rafmacalaba/datause-encoder-data huggingface dataset:
While there is much to celebrate, two negative developments have emerged: first, there is evidence of growing income inequality in South Asia; and second, there is growing imbalance between regions within countries and among the countries themselves. With fairly large and open borders, the growing imbalances in incomes and opportunities among South Asian countries present similar social and economic problems to the prosperous neighbors as imbalances within.

Poverty, income growth and lagging regions are inter-related. South Asia’s experience shows that the incidence of poverty and income growth is strongly and negatively correlated (Figure 3). With few exceptions lagging regions exhibit a higher than average rate of poverty and lower than average per capita incomes. The growing divergence between lagging and leading regions also suggests that lagging regions on average are growing more slowly than leading regions.

**Figure 1. Real GDP Growth**

<!-- Start of picture text --> East Asia and Pacific<br>Sub-Saharan Africa<br>OECD<br><!-- End of picture text --> _Source:_ World Bank's World Development Indicators. _Notes:_ Data are averages. South Asia’s data include the 2007 growth rate, while the rest of the regions do not.

So, a substantial part of the poverty and lagging regions challenges is a growth challenge. Two major development issues facing South Asia are: first, how can South Asia grow even faster than in the recent past? And second, how can lagging regions accelerate growth to catch up with growth in the leading regions?

The problem of inequality is, however, a more complex challenge. Growth acceleration in the lagging regions might help reduce inequality. But this is only a part of the larger task of making growth more inclusive. The pattern of growth that benefits 2
level, we predicted the missing observation with the value obtained by running a regression using other characteristics of the scheme as predictor.

# 4.3 _Methodology_

We compared the means of the indexes and their underlying indicators for Jalanidhi, KWA-BM, and other community managed schemes to assess their relative performance and strength and weakness. This was supplemented by a propensity score matching (PSM) analysis using the data from the technical audits of our sample to estimate the average treatment effect on treated (ATT) of the Jalanidhi schemes vis-à-vis KWA-BM schemes and other community managed schemes. PSM exercise was carried out to improve upon the initial matching exercise which was constrained by the availability of reliable secondary data.

For the PSM exercise, Jalanidhi was defined as the treatment and the control group was selected from the KWA-BM/other community managed schemes. The objective of the PSM was to construct two statistically matched samples from treatment and control groups based on various scheme characteristics. In other words, PSM would select schemes from Jalanidhi and match them with those schemes from KWA-BM that have characteristics similar to the selected Jalanidhi schemes. The matching is done based on propensity scores or probabilities estimated from a Probit model using Jalanidhi schemes as a binary dependent variable and scheme characteristics as regressors. For the Probit specification, we used age of scheme, designed per capita supply of water, availability of water treatment facility, and reliance on dependable sources of water (such as perennial river, deep tube wells, and bore wells) as proxies for scheme characteristics.

For constructing the matched sample, we implemented a radius matching with caliper. Following Stuart (2010), we chose a caliper of 0.2 standard deviation of the estimated propensity scores. We also tested for balance in the matched sample to ensure that the treatment and control groups are comparable. Finally, we estimated the ATT on various performance and sustainability indexes to capture the impact of Jalanidhi schemes vis-à-vis KWA-BM/other community managed schemes.

11
**Table 4:  Capital Inflows, Foreign Exchange Reserves and the Stock market**

||1996||19<br>|97<br>|||19<br>|98<br>||
|---|---|---|---|---|---|---|---|---|---|
|||Q1|Q2|Q3|Q4|Q1|Q2|Q3|Q4|
|Current account, $ mln|10847|2823|-714|-1503|-687|-2957|-3637|775|6038|
|Net portfolio inflows, $ mln<sup>a/</sup>|4411|6089|8020|2042|1338|4238|3900|928|-448|
|_of which:_||||||||||
|GKO/OFZ|1613|4013|4789|1859|220|3098|-331|-2267|-862|
|Eurobonds|1004|1229|1987|98|369|689|4097|2318|-140|
|Equity|2035|424|307|372|194|25|33|-20|666|
|Other portfolio inflows, net|-241|422|936|-287|555|425|101|898|-112|
|Other capital flows, net, $ mln<sup>b/</sup>|-19292|-7117|494|-2829|-7891|-2375|-882|-4569|-7426|
|Reserves, $ bn<sup>c/</sup>|15.3|16.5|24.5|23.1|17.8|16.9|16.2|12.7|12.2|
|RTS index<sup>d/</sup>|201|301|419|498|396|326|151|44|59|

Source: CBR, authors’ calculation a/ Includes both private and public sector. b/ Includes errors & omissions, “-“ equals outflow. c/ CBR gross reserves, including gold. d/ Official index of the Russian stock exchange, end of period, dollar-based.

The third and final round of speculative attacks on the ruble began in May 1998. In the second quarter of 1998, three things happened: by mid-May, the realization had set in that fiscal fundamentals were weak and international liquidity low. Falling oil prices were reflected in a growing current account deficit, so policymakers were battling a triple whammy of unsustainable debt dynamics, low reserves and a fast-deteriorating current account. But as Table 4 shows, the second quarter current account deficit was offset by net portfolio inflows largely because the government had started issuing Eurobonds as part of a move to lengthen maturities and lower borrowing costs.

The ruble peg was finally abandoned in August 1998. Table 4 shows the large decline in reserves in the third quarter of 1998, explained by the exit from GKOs and the big increase in the size of “Other capital flows, net” as domestic residents shifted out of ruble assets into dollars. Net portfolio flows remained positive overall because of the large volume of official borrowings as an international rescue package kicked in after July 20, 1998. The impact of the meltdown also shows up in the plunge in the RTS index to less than a third of its value at the end of the previous quarter as the ruble collapsed. But the current account sharply reversed following the 18
DIALOGIC READING AND STORYBOOK EVALUATION IN KENYA 28 PRIMR, and Mango Tree – but these programs are center-based, emphasizing teacher training, and targeting school-aged children (Dowd et al., 2013; Piper, Destefano, Kinyanjui, & Ong’ele, 2018; Piper et al., 2015; Kerwin & Thornton, 2018). Modifications to center-based programs may have logistical advantages in some settings, but they may be less likely to reach younger children. In the present study, only 42 percent of 3-year-olds were enrolled in pre-schools. In a study in Mozambique, even conditional on the availability of high-quality pre-schools in the community, only 55.6 percent of 3- and 4-year-olds were enrolled in pre-schools (Martinez et al., 2012). Scaling up access to high-quality preschool programs in LMICs may be costlier and require more political will than implementing parenting education programs; parenting programs may have broader reach in rural areas, especially for younger children (Daelmans et al., 2017). A comparative study of preschool and parenting education programs in Ethiopia by Save the Children found the parenting program to be as effective at improving children’s early literacy skills and language development as the preschool program (Borisova, Pisani, Dowd, & Lin, 2017). Interventions targeting parents and center-based programs can easily be complements: there is no reason a policy maker could not aim for improvements on both margins. For example, a study of a childcare center in Brazil found that adding parent reading training to their program yielded significant improvements in parent reading behaviors, and higher child receptive vocabulary and working memory scores after 9 months (Weisleder et al., 2017). In reflecting on the policy options, we think it is helpful to be clear on the relative advantages of each approach.

# **Limitations**

There were several limitations of this study. First, the follow-up was conducted five to six weeks after the intervention, giving us the opportunity to see only short-run effects of the intervention. This timing is in line with existing work documenting the effects of dialogic
Table 5: Job search spell and the no. of vacancies applied for: Comparing the employees who used social networks and those who did not

|||Log(searc|h spell)|||Log(no. a|pplications)||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Social networks|-0.437<sup>_∗∗∗_</sup>|-0.243<sup>_∗∗_</sup>|-0.202<sup>_∗∗_</sup>|-0.223<sup>_∗∗_</sup>|-0.428<sup>_∗∗∗_</sup>|-0.225<sup>_∗∗_</sup>|-0.203<sup>_∗∗_</sup>|-0.163|
||(0.086)|(0.104)|(0.096)|(0.094)|(0.099)|(0.097)|(0.092)|(0.111)|
|Schooing in years||0.069<sup>_∗∗∗_</sup>|0.045<sup>_∗∗∗_</sup>|0.022||0.043<sup>_∗∗∗_</sup>|0.023<sup>_∗∗∗_</sup>|0.023<sup>_∗_</sup>|
|||(0.011)|(0.010)|(0.015)||(0.012)|(0.008)|(0.012)|
|Age at hire||-0.013|-0.008|-0.010||0.013<sup>_∗∗_</sup>|0.015<sup>_∗∗∗_</sup>|0.013<sup>_∗∗_</sup>|
|||(0.008)|(0.009)|(0.009)||(0.006)|(0.005)|(0.006)|
|Father’s pri.school||0.016|0.103|0.138<sup>_∗_</sup>||0.034|0.091|0.076|
|||(0.078)|(0.079)|(0.076)||(0.068)|(0.067)|(0.076)|
|Mother’s pri.school||0.045|0.126<sup>_∗_</sup>|0.061||0.021|0.125<sup>_∗∗_</sup>|0.137<sup>_∗∗_</sup>|
|||(0.072)|(0.066)|(0.056)||(0.061)|(0.055)|(0.067)|
|Math z-score||0.004|0.011|-0.012||0.046|0.029|0.021|
|||(0.034)|(0.033)|(0.044)||(0.042)|(0.035)|(0.041)|
|Language z-score||-0.101<sup>_∗∗_</sup>|-0.100<sup>_∗∗_</sup>|-0.060||0.015|-0.014|-0.031|
|||(0.042)|(0.050)|(0.054)||(0.057)|(0.058)|(0.061)|
|Big 5 index z-score||-0.005|0.134|0.079||-0.072|-0.113<sup>_∗∗_</sup>|-0.133<sup>_∗∗∗_</sup>|
|||(0.083)|(0.100)|(0.118)||(0.051)|(0.045)|(0.047)|
|Firm fxed efect|||X||||X||
|Firm-occ fxed efect||||X||||X|
|R-squared|0.150|0.213|0.159|0.116|0.100|0.166|0.119|0.078|
|N|3396|3390|3390|3390|3396|3390|3390|3390|

_Note_ . The variable, _Social networks_ , is the dummy indicating that social networks were used to find jobs. The other controls included are entry year fixed effects and geographical division fixed effects. Occupations are grouped into manager, professional, semi-skilled, and elementary. Standard errors clustered within an establishment are in parentheses. Significance levels:<sup>_∗∗∗_</sup> = 1%,<sup>_∗∗_</sup> = 5%,<sup>_∗_</sup> = 10%.

27
37 Table 15: Hovrs a Week Spent by Females in Household Work by Region

|||In-s|chool v|outha|Weekl<br>_|y Hours|Worked<br>Out-<br>o|<br> <br>of-sch|ool<br>youths||
|---|---|---|---|---|---|---|---|---|---|---|
|||egR_|_ 0<br>1<br>1iS:t|-14<br> _52?z_k|42+|o|. -A|1_- 28_|_29-42_|_42+_|
|5-7|56.1|42.4|1.4|0.0|0.0|50.0|25.0|25.0|0.0|0.0|
|8-10|23.4|69.0|5.9|0.8|0,8|16.7|83.3|0.0|0.0|0.0|
|11-13|11.0|64.3|22.0|1.8|0:9|16.7|83.3|0.0|0.0|0.0|
|14-16|9.1|55.9|26.4|5.0|3.6|6.7|60.0|13.3|0.0|20.0|
|17-19|6.3|46.8|29.1|15.2|2.5|9.0|26.9|25.6|17.9|20.5|
|Lima|19.2|<br>57.7|<br>17.3|<br>4.2|1.6|12.4|37.2|<br>21.2|<br>12.4|<br>16.8|
|5- 7|49.1|46.1|<br>3.9|<br>0.9|0.0|36.1|55.6|<br>5.6|<br>2.8|<br>0.0|
|8-10|20.1|<br>65.9|<br>11.7|<br>2.0|0.3|11.5|69.2|<br>11.5|<br>3.8|<br>3.8|
|11-13|9.7|<br>66.1|17.7|<br>5.6|0.9|18.5|55.6|<br>18.5|<br>0.0|7.4|
|14-16|6.7|<br>52.5|<br>29.4|<br>9.7|1.7|8.8|<br>28.1|<br>26.3|24.6|<br>12.3'|
|17-19|9.6|38.4|31.6|14.7|5.6|5.8|27.3|25.2|20.9|20.9|
|Other|urban||||||||||
|areas|18.2|<br>56.3|<br>18.2|<br>5.9|1.4|11.9|37.5|<br>21.1|<br>15.8|13.7|
|5- 7|32.3|<br>58.7|<br>7.8|<br>0.6|0.6|37.5|54.8|<br>4.8|<br>1.9|<br>1.0|
|8-10|12.5|<br>67.5|<br>15.5|<br>3.2|1.3|8.4|66.3|<br>15.7|<br>6.0|3.6|
|11-13|6.8|<br>62.7|<br>22.2|<br>5.9|2.5|4.4|<br>52.2|<br>23.3|<br>16.7|<br>3.3|
|14-16|7.6|<br>58.2|22.2|<br>8.9|3.2|9.7|34.2|<br>30.6|<br>19.9|<br>5.6|
|17-19|10.0|<br>56.0|<br>22.0|<br>10.0|2.0|6.9|26.7|<br>36.1|<br>19.8|<br>10.4|
|Rural|||||||||||
|areas|13.0|<br>62.8|<br>17.6|<br>4.7|1.9|12.3|41.5|<br>25.5|<br>15.0|<br>5.8|

# Conclusions

Since the mid-SOs the government of Peru's education policies have been designed to raise skill levels and make education available to broader segments of the population. Those policies rested primarily on expanding the
Tatyana Bogomolova, Gregorio Impavido, Montserrat Pallares-Miralles

**Figure 25: In a pure DC reform, rate of return on assets makes a big difference on the replacement rates* in the DC component for teachers****

<!-- Start of picture text --> 50.0%<br>40.0%<br>30.0%<br>20.0%<br>10.0%<br>0.0%<br>2007 2012 2017 2022 2027 2032 2037 2042 2047 2052 2057 2062 2067 2072<br>Year<br>ref I.1A ref I.1B<br>Replacement rate<br><!-- End of picture text -->

**Notes** : * Average pension as percentage of average group wage; ** Comparison of replacement rates* in the DC component for teachers – Reform I.1A with I.1B. **Source** : WB calculations based on PROST.

# **V.A.2 Reforms I.2 and I.3**

Same shift to a pure provident fund but with a different transition path is modeled in the Reform I.2 and Reform I.3 scenarios:

- Only new entrants join the new system.

- Only one variant is modeled with respect to the assumed real interest rate earned on individual accounts (5%).  The qualitative impact previously discussed if assets are mismanagement obviously applies in this reform scenario as well.

- All other design features assumed to be the same as in Reform I.1.

The only difference between the two scenarios is the indexation rules in the remaining old system: Reform I.2: same indexation rules (wage indexation) as in the current system; Reform I.3: price indexation applied to full pensions with no indexation applied to the commuted part The impact of the transition path can be seen by comparing Reform I.2 and I.1A. In the first years, government obligations in the former are lower as with gradual transition contributions will have to be paid only for few new employees who join the system after the reform. However, later in the future the option with gradual transition becomes much more costly than the radical shift given that the contributions required to be paid to the

- 58 -
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12 Table 3. The Relative IMportnce of Primay (Uaproceused) Stage Commodities in the Total Export of 30 Asian Countries: 1970, 1980and 1990

||Primary S<br>Share of|tage Conm<br>Totai Expo|modides'<br>rt (%)|Countr<br>Exposb o<br>Co|y's Shame In<br>f All Primry<br>mmodities (%|Asian<br>Stage<br>)|Value of<br>Comnodi|Nominal<br>Aii Primary<br>ty Exports (S|Staep<br>miilion)|
|---|---|---|---|---|---|---|---|---|---|
|Asian Expoter|1970|1980|1990|1970|1980|1990|1970|1980|1990|
|Ausalia|28.6|27.4|25.5|23.8|12.6|18.5|1,397|5,784|9,608|
|China|23.7|25.4|10.5|7.2|10.1|16.7|420|4,628|8,670|
|Indonesia|61.5|62.6|33.5|14.1|31.5|16.7|829|14,423|8,666|
|Malaysia|44.5|39.3|18.6|7.6|13.2|11.7|446|<br>6,033|6,101|
|TMailand|35.6|31.0|_18.5_|4.4|4.4|7.8|258|2,006|4,050|
|India|31.9|20.9|14.5|9.9|3.6|4.4|579|1,664|2,291|
|Taiwan, China|10.5|4.8|3.1|3.0|2.1|4.2|176|947|2,184|
|New Zeaand|14.7|17.0|22.7|3.4|2.1|4.0|200|966|2,090|
|Philippines|29.7|29.7|16.1|6.2|4.3|2.9|366|1,969|1,508|
|Rep. of Kora|14.3|4.2|2.2|2.0|1.4|2.4|115|6J6|1,254|
|Btunei|93.0|62.9|43.7|1.8|5.7|2.0|106|2,586|1,035|
|Japan|1.6|0.6|0.3|4.4|1.7|1.9|260|785|978|
|<br>Papua, New GuInea|68.9|83.1|r,.2|0.9|1.9|1.3|51|881|657|
|<br>Singapore|17.1|4.5|<br>1.6|1.9|1.4|1.2|112|620|626|
|Pldstan|20.2|18.6|12.6|2.0|0.9|1.1|120|423|595|
|Sri Lanka|69.9|45.4|21.0|3.6|1.0|0.7|209|469|381|
|Hong Kong|1.0|1.1|0.7|0.4|0.4|0.6|25|180|322|
|<br>angladeb|n|23.4|17.4|na|0.4|0.6|as|199|295|
|<br>F3ji|71.4|56.4|50.5|0.8|0.4|0.3|45|166|168|
|Korea, Democratic Rep.|243|6.2|17.3|0.3|0.1|0.3|17|S0|131|
|<br>Myanmr|12.8|33.1|19.7|0.3|0.3|0.2|15|156|98|
|<br>Guam|333|31.2|46.8|-|-|0.1|1|1|29|
|Maldives|33.3|36.4|36.8|-|-|0.1|2|4|28|
|Democratic Kampucbet|43.6|28.6|48.6|0.3|-|-|17|2|18|
|vanutu|90.9|56.6|69.2|0.2|-|-|10|17|18|
|Solomon Ilans|37.5|47.4|21.0|0.1|-|-|3|37|17|
|Mongolia|3.5|29.8|23.3|-|-|-|-|2|16|
|<br>Tonga|97.3|55.6|58.8|-|-|-|1|5|8|
|<br>Nepal|27.1|18.4|2.0|0.1|-|-|7|14|5|
|<br>Kinbati|99.5|66.6|66.6|0.1|-|-|6|4|2|

Source: Compiled from United Nation's COMTRADE ecords using partner country imports.
the values _Vt_<sup>_i, V_</sup> _t_<sup>1backwards using (3).10We then calculate the gross flows,</sup><sup>_mij_</sup> _t_<sup>and,with them,we</sup> predict the next period labor allocation in sector _j_ , _L_<sup> ̃</sup><sup>_j_</sup> _t_ +1<sup>,</sup> where _L_<sup>�</sup><sup>_i_</sup> 1<sup>=</sup><sup>_Li_</sup> 1<sup>for</sup><sup>_t_=1.Thesepredictionsarethencomparedwiththedataandtheguessesare</sup> updated until convergence. To achieve efficiency, the model is estimated in two steps. We first use the identity matrix as the weighting matrix and we then plug in the residuals from this step in _ωt_<sup>_i_.</sup> We should note here that, in order to simplify the numerical search given the large number of parameters and the limited data, we find the utility differentials _u_<sup>1</sup> _t_<sup>and the compensating differentials</sup> _η_<sup>_i_</sup> that simultaneously solve the following system of equations: for all _t_ , and, for each sector _i_ , where _T_ is the number of time periods in the data. This is a calibration procedure where we match _u_<sup>1</sup> _t_<sup>and</sup><sup>_η_suchthatthepredictionsofthemodelmatchboththewholetimeseriesofnon-</sup> manufacturing sector labor allocations and the average employment for each sector _i_ . The vectors **u**<sup>1</sup> and _η_ are re-calibrated in each step of the minimization search.<sup>11</sup> The variance can be computed numerically. Given the solution _C_<sup>�</sup> , let Λ<sup>�</sup><sup>_i_</sup> _t_ +1<sup>=</sup><sup>_L_�</sup><sup>_i_</sup> _t_ +1<sup>(</sup><sup>_C_�;</sup><sup>_η,_</sup><sup>**u**1)</sup><sup>_−_</sup> � _L_<sup>_i_</sup> _t_<sup>(</sup><sup>_C_�;</sup><sup>_η,_</sup><sup>**u**1)bethepredictedemploymentchangesandletΛ</sup><sup>_i_</sup> _t_ +1<sup>=</sup><sup>_Li_</sup> _t_ +1<sup>_−L_</sup> _t_<sup>_i_betheobservedem-</sup> ployment changes. Define

> 10Note that, for time _T_ , the values are simply equal to the instantaneous utility, _VTi_<sup>=</sup><sup>_w_</sup> _T_<sup>_i_+</sup><sup>_ηi_and</sup><sup>_V_</sup> _T_<sup>1=</sup><sup>_w_</sup> _T_<sup>1+</sup><sup>_η_</sup> _T_<sup>1.</sup>

> 11Our estimator has a flavor similar to the estimator in Goolsbee and Petrin (2004) in the sense that they match micro-data to recover the parameters and aggregate moments to recover the fixed-effects. The key difference is that Goolsbee and Petrin have much more detailed disaggregated data and thus can estimate rather than calibrate the fixed-effects, as we do here.

9