!-- End of picture text --> Note: Central Statistics Office data. Years refer to respective fiscal years.

Below we analyze the period of economic boom and the succeeding period of deceleration, situating it in a global context. We compare the Indian experience with that of the other large emerging markets, and track its constituents. Comparing growth acceleration in 2004-2008 with that of
0.17]|0.09<br>[0.29]|-0.06***|-0.01|<br>0.01|
|Notes:|||||||||

(1) Source: UNPS 2013-14 round. The question on desire to work additional hours was asked, for those employed, as follows: “In the last 7 days, would [NAME] have liked to work more hours than [NAME] actually worked, provided the extra hours had been paid?” (1=Yes, in the current job 2=Yes, in taking an additional job 3=Yes, in a different job with more hours 4=No, and 9=Don’t know).

On availability to work, recent LSMS surveys ask respondents if they would “want to work if a job or business opportunity becomes available.” In the ILO LFS study, respondents were asked two separate questions on (a) whether they would want to work more paid hours per week than usual (desire to work), and (b) whether they could start working those hours in the next two weeks (availability to work).

(2) T-tests of equality of means were conducted between proxy and own-reporting, as well as across men and women within each of these categories. *** p<0.01, ** p<0.05, * p<0.1.

11
Policy Research Working Paper 5856

# **Abstract**

This paper analyzes firm growth patterns in South Asia, using establishment level data from an Interim Enterprise Survey. The survey was conducted by the World Bank in 2009 and 2010 and covers seven countries in the region. The first finding suggests that size in the base year gains importance for employment growth and firm age is statistically insignificant for growth. This contradicts the thought that young and small firms are the bearers of growth. Second, establishments in larger localities expanded faster, confirming the observation of urban centers as growth poles. Third, establishments in areas of severe conflict performed worse than establishments in other areas. Interestingly, the distribution of growth rates shows that both firm growth and fast-growing firms exist in conflict regions.

This paper is a product of the Finance and Private Sector Department, South Asia Region. 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://econ.worldbank.org. The author may be contacted at kfriesenbichler@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
9 _Table3: Affordability :Per Capita Household Expenditures on Medical and Health Care, 1992 (As percentage of Non-foodper capita expenditure)_

||_Quintile_<br>_I_|_s of house_<br>_II_|_holdper c_<br>_III_|_apita inc_<br>_IV_|_ome_<br>_V_|_Expenditure_<br>_Elasticity_|_T-ratio_|
|---|---|---|---|---|---|---|---|
|Instrument|0.04|0.04|0.31|0.41|0.44|<br>0.85|1.04|
|Primaryhealthcare|0.96|0.11|0.12|0.14|0.13|<br>1.45|1.93|
|Drugs|5.65|4.23|4.08|3.68|2.91|<br>8.11|4.56|
|Herbal medicine|0.47|0.42|0.44|0.45|0.50|<br>6.63|2.87|
|Service charges|2.05|0.92|0.62|0.76|0.60|<br>1.15|0.77|
|Othera|0.11|0.08|0.09|0.10|1.05|<br>0.94|1.23|
|Total|9.28|5.81|5.66|5.54|5.63|||

a. Home visits.

_Source:_ SSB: Urban Income Expenditure Survey, 1992.

_Table 4: Quality - Medical Personnel Input Mixes by Province (1994)_

|_Province_<br>_by quintile_<br>_ofper capit_<br>_income_|_a_<
_Brazilian spatial labor dynamics Page 25_

|Dependent Variable:<br>Delta ln Employment|2SLS Est.|2SLS SE|Spatial GMM<br>Est.<br>(Cutoff = 0.5)|Spatial GMM<br>SE<br>(Cutoff = 0.5)|
|---|---|---|---|---|
|Intercept|-1.0038159|.14846142|-.97731119|.21782738|
|ln Wage in 1991|.19207975|.01553089|.190244|.02302395|
|ln 5 to 15 over 15 to 55 yrs Ratio|.25067678|.03711903|.24860755|.0545722|
|Proportion of Natives in 1991|.292077|.04174566|.31104589|.06760336|
|Delta ln Market Potential|.2035026|.02778313|.19855203|.04571589|
|ln Teacher Qualification in 1991|-.00699189|.00239773|-.00710868|.00376014|
|Homicides in 1991|-9.7408304|4.5089288|-13.952994|12.869347|
|Employment in Farming in 1991|-.55168569|.17153628|-.62525272|.27117524|
|Bank Dummy|-.0029771|.00978021|.00085738|.01357927|
|ln Population in 1991|-.02653262|.00623792|-.02801059|.009894|
|Mean Temperature|-.00246354|.00267179|-.00177658|.00416755|
|Total Rainfall|3.313e-06|.00001244|7.357e-06|.00002025|
|Rainfall - 1o Princ. Component|.00715906|.00194123|.00712434|.0032946|
|Rainfall - 3o Princ. Component|.01638063|.0044276|.01515498|.00688489|
|Employment rate in 1991|.17582282|.14548762|.21597567|.23001887|
|crit. fn. test of overid. restrictions|
have estimated capital stocks by province and for three types of asset (Holz and Sun, 2017). There appears to be only two studies that provide estimates of gross fixed capital formation and capital stock on a sectoral basis. (Wu, 2015) provides a breakdown into 37 sectors with two assets per sector but it is limited to the period 1980 to 2010. The Penn World Tables provide a breakdown of the capital stock into four assets but with no sectoral breakdown. The objective of this note is to provide estimated of capital formation and the capital stock for four sectors (housing, infrastructure, government and business) for the period 1952 to 2016.

# **Estimating the capital stock**

An estimate of the capital stock has been made using by cumulating gross fixed capital formation from a base year and deducting the extent to which the capital stock falls in value as time passes.

This methodology, known as the perpetual inventory method, requires the following information:

- The value of the capital stock in an initial year.

- A continuous series for the value of investment (known technically as gross fixed capital formation) broken down into sectoral detail.

- A set of price indices that can be used to convert nominal investment outlays into real outlays.

- • A measure of the extent to which different types of assets fall in value over time.

The nature of the dependence of the capital stock on investment, the growth rate of investment and the depreciation rate is shown in Box 1. The inherent nature of the perpetual inventory means that for periods of time long-removed from its starting point, the current value of the capital stock is almost independent of the initial value of the capital stock. In addition, when there have been long periods of rapid growth in capital formation, as in China, the impact of different rates of depreciation on the estimates of the current growth rate of the capital stock is very limited – though the level of the capital stock does depend on the depreciation rate to a limited extent
|**Mean**<br>**TABLE 1**<br>**Sample Summary Statistics**|**P**|**ercentiles**||
|---|---|---|---|
||**10th**|**50th**|**90th**|
|**I. Household-level Data (Panel households;****_N_ = 3,424)**||||
|**Per Capita Income (Constant 1986 Yuan):**||||
|1987-88<br>526|229|448|896|
|1990-91<br>518|234|425|870|
|1995-96<br>709|316|593|1,192|
|1997-98<br>742|330|630|1,223|
|1999-00<br>749|310|614|1,312|
|2001-02<br>826|334|678|1,443|
|**Annualized Growth Rates from 1987-88 to:**||||
|1990-91<br>-0.009|-0.178|-0.009|0.157|
|1995-96<br>0.036|-0.045|0.039|0.116|
|1997-98<br>0.034|-0.034|0.034|0.102|
|1999-00<br>0.027|-0.035|0.029|0.089|
|2001-02<br>0.030|-0.026|0.031|0.085|
|**Composition of Income (Shares):**||||
|Agriculture, 1987-88<br>0.55|0.17|0.56|0.89|
|Wages, 1987-88<br>0.22
This section of the paper discusses some of the stylized facts found after analyzing the census information for 12 countries in Latin America, as well as their correspondent harmonized household surveys.

# **3.1. The evolution of growth and poverty in Latin America**

Economic growth in the region was an important driver for reducing poverty and improving the lives of many poor people during the last 50 years. The trend of the gross domestic product (GDP) in the LAC region was positive during half a century, although growth in the LAC region lagged other economies in the globe, such as the Four Asian Tigers.<sup>5</sup> At the same time, during the last 20 years the increase in GDP has been accompanied by a decline in poverty rates at the regional level (Figure 1), although recent weakening of economic conditions during 2015 and 2016 were accompanied by a relative slowdown of the social gains in the region (World Bank, 2016a).

Over the last decade, poverty reduction in LAC was driven mainly by economic growth, such that weak economic conditions in 2015 and 2016 raised concern on the challenge faced by further reductions in poverty and the growth of the middle class (World Bank, 2016a). Along with the reduction in poverty rates, improvement in other welfare indicators associated with development outcomes suggests that inequality has also been falling (the Gini index declined from 0.57 to 0.51 5 The Four Asian Tigers are Hong Kong SAR, China; Singapore; the Republic of Korea; and Taiwan, Chian, which experienced and maintained high growth rates between the early 1960s and the 1990s.

9
% 8 1 2<br>0.2 NOR 10% 3 2 4<br>ZAF 1<br>0 0% 0<br>10 30 50 70 90 EAP ECA LAC MENA SA SSA<br>EIU banking risk index (100=High), 2017 low both high climate high banking high both<br>E. Physical (disaster) risk vs. public debt risk F. Weather event & public debt risks by region<br>1.8 100%<br>1.6 BIH 90% 2 1 3<br>4 4 1<br>1.4 80%<br>6<br>1.2 THA MMR 70% 0 3 10 0<br>60%<br>1 KHM<br>OMN<br>SRB VNM 50% 5 8<br>0.8 6<br>0.6 BGD NIC 40%30% 7 1 2<br>0.4 SWENZLCHE AUS BGR HRV MOZ SDN 20% 8 5 2<br>0.2 CHL 10% 3<br>0 LUX SGP VEN 0% 1 0 1<br>10 30 50 70 90 EAP ECA LAC MENA SA SSA<br>EIU debt risk index (100=High), 2017 low both high climate high debt high both<br>Average Losses natural disasters (%GDP)<br>Average losses natural disasters (%GDP)<br>Average losses natural disasters (%GDP)<br><!-- End of picture text -->

Note: Panels A, C, and E: Countries above (below) country median in Notre Dame Readiness indicator in green (red). Panel A: average losses (% GDP) between 2005 and 2017.

Panels B, D, and F: columns show proportion of countries in each category (i.e., low climate and macro‐financial risks, high climate‐low
was in a private school the year before but in a public school today (switched to public) or if a student was in a public school the year before but in a private school today (switched to private). Approximately half the students are male, are 10 years old on average, less than half reside in urban areas and about 4% were graduating from upper secondary in the year we observe them in the panel. Among the students, 14.6% are in initial or pre-primary schooling, 57% in primary (grades 1-6), 18.6% in lower secondary (grades 7-9), and 9.8% in upper secondary (above 9<sup>th</sup> grade). About 25% attend bilingual schools, which may provide instruction in both Spanish and an indigenous language. Just over 22% of students attend a private institution, and we can also see that the COVID-19 positivity rate after 2020 was on average 18% but ranged between 0 and 41 percent across municipalities.

While having longitudinal data on students is ideal to observe changes over time, there are limitations due to their annual frequency. In an ideal situation, we would like to follow the same students on a weekly or monthly basis to better gauge the changing conditions with respect to the spread of COVID-19. However, the data are unable to provide this ideal setting that would better allow capturing the dynamics of school outcomes during the pandemic. This is not only an issue in Guatemala, but with most administrative educational records in developing countries. In the next section, we discuss how we reconcile the rapidly changing nature of infection rates with the available annualized administrative data and define an identification strategy to approximate the causal effects of differential COVID-19 exposure on educational outcomes in Guatemala.

11
Paraguay, and El Salvador. Surveys were processed following the protocol of the Socioeconomic Database for Latin America and the Caribbean (SEDLAC), a joint project between CEDLAS at the Universidad Nacional de La Plata and the World Bank.<sup>3</sup> Household surveys are not uniform across Latin American countries and in most cases not even within a country over time. The issue of comparability is of great concern. Owing to that situation, we make all efforts to make statistics comparable across countries by using similar definitions of variables in each country and by applying consistent methods of processing the data (SEDLAC, 2021).

For each country in our sample, we pool the available survey waves from 2010 (2009 in some countries) to 2020. The final sample includes children and young people in the age range from 6 to 24. To estimate the change in enrollment in 2020, we run a regression of the outcome of interest (whether the child is enrolled in education or not) on a linear time-trend and a dummy variable indicating that the observation refers to the year 2020.<sup>4</sup> Several control variables are also included to abstract from potential changes in sample composition, namely age, sex, urban or rural area of residency, and parental education. Formally, we estimate the following equation: where _Y_ indicates whether individual _i_ surveyed in year _t_ is enrolled in education (or in private education), _A2020_ is a dummy variable that takes the value 1 for the year 2020, τ stands for a linear time trend, _X_ is a vector including the control variables mentioned above and ε is the error term. The coefficient of interest is β. It indicates the deviation, in percentage points, of the year 2020 with respect to the overall trend in enrollment rates, net enrollment rates, or enrollment in private education, conditional on all the above-mentioned controls. Enrollment

> 3 Table A.1 in the Appendix shows basic information on the Latin American national household surveys used
inputs (public spending as a share of gross domestic product (GDP)) to outcomes, while _Living Life_ measures the ease of access to those basic public services as opposed to the outcomes or the quality of those outcomes. The WGI’s Government Effectiveness Dimension captures “perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies” (Kaufmann et al. 2010, 223). _Living Life_ , on the other hand, attempts to establish more objective measures of bureaucratic performance by accounting for time, processes, steps, and costs. The World Economic Forum’s Global Competitiveness Index’s performance measurement of government institutions and the IMD Business School’s world competitiveness rankings both examine enhanced economic growth and prosperity, whereas _Living Life_ focuses on the quality of interaction between citizen and governments.

Because bureaucratic complexity is often an effective proxy for corruption, _Living Life_ offers complementary insights on issues addressed by global, opinion-based and qualitative indicators such as Transparency International’s Corruption Perceptions Index and the Global Corruption Barometer. Other relevant performance measurements include the Open Budget Index, the Public Integrity Index, and Freedom House’s Civil Liberties and Political Freedoms Index.

Topic-specific initiatives such as those that collect data and analyze election outcomes dedicate efforts to understanding the societal impacts of voting. Extensive databases exist with relevant data on voting percentages of registered population, legislative frameworks for elections, and electoral management. Little research exists, however, focusing on the actual procedures and steps undertaken by citizens to register to vote and to fulfill their 6 | P a g e