rafmacalaba/datause-encoder-data huggingface dataset:
# Table A.6: **Alternative Models of Intergenerational Educational Mobility**

**Panel A: Normalized Years of Schooling**

||**Linea**|**r Model**|**Quadra**|**tic Model**|
|---|---|---|---|---|
||**Sons**|**Daughters**|**Sons**|**Daughters**|
||(1)|(2)|(3)|(4)|
|Born 1968-72_×_Inpres|0.382***|0.113|0.370***|0.104|
||(0.093)|(0.102)|(0.091)|(0.108)|
|(Father’s Edu /Sub-Group Sd)_×_Born 1968-72_×_Inpres|-0.139**<br>(0.058)|0.044<br>(0.063)|-0.274***<br>(0.093)|-0.020<br>(0.100)|
|(Father’s Edu /Sub-Group Sd) Sq_×_Born 1968-72_×_Inpres|||0.071**<br>(0.031)|0.037<br>(0.038)|
|R2|0.322|0.409|0.323|0.409|
|Observations|1199814|848350|1199814|848350|

**Panel B: Rank in Schooling Distribution**

||**Linea**|**r Model**|**Quadra**|**tic Model**|
|---|---|---|---|---|
||**Sons**|**Daughters**|**Sons**|**Daughters**|
||(1)|(2)|(3)|(4)|
|Born 1968-72_×_Inpres|0.087***|-0.032|0.085**|-0.024|
||(0.031)|(0.042)|(0.033)|(0.047)|
|Father’s Edu (Mid) Nat. Rank_×_Born 1968-72_×_Inpres|-0.084|0.104|-0.132|0.015|
||(0.051)|(0.080)|(0.152)|(0.168)|
|Father’s Edu (Mid) Nat. Rank Sq_×_Born 1968-72_×_Inpres|||0.075<br>(0.157)|0.099<br>(0.175)|
|R2|0.333|0.424|0.341|0.430|
|Observations|1199814|848350|1199814|848350|

Notes: Robust standard errors in parentheses clustered at the district of birth (*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1). Quadratic model is adopted following Becker et al. (2015). Family background is measured by father’s schooling. Father’s Edu is father’s years of schooling. The normalized schooling measure was constructed for sons and daughters separately. The Sub-Group SD refers to standard deviation of father’s years of schooling for that specific sub-samples (sons/daughters). The rank in schooling is constructed based on combined sample (Sons+Daughters), following Chetty et al. (2014). Sample corresponds to children born between 1957 and 1962, or 1968 to 1972. Covariates include birth district district FE, year of birth×1971 enrollment, year of birth×1971 number of children, year of birth×water sanitation program, year of birth dummies, following Duflo (2001). The variable Inpres measures the number of Inpres schools per 1000 children at the district level divided by the highest number of schools received by one district. For the sake of parsimony, only intercept, linear, and quadratic terms are reported in this table; full table, with all coefficients, is available upon request. Data sources: Indonesia’s full count census 2000 and Duflo (2001).

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45 Table 7 Public Sector Financing Requirement (Percent of GDP)

||1985|1986|1987|1988*|
|---|---|---|---|---|
|Overall Public Sector Deficit|4.4|6.7|9.9|6.1|
|Budget Deficit|2.4|4.9|6.5|5.3|
|Central Bank Losses|1.9|1.8|2.8|0.8|
|Credit to Development Banks|0.8|1.4|1.9|0.8|
|Public Sector Financing Requirement|5.1|8.1|11.2|6.9|
|Domestic Financing|1.2|5.7|9.8|6.9|

*Estimate

Source: World Bank, Central Bank and Ministry of Finance The large budget deficit had increasingly detrimental effects on the economy's performance. It directly contributed to inflation, but it also affected the allocation of credit and hence investment. While foreign exchange reserves lasted, the government could sell off foreign exchange rather than borrow in the home market or print money. Now, with reserves precariously low, there was no room left for further reserve sales to finance the deficit. 30 (See Figure 6) The declining reserves now forced a more consistent exchange rate policy which immediately raised the inflation rate.<sup>31</sup> Thus external financing of the budget deficit ultimately had an inflationary cost, even though it may have been delayed by a year or two. Another way to finance the deficit was through the banking system. High reserve ratios for banks or direct financing requirements 3OThe net reserves included on the liability side $800 million of arrears to the IMF. The gold position of the central bank had been revalued at various points and gm,ounted to $659 billion in December, 1988. 'By April 1989 the decline in economic activity and the restrictions of imports had become so massive that an actual reserve recovery had taken place. The reserve gains were sufficient to feed a rumor of another reactivation program.
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# **TABLE 6.2: Household Level - Univariate Probit Regressions**

Source of Data: Households' Survey Dependent variable: Household discouraged in at least two institutions (0,1) Marginal Effects evaluated at mean values are reported

|Middle Income<br>0.06<br>[2.27]**<br>High Income<br>0.075<br>[1.26]||||||||||
|---|---|---|---|---|---|---|---|---|---|
|Secondary Education|0.125<br>[3.09]***|||||||||
|University Education|0.207<br>[4.61]***|||||||||
|female|-0.039|||||||||
||[1.84]*|||||||||
|age||0<br>[0.26]|0.005<br>[0.94]|||||||
|age2|||0<br>[0.90]|||||||
|Dishonesty of 42 institutions - Number<br>||||0.004<br>[3.69]***||||||
|Dishonesty of 42 institutions - Average|||||-0.018<br>[1.74]*|||||
|Trust in State Institutions||||||-0.02<br>[2.18]**||||
|Knowledge report corruption|||||||0.088<br>[4.13]**|*||
|Member of association||||||||0.044<br>[5.38]*|**|
|Member of at least one association|||||||||0.086|
||||||||||[4.06]***|
|Observations<br>1558|1696<br>1696|1696|1696|1696|1696|1696|1696|1696|1696|
|Pseudo-Rsq<br>0|0.01<br>0|0|0|0.01|0|0|0.01|0.01|0.01|

Absolute value of z statistics in brackets

* significant at 10%; ** significant at 5%; *** significant at 1%

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time. Finally, this paper reviews several proposals for using alternate thresholds for income categories based on the current GNI per capita indicator. It also cites alternative methods of classification based on other variables, such as poverty levels or multidimensional indices.

The paper reflects information gathered during interviews and discussions with a number of key internal users of the current system, such as chief economists and selected directors and managers. It incorporates existing literature, including papers prepared for the Board of Executive Directors on the operational guidelines, and views of external users gathered through various channels, including blog posts and online discussions.

# **2. Main findings**

The paper finds that per capita GNI continues to be a readily available and reasonable measure for the purpose of classifying countries for analytical purposes. It correlates well, especially in terms of rankings, with a number of accepted indicators of development outcomes. Consequently, the income classification is widely used within and outside the World Bank for comparing attributes between groups of countries.

While local currency conversion factors based on purchasing power parities (PPP) are preferable to the use of market exchange rates for comparing per capita GNI using a common numeraire, the lack of consistent annual time-series estimates from the International Comparison Program (ICP) limits their use for the annual classification of economies. Had they been used, the classification would likely be subject to substantial revision when new benchmarks are published, which is not a desirable feature of such a system.

A known shortcoming with the use of market exchange rates is instability from one year to the next, and this can also introduce undesirable volatility in the classification system. The current method employed to mitigate this is known as the “Atlas” method, since it was first used in the World Bank’s _Atlas of Global Development_ publication. It uses an average of three exchange rates: the current year, and the previous two years inflated to current year prices. We find that the method is effective in achieving its objective of reducing volatility of the income classification.

5
# **Table 4: Firms in El Salvador during COVID**

|||Round 1<br>No-||R|ound 2<br>No-||
|---|---|---|---|---|---|---|
||Support|<br>Support|N|Support|Support|N|
|Percentage change of permanent full-time<br>employees since December 2019|-0.053|-<br>0.161***|377|-0.022|-0.133|401|
|Firms that the total number of temporary workers<br>decreased|0.210|0.335|372|0.366|0.302|395|
|Firms that total hours of operation per week<br>decreased|0.850|0.906|383|0.476|0.520|410|
|Firms that anticipate falling in arrears on<br>outstanding liabilities within next 6 months|0.041|0.524***|369|0.219|0.181|407|
|Cash transfers|0.118||387|0.270||404|
|Deferral of payments|0.265||387|0.238||405|
|Access to new credit|0.179||387|0.334||404|
|Fiscal relief|0.541||387|0.190||404|
|Wage subsidies|0.013||387|0.861||402|
|Other support|0.378||387|0.087||319|
|Average change monthly sales compared to one<br>year ago|-0.476|-0.589|387|-0.269|-0.229|406|
|Overdue on obligations to financial institutions|0.111|0.272*|379|0.535|0.269*|373|
|Adjusting or converting their production or services|0.034|0.228***|359|0.756|0.788|410|
|Decreased demand for products or services compar<br>to one year ago|0.882|0.749|385|0.731|0.613|410|
|Decreased liqdt or cash flow avail. since COVID-|||||||
|19 began|0.832|0.835|385|0.848|0.811|408|
|Started or increased online business activity|0.055|0.327***|385|0.540|0.573|409|
|Started or increased delivery of goods, services or<br>carryout|0.562|0.362|387|0.527|0.604|409|
|Started or increased remote work|0.631|0.472|387|0.638|0.473|404|
|Exporting directly (at least 10% of sales)|0.111|0.051|387|0.179|0.058|409|

Note: This table shows the firm performance and government support types in El Salvador in Round 1 and Round 2 surveys. Support columns show the respective statistics of firms that have received government support. No-Support columns show the respective statistics of firms that have not received any government support. N denotes the observation number. Means are weighted using the sampling weights from the ES. Asterisks in the No-Support columns are for unconditional t-tests comparing the respective statistic with the same statistic for the firms that received support. All standard errors are clustered at survey strata, and *, **, *** denotes the significance level at 10%, 5%, and 1%, respectively. See Section 4 for more details.

37
# **References**

Altonji, J.G. and R. Shakotko, 1987, Do wages rise with seniority? _Review of Economic Studies_ 54: 437459.

Altonji, J.G. and N. Williams, 1997, Do wages rise with job seniority: A reassessment, _Industrial and Labor Relations Review_ , 58: 370-397.

Abraham, K.G. and H,S, Farber, 1987, Job duration, seniority and earnings, _American Economic Review_ 77: 278-297.

Farber, H.S., 1999, Mobility and stability: The dynamics of job change in labor markets, Chapter 37, Ashenfelter and D. Card, eds. _Handbook of Labor Economics_ , Volume 3, Amsterdam: Elsevier.

Farber, H.S., and R. Gibbons, 1996, Learning and Wage Dynamics, _Quarterly Journal of Economics_ 111: 1007-1047.

Flinn, C.J., 1986, Wages and job mobility of young workers, _Journal of Political Economy_ 94: S88-S110.

Hall R.E. and R.A. Kasten, 1976, Occupational mobility and the distribution of occupational success among young men, _American Economic Review: Papers and Proceedings_ , 66: 309-315.

Johnson, W.R., 1978, A theory of job shopping, _Quarterly Journal of Economics_ 92: 261-277.

Jovanovic, B., 1978, Job matching and the theory of turnover, _Journal of Political Economy_ 87: 972-990.

Jovanovic, B. and Y. Nyarko, 1997, Stepping-stone mobility, _Carnegie-Rochester Conference Series on Public Policy_ 46: 289-325.

Jovanovic, B. and Y. Nyarko, 1996, Learning by doing and the choice of technology, _Econometrica_ 64: 1299-1310.

Miller, R.A., 1984, Job matching and occupational choice, _Journal of Political Economy_ 92: 1086-1120.

Neal, D., 1999, The complexity of job mobility among young men, _Journal of Labor Economics_ 17: 237261.

Parente, S.L., 1994, Technology adoption, learning-by-doing, and economic growth, _Journal of Economic Theory_ 63: 346-369.

Prendergast, C. and L. Stole, 1996, Impetuous youngsters and jaded old-timers: Acquiring a reputation for learning, _Journal of Political Economy_ 104: 1105-1134.

Rosen, S., 1972, Learning and experience in the labor market, _Journal of Human Resources_ 7: 325-342.

Rosenzweig, M., 1995, Why are there returns to schooling? _American Economic Review: Papers and Proceedings_ , 85: 153-58.

Rosenzweig, M., 2010, Microeconomic approaches to development: Schooling, learning and growth, _Journal of Economic Perspectives_ 24: 81-96.

12
Bastos (2018) for the average BRI country and for each developing East Asian country separately. The results suggest that China‐related supply shocks may have intensified in the last decade compared to the previous. Indeed, while competition pressures from China appear to have been innocuous for the multilateral exports of the developing East Asian countries during the period 1995‐2015, they have had a negative and significant impact for the exports of Indonesia, Malaysia, the Philippines, Thailand and Vietnam during period 2005‐2015. Demand shocks associated with the rise in China’s imports are found to have significantly boosted multilateral exports of Cambodia, Indonesia and Thailand, during 1995‐2015, and of Indonesia and Mongolia, during 2005‐2015.

The positive effects associated with the rise in Chinese imports may have offset China’s competition pressure on developing East Asian countries in external markets, but not consistently throughout the last two decades. The finding in Bastos (2018), that China‐driven supply shocks may have intensified in the last decade, holds for the entire group of BRI countries, as suggested by the changing magnitude of the estimate for the average BRI country (Table 3, last row). The implication is that up until 2005, China’s competition pressure in external markets, for countries in East Asia specifically, and for BRI countries in general, was offset by the positive effects associated with the increasing Chinese imports. This insight corroborates the findings in World Bank (2007) about the effect of the rise of China on the exports of East Asian countries during period 1985‐2004.<sup>10</sup> 1995. Regressions control for period effects (given that up to four 5‐year periods from the interval 1995‐2015 are being used) and panel fixed effects (either country‐industry level, for the regressions including all countries, or industry‐level, for the country‐specific regressions). Potential endogeneity concerns are addressed by employing 2SLS estimation, whereby the change in China’s multilateral exports is instrumented using changes in sectoral Chinese exports and imports to the top 10 destinations and sources.

10 Using a different methodology, the chapter on trade in World Bank (2007) finds that export growth to non‐China markets in industrial products was affected negatively by Chinese exports in the same products during period 1984‐2004 and more so during 2000‐2004. In addition, it finds that the negative effect was offset by the positive impact of China‐related demand shocks (with countries more seriously affected by the supply shocks also being the ones gaining from increased exports to China, notably Korea, Singapore, and Taiwan, China). The chapter further indicates that China appears to have been pushing other exporters up the value chain in parts and components, so that everyone ended up benefitting via specialization in specific tasks, and engagement in GVC and triangular trade.

22
These facts about recall bias in the NSS have two implications for the analysis. First, assuming that the 7-day recall period is more accurate, the overall level of caloric intake in the NSS data using a 30-day recall period is substantially biased downward. The difference in levels of caloric between the two recall periods in 2010-2012 is as large as the entire measured decline between 1983-2005 or 1983-2012 using the consistent 30-day recall period, and this underestimates the overall bias since categories like cereals and milk (which in 2010 and 2012 are measured using a 30-day recall period for all households) also exhibit a downward bias for 30-day recall, albeit to a lesser degree ((National Sample Survey Organization 2000)). A higher estimate of caloric intake is more in line with the reported TEE estimates, particularly in recent years, when 30-day measures of intake are well below the population TEE estimates. Second, the change in caloric intake over time using the 30-day recall period likely overstates the decline in caloric intake that would have been measured using 7-day recall, because the share of expenditures on the goods with the highest degree of bias rose from 36% to 50% between 1983 and 2010. Column 3 provides an illustrative estimate of what caloric intake in earlier rounds would have looked like under the assumption that the degree of bias for the 7-day recall categories in 2010 and 2012 remains constant over time. This assumption seems reasonable going back to 1994 in light of the results in (National Sample Survey Organization 2000) but these estimates are intended to be illustrative and need to be treated with caution, particularly for the 1980s where there is no direct measurement of 7-day vs. 30-day recall bias. Column 3 reports the official results using the 7-day recall period for select foods in 2010 and 2012 (from (NSSO 2013)) and imputations for earlier years that adjust caloric intake upwards but by a smaller percentage for earlier years (reflecting the change in the expenditure share of these categories reported in column 4). The result of this exercise is that the decline in caloric intake is about 20% smaller over the 1983-2005 period considered in (Deaton and Dr`eze 2009) or the entire 1983-2012 period.

Beyond the potential effects of recall periods on measurement error, numerous imputations are required to estimate caloric intake from a given NSS survey and these imputations have become more important over time. Al4 whereby caloric intake rose from 1994-2000 and fell from 2000-2005. The discrepancy between 30-day and 7-day recall quantities appear smaller when the recall periods are implemented simultaneously for the same households, but the level of 30-day recall is likely to be higher than for households that are not subjected to an additional 7-day recall period (see (Deaton and Kozel 2005) for a discussion).

13
services provided at the local level to determine the appropriate level of government to provide them.

**Table 1: Allocation of Expenditure Responsibilities in a Two-Tier Model**

|**Function**|**Upper**<br>**Tier**|**Lower**<br>**Tier**|**Justification**|
|---|---|---|---|
|Social services:||||
|Welfare assistance|X||Income redistribution; externalities|
|Child care services|X||Income redistribution; externalities|
|Social housing|X||Income redistribution; economies of scale; externalities|
|Public health|X||Income redistribution; economies of scale; externalities|
|Land ambulance|X||Economies of scale; externalities|
|Roads and bridges|X|X|Local versus regional roads|
|Public transit|X||Externalities; economies of scale|
|Street lighting||X|No externalities|
|Sidewalks||X|No externalities|
|Water system|X||Economies of scale|
|Sewer system|X||Economies of scale|
|Garbage collection|X||Economies of scale; externalities|
|Garbage disposal|X||Economies of scale; externalities|
|Police protection|X||Externalities; economies of scale|
|Fire suppression||X|Local responsiveness; scale economies for specialized services|
|Fire prevention/training|X||Economies of scale|
|Local land use planning||X|Local access, responsiveness|
|Regional land use planning|X||Externalities|
|Economic development|X||Externalities|
|Parks and recreation||X|Local responsiveness|
|Libraries||X|Local responsiveness|

Source: Bird and Slack (2004a) The Comunidad Autonoma de Madrid (CAM) is an example of a two-tier system with 179 lower-tier municipalities. CAM was created in 1983 and has a population of 5.2 million people. It is administered by a directly elected regional council; the president is elected by the council members. The region took over the previous powers of the Province of Madrid; the lower tiers have fewer powers and responsibilities. The responsibilities of CAM include: transport and infrastructure, education, health, planning, economic development, environment, and culture and research (Lefèvre 2003).

19
28

|_AUTH77OR_<br>Donovan<br>(1982)|_COVERAGE_<br>78 IMF-supported devaluations (1971 -0)|_METOD_<br>Control group *pproach|_DEVALUA77ON EFFECT_<br>Output rall more than LDCa<br>avercge in one year comparisons<br>but by les in 3_yearm_<br>conpariuons|
|---|---|---|---|
|Gylrason<br>(1987)|IMF supponed programs (1977-79)|Control group approach|B&P improves, effect on output<br>is inconclusive|
|Khan (1990)|69 LDCs (1973-88)|Control group approncli|Negative effect on growth<br>(however thc coeircients arc not<br>statisticafly<br>significant)|
|Solimno<br>(1986)|Chile|Macro-simulation model|Contraction in the short-run and<br>in the medium-run|
|Branson (1986)|Kenya|Macro-simulation model|Contractionasy effect|
|Rocaand<br>Priale (1987)|Peru(1977-78)and (1980-82)|Macro-simulation model|Contractionary effect due to<br>increase in cost of working<br>capitalof_firms_|
|Agenor (1991)|23 LDCs (1978-87)<br>(SSA: Burundi,. Canmeroon, Gabon,<br>Malawi, Nigeria, Sierra Leone, Togo.<br>ZaireandZambia)|Econometric approach<br>1-|Anticipated depreciation is<br>contractionary while<br>unanticipated depreciation is<br>expansionary|
|Bhagwat and<br>Oatsuka<br>(t974)|LDCs (1960-70)<br>_|'Before and after' approach|PosiLive expon response in the<br>long-run, litle indication of<br>significant imnpoutrespOnSe|
|Edwards and<br>Santelln (1992)|48 devaluations in LDCs (SSA:Ghana,<br>Zaire)|Control group approach and<br>econometric approach|Devaluation when accomnied<br>by restrictive and consistent<br>macro-policies an effective|
|Taylor (1979)|22 countrics<br>-______________________________|Econometric approach<br>____________________<br>_|Support of expansionary<br> <br>-hypothesis|
|Salant (1976)|101 devaluation in LDCs and DCs|'BfGore and aficr' approach|Balance of trade impsroves only<br>in 46 cases, B&P improves in<br>76 cases|