rafmacalaba/datause-encoder-data huggingface dataset:
_Chen and Kee: Knowledge and Endogenous Growth_ 18

# **References:**

Aghion, Philippe and Peter Howitt (1992). “A Model of Growth Through Creative Destruction.” _Econometrica_ . Vol. 60, No. 2 (March), pp. 323-351.

Aghion, Philippe and Peter Howitt (1998). _Endogenous Growth Theory_ . MIT Press.

Barro, Robert J. (1991). “Economic Growth in a Cross-Section of Countries.” _Quarterly Journal of Economics_ . Vol. 106, No. 2, pp. 407-443.

Chen, Derek H. C. and Carl J. Dahlman (2004). “Knowledge and Development: A Cross-Section Approach.” World Bank Policy Research Working Paper No. 3366. The World Bank, Washington D.C., August. Dinopoulos, Elias and Peter Thompson (1998). “Schumpeterian Growth without Scale Effects.” _Journal of Economic Growth_ . Vol. 3, No. 4 (December), pp. 313-335.

Grossman, Gene and Elhanan Helpman (1991). _Innovation and Growth in the Global Economy_ . MIT Press.

Hall, Robert E. and Charles I. Jones (1999). “Why Do Some Countries Produce So Much More Output per Worker than Others ?” _Quarterly Journal of Economics_ . Vol. 114, February, pp. 83-116.

Howitt, Peter and Philippe Aghion (1998). “Capital Accumulation and Innovation as Complementary Factors in Long-Run Growth.” _Journal of Economic Growth_ . Vol. 3 (June), pp. 111-130.

Jones, Charles I. (1995). “R&D-Based Models of Economic Growth.” _Journal of Political Economy_ . Vol. 103, No. 4 (August), pp. 759-784.

Jones, Charles I. (1999). “Growth: With or Without Scale Effects?” _American Economic Review_ . Vol. 89, No. 2 (May), pp. 139-144.

Jorgenson, Dale W. and Kevin Stiroh (2000). “Raising the Speed Limit: U.S. Economic Growth in the Information Age.” _Brookings Papers on Economic Activity_ . Vol. 1, pp. 125-211. Kortum, Samuel (1997). “Research, Patenting and Technological Change.” _Econometrica_ . Vol. 65, No. 6, pp. 1389-1419.

Krueger, Allan B. and Mikael Lindahl (2000). “Education for Growth: Why and For Whom?” National Bureau of Economic Research Working Paper No. 7591.
in Sub-Saharan Africa). In South Asia, all countries experienced a decline in the intergenerational share, with India registering the largest reduction. The evidence suggests that the evolution of intergenerational share does not depend systematically on the level or evolution of sibling correlation in a country. The share of intergenerational transmission declined in both Pakistan and Bangladesh even though their trajectories for sibling correlation were opposite (an increasing sibling correlation in Pakistan, and a declining one in Bangladesh).

# (7) Conclusions

We provide comparable estimates of intergenerational educational mobility for 53 developing countries using sibling correlation as an omnibus measure, and data from 230 waves of Demographic and Health Surveys. Sibling correlation is an omnibus measure because it captures all the observed and unobserved family and neighborhood factors shared by the siblings when growing up together. Sibling correlation is thus a much broader measure compared to the other widely used measures in the literature such as intergenerational regression coecient, intergenerational correlation (Pearson correlation), and intergenerational rank-rank slope. Another important advantage is that sibling correlation is less susceptible to the biases caused by coresidency restrictions in the surveys as missing older children who grew up many years ago in dierent family and neighborhood environment does not bias the estimates. The Demographic and Health Surveys provide high quality data on schooling of children and father (years of schooling, not categorical), and the data on father's schooling are not based on children's recall. To the best of our knowledge, this is the rst paper to provide estimates of sibling correlation in schooling for a large number of developing countries using high quality data standardized across countries.

The estimates suggest that sibling correlation in schooling in developing countries is much higher (average 0.59) than that in developed countries (average 0.41). We nd substantial spatial heterogeneity across regions, Latin America and Caribbean with the highest (0.65) and Europe and Central Asia with the lowest (0.48) estimates. Country level heterogeneity within a region is more pronounced. The evolution of sibling correlation suggests a variety of mobility experiences, with some regions registering a monotonically declining trend from the 1970s birth cohort to the 1990s birth cohort (Latin America and Caribbean and East Asia and Pacic), while others remained trapped in stagnancy (South Asia and Sub-Saharan Africa).

24
Salehyan, Idean and Kristian Skrede Gleditsch, 2004. Refugee Flows and the Spread of Civil War. Paper Present at the 100th _Annual Meeting of the American Political Science Association_ , Chicago, IL, 2-5 September 2-5.

Sambanis, Nicholas, 2001. “Do Ethnic and Non-Ethnic Civil Wars Have the Same Causes? A Theoretical and Empirical Inquiry (Part 1).” _Journal of Conflict Resolution_ 45 (3): 259-82.

Sambanis, Nicholas, 2002. “A Review of Recent Advances and Future Directions in the Quantitative Literature on Civil War.” _Defense and Peace Economics_ , 13(3): 215-243.

Sambanis, Nicholas, 2003. "Using Case Studies to Expand the Theory of Civil War." Conflict Prevention and Reconstruction Working Paper No. 5 (150pp). May 2003. [http://lnweb18.worldbank.org/ESSD/essd.nsf/CPR/WP5] Sniders, A. B. and R. Bosker, 1999 _. Multilevel Analysis._ London, Sage.

Starr, Harvey, 1991. “Diffusion Approaches to the Spread of Democracy.” _Journal of Conflict Resolution_ 35(2): 356-81.

Rabe-Hesketh, S., Skrondal, A. and Pickles, A. (2004). Generalized multilevel structural equation modelling. _Psychometrika_ 69 (2): 167-190.

Walter, Barbara, 2004. Does Conflict Beget Conflict? Explaining Recurring Civil War.” _Journal of Peace Research_ , 41(3):371-388.

Woodwell, Doug, 2004. "Unwelcome Neighbors: Shared Ethnicity and International Conflict during the Cold War." _International Studies Quarterly_ 48(1):197-233.

31
# TABLE OF CONTENTS

||Page|
|---|---|
|I. EXCHANGE RATE UNIFICATION AND INFLATION<br>.|.<br>1|
|II. MODEL..<br>.|4|
|Government Spending and Foreign Exchange Rationing<br>.|.<br>4|
|Production and Real Exchange Rate.<br>.<br>.|6|
|Inflation Tax, Portfolio Balance and the Premium<br>.<br>|.<br>8|
|Trade-off between Tax on Exports and Inflation Tax<br>Unification Through Overnight Floats and Inflation<br>Exchange Rate Rules ......................................|.<br>.<br>10<br>.<br>.<br>13<br>...........<br>17|
|III. EXAMPLES FROM SUB-SAHARAN AFRICA<br>..<br>Overnight vs. Gradual Reform<br>.<br>.|19<br>20|
|IV. CONCLUDING REHA KS ..........................................|.... ......<br>22|
|Footnotes .......................................................|.....<br>24|
|ApDendix<br>.........<br>|27|
|References<br>.....<br>..........<br>.........................|....................<br>29|
|Figures 1-<br>6||
|___________<br>Table 11:|______________<br>Export starters|________<br>and non-|____________<br>starters thre|__________<br>e years bef|___________<br>ore the star|__________<br>t|___________|_______________|_______________|
|---|---|---|---|---|---|---|---|---|---|
|Country|Year of||Productivity|premia of e|xport starters|[p-values]||No. of|No. of|
|___________|start<br>______________|Sales/<br>________|worker<br> <br>____________|Value added<br>__________|/worker<br>___________|TF<br>__________|P<br>___________|observ.<br>_______________|starters<br>_________________|
|Belgium|All years|7.5|[0.14]|3.7|[0.18]|-1.0|[0.83]|1684|160|
||1999|29.2|[0.04]|13.2|[0.10]|9.7|[0.14]|202|13|
||2000|6.6|[0.68]|22.1|[0.01]|2.2|[0.84]|223|16|
||2001|13.1|[0.38]|9.9|[0.38]|5.4|[0.48]|283|26|
||2002|-1.0|[0.91]|5.9|[0.36]|13.6|[0.14]|267|32|
||2003|3.0|[0.83]|-8.2|[0.24]|2.0|[0.78]|253|33|
||2004|2.7|[0.86]|7.3|[0.14]|-8.0|[0.12]|234|24|
||2005|6.1|[0.65]|3.1|[0.83]|-0.7|[0.96]|222|16|
|China|All Years|14.7|[0.00]|12.6|[0.00]|7.9|[0.00]|202,498|8,738|
||2001|17.1|[0.00]|13.6|[0.00]|3.4|[0.22]|34,714|863|
||2002|21.8|[0.00]|14.4|[0.00]|7.4|[0.01]|36,446|999|
||2003|19.9|[0.00]|12.5|[0.00]|4.9|[0.07]|38,420|999|
||2004|13.8|[0.00]|11.6|[0.00]|5.3|[0.00]|44,058|2,878|
||2005|9.4|[0.00]|11.4|[0.00]|12.7|[0.00]|48,860|2,999|
|Colombia|All Years|12.3|[0.00]|11.4|[0.00]|-10.2|[0.00]|20,504|696|
||1984|-1.9|[0.80]|5.4|[0.46]|-37.4|[0.00]|2,275|47|
||1985|-3.3|[0.58]|3.5|[0.56]|-20.3|[0.05]|2,375|65|
||1986|6.3|[0.30]|10.9|[0.07]|-27.5|[0.00]|2,497|74|
||1987|8.2|[0.27]|8.0|[0.27]|7.5|[0.56]|2,537|54|
||1988|11.2|<br>[0.12]|12.8|<br>[0.06]|-16.8|<br>[0.10]|2,636|59|
||1989|21.7|<br>[0.00]|14.6|<br>[0.01]|-7.4|<br>[0.39]|2,711|84|
||1990|8.8|[0.11]|11.9|[0.03]|-5.2|[0.48]|2,777|119|
||1991|21.9|[0.00]|12.6|[0.00]|3.0|[0.61]|2,696|194|

55
|S|t|Ef<br><br>t|fect Si<br><br>NEH|ze<br><br>C|Weight<br><br>H)|
|---|---|---|---|---|---|
|2|———#*———|— 0.38[|<br>0.13,|<br> 0.63)|<br>2.19|
|2<br>|+|0.01 [|-0.24,|0.25}|2.25|
|5|—i|0.32[|0.18,|0.46]|3.70|
|5|—l—|040[|0.28,|0.52]|4.03|
|5|—i—|0.26[|0.12,|0.40)|3.74|
|5|He|046[|0.38,|0.54)|4.59|
|5|—_i—-|0.19[|0.03,|0.36]|3.27|
|5|———_TM»———|0.31[|0.03,|0.59}|1.92|
|6|||0.19[|0.19,|0.19]|5.33|
|6|be]|0.11[|0.11,|0.11]|5.33|
|6|Fal|0.07{|0.07,|0.07]|5.33|
|6|fie||0.04[|0.03,|0.05}|5.32|
|6|B|0.04[|0.04,|0.04]|5.33|
|6|a|0.05[|0.04,|0.05}|5.33|
|6|Lal|0.12[|0.12,|0.12}|5.33|
|7|—|0.16[|-0.03,|0.35}|2.97|
|8|gg|0.09[|0.06,|0.13]|5.17|
|8|GS|0.08[|0.05,|0.12}|5.20|
|8|&|O.10[|0.06,|0.13]|5.20|
|8|—|0.16[|0.09,|0.23}|4.76|
|8|Oo|0.12[|0.08,|0.16]|5.17|
|8|El|0.10[|0.09,|0.11]|5.32|
|8|—_@|0.23[|0.06,|0.40)|3.24|
|Overall|ae|0.16[|0.11,|0.21]||
|Heterogeneity: 1?=0.01, F= 100.00%, H*= 1.58e+09||||||
|Test of 8, = 8: Q(22) = 1.29e+10, p =0.00||||||
|Testof8=0:2=6.36,p=0.00||||||

Random-effects REML model ~2 0 2 4 6
We focus on the most important ones here. It becomes clear that the signs and significance of the coefficients on institutional quality variables are robust with respect to the estimation procedure: all coefficients are significant at least on a 5 percent confidence level with the exception of coefficient on corruption, in which case the t statistic is only slightly below the 5 percent critical value. Coefficients on Tertiary Education appear significant and imply somewhat larger returns on education than in previous regressions. The above specification predicts a larger rate of conversion to a common cross-country steady state of per capita industrial output than that suggested by previous specifications. Another set of findings is worth paying special attention to: the results above suggest that developed legal and regulatory framework positively influences industrial growth both through investment and total factor productivity. To put it differently, the fact that after controlling for an estimated rate of investment, coefficients on PRS indicators remain strong (see Table 3), suggests that institutional quality affects both the amount of investment in the economy as well as the efficiency of resource allocation (at least in the industrial sector).

It can be inferred from the regression results that _ceteris paribus_ a one percent increase in per capita industrial value added can be achieved by either a 7.7 percent increase in the level of Tertiary Enrollment or a 2.2 percent increase in the rate of investmentl'. Our findings are not significantly different from those of Mankiw _et al._ (1992) who report the elasticity of per capita output growth with respect to the investment stage regression without including PRS indicators. The outcome of the regression is summarized below: Dep. Var. = -1.6 - 0.16*Log(Initial) + 0.8*Log(TerEdu) + 0.092*Est. Log(I/GDP); R<sup>2</sup> _=0.45_ (-2.31) (-2.32) (0.86) (3.08) The results are consistent with those reported in Table 3 and if contrasted with the latter can hardly be viewed as indicative of multicollinearity.

10 Reported elasticities are based on the sixth specification, i.e. where the PRS Composite measure is used.

12
Policy Research Working Paper 9730

# **Abstract**

Recent empirical work suggests that there are large agglomeration gains from working and living in developing country cities. These estimates find that doubling city size is associated with an increase in productivity by 19 percent in China, 12 percent in India, and 17 percent in Africa. These agglomeration benefits are considerably higher relative to developed country cities, which are in the range of 4 to 6 percent. However, many developing country cities are costly, crowded, and disconnected, and face slow structural transformation. To understand the true productivity advantages of cities in developing countries, this paper systematically evaluates more than 1,200 elasticity estimates from 70 studies in 33 countries. Using a frontier methodology for conducting meta-analysis, it finds that the elasticity estimates in developing countries are at most 1 percentage point higher than in advanced economies, but not significantly so. The paper provides novel estimates of the elasticity of pollution, homicide, and congestion, using a large sample of developing and developed country cities. No evidence is found for productivity gains in light of the high and increasing costs of working in developing country cities.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice, the Urban, Disaster Risk Management, Resilience and Land Global Practice, and East Asia and the Pacific Region Office of the Chief Economist. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank. org/prwp. The authors may be contacted at agrover1@worldbank.org, slall1@worldbank.org; and jtimmis@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
making their lending relationships more stable and generally improving access to credit for all firms (Giannetti and Ongena, 2009b). Similarly, other firm-level evidence showed that lending by foreign banks was associated with growth in firms‘ sales, assets, and leverage (Giannetti and Ongena, 2009a). Again, however, those results were from before the global crisis studied in this paper.

Although the signs of the coefficients for the foreign participation variables are the same when we run these regressions on the full BEEPS sample, magnitudes and significance levels are smaller than for the balanced panel of BEEPS respondents (regressions not reported). It should therefore be kept in mind that these results hold for a sample that differs slightly from the full one as described above.

# **D. Financial Constraints during the Crisis – Discussion**

At first blush, it may seem surprising that the changes in reported financial constraints were more severe for large firms than others during the crisis. Their size and age makes them more transparent for lenders to evaluate, often they have more and better collateral to pledge, and they are more likely to have long-standing relationships with multiple banks, all of which could help them obtain or retain external finance to a greater extent than other firms during the crisis. In this sub-section, we therefore compare the tendency to seek external finance and the evolution of financial constraints from 2005 to 2008-9 for large versus small firms.

While all firms were more likely to apply for loans in the 2008-9 BEEPS than in 2005 or 2002, the increase was especially pronounced for large firms (Table 6). Whereas 39.2% of large firms applied for a loan in the 2005 survey, 71.8% did so in the 2008-9 survey. For small firms the share that applied for loans hardly increased over the two surveys (from 25.8% to 26.7%). This does not necessarily imply that small firms were less affected by the drop in demand that accompanied the crisis than large ones. Both groups were affected, but large firms likely felt that their likelihood of obtaining credit in 2008-9 was reasonably high. By contrast, small firms, which Brown et al. (2011) shows were more likely to be discouraged from applying for loans in 2005 because of procedural hurdles and their belief that the application was likely to be rejected, remained much less likely to seek bank loans in 2008-9. If they were discouraged in 2005 about 28
Policy Research Working Paper 10168

# **Abstract**

The COVID-19 pandemic has disrupted survey and data systems globally and especially in low- and middle-income countries. Lockdowns necessitated remote data collection as demand for data on the impacts of the pandemic surged. Phone surveys started being implemented at a national scale in many places that previously had limited experience with them. As in-person data collection resumes, the experience gained provides the grounds to reflect on how phone surveys may be incorporated into survey and data systems in low- and middle-income countries. This includes agricultural and rural surveys supported by international survey programs such as the World Bank’s Living Standards Measurement Study—Integrated Surveys on Agriculture, the Food and Agriculture Organization’s AGRISurvey, or the 50x2030 Initiative. Reviewing evidence and experiences from before and during the pandemic, the paper analyzes and provides guidance on the scope of and considerations for using phone surveys for agricultural data collection. It addresses the domains of sampling and representativeness, post-survey adjustments, questionnaire design, respondent selection and behavior, interviewer effects, as well as cost considerations, all with an emphasis on the particularities of agricultural and rural surveys. Ultimately, the integration of phone interviews with in-person data collection offers a promising opportunity to leverage the benefits of phone surveys while addressing their limitations, including the depth of content constraints and potential coverage biases, which are especially challenging for agricultural and rural populations in low- and middle-income countries.

This paper is a product of the Development Policy Team, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at pwollburg@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
twice as likely to be poor than divorced men in that same age group. For women, widowhood, at all ages is a poverty increasing factor, although of declining value as women age. For men and women, remaining single by age 50‐64 puts them at a higher poverty rate than their married peers of the same age.

_Figure 6. Female and male poverty rate by age group and marital status_

# _6.a. Female poverty rate_

<!-- Start of picture text --> Poverty rate for age group<br>50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>15_17 18_49 50_64 65+<br>6.b. Male poverty rate<br>50<br>45 Poverty rate for age group<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>15_17 18_49 50_64 65+<br>Poverty rate<br>Never married Living together Married Divorced/separated Widowed Never married Living together Married Divorced/separated Widowed Never married Living together Married Divorced/separated Widowed Never married Living together Married Divorced/separated Widowed<br>Poverty rate<br>Never married Living together Married Divorced/separated Widowed Never married Living together Married Divorced/separated Widowed Never married Living together Married Divorced/separated Widowed Never married Living together Married Divorced/separated Widowed<br><!-- End of picture text -->

# _6.b. Male poverty rate_

Notes: The size of the dot represents the relative weight of each group within the total poor for the age group. Total sample 89 countries Source: WB Staff’s calculations based on GMD.

15
# **Log population in neighboring squares**

To test hypotheses concerning population concentrations in the immediate neighborhood of the squares, information is coded in three variables. The first `ln(population in neighborhood 1st order)' is the sum of the population sizes in the eight immediately contiguous squares. The second is the 2nd order neighborhood population – the sum of the populations in the 16 squares contiguous to these again. The third is the sum of the populations in the 24 3rd order neighbors. The variables were thereafter log-transformed.

# **Population growth in square**

Population growth data was derived from the `log population in square' variable. We coded local population growth as the difference in square population from one period to the next. For observations in the period 1.1.1960–31.12.1964 we coded population growth as missing. For observations in the period 1.1.1965–31.12.1974 we used log population for 1970 minus log population for 1960, etc.

# **Log population in country**

To allow distinguishing between the local-level and national-level mechanisms, we added information for the total population of the country, log-transformed. The data were taken from the _Times Concise Atlas of the World_ (2000).

# **Log area of country**

We also added information on the total extent of the country in log square kilometers. The data were taken from the _Times Concise Atlas of the World_ (2000).

# **Distance from Capital**

To test hypotheses regarding the distribution of populations, we coded the distance from each square to the capital of the country. The variable was coded as the distance in terms of squares and log-transformed.

20