rafmacalaba/datause-encoder-data huggingface dataset:
# B. **Pension Reserves and Investment Returns**

Pension Reserves: Definitions and Sources. Pension reserves represent accumulated pension scheme surpluses which occur as the result of a decision to set payroll taxes higher than necessary to cover annual pension outlays. This typically occurs during the early years of a pay-as-you-go pension scheme when the ratio of contributors to pensioners is low because the scheme is immature and the demographic structure of the country is young. In some cases however, governments may choose contribution rates which will produce surpluses even after the scheme has matured. Such a decision was made, for example, in the early 1980s in the United States. By definition, provident fund countries that offer publicly-managed defined contribution plans have funded pensions which are classified here as pension reserves.

Public pension reserves were common in Europe, the United States and some Latin American countries during the first half of this century. Today most of these schemes, including those in Eastern Europe, have exhausted past reserves and are running deficits. In contrast, many developing countries are still in the immature part of the pay-as-you-go life cycle and have significant reserves. All public definedbenefit plans are underfunded to some degree but some continue to run surpluses and therefore can be considered partially-funded. There is no case of a publicly-managed, fully-funded, defined-benefit scheme.

The figures reported in the book represent the reserves of the old age, survivor and disability programs; reserves of other social insurance schemes are excluded. Some countries have more than one public pension scheme; in most such cases, only the pension reserves of the main scheme are available. In some cases such as Indonesia and the Philippines, reserves of more than one scheme are available. For example, the reserves of Indonesia's public employees' scheme (TASPEN) are added to those of the ASTEK, the largest scheme covering private employees, to produce the total pension reserve estimate. Pension reserve data, including data not presented in the report, are shown in Annex Table 3 below in national currency units and as a proportion of GDP. It should be noted that the real value of reserves can change dramatically over short periods of time. In Venezuela, for example, pension reserves of the IVSS lost almost half their real value in one year - 1989 - due to investments in fixed interest rate bonds under conditions of high inflation. In Chile, pension reserves in the new scheme have risen rapidly due to annual contributions to funded accounts and high average real investment returns.

16
||(0.0502)|(0.0501)|(0.050)|(0.050)|(0.050)|(0.050)|(0.050)|(0.050)|
|---|---|---|---|---|---|---|---|---|
|lage|0.0731|0.0729|0.072|0.072|0.072|0.072|0.073|0.073|
||(0.0507)|(0.0506)|(0.051)|(0.051)|(0.051)|(0.051)|(0.051)|(0.051)|
|SOE|-0.0414|0.0143|-0.067|-0.009|-0.087|-0.087|-0.064|-0.014|
||(0.2736)|(0.2824)|(0.273)|(0.282)|(0.273)|(0.274)|(0.268)|(0.274)|
|FEM|0.0596|0.0616|0.056|0.059|0.056|0.055|0.058|0.060|
||(0.1049)|(0.1048)|(0.106)|(0.106)|(0.106)|(0.106)|(0.105)|(0.105)|
|Constant|-0.8817***|-1.0626***|-0.897***|-1.026***|-0.925***|-0.985**|-0.909***|-1.082***|
||(0.2276)|(0.3753)|(0.221)|(0.396)|(0.218)|(0.391)|(0.217)|(0.365)|
|Observations|9,802|9,802|9,802|9,802|9,802|9,802|9,802|9,802|
|R-squared|0.1481|0.1500|0.148|0.151|0.149|0.149|0.148|0.150|

_Table A- 10: Employment growth and government-owned banks_

|VARIABLES|emp_gr|emp_gr|emp_gr|
|---|---|---|---|
|SOB|-0.471|0.021|4.914*|
||(1.294)|(1.324)|(2.941)|
|SOB_MNA|-6.542**|-6.583**|-12.703**|
||(3.286)|(3.013)|(5.422)|
|INTER||-1.202*|-6.840**|
|||(0.690)|(3.339)|
|INTER_MNA||0.627|8.189|
|||(1.105)|(6.904)|
|lagempl|-80.439***|-80.368***|-80.353***|
||(2.818)|(2.809)|(2.768)|
|lsize|80.452***|80.406***|80.438***|
||(2.849)|(2.828)|(2.769)|
|lage|0.602|0.526|0.434|
||(0.868)|(0.855)|(0.843)|
|SOE|-0.370|-0.428|-0.273|
||(1.795)|(1.819)|(1.824)|
|FEM|1.846**|||
||(0.884)|||
|Constant|-0.954|-0.832|-0.787|
||(2.896)|(2.905)|(2.916)|
|Observations|13,753|13,492|13,492|
|R-squared|0.781|0.781|0.782|

ECA

|||_Table A- 11:_|_Credit constrain_|_ed firms and go_|_vernment-owne_|_d banks_|||
|---|---|---|---|---|---|---|---|---|
|VARIABLES|CRCO|CRCO|CRCO|CRCO|CRCO|CRCO|CRCO|CRCO|
|SOB|0.1726|0.2121*|0.621**|0.588**|0.619**|0.582**|0.632**|0.597**|
||(0.1143)|(0.1158)|(0.288)|(0.287)|(0.289)|(0.286)|(0.297)|(0.293)|
|INTER|||-1.078*|-0.927|-1.093*|-0.924|-1.265**|-1.067*|
||||(0.565)|(0.568)|(0.589)|(0.586)|(0.636)|(0.619)|
|ROA||0.0217||0.009||0.011||0.014|
|||(0.0320)||(0.031)||(0.031)||(0.032)|
|LTA||-0.0828*||-0.060||-0.064||-0.074*|
|||||40|||||
option multiplied the estimated proportion of non-poor by a constant in each region, chosen to ensure that the proportion of non-poor in the small area estimates matched the proportion of non-poor from the survey at the regional level. The final benchmarked head count estimates were then obtained by subtracting the estimated share of non-poor from 100 percent. Benchmarking the proportion of people that are not poor instead of the proportion of poor avoids generating poverty estimates that exceed 100 percent.

Estimates of mean squared error were calculated using the parametric bootstrap procedure implemented by the povmap package (Gonzalez-Manteiga et al, 2007). The Povmap package implements the bootstrap procedure in a way that takes the benchmarking procedure into account. As is standard when estimating uncertainty using this method, the estimates are assumed to be unbiased, which implies that the standard error is the square root of the estimated mean squared error (Tzavidis et al, 2018). This allows for the estimation of standard errors, and CVs for each target area.

Comparisons of the model-based and direct estimates at the regional level suggest modest amounts of model and sampling error. Comparing the estimated precision of the small area estimates with the direct estimates show a large efficiency gain from incorporating auxiliary data from the census. Unfortunately, due to the age of the census, and the fact the COVID – 19 pandemic happened between the census and the budget survey, the estimates will only slightly reflect the changes caused by the COVID – 19 pandemic and are better measures of long-term structural poverty.

# 5. Study Findings

Small area poverty maps The variables selected to predict per capita consumption as well as the resulting coefficients from the linear mixed model are provided in table A1 of the annex. Table 2 presents a set of model diagnostics relating to the fit of the model and the consistency of the normality assumptions with the sample data. Overall, the model explains 57 percent of the variation in log per capita consumption when not considering the conditional random effect, and 61 percent when the conditional random effect is included. The model was estimated in a subsample of 16,052 households, because data from 819 households, or about 5 percent of households, were dropped due to missing data in the set of predictors. Similarly, approximately 16,500 census households, or about 3 percent of households, were dropped from the census due to missing predictors.

Regarding the distribution of the residuals, the conditional random effect has a skewness of -0.30 while the error term has a skewness of 0.38. This is slightly different from the benchmark values of 0 characterizing the normal distribution. The estimated kurtosis, which is 4.2 for the random effect and 4.1 for the idiosyncratic error term, differs moderately from the benchmark value of 3 that characterizes a perfectly normal distribution.

**Table 2: SAE diagnostics for monetary poverty**

|**Characteristic**|**Value**|
|---|---|
|Marginal R<sup>2</sup>|0.568|
|Conditional R<sup>2</sup>|0.613|
|Random effect||

9 | P a g e
**of net commitments, of all networks, in the FCS IDA portfolio (see Figure 4).**<sup>2</sup> SDN had about 240 projects per year and over $7bn in net commitments between FY2007 to FY2013. The Human Development Network has the next largest portfolio with around 120 projects per year and $3.5bn in net commitments between FY2007 to FY2013. The Poverty Reduction and Economic Management Network (PREM) and the Financial and Private Sector Development Network (FPD) have much smaller portfolios in FCS countries.

**Figures 5 and 6 compare the regional and network decomposition in the FCS portfolio to the same decomposition in other non-FCS IDA-only borrowing countries and other non-IDA borrowing countries.** These figures demonstrate that across the IDA portfolio, Africa is the dominate region with the highest number of projects and largest level of net commitments, both among FCS and non-FCS countries. However, when non-IDA borrowing countries are considered, much larger portfolios can be observed for other regions relative to their IDA lending portfolio. With regards to the network decomposition SDN has the largest portfolio among non-FCS IDA and non-IDA borrowing countries, followed by HDN.

**Figures 7 shows a more detailed decomposition of the FCS, non-FCS and non-IDA portfolios between FY2007 and FY2013.** These figures show that across the portfolios the relative number of projects and proportion of resources going to different sector boards is reasonably similar. Discrepancies include that the size of Social Protection (SP) projects in FCS countries tend to be smaller than in non-FCS and non-IDA countries, PREM Economic Policy (EP) projects are largest in non-IDA countries, while Water (WAT), Urban Development (UD) and Transport (TR) projects are largest in FCS countries.

> 2Given that this analysis uses historical data between FY2001 to the end of FY2013 the previous network and sector board structure is used.

8
We combine data from multiple sources to create a monthly-city level panel of _crime reports from migrant and host victims_ and _crimes committed by migrants_ for the largest urban centers in Colombia. To examine the effects of the PEP we use a difference-in- program differences specification, which compares crime in cities with different program take-up rates, before and after the program roll-out in August, 2018. We complement our analysis with event studies that examine the evolution of quarter-by-quarter crime outcomes in the main urban centers with different program take-up rates. The event studies support the validity of our identification strategy since we observe parallel trends before program implementation.

As a robustness test, we complement the difference-in-difference estimates exploiting the fact that each undocumented migrant eligible for the permit received a registry number exogenously allocated during registration. Based on this number, migrants could apply for (and subsequently get) the PEP migratory status in one of 22 time windows in late 2018. We use these assignments to estimate the average number of registration days available to undocumented immigrants in each city and use it as an instrumental variable for the program take-up rates. Our estimates are similar when employing the instrumental variables, reinforcing the validity of the difference-in-difference results. Additionally, our results are robust to multiple hypothesis testing, changes in geographic aggregation level (e.g., department-month aggregation instead of city-month aggregation), controls choice (e.g., dropping total crime controls), corrections on inference to a low number of clusters, and potential violations to the parallel trend assumption (Bilinski and Hatfield, 2018).

Our main analysis points to two key findings. First, places where more undocumented Venezuelan migrants applied for the PEP permit experienced an increase in total crime reports by Venezuelan migrants. These effects are explained by increments in reports of sex crimes and domestic violence, and they are entirely driven by female Venezuelans migrants, an especially vulnerable population. Notably, we find that Colombian cities 3
are assumed to have zero per capita growth in real GDP, i.e. their real GDP growth rate is the same as their population growth rate.

# **4. Results**

## **4.1 Impacts on Growth**

Given the demographic characteristics of most African countries, the _ex ante_ expectation is that most countries will experience a boost in per capita growth as a result of the demographic changes, through the faster labor supply growth (relative to the population growth) and the greater savings and investment. Given its inherent neoclassical growth underpinnings, growth in the forward-looking scenarios is driven by productivity, labor changes, and investment. Productivity growth rates are held constant across the various scenarios so that differences between the baseline and the frozen age structure scenarios are driven by the differences in labor supply growth and capital formation. Decompositions of the growth through the partial demographic freeze (no savings or no labor supply effect) scenarios illustrate this.

This can be seen clearly in how the direct labor supply effect and the direct savings effect of demographic change are expected to contribute to African countries’ average annual GDP per capita growth rates for the 2011-2030 period in the high growth baseline scenario (Figure 3). With the exception of South Africa – a special case to be discussed shortly – demographics account for between 0.2 percentage points (Botswana) and 2.3 percentage points (Ethiopia) of average annual growth, and 0.4 percentage points for the region as a whole. Putting this in perspective, SSA’s real GDP per capita was USD 1,188 (constant 2007) in 2011, and can grow to USD 2,359 if demographic effects are considered, or USD 2,183 if there is no demographic dividend.

**Figure 3: Impact of demographic changes on real GDP per capita growth rate for 2011-30, as difference from high growth baseline and decomposed by direct labor supply and savings effects**

<!-- Start of picture text --> 2.50<br>2.25<br>Direct Labor Supply Effect Direct Savings Effect<br>2.00<br>1.75<br>1.50<br>1.25<br>1.00<br>0.75<br>0.50<br>0.25<br>0.00<br>-0.25<br>-0.50<br>Percentage Points<br>Ethiopia Zimbabwe Burkina Faso Rwanda Uganda Senegal Cameroon Malawi Ghana Kenya Madagascar Zambia Rest of SSA Mozambique Côte d’Ivoire Tanzania Nigeria Botswana South Africa SSA<br><!-- End of picture text --> Note: Direct savings effect based on subtracting the no savings effect scenario results from high growth baseline; direct labor supply effect based on subtracting the no labor effect scenario results from high growth baseline. Source: Simulation results 14
0.02045 (cost) and 0.01353 (profit) or about Ihto 2 percentage points.

# **4.3. Results for developing nations using SMALL banhs as community banks**

Table 4 shows our first set of results for developing nations, which specify only SMALL banks as community banks. In Panel A, the estimated coefficients on SMALL SHARE, SMALL EFF RANK, and the interaction term SMALL SHARE x EFF RANK are positive and statistically significant at at least the 10% level, and are robust across the specifications. Consistent with developed-nations results, the developing-nations data support the hypotheses that relatively healthy SMALL banks are associated with faster economic growth, and that the marginal benefit of a higher market share for these banks is higher when these banks are more efficient.<sup>18</sup> With respect to economic significance, an increase in SMALL SHARE of 0.10 is predicted to increase GDP GROWTH by 0.00947 (cost) and by 0.0 1945 (profit) or about I to 2 percentage points, which are large in magnitude. An increase in SMALL EFF RANK of 0.10 is estimated to increase GDP GROWTH by 0.0068 (cost) and 0.00538 (profit) or about **1/2** of one percentage point.

In Regressions 7 and 8 in Panel B of Table 4, the estimated coefficients of SME EMP are positive and statistically significant at the 5% level. An increase in SME EMP of 0.10 is estimated to increase GDP GROWTH by about _V 2_ of one percentage point. The estimated BKLENDGDP coefficients are positive but not statistically significant (and so we do not calculate their economic significance). Again, the measured effects of community bank health on GDP GROWTH do not appear to be substantially diminished by the inclusion of SME EMP and BKLENDGDP.

In Regressions 9-12, the estimated coefficients on SMALL SHARE and SMALL EFF RANK are positive and statistically significant in all cases for both the SME EMP and BKLENDGDP equations, and the interaction term SMALL SHARE x EFF RANK is positive in all cases, and but not always statistically significant. In terms of effects on SME employment, an increase in SMALL SHARE of 0.10 is estimated to increase SME EMP by 0.0091 (cost) and by 0.0266 (profit), or about 1 to 3 percentage points. An increase in SMALL EFF RANK of 0. 1O is estimated to increase SME EMP by 0.021 (cost) and by 0.0205 (profit) or about 2 percentage points. In terms of effects on overall bank lending, an increase in SMALL SHARE of 0.10 is estimated to increase BKLENDGDP by 0.0098 (cost) and by 0.03135 (profit) or about 1 to 3 percentage points, and an increase in

> 18 A related finance-growth study finds that bank efficiency is positively related to economic growth in four developing Asian nations (Ferrier 2001), but it is focused on the efficiency of all banks, rather than the relative efficiencies of community banks versus other banks.

20
# **Table 12. HIES** **<u>to HIES imputation from 2006/07 to 2009/10 for urban areas</u>**

||||Mean|of X in|
|---|---|---|---|---|
|Description|Coefficient|Std. Err.|2006/7|2009/10|
|Intercept|8.3644|0.0533|||
|Bicycle|0.1162|0.0158|0.311|0.271|
|Boats|0.5614|0.1153|0.012|0.004|
|Bus lorry|0.4368|0.1016|0.015|0.014|
|Motor car van|0.4174|0.028|0.122|0.120|
|Computers|0.152|0.0225|0.176|0.263|
|Cookers|0.173|0.0176|0.745|0.772|
|Lighting-electricity or solar electricity|0.2067|0.0337|0.953|0.965|
|Electric Fans|0.1107|0.02|0.785|0.804|
|Flat house|0.0986|0.0308|0.057|0.076|
|Fridge|0.1156|0.019|0.604|0.656|
|Head’s major industry: Agriculture|0.1232|0.0675|0.012|0.014|
|Household size|-0.2769|0.0126|4.4|4.3|
|Household size squared|0.0136|0.001|22.4|21.7|
|Head’seducation completed primary|0.1144|0.018|0.408|0.445|
|Head’seducation passed OL|0.1309|0.022|0.221|0.186|
|Head’seducation passed AL|0.2722|0.0273|0.176|0.199|
|Telephone|0.1329|0.0182|0.503|0.554|
|Floor>750 sqft|0.0744|0.0162|0.409|0.458|
|Line room/Row house|-0.0743|0.0221|0.104|0.072|
|Motor Bicycle|0.1012|0.0207|0.150|0.179|
|Only paid income|-0.2023|0.0299|0.575|0.573|
|Only self-income|-0.2085|0.0315|0.192|0.180|
|Paid and self-income|-0.1613|0.0415|0.122|0.116|
|Per capita wage index|0.0062|0.0006|20.9|23.6|
|Three wheeler|0.1024|0.0308|0.057|0.066|
|Has own or shared toilet|-0.07|0.036|0.965|0.935|
|TV|0.0558|0.0235|0.881|0.901|
|VCD|0.0879|0.0156|0.448|0.517|
|Washing machine|0.1618|0.0209|0.283|0.317|
|Waterpumps|0.1784|0.0618|0.017|0.002|
|Source: Authors’ calculations usingHIES 2006|/06 and 2009/10 data||||

19
10 official, banking channels; they do not include the large (and unknown) amount of remittance monies which are sent home through private, unofficial channels. For example, in one major labor-exporting country – Egypt – it has been estimated that unofficial remittances amount to between one-third and one-half of total official remittances.<sup>18</sup> For this reason, it is likely that the “official remittance” figures recorded in Annex Table A1 are gross under-estimates of the actual level of remittances (official and unofficial) entering each labor-exporting county.

4. Migration, Remittances and Poverty Reduction: Econometric Model and Results In this section we use the cross-country data to analyze how international migration and remittances affect poverty in the developing world. Using the basic growth-poverty model suggested by Martin Ravallion and Shaohua Chen,<sup>19</sup> the relationship that we want to estimate can be written as Where P is the measure of poverty in country _i_ at time _t_ , β1 is the “elasticity of poverty” with respect to mean per capita income given by μ, β2 is the elasticity of poverty with respect to income distribution given by _g,_ β3 is the elasticity of poverty with respect to variable x (such as international migration or remittances) and ε is an error term that includes errors in the poverty measure.

The income variable in equation (1) can be measured in two different ways: (1) per capita GDP, in purchasing power parity (PPP) units, as measured from national accounts data; and (2) per capita survey mean income (expenditure), as calculated from household budget surveys done in the various developing countries. As Angus Deaton
- Brune, Lasse, Xavier Giné, Jessica Goldberg, and Dean Yang. 2016. “Facilitating Savings for Agriculture: Field Experimental Evidence for Malawi.” _Economic Development and Cultural Change_ 64(2): 187-220.

- Chen, Rong. 2021. “Mapping Data Governance Legal Frameworks Around the World Findings from the Global Data Regulation Diagnostic.” World Bank Policy Research Working Paper 9615.

- Chiplunkar, Gaurav, and Pinelopi Koujianou Goldberg. 2022. “The Employment Effects of Mobile Internet in Developing Countries.” NBER Working Paper 30741, National Bureau of Economic Research, Cambridge, MA.

- Claessens, Stijn, Jon Frost, Grant Turner, and Feng Zhu. 2018. “Fintech Credit Markets Around the World: Size, Drivers and Policy Issues.” _BIS Quarterly Review_ (September): 29–49.

- Cohen, Michael, and Marc Rysman, 2013. “Payment Choice with Consumer Panel Data.” Working Paper No. 13-6, Federal Reserve Bank of Boston.

- Collins, Daryl, Jonathan Morduch, Stuart Rutherford, and Orlanda Ruthven 2009. _Portfolios of the Poor: How the World’s Poor Live on $2 a Day_ . Princeton, NJ: Princeton University Press.

- Dabla-Norris, Era, Yixi Deng, Anna Ivanova, Izabela Karpowicz, Filiz Unsal, Eva Van Leemput and Joyce Wong. 2015a. “Financial Inclusion: Zooming in on Latin America.” IMF Working Paper No. 15/206, International Monetary Fund, Washington, DC.

- Dabla-Norris, Era, Yan Ji, Robert Townsend, and D. Filiz Unsal. 2015b. “Distinguishing Constraints on Financial Inclusion and Their Impact on GDP, TFP, and Inequality.” NBER Working Paper No. 20821, National Bureau of Economic Research, Cambridge, MA.

- De’, Rahul, Neena Pandey, and Abhipsa Pal. 2020. “Impact of Digital Surge during Covid-19 Pandemic: A Viewpoint on Research and Practice.” _International Journal of Information Management_ , 55(C): 102171.

- Deléchat, Corinne, Monique Newiak, Rui Xu, Fan Yang, and Goksu Aslan. 2018. “What is Driving Women’s Financial Inclusion Across Countries?” IMF Working Paper No. 18/38, International Monetary Fund, Washington, DC.

- Demirguc-Kunt, Asli, Leora Klapper, Dorothe Singer, and Saniya Ansar. 2022. _The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19_ . Washington, DC: World Bank.

- Dupas, Pascaline, Dean Karlan, Jonathan Robinson, and Diego Ubfal. 2018. “Banking the Unbanked? Evidence from Three Countries.” _American Economic Journal: Applied Economics_ 10(2): 257–297.

28