rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Table 5 System generalized method of moments (GMM) estimation of the model with lagged log per capita income.</u>**

|<br>**capita income.**<br>Dependent variable: Log GDP per<br>capita|<br>(1)|<br>(2)|<br>(3)|<br>(4)|
|---|---|---|---|---|
|Intergenerational persistence|||||
|HIC|-0.070 (_0.641_)||||
|ECA|0.336 (_0.562_)||||
|LAC|0.483 (_0.892_)||||
|Others|0.909 (_1.056_)||||
|Intergenerational correlation|||||
|HIC||-0.016 (_1.334_)|||
|ECA||0.254 (_0.947_)|||
|LAC||-0.018 (_0.901_)|||
|Others||0.886 (_1.877_)|||
|Upward mobility gap|||||
|HIC|||-0.066 (_0.058_)||
|ECA|||-0.039 (_0.079_)||
|LAC|||-0.076 (_0.076_)||
|Others|||-0.046 (_0.129_)||
|Upward mobility in higher education|||||
|HIC||||-0.085 (_0.556_)|
|ECA||||0.612 (_0.678_)|
|LAC||||-0.419 (_0.896_)|
|Others||||1.106 (_0.972_)|
|Lagged Log GDP per capita (one year)|0.920<sup>***</sup>|0.915<sup>***</sup>|0.974<sup>***</sup>|0.913<sup>***</sup>|
||_(0.078)_|_(0.124)_|_(0.071)_|(_0.065_)|
|Number of observations|1158|1158|1158|1158|
|Number of countries|68|68|68|68|
|Arellano-Bond test for AR(1):|0.012|0.023|0.015|0.010|
|Prob > χ<sup>2</sup>|||||
|<br>Arellano-Bond test for AR(2):|0.001|0.001|0.001|0.001|
|Prob > χ<sup>2</sup>|||||
|<br>Sargan<br>test<br>for<br>overidentifying|0.714|0.798|0.813|0.813|
|<br> <br>restrictions (p-value)|||||
|<br>Hansen<br>test<br>for<br>overidentifying|0.307|0.320|0.365|0.317|
|restrictions (p-value)<br>_Source:_All specifications include country<br>specifications of table 2. Standard errors (|and year fixed ef<br>in parentheses) ar|fects as well as all<br>e clustered at the c|of the control varia<br>ountry level.|bles included in the|

_<mark>Source:</mark>_ <mark>All specifications include country and year fixed effects as well as all of the control variables included in the specifications of table 2. Standard errors (in parentheses) are clustered at the country level.</mark> *** <mark>significant at the 1% level,</mark> ** <mark>significant at the 5% level,</mark> * <mark>significant at the 10% level.</mark> 30
<!-- Start of picture text --> 2<br>1.5<br>0.5<br>-1.5 -1<br>1997 2003 2009 2015 2021 2027 2033 2039 2045 2051 2057 2063 2069<br>| ~~~PAYG Reforms<br>- M ultipillar Reforms with 6% Contribution<br>-MultipillarReforms with 8% Contribution<br><!-- End of picture text -->

On the whole, there is no reason to believe that the direct impact of the reform on national savings would be substantially stronger than indicated in Figure 12. Of course, to the extent that the reform has a positive impact on Hungary's growti performance, there could be additional indirect effects on savings, resulting from endogenous interactions between savings and growth, and these effects are not reflected in Figure 12.14 The perception that the Hungarian transition would be primarily "tax-financed", due to the PAYG reforms and the overall reduction in the General Government deficit, led many observers to state that the reform would benefit excessively future generations, to the detriment of current generations, who would be forced to "pay twice". However, whereas it is true that the reform package has reduced significantly the burden on future generations, it has not been sufficient to fully restore intergenerational balance, given the severe initial bias against these generations. Calculations of generational accounts for Hungary indicate that the pension reform has reduced the net burden on future generations by roughly three fourths, but that future generations are expected to remain net contributors to the system.1<sup>5</sup> The reduction of the initial intergenerational imbalance has been achieved by increasing slightly the burden on current workers, as older workers and pensioners have not been significantly affected by the reform and have remained net beneficiaries of the system. The calculations of generational accounts for Hungary also show that current workers would have incurred even greater losses if the second pillar had not been introduced. What the calculations do not capture, however, is the possible positive impact of the reform on Hungary's growth performance and on the welfare of current generations.

> 14 The interactions between pension reform, growth, and the welfare of different generations are examined in Kotlikoff (1995) and Corsetti and Schmidt-Hebel (1995). Loayaza, Schmidt-Hebel, and Serven (2000) provide a recent survey of the empirical literature on savings.

> **'5** Gal, Simonovits, and Tarcali (2000).

**1X**
_(Table C.4 continued.)_

|Household members|Number of household members|
|---|---|
|Household head|Dummyvariable =1 if respondent is the household head|
|Migrant|Dummyvariable =1 if respondent is not born in Dhaka|
|Educationyears|Highest educational attainment inyears|
|Nr Children|Number of children(onlyavailable if ever married)|
|Child outside HH|Dummy variable =1 if any children live outside the household (only<br>available if Nr Children _≥_1)|
|Combined<br>income<br>of<br>spouses|Sum of husband and wife’s income from economic activities last<br>month(if married). Onlyavailable forpromotion sample.|
|Spousal education dif-<br>ference|Wife’s - husband’s education years (if married)|
|Agegapof spouses|Wife’s - husband’s age(if married)|
|Woman’s<br>spousal<br>income share|Ratio of wife’s income from economic activity and combined income<br>of spouses last month (if married). Only available for promotion sam-<br>ple.|
|Woman’s household in-<br>come share|Ratio of wife’s income and household income last month (if married)|
|Decision-making|See Panel A.|
|Supportive family|standardized index of the following 3 questions measuring support<br>for female relatives working in garment industry, computed follow-<br>ing Anderson (2008) and O’Brien (1984):<br>1. Whether the female relatives were ever asked to quit their jobs<br>(=0 if Yes)<br>2. How supportive the family is towards female relatives working<br>in the garment industry (categorical variable)<br>3. How supportive the family would be if a female relative re-<br>ceived an offer for a promotion to supervisor (categorical vari-<br>able)|
|Experience in garment<br>sector|Number of completed years in the garment industry|
|Exposure|Dummy variable =1 if respondent has worked under a female super-<br>visor or manager|
|Internal locus of control|standardized index following Anderson (2008) and O’Brien (1984)<br>of 8 locus of control questions indicating whether nominee chooses<br>the internal option,based on Rotter(1966)|
|Grit|standardized index following Anderson (2008) and O’Brien (1984)<br>of 6 grit items, higher score means higher grittiness, based on Duck-<br>worth andQuinn(2009)|

60
deposit funding shares of 0.04 and 0.47, respectively.<sup>10</sup> Qualitatively, these results are consistent with the inverted U-shaped relationships between the Z-score on the one hand and the fee income and non-deposit funding shares on the other in Figure 8. Thus, there may only be a limited potential to reduce bank risk by venturing into fee-generating activities, while bank risk is potentially reduced by combining deposit and non-deposit funding.

The Z-score regressions with 2004 data reported in Panel B of Table 7 are very similar to those reported in Panel B. Specifically, in regressions 1 and 3 the fee income share continues to obtain negative and significant coefficients. In regression 4, the linear and quadratic terms in the fee income share similarly continue to obtain negative and positive coefficients, respectively, but now both coefficients fail to be statistically significant. This regression does not provide support for the hypothesis that the relationship between the Z-score and the fee income share is non-linear.

Our fee income variable reflects non-interest income in the form of fees, commissions and trading income. To conclude this section, it is interesting to break this variable down into a trading income part and a non-trading, non-interest income part (with both variables defined as shares of total operating income). Specifically, columns 1 to 3 of Table 8 report regressions of the bank rate of return that include a trading income variable, a non-trading, non-interest income variable and these two variables together, respectively. The trading income variable enters columns 1 and 3 with positive and significant coefficients, while the non-trading income variable obtains insignificant coefficients in columns 2 and 3. Trading income rather than non-trading, non-interest income appears to increase a bank’s rate of return. Analogously, columns 4 to 6 of Table 8 relate the Z score to trading income, non-trading, non-interest income and both, respectively. Now we see that both types of non-interest income (of the trading and non-trading type) reduce the Z-score significantly.

> 10 Note that these results suggest that for all but a few percent of banks a higher fee income share increases risk at the margin (unlike in Figure 8), while they confirm that for the great majority of banks a higher non-deposit funding share reduces risk (as in Figure 8).

22
in the absence of such auxiliary mechanisms which are probably very important in practice, trade could result in asymmetric sectoral output growth as well as employment shifts, and that small changes in preferences can result in sharply discontinuous patterns of spatial specialization. Since facilitating trade is likely to bene...t the non-farm sector most, market integration appears an e¤ective way of catalyzing economic development in economies dominated by agriculture, i.e. much of the developing world.

# References

Caselli, F. and Coleman II, W. 2001 "The US structural transformation and regional convergence" Journal of Political Economy 109, pp 584–616.

Dasgupta, P. and Ray, D. 1986 "Inequality as a Determinant of Malnutrition and Unemployment, 1: Theory." Economic Journal 96(4), pp 1011-1034.

Dasgupta, P. and Ray, D. 1987 "Inequality as a Determinant of Malnutrition and Unemployment, 2: Policy." Economic Journal 97(1), pp 177-188.

de Janvry, A., Fafchamps, M., and Sadoulet, E. 1991 “Peasant Household Behaviour with Missing Markets: Some Paradoxes Explained”Economic Journal 101(405) pp 1400- 1417.

Grigg, D. 1994 “Food Expenditure and Economic Development”GeoJournal 33(4), pp 377-382 Harris, D.J. 1985. "The theory of economic growth: from steady states to uneven development." In Contemporary Issues in Macroeconomics and Distribution, ed. G. Feiwel. London: Macmillan.

Krugman, P. 1991 "Increasing Returns and Economic Geography" Journal of Political Economy 99(3) pp. 483-499.

Krugman, P. 1998 "What’s New about the New Economic Geography" Oxford 10
# Research Components of World Bank Education Projects, since 1982.

|Area|Country and Loan/Credit<br>Nunke<br>|Approved<br>I|Research Components|Current Status of Studies,<br>including<br>Completed Documents/Data|
|---|---|---|---|---|
||||(iv)<br>Strengthen public<br>sector management to<br>enable studies of teacher motivation<br>(para. 2.11 p.9)||
|LAC|HondurasLN2804-HO|1987|Rural Primnry EducationManagement Proiect.<br>Project revolves around micro-planming<br>of rural<br>primary education. Diagnostic survey of six<br>Departments (para. 2.06 p.<sup>9</sup>).|Two micro plans completed; Eight others in<sup>draft</sup><br>form being prepared by the Goverrnent.<br>Ministerio de Educacion (1989). "nicroplanes<br>educativos deprimeria<br>par los distritos<br>de<br>Choloma, departementode Cortes y Olanchito,<br>departemento<br>de Yoro." (doc. iG34349)<br>also<br>J.PraudayL.Figueroa .1989). "Terminacion<br>del proyecto Administracion<br>de le Educacion<br>Primaria Rural."<br>(doc.4G34819).|
|LAC|JamaicaON 2070-Jm|1982|Third Educational<br>(Technical<br>and Vocational).<br>Mi) Pre-investment<br>study on Primary Education<br>learning materials (para. 3.15 p.18).<br>(ii) Evaluation of each project institution<br>using graduate tracer studies (3.14 18).|See LAC files.<br>(ii) NYCD (1966). "Development<br>and utilization<br>of<br>the tracer system for placement and follou-up<sup>of</sup><br>vocational and technical program institutes:<br>Evaluation<br>plan and guidelines." (doc. SG00360)<br>[No document found for finalstudy].|
|LAC|JamaicaLM 2899-JN|1988|Education Program Preparation<br>and StudentLoan<br>Proiect. Studies of: i)administrative<br>organization<br>and school cooperation;<br>(ii) financialevaluation<br>of secondary school<br>reforms (emphasize<br>unified lower cycle);<br>(iii) determination<br>of family incomes and<br>student expenditures<br>at higher educationto<br>determine appropriateness<br>of loans, determin-<br>ationof unit costs of public<br>education.<br>(para.2.7<br>p.10 AmexVil).|See LAC files.<br>(i) MOE (1988). "School maintenance study."<br>(doc. SG16014);<br>(ii) MOE (1988) "Study of student loan scheme."<br>(doc.**_U_**G16018);<br>(iii)<br>CVsfor unitcost studif<br>(1989)<br>(doc.#G33899-900); for schoot administration<br>study (doc.# G33901;for Reform of<sup>Seco.,dary</sup><br>Education study (doc.SG33902); for Eckvationat<br>Planming study (doc.SG33904).|

63
<!-- Start of picture text --> 25 24.4<br>20<br>15<br>10.6<br>10<br>5<br>0<br>Offline Online<br>Conditional on having each channel<br>P90 vs Median<br>Gap in sales per channel per worker<br><!-- End of picture text --> Figure 16: Ratio of sales value by channel P90 relative to median

# **6. What drives these results?**

Several factors may explain firms’ decisions to increase digital investments as a response to the pandemic, and the heterogeneous response that explains an increasing digital divide. This section tries to understand more formally what are the firm characteristics pre-pandemic that may explain these firm digital choices. For this exercise, The analysis is restricted to the sample to countries for which pre-pandemic information from WBES was available. Specifically, this analysis estimates Equation 1 using some variables extracted from the baseline WBES survey,<sup>15</sup> and check the correlation for two potential channels: i) The technological capabilities of firms before the shock; ii) Access to finance. Table 1 and Table 2 summarize the main results.

The first hypothesis is that firms with higher levels of technology capabilities pre-pandemic were able to adjust faster to digital adoption. Comin et al. (2022) combine data from the Firm-level Adoption of Technology survey with the BPS for Brazil, Senegal, and Vietnam and show that firms using a higher level of technology sophistication pre-pandemic were significantly more likely to increase the use to digital technology, which also contributed to a better sales performance at the initial stage of the shock. Using a different sample and simpler proxies for technological capabilities pre-pandemic - such as having own website and spending on R&D - the data also shows that these characteristics are significantly and positively associated with the likelihood that firms start or increase the use of digital technologies, increase the share of remote workers, and the share of online sales after COVID-19. Thus, technological capabilities pre-pandemic are also associated with larger digital adoption during the pandemic, which suggests persistence and an increasing risk of the digital divide moving forward.

> 15The surveys was implemented over the 2018-2019 period.

19
income<sup>5</sup> ). Whether or not a change in the price of commodity **k** is favorable to the poor (i.e. pro-poor) depends on the distribution of these budget shares over the entire population.

We need a decision rule for declaring a price change pro-poor. For our purpose, an increase (reduction) in the price of commodity **k** is considered pro-poor if it induces an absolute increase (reduction) in poverty smaller (greater) than it would in a _benchmark_ case<sup>6</sup> . As a benchmark we select a situation where a one percent change in the price of **k** would have the same relative impact on real income **x** . We construct such a hypothetical by assuming that everybody assigns the same proportion of real income, **w0k** , to the purchase of commodity **k**<sup>**7**</sup> . This constant budget share can be computed as the average budget share for commodity **k** . That is: where **mx** represents the highest level of real income in the population. The corresponding price elasticity of poverty is: Since budget shares do add up to unity, the price elasticity defined by equation (2.9) can

> 5 Interestingly, this price elasticity is analogous to the growth elasticity of poverty proposed by EssamaNssah and Lambert (2006) in the context of measuring the pro-poorness of economic growth. 6 The choice of a benchmark is analogous to selecting a level of significance in statistical analysis. 7 This idea is similar to the use of a Lorenz curve to assess inequality.  Indeed, within the Lorenz framework, the amount of observed inequality is inferred from a comparison of the actual distribution of economic welfare (as represented by income or expenditure) with a hypothetical distribution where everybody would have equal share.

9
for each household group available income is computed in terms of dollars per day. Three of the household groups fall in between the one and two dollar per day poverty lines. The notable exception is the household group with an outside link and lacking draught power with just over two dollars per day. This is in line with the rural-urban income differences since this household group specializes in outside province migration.

To get a clear view of differences in household endowments that affect household response, the middle part of Table 2 details income sources. Specific features of the village SAM and equilibrium model arise in this table. For example, income from labor and irrigated land is split between shadow income<sup>4</sup> and above shadow income. The household survey data reveal imperfect labor and land markets. Households are involved in a variety of off-farm activities with different wages. These wages are well above the estimated shadow wage, indicating restricted access to off-farm employment and suggesting a situation of labor surplus at the local level. This is not unexpected, given the high population density in rural China and similar findings in a study by Bowlus (2003).

In the SAM and village equilibrium model the demand-constrained labor market is accounted for by valuing labor against household-specific shadow wages, estimated using the household survey data. In case of off-farm activities, labor then earns revenue above the shadow wage which is tracked in a separate account of the SAM. For example, for the unlinked households with no draught power labor is the most important endowment, contributing 74.9 percent to its income, broken down into 57.4 percent coming from shadow wages and 17.5 percent from above shadow wages.

> 4  Nonseparability results in household-specific shadow prices that balance households’ unobservable demand and supply. We therefore estimated an agricultural production function, explaining the total value of household output in terms labor-, land-, manure-, feed- and external inputs. The shadow prices are derived from this estimated production function as the marginal value product of each input. Specifically, for each household in the sample the household-specific shadow prices for household non-tradables are derived as the marginal value product of each input. Averaging over the households within a household group yields a

14
McDonald, Ian M. and Robert M. Solow, 1985. Wages and Employment in a Segmented Labor Market. Quarterly Journal of Economics, Vol. 100, No. 4., pp. 1115-1141.

Meghir, Costas and Edward Whitehouse, 1995. The Evolution of Wages in the United Kingdom: Evidence from Micro Data. Journal of Labor Economics, Vol. 14, No. 1., pp. 1-25.

Micco, Alejandro and Carmen Pages, 2004. Employment Protection and Gross Job Flows: A difference-in-difference Approach. IADB Research Department Working Paper 508.

Navarro-Lozano, S. and P. Schrimpf. 2004. The Importance of Being Formal: Testing for Segmentation in the Mexican Labor Market. Mimeo, University of Chicago.

Neri, Marcelo C., 2002. Decent work and the informal sector in Brazil. EPGE, Ensaios Economicos, 461.

Petrin, Amil and Jagadeesh Sivadasan, 2006. Job Security Does Affect Economic Efficiency: Theory, a New Statistic, and Evidence from Chile, NBER WP 12757.

Rajan, Raghuram and Luigi Zingales, 1998. Financial Dependence and Growth, American Economic Review. American Economic Association, vol. 88(3), pages 559-86.

Saint-Paul, Gilles, 1995, ''The High Unemployment Trap'', _Quarterly Journal of Economics_ , May 1995, CX, 2, 527-550 Schneider, Friedrich and Dominik H. Enste, 2000. Shadow Economies: Size, Causes, and Consequences. Journal of Economic Literature, XXXVIII, 77-114.

Summers, Lawrence, 1989. Some Simple Economics of Mandated Benefits, American Economic Review, 79(2), 177-183.

Stock, J. H., and M. Yogo, 2003. Testing for Weak Instruments in Linear IV Regression, in Identification and Inference for Econometric Models: Essays in Honor of Thomas J. Rothenberg, ed. by D. W. K. Andrews and J. H. Stock Cambridge, U.K: Cambridge University Press, 80– 108.

Verhoogen, Eric, 2008, Trade, Quality Upgrading and Wage Inequality in the Mexican Manufacturing Sector, _Quarterly Journal of Economics_ , 123(2), 489-530.

World Bank, 2007. Latin America and the Caribbean Flagship Report: Informality in LAC: A barrier for development. The World Bank, Washington, DC.

33