rafmacalaba/datause-encoder-data huggingface dataset:
Development Bank (IADB) was created in 1959, the Asian Development Bank (ADB) in 1966, the African Development Bank (AfDB) 1964, the Islamic Development Bank (IsDB) in 1973, the European Bank for Reconstruction and Development (EBRD) in 1991, and the European Investment Bank (EIB) in 1958. All of these multilateral institutions have an economics department that produces research and economic analysis, and they all have a Chief Economist or a Director of Economics to provide intellectual leadership. Many bilateral aid agencies have also enhanced their knowledge function: for example, both the US AID and the UK‘s DFID have Chief Economists.

In a multi-polar world, the World Bank Group‘s financing function remains important, but in the future, it is possible that it will be limited to a smaller number of low-income countries and a few global public goods. Thirty-six countries have graduated from IDA since its creation; however, eight of those graduates have slipped back into IDA status.<sup>63</sup> From the IBRD perspective, twenty-five countries have graduated since 1970; however, six later ―degraduated.‖<sup>64</sup> In fiscal year 2008 – prior to dramatic increase in crisis lending— _gross_ disbursements for the entire World Bank Group (including IFC) were $27.2 billion. This is a tiny amount compared to _net_ capital flows to the developing countries that reached a record $1.1 trillion in 2007 and reached over half a trillion dollars in 2010. In terms of net disbursements, in fact, the World Bank (IBRD+IDA) has been a small share of developing country GDP—never surpassing 1⁄2 of one percent of developing country GDP since the 1970s (Figure 18a). In typical years, net lending was on the order of 1⁄4 of one percent of GDP. If one considers that investment to GDP ratios average about 25 percent of GDP, then typically IBRD-IDA might finance about 1 percent of developing country investment in a given year. IDA has represented a somewhat larger share of the subset of low-income country GDP (Figure 18b); however, the share still has remained below 2 percent – even during the peak years.

> 63 See data at

> http://web.worldbank.org/WBSITE/EXTERNAL/EXTABOUTUS/IDA/0,,contentMDK:20054573~pagePK:83988~ piPK:84004~theSitePK:73154,00.html.

> 64 See Heckelman et al (2011) for more information and for an empirical analysis of the determinants of country graduation.

41
**Table 2. Student Reading Achievement Trends and Estimated Decline in Scores due to School Closures, All ECA+ Countries**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Departure from linear trend in 2021|-15.27***|-16.43***||-15.69***|-7.90|
|_Principal report on how many weeks sc_|_hool was affected_|||||
|>8 weeks|||-19.16***|||
|0-2 weeks(difference from_>8 weeks_)|||5.97*|||
|2-8 weeks(difference from_>8 weeks_)|||1.43|||
|_UNESCO data on school closures_||||||
|weeks with full closures||||-0.08||
|weeks full or partial closures|||||-0.32*|
|_Additional controls_||||||
|country fixed effects|Yes|Yes|Yes|Yes|Yes|
|country*year|Yes|Yes|Yes|Yes|Yes|
|student grade|No|Yes|Yes|Yes|Yes|
|student age|No|Yes|Yes|Yes|Yes|
|country average student age|No|Yes|Yes|Yes|Yes|
|N of students|582112|581887|570955|581887|581887|

* p<0.05; ** p<0.01; *** p<0.001

# _Estimates for individual countries_

Under some assumptions, we can identify the learning loss for the EU student population as a whole. We take into account differences between countries that are stable over time using country fixed effects. We also control for time-variant effects of changes in the student sampling, namely for changes in individual student age and grade and country-average student age. We estimate the linear trend in student achievement, separately for each country. Assuming that these are valid controls for time variant and time invariant country characteristics, we identify the average departure from the achievement trend for the population of students in EU or ECA+ countries participating in PIRLS 2021. This departure can be interpreted as the learning loss caused by the COVID-19 pandemic as the pandemic was the only factor affecting achievement results in all countries. To separate the overall negative impact of the pandemic from the learning disruption caused by school closures, we also provide comparisons between schools and countries affected by shorter or longer closures.

Providing similar estimates for individual countries is possible with the model presented in the section discussing our empirical strategy. However, in this case, it is not possible to interpret achievement change in 2021 as a measure of learning loss as for individual countries other factors could change between 2016 and 2021 affecting student scores. Anyhow, we provide these results to allow comparisons between countries that are adjusted for the overall time trends and changes in the sampling (age and grade).

Table A1 in the annex compares results for ECA+ countries, using separate data for each country. The first three columns provide results for 2016, 2021, and the difference. These unadjusted results should not be interpreted as measures of the learning loss as they could reflect long-term trends in student achievement. Column 4 compares 2021 results to the 8
for 1967-87 and 1981-87 for each variable.' Table 1 estimates for 'level" from shows that only one measure of openness is significant at 10% level for Sub-Saharan Africa for the period 1967-87. Though cross-section equations using period-difference data (Le.equation 6) can be expected to capture the total change in openness over the period better than the period average, the coefficients on openness shown in Table 2 are insignificant.

The weakness of the openness-performance link in the cross-section results is perhaps not surprising.' Most developing countries, including those in Sub-Saharan Africa experienced large annual swings in commercial and exchange rate policies over the last two decades, which is not well captured by any measure of change averaged over such long periods. The average degree of openness for a period of several decades or even the average change in openness over such a long period hides significant variations in individual country policy and performance.

# Fxed Effect Estimation

The two equations (i.e. 5 and 6) are re-estimatec, using annual pooled data for the same variables. To control for unobserved country-specific differences affecting the level or growth rate of GDP, we included a dummy variable for each country. Tables 3 and 4 report estimates from regressions using pooled annual 'level' data and annual "change" data respectively.

The fixed effect results for the 'level' form in Table 3 shows that all four measures of openness are significant at the 1 % level for the period 1967-87. Except for the "premium" measure of openness, the same is true for the sub-period 1981-87. Thus after controlling for changes in capital and labor inputs, increased openness accounts for better economic performance in Sub'The Halevi-Thomas index and the Dollar index are available for shorter periods i.e. 1978 -87 and 1977-87 respectively, than the other two measures.

2'Harrison (1991) also finds considerably poorer estimates of openness-performance links in the cross-section estimates relative to the fixed-effect estimates.

20
# **Figure 4: Shift into services in developing countries**

<!-- Start of picture text --> Structure of output in 2005 and 2030<br>100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>EU US CHN ARG BRA CHL COL MEX PER CAC XLC<br>Agriculture and Food Fossil fuels Manufacturing Services<br>2005 2030 2005 2030 2005 2030 2005 2030 2005 2030 2005 2030 2005 2030 2005 2030 2005 2030 2005 2030 2005 2030<br><!-- End of picture text --> Source: World Bank simulations using the Linkage model. Notes: Top line represents 2030 shares, bottom line - 2005 shares of sectors in total national/regional output. ARG-Argentina, BRA-Brazil, CAC-Central America & Caribbean, CHL-Chile, CHN-China, COL-Colombia, ECA-Europe & Central Asia, EUREU27 & EFTA, MEX-Mexico, MNA-Middle East & North Africa, PER-Peru, SAS-South Asia, SSA-Sub-Saharan Africa, USA-United States, XEA-Rest of East Asia, XHY-Rest of high-income, XLC-Rest of Latin America The growing share of services will be associated with a falling contribution of other sectors, mainly agriculture. China’s agricultural output share is likely to decrease by more than one-half. A similar trend can be observed in East and South Asia and ECA, where agricultural share of output contracts by a few percentage points. Developments in LAC are more diverse. Agricultural output in countries with a comparative advantage in agriculture, such as Argentina, Brazil and Colombia, is expected to expand. On the other hand in Peru where the productivity growth in manufacturing and services is much faster than in agriculture, the share of relatively uncompetitive agricultural output shrinks.

The structural shift towards services leads to an increase in the demand for skilled workers and could lead to the increase of skilled wage premium and relative income of skilled workers. However, we find that in all countries/regions the fast growth of supply of skilled workers outpaces the growth of demand for their services leading to a decline of their relative wage (or skill premium).

13
Now assume that the estimated marginal effects of education on women’s labor force participation measure the pure ability-selection effect. The 1987 education effects β1987E quantify the effect of ability on labor force participation for each ability type _k_ . The education effects in other years should differ from those in 1987 only by the change in the ability composition of education groups. That is, they are a weighted average of the 1987 estimates. Table A1.5 in the appendix shows the original estimated marginal effects of education in columns 1–5, and the reweighted effects for 1999, 2004, 2009, and 2011 in columns 6 – 9. Results for secondary and graduate education are summarized in Figure 6.

As Figure 6 shows, the reweighting exercise predicts the change in the marginal effects of education from 1987 to 2011 quite well. While this is by no means conclusive evidence that the education effects are largely driven by selection,<sup>25</sup> it does show that declining selection could potentially play a large role in the declining effect of higher education on female labor force participation, where declining selection means that women’s selection into education is increasingly based on characteristics that are not positively related to labor force participation.

# **_Cohort Analysis_**

In a sample of women aged 25 to 54, educational attainment changes over time primarily because younger cohorts enter the sample with higher educational attainment while older cohorts with lower educational attainment age out of the sample. Consequently, the educational attainment by age group increases continuously, while the age composition of the sample is stable over time. To further probe the selection channel discussed above, we estimate age-group-specific marginal effects of education on labor force participation. If the declining effect of higher education indeed

> 25 If purely selection-driven, one would not find negative estimates at intermediate education levels.

34
# **_4.2 Lessons from the 1990s Forecasting Experience_**

Now that the new millennium is more than a decade old, we have a much fuller perspective on the last decade that preceded it. What were the main developments in the 1990s, and how successful were we in the late 1980s in anticipating them?

A number of retrospective analyses of the 1990s have appeared in recent years. A 2005 World Bank study analyzes the policy and growth experience of the 1990s, a period characterized as a decade of reforms. This volume is complemented by a volume of reflections on the 1990s development experience by influential policy makers (Besley and Zagha 2005) and by firsthand experiences of senior staff of the Bank (Gill and Pugatch 2005). Also of interest is the incisive review by a prominent economist and development intellectual (Rodrik 2006). A good retrospective account of the 1990s from a broader world economy standpoint can be found in _The Global Economy in the 1990s: A Long-Run Perspective,_ in which the editors, Paul W. Rhode and Gianni Toniolo, and a number of economic historians present their analyses of important facets of the decade (Rhode and Toniolo 2005). The main features of the decade identified in this study have been helpful in structuring our account of the decade of the 1990s.<sup>6</sup> Eight key developments of the 1990s set the stage for the 21st century:

- a. The continued spread of globalization. (In the event, globalization became deeper in the 2000s.)

- b. The consolidation of China’s rapid economic growth, the beginning of India’s catch-up, and the contrast offered by the continuing poor performance of Africa. (Rapid growth in China and India, as well as in Brazil and a few other emerging economies, continued in

6 In addition, the IMF has conducted several assessments of its economic and financial forecasts, which are published twice a year in the _World Economic Outlook (WEO)_ . The forecasts are short term (and not comparable to those in this paper) and typically one to two years ahead of the current year, often based on incomplete data. However, the analysis provided in the _WEO_ is often set in a medium-term context of potential major economic/financial threats to the global or regional economies. These assessments have generally found the IMF forecasts to be consistent with forecasts prepared by other international organizations and the private sector. They also find that the IMF’s short-term forecasts tend to be on the optimistic side (Boughton 2001; Masi 1996; IMF 2001; Timmermann 2006).

17
21 management. It would also satisfy depositors’ demand for greater involvement in the strategic management of the bank.<sup>42</sup> However, the election of UIA holders’ representatives may fragment the board of directors along conflicting demands of different groups. Operationally, this could lead to decisional deadlocks to the detriment of efficient management and profit performance. In addition, the extension of shareholders’ rights to UIAH raises a legal issue. This category of depositors and shareholders are subject to two very different types of legal liabilities. The liability of investment account holders is limited to losses occurring on their investments. On the other hand, the liability of shareholders covers _all_ losses which the bank may incur in the course of its business, including losses from funds provided by current account holders. In this light, the power to elect board representatives would give UIA holders a role in the strategic management of the entire institution that might not be commensurate with their risks.

If extending shareholders’ rights to UIA holders is deemed impractical, depositors’ protection could be another option. In most financial systems, regulators act on behalf of debt-holders by requiring insurance on all deposits and taking control away from equity holders in case of distress. The PLS nature of investment accounts prevents however the application of deposit insurance as it is. Accordingly, a _Shariah_ -compliant version of deposit insurance could be put in place. It would cover current account holders under all circumstances of bank insolvency and UIA holders only in cases of insolvency resulting from proven fraudulent mismanagement.<sup>43</sup> Such a measure may reduce systemic risk associated with bank panic behavior and permit redress for UIA holders affected by the failure of an Islamic financial institution.<sup>44</sup>

> 42 In a survey of IIFS consumers’ preferences, Chapra and Ahmed (2002) record an interest by depositors to be involved in the strategic management of the bank.

> 43 In case of bank liquidation, the Central Bank of Jordan distinguishes between investment accounts and deposits accounts. While the latter can be covered by deposit insurance, the former are charged with the expenses and disbursements of the liquidator and only subsequently their entitlements are distributed according to PLS ratios. See Banking Law Art 56.

> 44 This is the system that is currently in place in Turkey. As outlined in the paper “Corporate Governance: Overview of Issues and Options” by the same authors, it was introduced following the runs on Special Finance Houses’ deposits following the collapse of IFH.
Also, from Table C4, the impact of the NEN on real rural wages does not systematically change with increasing <sup>_U_</sup> , although there are some significant differences.

**Table C4: Impact on real wages across levels of the factor** <sup>**_U_**</sup>

|**(Efficiency adjusted)**<br>**real urban wage in the**<br>**"before" scenario(**<sup>**_U_**</sup>**)**|**%****in real urban wage**|**%****in real rural wage**|**No. of regions**|
|---|---|---|---|
|0.308|4.144|2.030|82|
|0.444|2.222|3.223|83|
|0.552|1.831|4.007|83|
|0.830|5.446|3.449|83|

Table C5 shows that larger impacts on real urban wages appear to have been experienced in regions with larger values of _REL_TIME_ , implying that, in the urban sector, the benefits of the NEN have been more intensely felt by the initially most remote regions. However, the relationship is relatively weak. In contrast, for real rural wages, we see a very significant relationship, although in the opposite direction to that for real urban wages. For real rural wages, the initially most accessible regions exhibit the largest increases, and the size of increase declines monotonically with increasing remoteness. This indicates that rural wage growth as a result of the NEN has not had so much of a beneficial effect on the most remote rural regions - it appears as though the policy of building the NEN has yet to impact remote rural economies.

**Table C5: Impact on real wages across levels of the factor** **_REL_TIME_**

|**Aggregate travel time to**<br>**otherregions, "before"**<br>**scenario (****_REL_TIME_) **|**%****in real urban wage**|**%****in real rural wage**|**No. of regions**|
|---|---|---|---|
|9172|1.945|5.046|82|
|10509|3.979|3.037|83|
|12474|3.262|2.888|83|
|16166|4.431|1.775|83|

Table C6 reaffirms the earlier finding that the NEN seems to have impacted most positively on urban economies. We see that as _S_<sup>_R_</sup> – the share of the rural sector in a region's total employment – increases, the positive impact of the NEN on real urban wages decreases, becoming negative for the 83 most rural regions. The corollary of this, of course, is that as the urban share increases, we see a very significant increase in real urban wage growth attributable to the NEN. In contrast, the 82 least rural regions experienced the smallest mean increase in real rural wages and increasing rurality is associated with marginally increasing impacts.

45
custody, to be carried out by a Canadian auditor, Michael Mackey. However, the strict bank secrecy laws in Mexico inhibited a thorough examination of the debts transferred to FOBAPROA and allegations of corruption.<sup>1</sup> The FOBAPROA experience shows that corporate restructuring is hindered if there are inadequate bankruptcy laws and a lack of experienced judges and bankruptcy trustees to effectively manage bankruptcies. Above all, the Mexican experience shows that politicaleconomy factors can profoundly affect the success of government policies through the corruption of the recapitalization process. Recapitalization and asset resolution programs per se do not encourage a restart of lending, especially in a country where imprudent lending behavior and politically-connected lending are widespread.

# _Asset Management Companies in Indonesia and Korea_

While it is beyond our scope to review other countries’ experiences with AMCs in similar detail, it is fair to say that the failures in Mexico were representative of many countries’ experiences, documented elsewhere. Here we briefly compare and contrast the experiences of Indonesia and Korea after the Asian crises. For reviews of these crises and resolution strategies, see World Bank (1999), and Scott (2002) for Korea, and Lindgren et al. (1999), IMF (2000), and Enoch et al. (2001) for Indonesia.

As a response to the financial crisis that erupted in Indonesia in the wake of the floating of the Thai bhat in 1997, the Indonesian government set up the Indonesian Bank Restructuring Agency (IBRA) with a wide set of responsibilities. Those included the restructuring of banks that were transferred to it, the recovery and restructuring of bank assets (including both physical

> 1 Mackey (1999) reports that about $7.3 billion of the loans (or more than 10 percent of total loans) transferred to FOBAPROA did not meet the criteria established for inclusion in the Loan Purchase and Recapitalization Program, and that just over $600,000 of those loans was of illegal origin.

38
# **Appendix 9: Effects of participation in the non-farm activities on food security and subjective well being**

||Food in|secure|Adequacy of food c|onsumption|
|---|---|---|---|---|
|VARIABLES|CRE Probit|Linear FE|CRE ordered Probit|Linear FE|
|Wage employment participation|-0.047+|-0.065**|0.177**|0.104**|
||[0.125]|[0.041]|[0.037]|[0.021]|
|Self-employment participation|-0.034+|-0.027|0.057|0.025|
||[0.140]|[0.281]|[0.379]|[0.482]|
|Age of the household head|0.000|-0.000|-0.002|-0.000|
||[0.743]|[0.952]|[0.591]|[0.793]|
|Male-headed household (0/1)|0.016|0.009|0.103|0.045|
||[0.596]|[0.792]|[0.268]|[0.372]|
|Highest level of formal education acquired by household head|||||
|PSLC|0.071+|0.074+|-0.068|-0.027|
||[0.121]|[0.146]|[0.576]|[0.691]|
|JCE|-0.053|-0.067|0.055|0.038|
||[0.355]|[0.252]|[0.730]|[0.607]|
|MSCE|-0.141**|-0.119|0.344+|0.180+|
||[0.036]|[0.151]|[0.116]|[0.108]|
|Maximum level of formal education acquired in the household|||||
|PSLC|-0.029|-0.029|0.107|0.042|
||[0.385]|[0.430]|[0.290]|[0.442]|
|JCE|0.087*|0.095*|0.177|0.083|
||[0.072]|[0.055]|[0.169]|[0.236]|
|MSCE|0.092|0.113+|-0.069|-0.066|
||[0.188]|[0.137]|[0.698]|[0.504]|
|Number of infant (<5yo) in HH|0.023*|0.026*|-0.063*|-0.032+|
||[0.073]|[0.056]|[0.093]|[0.102]|
|Number of children (5-14yo) in the household|<br>0.019**|<br>0.024***|<br>-0.057**|<br>-0.031**|
||[0.029]|[0.007]|[0.025]|[0.032]|
|Number of prime adults (15-60yo) in HH|0.008|0.010|-0.073**|-0.034*|
||[0.461]|[0.506]|[0.011]|[0.089]|
|Number of elderly (60yo+) in HH|<br>0.035|<br>0.021|<br>-0.157*|<br>-0.073+|
||[0.301]|[0.613]|[0.095]|[0.117]|
|Household access to loan (0/1)|<br>0.022|<br>0.024|<br>-0.135*|<br>-0.067*|
||[0.377]|[0.403]|[0.052]|[0.087]|
|Normalized TLU index|-1.001+|-0.904*|1.198|1.257|
||[0.103]|[0.068]|[0.295]|[0.163]|
|Normalized agricultural asset index + Normalized land holdings|-0.179***|-0.173**|0.876***|0.414***|

56
# **Why Did Poverty Decline in India?**

## **A Nonparametric Decomposition Exercise**

**Carlos Felipe Balcázar, Sonal Desai, Rinku Murgai and Ambar Narayan**<sup>*****</sup>

**JEL Codes:** J30, I32, D31.

**Keywords:** poverty, nonparametric decomposition, labor income, dependency ratios, remittances.

> *Balcázar, Murgai and Narayan: The World Bank, 1818 H St. NW. Washington DC, United States of America. Desai: Department of Sociology, University of Maryland, College Park. We thank the outstanding collaboration and useful comments of Urmila Chatterjee.