rafmacalaba/datause-encoder-data huggingface dataset:
In the data set we use to analyze intergenerational mobility (i.e., Perceptions of Poverty, Prosperity and Economic Mobility in Nepal) we have about 8,000 unique father-offspring pairs with information on completed education level. Limiting our attention to respondents over 30 years of age to filter out individuals who may not have completed education, we end up with a little over 5,000 observations. Father’s educational attainment is presented in rows while the son/daughter’s education level is presented in columns. The data show an overwhelming improvement in educational outcomes among the generation of “children” in comparison to the earlier generation in the sample. Overall illiteracy is a third of its level among fathers, primary school completion has more than doubled and the completion of SLC, Intermediate and some university level education has increased four-fold. This is consistent with the rapid expansion of education in Nepal in the last three decades (Table 1).

**Table 1: Intergenerational Mobility in Educational Attainment**

|Father's education|Illiterate|Basic<br>Literacy|Primary|Son/daughter'<br>Secondary|s educational atta<br>SLC Passed|inment<br>Intermediate|University +<br>Technical|_N_|
|---|---|---|---|---|---|---|---|---|
|Illiterate|35%|22%|17%|16%|6%|2%|1%|2,777|
|Basic Literacy|6%|21%|13%|24%|17%|12%|7%|1,273|
|Primary|10%|11%|26%|29%|16%|5%|3%|325|
|Secondary|8%|8%|14%|28%|19%|13%|10%|586|
|SLC Passed|6%|3%|6%|22%|27%|19%|17%|149|
|Intermediate|2%|0%|2%|13%|11%|31%|39%|89|
|University + Technical|0%|5%|2%|2%|24%|27%|41%|59|
|_N_|1,136|970|815|1,069|619|385|264|5,258|

_Note:_ Rows add up to 100%. _Source_ : World Bank Staff estimates based on data from Perceptions of Poverty, Prosperity and Economic Mobility in Nepal. 2014.

In addition to average increases, there have been appreciable improvements within father-son pairs as well. For example, 22 percent of all sons born to illiterate fathers have attained basic literacy, 17 percent have attained primary level education, 16 percent secondary, and about 9 percent have gone beyond the secondary level. Similar patterns can be observed for various other levels of father’s education level. In general, there is a greater “mass” above the diagonal than below in this symmetrical transition matrix, suggesting that children have generally done better than their fathers on education. This is not to say that there has not been any downward mobility though; there are non-zero values below the diagonal as well. For example, about 5 percent of children born to parents who had university level education or higher remained at basic literacy status. But these numbers are generally small.

4
Schultz, Theodore W. 1972. “Human Capital: Policy Issues and Research Opportunities.” Pp. 1– 84 in _Economic Research: Retrospect and Prospect, Volume 6, Human Resources_ . NBER. Strumilin, Stanislav. 1924. “Khoziaistvennoe Znachenie Narodnovo Obrazovaniia (Economic Significance of National Education).” _Planovove Khoziastvo (Planned Economy) No_ 9–10. Telezhkina, Marina. 2019.“Massification of Higher Education System in Russia,” July 8-12, WBHSE Summer School on the Economics of Education (Moscow).

Vernon, Victoria. 2002. “Returns to Human Capital in Transitional Russia.” _Department of Economics, The University of Texas at Austin_ .

21
<!-- Start of picture text --> Pl _ Basis4 mpointsEuro Area = SwedenPercentage points4<br>= Denmark = Switzerland<br>2 = Japan 2<br>— 00<br>a<br>|| -2 -2<br>— -4 -4<br>6 6<br>Financial Bank equity Financial<br>sector CDS price (RHS) sector<br>equities<br>(RHS)<br><!-- End of picture text -->
The impact on _LAYS_ of using one of these assessment systems versus another is illustrated in Figure 4.3. The _LAYS_ results (right panel) are highly consistent: the correlation between the two _LAYS_ measures is 0.98, and the rank correlation is 0.96.<sup>16</sup> Moreover, as the left panel shows, the tight relationship is driven by the strong correlation between the PISA and TIMSS measures of learning per year, and not solely by the fact that we are using the same measure of average years of schooling to calculate _LAYS_ for both countries.

<!-- Start of picture text --> Figure 4.3: Comparing PISA and TIMSS 2015<br>Relative Learning per Year LAYS<br>0.0 0.2 0.4 0.6 0.8 1.0 0 5 10 15<br>TIMSS: Learning per year rel. to Singapore LAYS using TIMSS<br>Source: Authors’ analysis of TIMSS 2015, PISA 2015, and Barro‐Lee data.<br>Notes: Numeraire country is Singapore. Illustration for case where learning starts at Grade 0 for TIMSS and age<br>6 for PISA. For relative learning per year: Correlation coefficient is 0.91. Spearman rank correlation is 0.79. For<br>LAYS : Correlation coefficient is 0.98. Spearman rank correlation is 0.96.<br>1.0 15<br>0.8<br>10<br>0.6<br>0.4 LAYS using PISA<br>5<br>PISA: Learning per year rel. to Singapore 0.2<br>0<br>0.0<br><!-- End of picture text -->

# **_4.3 The subject used to derive LAYS: Mathematics, Science, and Reading_**

The calculations above, whether based on TIMSS 2015 or PISA 2015, all use the assessment’s mathematics score to derive the _LAYS_ adjustment. It is natural, therefore, to ask whether _LAYS_ estimates are sensitive to the subject used to quantify average learning-per-year. Each of these international assessments covers multiple subjects: TIMSS assesses Math and

> 16 See Annex 1 for the implications of using the share who reach “Level 1” on the PISA scale.

21
8

# **3.1.2 Pure forward GVC-trade participation**

‘Pure forward’ is close to the origin of the chain. It comprises the exports of sector _n_ of value-added generated within the domestic chains, which are then re-exported by the partner.

The pure forward participation is simply the difference between the entire domestic value-� added that is exported ( **V** _s_ **L** _ss_ **E** _sr_ ) and the one that is directly absorbed by the importer ( **DAVAX** _sr_ ). Schematically one can represent this as in Diagram 1

**Diagram 1: Pure-forward GVC-trade - Origin of the chain: first exporting sector**

<!-- Start of picture text --> Intermediates<br>n<br>s r<br><!-- End of picture text --> Notes: letters denote either countries (red) or sectors (black) traced by our accounting framework. Boxes represent countries while sectors are identified by circles. Arrows indicate sales, direct ones (solid arrows), or direct and indirect ones (dashed arrows).

The rest of GVC-related trade is given by the sum of the _pure_ backward participation and the _two-sided_ participation. This is what Hummels et al. (2001) call vertical specialization, i.e. the import content of exports. We discuss it, as well as its two components, in the next paragraphs.

# **3.1.3 Pure backward GVC-trade participation**

‘Pure backward’ is the portion of the import content of exports closer to the end of the chain: where **u** _N_ is the 1 _× N_ unit row vector whose purpose is to reduce the dimension of the matrices. Pure backward GVC-trade consists of imported inputs bought by sector _n_ in country _s_ directly from the foreign country _t_ or indirectly through domestic chains and exported by _G_ � the same sector to the final market _r_ , as final products � **Y** _sr_ or intermediates Å _t_ = _s_<sup>**u**</sup><sup>_N_</sup><sup>**A**</sup><sup>_ts_</sup><sup>**L**</sup><sup>_ss_</sup> ã _G_ � � **A** _sr_ **L** _rr_ **Y** _rr_ , as shown schematically in Diagram 2. Å _t_ = _s_<sup>**u**</sup><sup>_N_</sup><sup>**A**</sup><sup>_ts_</sup><sup>**L**</sup><sup>_ss_</sup> ã
*We are grateful to Aart Kraay, Sebastian Horn, Kathryn Holston, and Eduardo Olaberria for discussion of the paper and valuable comments.

# **<mark>References</mark>**

<mark>Abadie, A., L’Hour, J., 2021. A Penalized Synthetic Control Estimator for Disaggregated Data. Journal of the American Statistical Association 116 (536): 1817–34,</mark> <u><mark>https://doi.org/10.1080/01621459.2021.1971535.</mark></u> <mark>Abadie, A., Diamond, A., Hainmueller, J., 2010. Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program. Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 493505. https://doi.org/10.1198/jasa.2009.ap08746.</mark> <mark>Abadie, A., Diamond, A., Hainmueller, J., 2015. Comparative Politics and the Synthetic Control Method. American Journal of Political Science. 52 (2): 495-510</mark> <mark>https://doi.org/10.1111/ajps.12116</mark> <mark>Abadie, A., Gardeazabal, J., 2003. The Economic Costs of Conflict: A Case Study of the Basque Country. American Economic Review, 93 (1): 113-132. DOI:</mark> <mark>10.1257/000282803321455188</mark> <mark>Abadie, A. (2021). Using synthetic controls: Feasibility, data requirements, and</mark> <mark>methodological aspects. Journal of Economic Literature, 59(2), 391-425.</mark> <mark>Aguiar, M., Amador, M., 2021. The Economics of Sovereign Debt and Default. Princeton University Press.</mark> <mark>Aguiar, M., Amador, M., 2014. Sovereign debt, in Handbook of International Economics. ed. by Elhanan Helpman, K.R., Gopinath, G. Elsevier, vol. 5, chap. 11, pp.647–687.</mark> <mark>Aguiar, M., Amador, M., Farhi, E., Gopinath, G., 2013, Crisis and Commitment: Inflation Credibility and the Vulnerability to Sovereign Debt Crises. NBER Working Paper</mark> <mark>Aitken, A., 2020. Measuring Welfare Beyond GDP. National Institute Economic Review , Volume 249 , August 2019 , pp. R3 - R16. DOI: https://doi.org/10.1177/002795011924900110</mark> <mark>Albanesi, S., 2007. Inflation and inequality Journal of Monetary Economics, 54(4): 1088-</mark> <mark>1114 https://EconPapers.repec.org/RePEc:eee:moneco:v:54:y:2007:i:4:p:1088-1114</mark> 34
liberalization are maintained when FDI liberalization is dropped.

Although we control for public sector presence in all specifications so far and find the corresponding effects to be generally insignificant, it is possible that industries reserved for the public sector exhibit patterns of spatial concentration that are systematically different from those of other industries. Hence, in Column 4 of Table 4 we estimate Equation 2 including ERP again and focusing on the set of industries which were not reserved for the public sector and find that the basic qualitative results from<sup>Table</sup><sup>**3**continuetohold.</sup> Appendix Table B.5 presents the results from additional robustness checks. Column 1 shows the estimates weighing each industry-year observation according to its employment size which are qualitatively similar (though weaker) than those in Table **3.** However, BreuschPagan and White tests for heteroskedasticity imply a rejection of the hypothesis of constant variance. Columns 2 and **3** show the estimates downweighting, respectively, **just noisy in-** dustries and just noisy years. These corrections for heteroskedasticity reduce the standard errors for almost all coefficients when compared to those in Column 1 obtained when observations are weighted only **by** industry employment size. Moreover, it is important to note that all results **-** estimated using either weighted or unweighted regressions **-** are robust to restricting the sample to the years with less noisy **ASI** data **-** 1980-94.3' Appendix Table B.5 also presents the results from estimating Equation 2 including two or three year lags of the independent variables. Allowing for this longer response of geographic concentration to its determinants does not change the coefficients much relative to those in Table **_3.32_** It is interesting to note that Tables **3** and 4 show that the **EG** index responds reasonably quickly (within one year) to changes in policy variables. This result would be less plausible if we were examining .-, v1 ii using the more direct and interesting approach of modeling a plant's location decision as in Mayer et al. (2010). Unfortunately, our data do not allow us to consider a plant's location decisions since the data are repeated cross-sections of plants rather than a panel. The **EG** index which we use essentially measures the change in a location's employment share relative to the employment share in the average location. Thus, the **EG**

> 32"These An alternative results are;:- model **,** for **I** the uplon evolution request.process of spatial concentration would include the various lags of the independent variables in a single specification. However since tests reveal a very high degree of correlation between the lags, it is not clear that the results from such a specification could be relied upon.

**25**
# **Appendix 2: Balanced Sample**

_Table A2.1: Balancing Properties, Balanced Sample_

||Group|||T-Test|||
|---|---|---|---|---|---|---|
||Control<br>(1)|Treatment 1<br>(2)|Treatment 2<br>(3)|Difference<br>(1)-(2)|Difference<br>(1)-(3)|N|
|_Panel A: Outcomes, Baseline_|||||||
|Deliberate Practice Be-|||||||
|liefs|16.600|16.630|16.760|-0.032|-0.156**|16,575|
||_(2.410)_|_(2.417)_|_(2.368)_||||
|Grit|29.600|29.600|29.420|-0.002|0.175|16,575|
||_(4.067)_|_(4.039)_|_(4.071)_||||
|Grit: Perseverance-of-|||||||
|Effort Facet|16.310|16.370|16.220|-0.067|0.089|16,575|
||_(2.473)_|_(2.464)_|_(2.517)_||||
|Grit: Consistency-of-In-|||||||
|terest Facet|13.290|13.230|13.200|0.064|0.086|16,575|
||_(3.073)_|_(3.057_|_(3.089)_||||
|Frustration Reaction|11.033|11.124|11.062|-0.091|-0.029|16,575|
||_(2.535)_|_(2.528)_|_(2.526)_||||
|Motivation Framework|20.862|20.921|20.869|-0.059|-0.007|16,575|
||_(2.873)_|_(2.865)_|_(2.835)_||||
|Locus of Control|17.665|17.648|17.646|0.017|0.019|16,575|
||_(2.299)_|_(2.329)_|_(2.330)_||||
|Present Bias|4.203|4.216|4.237|-0.013|-0.034|16,575|
||_(0.991)_|_(1.006)_|_(0.988)_||||
|S/E Skills Index|0.053|0.073|0.084|-0.020|-0.031|16,575|
||_(0.993_|_(1.001)_|_(1.009)_||||
|GPAs|3.559|3.490|3.548|0.069|0.010|16,575|
||_(1.078)_|_(1.087)_|_(1.096)_||||
|_Panel B: Controls, Baseline_|||||||
|Age|12.490|12.480|12.500|0.002|-0.018|16,575|
||_(0.557)_|_(0.553)_|_(0.554)_||||
|Female|0.503|0.519|0.522|-0.016*|-0.019**|16,575|
||_(0.500)_|_(0.500)_|_(0.500)_||||
|Sixth Grader|0.483|0.486|0.467|-0.003|0.016|16,575|
||_(0.500_|_(0.500)_|_(0.499)_||||
|Macedonia|0.680|0.724|0.694|-0.044|-0.014|16,575|
||_(0.466)_|_(0.447)_|_(0.461)_||||
|Albanian|0.266|0.208|0.250|0.057|0.016|16,575|
||_(0.442)_|_(0.406)_|_(0.433)_||||
|Roma|0.017|0.019|0.022|-0.002|-0.005|16,575|
||_(0.130)_|_(0.135)_|_(0.148)_||||
|Other|0.037|0.049|0.033|-0.012|0.004|16,575|
||_(0.189)_|_(0.216)_|_(0.179)_||||

33
Also, the question of the operating variable is seldom dealt with in the statutes. And even if such a policy was stated explicitly, it may not necessarily prevail. Thus the characterization of banks requires a fairly intimate knowledge of their structure, organization, and working practices as well as the personalities in both the bank and government.

Several studies have examined the relationship between central bank independence and inflation. Using data from 1973 to the present--a period of floating exchange rates-they found a close correspondence between independence and good price performance. A colleague and I ran the test for 1890 to 1990, during which there were several periods of floating rates and therefore much more monetary independence. Our findings confirmed the earlier results.

# The Nature of Central Bank Independence

Institutions that were founded before the nineteenth century, but were not yet central banks, were acquiring central bank functions in the nineteenth century and beginning to behave like modem central banks. Chief among these functions was the responsibility for maintaining currency convertibility under the gold standard.

In the nineteenth century the philosophy of laissez faire dominated, and central banks enjoyed greater latitude compared with earlier (and later) times. World War I brought that freedom to an abrupt end. After the war there was a desire to return to prewar status, but movement toward this end was precluded by the Great Depression, World War 11, and then permanently by the rise of the managed economy. Only in recent years has the trend begun to reverse.

By around 1800 the Bank of England dominated the monetary system and held the reserves of the system. Also, there are examples of the Bank rescuing the market and individual banks. Freed from the obligation of converting its notes after 1797, the Bank was accused of being responsible for the 9
PSUs (11 states have established this), and (c) several technical committees and working groups on topical issues of cash and debt management.<sup>38</sup>

# **Debt Swap and Securitization: Move toward Market-based Financing**

The fiscal correction was given an impetus with the introduction of a ―debt swap scheme‖ during 2003–04 to lower the existing interest burden and increase market access. Loans from the center amounting Rs 1,000 billion (US$23 billion<sup>39</sup> ) with interest rates in excess of 13 percent were substituted with new market loans and small savings proceeds at lower rates of interest; the outstanding debt remained unchanged. The market conditions prevailing were fortituous and the rates were significantly lower, at 7.5 percent (RBI State Finances Study 2004–05, 24), enabling an interest savings for states of Rs 310 billion (US$7.1 billion<sup>40</sup> ) and 0.75 percent per year in revenue (Twelfth FC). This direction toward the market was reaffirmed by the Twelfth FC in conjunction with state debt relief, where it stated that, ―As regarding the future lending policy, the central government should not act as an intermediary and allow the states to approach the market directly‖ (Twelfth FC, p. 236).

States issued ―power bonds‖ to securitize the fiscal risks emanating from the losses of electricity utilities arising from the gap between the cost of producing power and the tariff charged. This gap between the cost and tariff had resulted in signifcant losses and an accumulation of arrears. With the securitization, arrears and accrued interest at about 1.5 percent of GDP were cleared by states through the issuance of 15-year tax exempt ―power bonds‖.<sup>41</sup> Cognizant of the moral hazard issue, this was clearly announced as a one-time settlement measure and was supplemented with reforms to ensure discipline going forward. Participating states qualified for funds on the basis of reform milestones and improvements in the reduction of commercial losses. Although, states‘ liabilities had increased by 22.8 percent during 2003-04 at

> 38 These include issues relating to debt sustainability, model fiscal responsibility legislation, pension liabilities, stategovernment-guaranteed advances and bonds, fiscal risk of state government guarantees, voluntary disclosure norms for state governments, state government guarantees, and methodology for compilation of outstanding liabilities and periodic revisions in the ways and means advances limit.

> 39 Using the e nd of March 2004 exchange rate at Rs 43.445 = US$1.0. ( _Source:_ IFS)

> 40 At an exchange rate of Rs 43.445 = US$1.0 (taken as representative of  that  period)..

> 41 Settlement of state electricity boards dues, May 2001 and World Bank (2005), State Fiscal Reform in India: Progress and Prospects, page 13. The balance accrued interest at Rs.100 billion was written off (see chapter by Rastogi, A. in India Infrastructure Report 2004, pg 24).

25
# **Table 5. Property rights and Population Growth The dependent variable is log (population)**

||Full|Full – high war casualty|
|---|---|---|
|Early Taiping*Year 1851|-0.005|-0.003|
||[0.020]|[0.020]|
|Early Taiping*Year 1880|-0.448***|-0.447***|
||[0.101]|[0.104]|
|Early Taiping*Year 1910|-0.426***|-0.418***|
||[0.095]|[0.097]|
|Early Taiping*Year 1953|-0.558***|-0.553***|
||[0.099]|[0.101]|
|Early Taiping*Year 1982|-0.473***|-0.467***|
||[0.089]|[0.091]|
|Early Taiping*Year 2000|-0.452***|-0.446***|
||[0.095]|[0.095]|
|Late Taiping*Year 1851|0.005|-0.000|
||[0.036]|[0.039]|
|Late Taiping*Year 1880|-0.504***|-0.417**|
||[0.194]|[0.184]|
|Late Taiping*Year 1910|-0.508***|-0.425***|
||[0.163]|[0.152]|
|Late Taiping*Year 1953|-0.314**|-0.236**|
||[0.142]|[0.117]|
|Late Taiping*Year 1982|-0.317**|-0.240*|
||[0.141]|[0.123]|
|Late Taiping*Year 2000|-0.340**|-0.288**|
||[0.151]|[0.144]|
|Observations|1,862|1820|
|R-squared|0.797|0.798|
|Number of prefectures|266|260|
|Control|Y|Y|
|Cluster at prefecture level|Y|Y|
|Year Fixed Effect|Y|Y|
|Prefecture Fixed Effect|Y|Y|

Note: The full sample contains the prefecture-year panel for 266 prefectures and seven time periods (from 1820 to 2000).

The time-varying control variables are the duration of treat ports, the duration of concessions and leased territories, the frequency of wars since year 1776. Also included are time dummies interacted with the following time-invariant controls: the distance to Yangzi River, to the coastline, and to the Grand Canal; the number of neighboring provinces; the pre-war level of land taxes per mu; the total number of palace graduates per million people from 1793-1820; silk and tea prefectures dummy; the four post designations classified by the Qing government in 1820.

Standard errors are robust, clustered at the prefecture level.

*** p<0.01, ** p<0.05, * p<0.1

50
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- 32 -

this allows one to see whether there is much movement in and out of poverty over a one year period.

The data in Table 10 present data on the persistence of poverty for the first two LSMS surveys for which multi-year (panel) data are available. It is evident that in both C6te d'Ivoire and Chana there is substantial movement in and out of poverty in a relatively 3hort period of time. Of the poorest 30X (approximately 22.6% + 7.1%) of the population in Cote d'Ivoire in 1985, nearly one fourth (7.1%) were not poor in 1986, and of the poorest 29% (16.41 + 12.3%) in Ghana in 1987-88, about 40% (12.3%) were not poor the following year. While this movement suggests that many of the poor are only poor for a year or two, a careful analysis is necessary before any further conclusions can be drawn.

TABLE 10: Persistence of Poverty in Developing Countries

|Cote d'Ivoire|||19|86|
|---|---|---|---|---|
||||Poorest 30%|Other 70%|
||1985|Poorest 30%<br>Other 70%|22.6%<br>16.1%|7.1%<br>54.2%|
|Ghana|||1988|-89|
||||Poorest 30%|Other 70%|
||1987-88|Poorest 30%|16.4%|12.3%|
|||Other 70%|15.7%|55.7%|