rafmacalaba/datause-encoder-data huggingface dataset:
**Table 5. Replacement Rates Under Multi-Pillar System for 19 Years of Expected Retirement**

|Year of<br>Retirement|No. years<br>work<br>Old Systema|No. years<br>work<br>New Systema|Transitional<br>+Basic<br> <br>Benefitb|D C<br>Pension"|Total<br>Replacement<br>Rate|
|---|---|---|---|---|---|
|2000|37|5|60|3|63|
|2020|19|25|42|15|57|
|2030|10|35|33|22|55|
|2040|0|45|23|30|53|
|2050|0|45|23|32|55|
|2060-2100|0|45|23|32|55|

a Expected working period increases as retirement age increases, but this is partially offset by schooling increase.

- _b_ This gives replacement rate of final year's wage for average member of retiring cohort. Formulas for transitional and basic benefits are given in text. Pension from DC individual accounts is based on baseline wage growth and interest rate assumptions in Table 2. Retirement age is reformed option in Table 2.

Expected period of retirement is 19 throughout. Increased longevity is offset by increased retirement age.

Price indexation of pension is assumed.

Contribution rate to DC pension is 8.5%, of which .3W goes toward administrative expenses.

For contribution rates to transitional + basic benefit seet Table 6.

**41**
|7|Conditional cash<br>transfer|Philippines|EAP|RCT|Enrolled in school 15‐<br>17yrs|**‐0.098**|Evaluation<br>report|Chaudhury et<br>al. 2013|
|---|---|---|---|---|---|---|---|---|
|8*|Sanitary products|Nepal|SA|RCT|Attendance|**‐0.083**|Journal|Oster and<br>Thornton<br>2011|
|9|Conditional cash<br>transfer|Uruguay|LAC|DID|School attendance,<br>18‐month follow up<br>School attendance,<br>30‐month follow up|**‐0.056**<br>**‐0.047**|Journal|Amarante et<br>al. 2013|
|10|Unconditional Cash<br>Transfer|South Africa|SSA|DID|Attendance|**‐0.043**|Working<br>Paper|Santana 2008|

Note: Interventions marked with an asterisk are girl‐targeted. 1 Adjusted negative value for comparison.

**Table 5: The 10 most effective interventions to improve learning for girls**

||**Program**<br>**description**|**Country**|**Region**|**Evaluation**<br>**design**|**Outcome**|**Effect**<br>**size(SD)**|**Type of**<br>**publication**|**Reference**|
|---|---|---|---|---|---|---|---|---|
||||||Letter sounding fluency|**2.563**|||
|1|Literacy<br>intervention|South Africa|SSA|DID|Word naming fluency<br>Reading comprehension|**1.840**<br>**1.757**|Evaluation<br>report|Piper 2009|
||||||Oral reading fluency|**1.658**|||
|2|Mother tongue<br>instruction|Kenya|SSA|RCT|Reading comprehension,<br>Lubukusu, class 1<br>Reading comprehension,<br>Kikamba, class 2|**1.36**<br>**1.25**|Journal|Piper et al.<br>2016|

30
<!-- Start of picture text --> 2.0% 0.3%<br>1.0% 0.0%<br>0.0%<br>-0.3%<br>-1.0%<br>-0.5%<br>-2.0%<br>-0.8%<br>-3.0%<br>-1.0%<br>-4.0%<br>-1.3%<br>-5.0%<br>-1.5%<br>-6.0%<br>-7.0% -1.8%<br>-8.0% -2.0%<br>U.S. Current Account (LHS) U.S.-Japan Bilateral Current Account (RHS)<br>U.S. Total C.A. Bilateral C.A.<br>Percentage of U.S. GDP Percentage of U.S. GDP<br>Mar-60 Sep-61 Mar-63 Sep-64 Mar-66 Sep-67 Mar-69 Sep-70 Mar-72 Sep-73 Mar-75 Sep-76 Mar-78 Sep-79 Mar-81 Sep-82 Mar-84 Sep-85 Mar-87 Sep-88 Mar-90 Sep-91 Mar-93 Sep-94 Mar-96 Sep-97 Mar-99 Sep-00 Mar-02 Sep-03 Mar-05<br><!-- End of picture text -->

**Figure II. U.S. Current Account and U.S. and Japan Bilateral Current Account**

This figure shows the quarterly U.S. total current account balance and one of its components, the bilateral current account with Japan, from 1960 to 2005. Values are shown as a percentage of U.S. GDP.

Source: Bureau of Economic Analysis (U.S. Department of Commerce).

56
the value of output grew at an average rate of over five percent per annum. Simultaneously, the Lake Victoria fishery was increasingly dominated by Nile perch. In 1990, Nile perch accounted for 44 percent of the landed volume of fish (Figure 11). By 1996 this had increased to 58 percent.

The dominance of Nile perch in the Lake Victoria fishery both reflects and induced an increase in the landed price of Nile perch relative to other fish, in particular tilapia<sup>25</sup> . Historically the difference in landed price of Nile perch, tilapia and R. _argentea_ (Omena) was not significant. However, in the mid-1990s the price of Nile perch increased significantly, whilst the price of tilapia actually declined.

The increase in landed price of Nile perch in the mid-1990s further enhanced its dominance within the economy of the Lake Victoria fisheries. By 1996, Nile perch accounted for over 75 percent of the landed value of fish in Kenyan waters of Lake Victoria. Indeed, the economic sustainability of the fishery at its current scale is largely dependent on Nile perch. There is limited scope for any shortfalls in the returns from Nile perch to be off-set by increased landings of other species.

**Figure 10. Nominal and real (constant 1995 prices) value of fish landings from Lake Victoria, 1980-2002):**

<!-- Start of picture text --> 180<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>Nominal Real (1995=100)<br>US$ million<br>1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2001<br><!-- End of picture text --> _Note: calculated using IMF GDP deflator for Kenya (1995=100). Source: CBS_ Despite its small contribution to national GDP, the Nile perch fishery plays a significant role in the economy of the region bordering Lake Victoria. Nyanza is a region of economic and social under-development, both within a Kenyan and a wider African context. It has a Human Development Index (HDI) of only 0.440 and a Gender Development Index (GDP) of 0.434, both

> 25 R. _argentea_ and Nile perch are products of, in effect, separate fisheries and so do not directly compete with one another in terms of the direction of the fishing effort.

31
Table 6 Percentage of Births and Difference-in-Differences Estimates in Utilization by Poverty Status between Villages with and Villages without a Voucher Client Resident in 2010–11

|Services|Vouch<br>resident<br>at fol<br>(|er client<br>in village<br>low-up<br>%)|No vouc<br>resident i<br>at foll<br>(|her client<br>n village<br>ow-up<br>%)|Differen<br>differe|ce-in-<br>nces|
|---|---|---|---|---|---|---|
||Poor|Nonpoor|<br>Poor|Nonpoor|Percentage<br>points<sup>a</sup>|Odds<br>ratios<sup>b</sup>|
|Four or more ANC<br>visits|51<br>(_N_=<br>397)|62<br>(_N_=<br>573)|37<br>(_N_= 82)|52<br>(_N_=<br>216)|−4|1.1<br>(0.6–<br>2.3)|
|Place of delivery|(_N_=<br>364)|(_N_=<br>517)|(_N_= 80)|(_N_=<br>198)|||
|Private facility|25|35|13|16|7|0.8<br>(0.3–<br>2.1)|
|Public facility|31|35|32|48|−12|1.5<br>(0.7–<br>3.3)|
|Public or<br>private<br>facility|59|74|45|69|−9|0.9<br>(0.5–<br>2.0)|
|PNC|45|59|51|52|13|0.5|
||(_N_=|(_N_=|(_N_= 82)|(_N_=||(0.3–|
||397)|573)||216)||1.1)|

_<mark>Source:</mark>_ <mark>Population Council, Household Survey 2010–11.</mark> a Based on differences in the proportions of poor and nonpoor women who used the health services in villages with and without a voucher client: a negative sign means the difference between the poor and nonpoor was greater in the comparison group.

29
Table 8: COVID-19 and Electricity Consumption in Lockdown Communities: Ghana

|||||logof C|onsumpti|on|||
|---|---|---|---|---|---|---|---|---|
||A|ll|Resid|ential|Non-Res|idential|HeavyI|ndustries|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|DID (Month> April=0)|0.404***<br>(0.002)|0.406***<br>(0.002)|0.452***<br>(0.002)|0.454***<br>(0.002)|0.118***<br>(0.006)|0.118***<br>(0.006)|-0.409***<br>(0.022)|-0.410***<br>(0.023)|
|DID (Month> April=1)|0.149***<br>(0.002)|0.160***<br>(0.002)|0.173***<br>(0.002)|0.185***<br>(0.002)|0.015***<br>(0.006)|0.020***<br>(0.006)|-0.180***<br>(0.023)|-0.181***<br>(0.023)|
|DID (Month> April=2)|0.173***<br>(0.002)|0.146***<br>(0.002)|0.193***<br>(0.002)|0.164***<br>(0.002)|0.053***<br>(0.006)|0.038***<br>(0.006)|-0.100***<br>(0.027)|-0.099***<br>(0.032)|
|DID (Month> April=3)|0.142***<br>(0.002)|0.174***<br>(0.003)|0.155***<br>(0.003)|0.191***<br>(0.003)|0.072***<br>(0.007)|0.088***<br>(0.007)|-0.112***<br>(0.025)|-0.106***<br>(0.028)|
|DID (Month> April=4)|0.113***<br>(0.003)|0.068***<br>(0.003)|0.128***<br>(0.003)|0.078***<br>(0.003)|0.035***<br>(0.007)|0.009<br>(0.007)|-0.138***<br>(0.026)|-0.122***<br>(0.028)|
|DID (Month> April=5)|0.196***<br>(0.003)|0.167***<br>(0.003)|0.213***<br>(0.003)|0.181***<br>(0.003)|0.104***<br>(0.007)|0.088***<br>(0.007)|-0.096***<br>(0.027)|-0.085***<br>(0.028)|
|DID (Month> April=6)|0.119***<br>(0.003)|0.073***<br>(0.003)|0.133***<br>(0.003)|0.082***<br>(0.003)|0.044***<br>(0.007)|0.019***<br>(0.007)|-0.112***<br>(0.025)|-0.106***<br>(0.027)|
|DID (Month> April=7)|0.150***<br>(0.003)|0.154***<br>(0.003)|0.168***<br>(0.003)|0.174***<br>(0.003)|0.048***<br>(0.007)|0.049***<br>(0.007)|-0.090***<br>(0.025)|-0.079***<br>(0.026)|
|DID (Month> April=8)|0.159***|0.175***|0.433***|0.452***|0.046***|0.053***|-0.071***|-0.060**|
||(0.013)|(0.013)|(0.063)|(0.063)|(0.014)|(0.014)|(0.026)|(0.028)|
|Climate Ctrls|No|Yes|No|Yes|No|Yes|No|Yes|
|Customer FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|District X Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|R-squared|0.697|0.697|0.624|0.624|0.795|0.795|0.869|0.869|
|Mean dep. Var|1032.762|1032.762|224.075|224.075|763.197|763.197|143144.950|143144.950|
|Obs|8635711|8635711|7327395|7327395|1268209|1268209|38885|38885|

Notes: Dependent variable is the log of monthly electricity consumption. DID (Month> April= _ω_ ) represents the DID estimates for the _ω_ months since the start of COVID lockdown (April, 2020). The DID estimates are derived from the expression _DID_<sup>_ω_</sup> = _β_<sup>ˆ</sup> 2020<sup>_ω−_</sup><sup><u>1</u></sup> 2 �2019 _k_ =2018<sup>_β_ˆ</sup> _k_<sup>_ω_.Climate Ctrls include the monthly average</sup> temperature and total precipitation. Standard errors clustered at district level in parenthesis. _∗_ Significant at 10 percent level _∗∗_ Significant at 5 percent level _∗∗∗_ Significant at 1 percent level 34
ways: i) the variance of both measures of diversification across countries declines with the level of aggregation; and ii) the colinearity between the measures of diversification rises with the level of aggregation (see section 4.1 for a discussion). Given the above, do changes in aggregation affect the coefficient of latent diversification in the regressions presented in Table 3?<sup>24</sup> To address this question we re-estimate the specifications in columns (1)-(7) of Table 3, Panel A using measures of diversification calculated with the SITC Rev. 1 2-digit, 3-digit and 5-digit classifications. The results of this exercise are presented in Figure 8.

The coefficients on our measure of latent diversification tend to be more precisely estimated when using data at higher levels of disaggregation. This is partly a result of the attenuation of variance across countries. The reduction in statistical significance of latent diversification estimates in specifications where we also control for cross-sectional diversification is larger when we use more aggregated data. This is partly a result of the large degree of colinearity between cross-sectional and latent diversification when using aggregated data. Likewise, Figure 8 shows that in most cases more aggregated data is associated with larger point estimates. This is also partly due to lower cross-country variance, because the coefficient is inversely proportional to the variance of the explanatory variables.

**6.2. Latent Diversification, Terms-of-Trade Volatility and GDP Volatility** The estimation results in Table 3 highlight the link between latent diversification and terms-of-trade volatility. Latent diversification is negatively correlated with ToT volatility (but not necessarily with GDP per capita volatility), whereas economic size and income per capita do not necessarily have a direct effect on either volatility measure. To explore these links further, we present the results of an econometric exercise that simultaneously explores the determinants of latent diversification, terms-of-trade volatility, and GDP per capita growth volatility. In the model, latent diversification affects GDP per capita growth volatility through its effect on terms-of-trade volatility. We estimate these relationships through a threestage least squares estimation (3SLS). The results are presented in Table 4.

The model of latent diversification is consistent with the results obtained throughout this paper—bigger and richer countries typically have larger latent export baskets. In addition, a higher prevalence of commodity exports does not have a statistically significant impact on latent diversification. Regarding

> 24 We focus our analysis exclusively on the link between latent diversification and ToT growth volatility. This choice was made given the fact that the coefficient for latent diversification in the GDP per capita growth volatility equation is significant only in a limited number of cases and, as is discussed in section 4.1, this expected to hold for higher levels of aggregation.

26
Testing Information Constraints on India’s Largest Antipoverty Program Martin Ravallion, Dominique van de Walle, Puja Dutta and Rinku Murgai<sup>1</sup>

**Keywords:** Poverty; public awareness; information campaigns; workfare; randomized trials

**JEL:** D83, I38, O12, O17

**Address for correspondence:** <u>mr1185@georgetown.edu</u>

1 Ravallion is with the Department of Economics, Georgetown University, and the NBER. Dutta, Murgai and van de Walle are with the World Bank. The authors are grateful to Sunai Consultancy Private Ltd and GfK Mode for support on the field work for this study. The movie was produced by the Institute for Participatory Practices (Praxis) with assistance from Soumya Kapoor. The authors are also grateful to the Rural Development Department, Government of Bihar, for providing insights into the challenges and ongoing initiatives in Bihar. Arthur Alik Lagrange and Maria Mini Jos provided very able research assistance. Useful comments were received from seminar participants at the Indian Statistical Institute, the Australian National University, the Norwegian School of Economics, the Stockholm Institute of Transition Economics and the World Bank. These are the views of the authors and do not necessarily represent those of their employers, including the World Bank or of any of its member countries.
Table _of QW=_

|_Abstnct_<br>_....................................._<br>_.................._|_..._<br>_........._<br>_i_|
|---|---|
|Summary ...............<br>.......................|fi|
|I. lNTRODUCIION<br>........................................<br>...<br>....|1|
|U.LEVELS OFMILTARYEXPENDITURE<br>...............................|2|
|m.MILITARY SPENDING AND ECONOMIC GROWTH ...........<br>............|4|
|A.<br>Hypothesis ......................................|4|
|<br>B.<br>Model Specification .......................................|6|
|<br>C.<br>Data<br>................................<br>.|_8_|
|D.<br>Results<br>.<br>.....................................<br>.|9|
|Tl e Relationship Between Milex and Ecowomic Growd<br>.<br>.|9|
|<br>Ot.&er Regressors..........<br>..............<br>.9||
|<br>Sensitivity Analysis..|_9_|
|<br>Changing the Specification<br>..|10|
|<br>Robustness ........................<br>,,|10|
|Robustness vis-a-vis Outliers .........|11|
|E.<br>Military Expenditres and Growth: Channels of Impat_..............._|_.._14|
|IV. OTHER ECONOMIC IMPACTS OF MILITARY EXPENDITURES .....<br>..........|16|
|A.<br>Impact on Government Social and Ifrastrucure_SpeWing._....<br>..........|16|
|<br>B.<br>hImact on Other Economic Variables. .......<br>.............<br>.|is|
|V. DETERMINANTS<br>OF MLITARY EXPENDITURE ......<br>...................|19|
|VI. CONCLUSIONS ................................................|20|
|REFERENCES..................................|30|
|Variable List||
|Table I<br>Patterms of Military Experditure||
|<br>Table 2<br>Cross Section Regressions||
|<br>Table 3<br>Regressions of the Difference in Growth Rata Betwee Succouive Mulid-yer||
|Periods on the Differences in the Regressors||
|<br>Table 4<br>Summary of Regressions by Geographic Regiom||
|Table S<br>Dropping Various Regions||
|Table 6<br>Channels of Imact||
|<br>p<br>Table 7<br>Testing the Efficiency Hypcthesis||
|<br>Table 8<br>Impact of Milex On The Level and Composiion of Cal<br>GovNment||
|Expediture 197089||
|Table 9<br>Impact of MES on Key Economic Variables||
|<br>Table 10<br>The Dem<br>ir of Military Exp<br>_end - MES||
**Table A3: Country-Specific Sibling Correlation Estimates (1980 Cohort)**

|**Country**|**Sibling**<br>**Corr.**|**S.E.**|**N**|**Country**|**Sibling**<br>**Corr.**|**S.E.**|**N**|
|---|---|---|---|---|---|---|---|
|East Asia & Pacific||||Sub-Saharan Africa||||
|Cambodia|0.657|0.009|6402|Benin|0.595|0.016|2439|
|Indonesia|0.616|0.007|10148|Burkina Faso|0.638|0.017|1636|
|Philippines|0.616|0.011|4746|Burundi|0.584|0.021|1396|
|Vietnam|0.616|0.021|1232|Cameroon|0.629|0.017|1755|
|||||Chad|0.727|0.020|887|
|Europe & Central Asia||||Congo, Rep.|0.532|0.022|1212|
|Albania|0.502|0.032|1106|Cote d'Ivoire|0.592|0.029|620|
|Armenia|0.592|0.022|1735|Ethiopia|0.656|0.011|3006|
|Kyrgyz Republic|0.438|0.036|575|Gabon|0.614|0.034|485|
|Tajikistan|0.461|0.023|1469|Ghana|0.617|0.020|1588|
|Turkey|0.561|0.015|3265|Guinea|0.515|0.028|983|
|||||Kenya|0.584|0.017|2587|
|Latin America & Caribb|ean|||Lesotho|0.592|0.013|3001|
|Bolivia|0.653|0.015|3118|Liberia|0.535|0.027|787|
|Brazil|n.a|n.a|n.a|Madagascar|0.790|0.010|2084|
|Colombia|0.582|0.009|9258|Malawi|0.602|0.015|2470|
|Dominican<br>Republic|0.538|0.014|4279|Mali|0.619|0.019|1595|
|Guatemala|0.697|0.016|1281|Mozambique|0.522|0.024|1176|
|Haiti|0.674|0.014|2594|Namibia|0.535|0.029|1072|
|Peru|0.591|0.006|19330|Niger|0.684|0.019|1031|
|||||Nigeria|0.666|0.011|4833|
|Middle East & North Af|rica|||Rwanda|0.550|0.015|3381|
|Egypt, Arab Rep.|0.581|0.008|11306|Senegal|0.584|0.016|2157|
|Jordan|0.468|0.012|7316|Sierra Leone|0.517|0.026|1029|
|||||South Africa|0.383|0.050|343|
|South Asia||||Tanzania|0.563|0.018|1952|
|Afghanistan|0.523|0.015|2727|Togo|0.570|0.039|430|
|Bangladesh|0.614|0.010|5228|Uganda|0.666|0.017|1489|
|India|0.631|0.003|53132|Zambia|0.681|0.018|1325|
|Nepal|0.618|0.016|2171|Zimbabwe|0.609|0.021|1481|
|Pakistan|0.633|0.004|27858|||||

_Notes_ : This table presents the sibling correlation estimates for each of the 53 developing countries in the Demographic and Health Surveys (DHS) using the 1980s birth cohort sample.

41
# **5.2 Empirical evidence**

To empirically investigate the role of small firms in cross-border e-commerce, we go back to the harmonized I2D2 and extract information on firm size. We compute, for each country and year the share of workers employed in firms with fewer than 10 employees.<sup>21</sup> It is on average 45 percent, and it oscillates between 15 percent in Russia and 74 percent in Costa Rica. This suggests that for many countries in our sample the share of employment in small firms is sufficiently large as to have an economically significant impact on the wage gap.

We also compute the share of skilled workers in small and large firms to check whether small firms are less skilled-intensive. Columns (2) and (3) of table 7 show that on average only 25 percent of workers in small firms (firms with fewer than 10 employees) are considered skilled. In large firms (firms with more than 50 employees) 49 percent of workers are skilled. Comparing each row in columns (2) and (3) of table 7 we observe in all countries a larger share of skilled workers in large The variation across countries in the share of employment in small firms suggests that if the mechanism at work is the one described above, then the impact of online exports on the wage gap is likely to be heterogeneous across countries. In order to test this heterogeneity and provide evidence for our mechanism, we re-estimate equation (1) with an interaction between online exports and two dummies indicating whether the country is above or below the median share of employment in small firms in our sample. The median is at 44 percent in our sample. We use a dummy for two reasons. First, the size of the firm is not systematically reported by all individuals in the I2D2 surveys and the resulting information may be quite noisy. In addition, the I2D2 does not provide the exact number of employees, but an interval with a lower and upper bound for firm size. Second, the relationship between the impact of online exports on the wage gap and the share of workers in small firms may not be linear. The solution to these two problems is to use a dummy variable instead of the actual continuous variable. We expect the coefficient on the interaction of online exports with the dummy indicating a share of employment in small firms above the median to be negative and larger than the coefficient on the interaction of online exports with the dummy indicating a share of employment in small firms below the median. Because we are ultimately endogenize the number of firms and solve for a full model with more general assumptions on the production side to show that the wage gap increases with the fixed costs faced by firms in the less skill-intensive sector, and that this effect is larger the larger is the endowment of unskilled to skilled workers.

> 21Information on firm size is unfortunately not available for Colombia, the Dominican Republic, Sri Lanka, and the Philippines.

14
<!-- Start of picture text --> ‘Partial regresions of DALP variables on DPI Party Age, 2009<br>—o —<br>8 ae °<br>Bo . 2s °. #S<br>oQji* $ ee e o * tof<br>oO o*8 °* . o °* @ © %6 «<br>D e* Y Do 2 e<br>x e Ps Ff e <x oe 3 e +<br>Bo es a Bo s ote o ee<br>on or “S<br>-50 0 50 100 -10 -5 0 8) 10<br>Partial Effect of Average Party Clientelism, DALP Average Party Appeals to Charisma, DALP<br>coef = -.42573831, (robust) se = .10391512, t = -4.1 coef = -1.1424425, (robust) se = .66470544, t = -1.72<br>58 °<br>Sic ,o°<br>5 @3DowD Septal*° Rtee AyJ:?°¢- tex. ;° .. °<br>ie}<br>a”<br>-5 0 cS) 10<br>Average Party Appeals to Competence, DALP<br>coef = 2.4727309, (robust) se = .83784331, t= 2.95<br><!-- End of picture text -->