rafmacalaba/datause-encoder-data huggingface dataset:
Vulnerability to unemployment increased during the pandemic for people living in urban areas, with ethnic self-identification, and aged between 18-28 years (Figures 14 - 15).

Furthermore, there are significant differences in labor incomes and hours worked between men and women. Those without higher education and those with children are particularly vulnerable to larger gaps and worse levels in these labor market indicators (Figure 16). Rural areas and ethnic populations have low labor incomes, below 50%, which is critical. However, higher education can significantly reduce the gap, up to 80 percentage points for women (Figure 18). Finally, the number of women engaged in unpaid family work has declined, while men with children experienced a rise in this type of work in 2020 (Figure 17).

The low labor income in rural areas and among the ethnic population is especially critical, with levels below 50%. Higher education improves the income for both groups, with or without children, and for women, it can structurally reduce the gaps up to 80 p.p. (Figure 18). In hours worked per week, the differences are like the previous ones, but proportionally of a smaller magnitude (Figures 19- 20).

# **5. Methodology**

The objective of this paper is to assess the influence of the pandemic on preexisting disparities in labor market outcomes by utilizing two methodologies that enable us to approximate the genderspecific impacts of the pandemic on labor participation and income.

# **Estimation of the probability of participating in the labor market**

Labor participation between men and women presents a high structural gap, as was observed in Section 1. To identify the factors that determine this gap and quantify its importance, we use a probabilistic model to estimate the probability for each group to participate in the labor market based on their characteristics. Given the type of survey, we do not have information that follows individuals over time which limits causal inference. Therefore, we conduct separate estimates _(cross-section)_ for the years 2015, 2017, 2019, and 2021 with the following specification:
**Exhibit 19: Contribution to Tax Growth by Subsector**

<!-- Start of picture text --> 20% Transport, Storage<br>and<br>Communications<br>15%<br>Wholesale and<br>Retail Trade<br>10%<br>Construction<br>Manufacturing<br>5% All Other<br>Mining & Quarrying<br>0%<br>Agriculture Industry Services mult<br>Contribution to tax growth<br><!-- End of picture text -->

**Exhibit 20: Growth in Taxes and Employment by Sector**

|**2008 – 2012**||**Taxes**|**Em**|**ployment**|
|---|---|---|---|---|
||CAGR|Contribution|CAGR|Contribution|
|**Agriculture**|2.86|0.00|0.00|2.31|
|**Industry**|1.58|0.04|0.06|1.43|
|**Services**|1.09|0.19|0.07|0.92|
|**Multiple Sectors**|2.23|0.01|0.03|2.54|

_<u>Note:</u>_ This table shows the Compound Annual Growth Rate (CAGR) in tax declarations and taxable employment of relevant companies by sector between 2008 and 2012. It also shows the contribution of relevant companies to the overall growth in tax declarations and taxable employment. “Relevant” companies are those registered after 2008 and active as of 2012.

25
effect of the pandemic is insignificant. The results for reading and science should be interpreted with caution. The negative trends in student achievement started before the pandemic. Thus, our identification strategy, which relies on stable long-term trends, cannot be applied without doubts to reading and science. Our robustness checks for reading and science results confirm that indeed in these two domains the time trends are not linear, and one cannot distinguish between the effects of the pandemic and the long-term decline in achievement. What causes these long-term declines is beyond the scope of this paper. We can only note that both reading and science were minor domains in PISA 2022, meaning the measurement of student achievement was less precise than in mathematics.

**Table 4. Impact of Pandemic on Student Reading and Science Achievement**

||Readi<br>|ng (2000 to 2<br>|022)<br>|Scien<br>|ce (2006 to 2<br>|022)<br>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Departure from linear|-12.10***|<br>-6.96***|<br>-8.43***|<br>-0.78|6.16***|<br>8.13***|
|trend in 2022|(0.43)|(0.75)|(1.10)|(0.46)|(0.84)|(1.15)|
|UNESCO: number of||-0.33***|||-0.44***||
|weeks with full closures||(0.04)|||(0.04)||
|PISA: country average<br>student-provided number|||-0.14***|||-0.38***|
|of weeks of closures|||(0.04)|||(0.04)|
|_Additional controls_|||||||
|Individual level: gender,|||||||
|immigrant background,|Yes|Yes|Yes|Yes|Yes|Yes|
|SES|||||||
|Country average: gender,|||||||
|immigrant background,<br>SES|Yes|Yes|Yes|Yes|Yes|Yes|
|•<br>country fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|•<br>country*time|Yes|Yes|Yes|Yes|Yes|Yes|
|N|3,080,007|3,080,007|2,966,140|2,663,094|2,663,094|2,565,541|

Standard errors in parentheses. * p<0.05; ** p<0.01; *** p<0.001. Full results available in the Annex.

17
level with limited coverage.<sup>17</sup> Another source of yield data is the community part of the Household Income and Expenditure Surveys (HIES) (2000, 2005, 2010). We define rice yield per acre as the average of yields of boro, aman and aus rice reported in the village/community part of HIES. The upazila (subdistrict) level rice yield is the average over villages surveyed within an upazila. Since the number of villages within an upazila is not large, these upazila level estimates of rice yield may involve significant measurement error. However, it is reassuring that the yield growth from the HIES data shows estimates comparable to that from the official estimates during the decade of 2000.

The rainfall data are taken from Bandyopadhyay and Skoufias (2012). The original data on rainfall come from the Climate Research Unit (CRU) of the University of East Anglia. The CRU reports estimated monthly rainfall for most of the world at the half degree resolution from 1902 to 2009. The CRU method combines weather station data with other information to arrive at the estimates.<sup>18</sup> To estimate the subdistrict (upazila/thana) level rainfall from the CRU data, Bandyopadhyay and Skoufias (2012) uses area weighted averages.<sup>19</sup> Travel times to metropolitan cities were computed using GIS software and road network from the mid-1990s. Data on agro-ecological zones are drawn from the Bangladesh Water Board database.

Over the years, a number of larger upazilas were split to form new upazilas, thus increasing the total number of upazilas from 486 in 1990 to 507 in 2000 to 543 in 2010. We use upazila maps to identify the borders of upazilas over time and matched all upazilas in 2000 and 2010 to 1990 upazilas. The upazila level panel is defined using 1990 upazila boundaries. To define the estimation sample, we take out the upazilas that are located in the two main metropolitan cities (Dhaka and Chittagong), leaving us with a sample of 464 upazilas. We divide the upazilas into two sub-samples on the basis of share of urban in total

> 17These data are reported at old (and much larger) district level – there are about 20 old districts. With newly created districts, there are now 64 districts in Bangladesh. These data are drawn from Statistical Yearbooks.

> 18Previous versions of the CRU data were homogenized to reduce variability and provide more accurate estimation of mean rain at the cost of variability estimation. The version 3.1 data are not homogenized and thus allows for better variability estimates. The estimates of rainfall near international boundaries are not less reliable as compared with those in the interior of the country, as the CRU estimation utilizes data from all the weather stations in the region.

> 19For example if an upazila/thana covers two half degree grid cells for which CRU has rainfall estimates, then upzila/thana rainfall is estimated as the average rainfall of the two grid-cells, where the weights are the proportion of the area of the upazila/thana in each grid-cell. For details, please see Bandyopadhyay and Skoufias(2012).

19
|0.003<br>0.003<br>0.859<br>0.211***<br>0.37<br>0.190*<br>-0.588<br>0.314*<br>0.754<br>0.251***<br>1.066<br>0.213***<br>410<br>0.24<br>d the share of<br>olds where the|
|---|
|Southern<br>-0.001<br>0.002<br>-0.348<br>0.156**<br>0.557<br>0.158***<br>410<br>0.18<br>ld members an<br>my for househ|
|0.002<br>0.003<br>-0.965<br>0.287***<br>0.554<br>0.144***<br>410<br>0.2<br>er of househo<br>e that a dum|
|0<br>0.001<br>0.302<br>0.062***<br>-0.076<br>0.07<br>-0.373<br>0.074***<br>0.123<br>0.060**<br>0.221<br>0.060***<br>2339<br>0.15<br>og of the numb<br>ughs owned (not|
|Eastern<br>-0.001<br>0.001<br>-0.333<br>0.052***<br>0.033<br>0.043<br>2339<br>0.13<br>ld head, the l<br>rows and plo|
|0<br>0.001<br>-0.445<br>0.066***<br>0.106<br>0.040***<br>2339<br>0.13<br>nt at 1%.<br>of the househo<br>d value of har|
|0.008<br>0.002***<br>0.213<br>0.112*<br>0.187<br>0.133<br>-0.327<br>0.159**<br>0.143<br>0.134<br>0.585<br>0.128***<br>669<br>0.2<br>; *** signifca<br> age squared <br>ising farms, an|
|Central<br>0.006<br>0.002***<br>-0.111<br>0.105<br>0.351<br>0.097***<br>669<br>0.18<br>gnifcant at 5%<br>ng the age and<br>for livestock ra<br> province.|
|All Provinces<br>0.002<br>0.001<br>0.002<br>0.008<br>0.001**<br>0.001<br>0.001***<br>0.002***<br>-0.513<br>-0.42<br>0.060***<br>0.138***<br>0.2<br>0.303<br>0.036***<br>0.092***<br>-0.298<br>0.045***<br>0.162<br>0.039***<br>0.339<br>0.054***<br>0.025<br>0.059<br>-0.401<br>0.068***<br>0.203<br>0.055***<br>0.399<br>0.054***<br>3418<br>3418<br>3418<br>669<br>0.15<br>0.14<br>0.16<br>0.19<br>ndard errors in parentheses: * signifcant at 10%; ** si<br>tion equations (frst stage) are run by province includi<br> log of total farm area, a farm type dummy, a dummy <br>le is not included, Tobit did not converge).<br> 3 columns (all provinces), Tobit regressions are run by|
|_φ_<br>_F_1<br><br>_F_2<br><br>_R_1<br>_R_2<br>1998<br>1999<br>2000<br>2001<br>2002<br>Obs<br>_R_2<br>Robust sta<br>Tobit selec<br>males, the <br>head is ma<br>In the frst|

37
funds<sup>8</sup> , and through the definition of the Minimum Return Guarantee.

The MRG imposes a lower threshold of returns that each individual PFA needs to guarantee for its investors. If a PFA fails to provide at least this return, the PFA must transfer part of its own net worth to the fund to make up the shortfall. The MRG is assessed by comparing the fund’s average annual return over the previous three years to the average of the six PFAs.<sup>9</sup> Between January 2004 and June 2007, the minimum return guarantee allowed for a shortfall of 30 percent from the average annual return over the previous three years for all PFA’s. In other words, if Π _t_ denotes the average across PFA’s of the average annual return over three years, then the MRG required a minimum return of 70%Π _t_ for each PFA. After June 2007, the government changed the formula for the lower threshold on the return for each PFA to min _{_ 70%Π _t,_ Π _t −_ 2 _._ 6% _}_ . Note that this new formula implies that for industry returns below 8 _._ 66%, the new formula implies a minimum return of Π _t −_ 2 _._ 6%.<sup>10</sup> Between December 31, 2006 and December 31, 2009 the industry annual average returns were 6.01%. With the new formula in place, the minimum return would be calculated as 3.41%, instead of the 4.20% that would have been calculated under the older formula. This effectively implied a loosening of the MRG requirement after June 2007.

# **2.2 The data**

The data in this paper were collected from three sources. Information on portfolio holdings by Colombian Pension Funds was provided by the Colombian Association of Pension Fund Administrators (ASOFONDOS). It includes the detailed security allocations for the funds managed by each of the six PFAs on a monthly basis for the period 2004:1 to 2010:12. We use Compustat Global for stock-specific information, i.e. price, trading volume, and firm size. However, Compustat Global has missing observations for several of the domestic stocks held in the pension fund portfolios. We collect missing information directly from the _Superfinanciera de Colombia_ (SFC), a supervisory agency within Colombia’s Finance and Public Credit Ministry which oversees all

> 8In June 2008 some of the limits were: (i) Maximum 50% on domestic government debt. (ii) Maximum 40% on equity securities. (iii) Maximum 40% on foreign securities.

> 9See Kritzer et al. (2011) for other Latin American countries with MRG requirements.

> 10Notice that 70%Π = Π _−_ 2 _._ 6% for Π = 8 _._ 66 percent. For Π _<_ 8 _._ 66 percent, 70%Π _>_ Π _−_ 2 _._ 6% and hence the minimum return required is Π _−_ 2 _._ 6%.

7
Finally, section 5 provides perspectives on the potential of public pensions to promote the dynamics of the demographic transition, discusses some model limitations, and provides suggestions for future research. Section 6 concludes.

# **2. The Model**

The overlapping generations (OLG) model is outlined in this section. We owe the basic structure of the model to Wigger (2002), who incorporates altruism and endogenous fertility decisions into an OLG model, ad modum Diamond (1965), which leads to an old age security motive for having children.<sup>7</sup> Our model extends this structure by incorporating child and adult survival rates, respectively. We assume that improved health conditions generate higher survival rates.<sup>8</sup> We follow Jorgensen and Jensen (2009) in their incorporation of the survival rates in order to analyze their impact on savings, fertility and economic growth. The model consists of different building blocks: demographics, budget constraints, household behavior and technology. We present these in turn, before outlining the solution method in section 3.

# **2.1 Demographics**

Individuals are assumed to live, as shown in Figure 1, for three periods: as children, as working age adults, and as retired adults.

**Figure 1. Child and Adult Survival Rates**

<!-- Start of picture text --> Childhood Working age Retirement<br>μ1 μ2<br>Child survival rate Adult survival rate<br>Source: Author<br><!-- End of picture text --> Parents are assumed to make economic decisions on behalf of their children. The child survival rate, 0< _μ1_ ≤1, is assumed to denote the survival of children immediately before they enter the labor force as adult workers. In that way, a higher _μ1_ translates into a larger labor force. A higher adult survival rate, 0< _μ2_ ≤1, is incorporated such that more workers survive into retirement. Labor is inelastically supplied by workers, _Nt_ , and is assumed to grow by 1+ _nt-1_ = _Nt_ /( _μ1Nt-1_ ), where _n_ >-1 is the growth rate. If child survival or fertility increases then the labor force will grow at a higher rate.

> 7 Blackburn and Cipriani (2005) also investigate the dynamics of fertility and altruistic intergenerational transfers in the context of the demographic transition. Neither they nor Wigger (2002) incorporate survival rates (or length of life) into their models. As a result, they are unable to investigate the role of child mortality in the first phase of the demographic transition. This is exactly the link that this paper provides.

> 8 We do not, however, incorporate a government health sector or private investments in health. Higher health investments would otherwise be expected to increase survival rates in line with Chakraborty (2004), but since we are interested in a closed form solution for key variables, which Chakraborty does not provide, a framework with endogenous health expenditure that endogenously affects survival rates will remain an issue for our future research.

5
**4. Policies to Make Public Servants More Accountable: Can Reforms That Only Increase Monitoring or Address Local Power Constraints Succeed?**

# **A Trade-Off Between Capacity and Power Constraints?**

First off, it is important to note that not all elites, be they public sector workers or other types of elites who can have direct influence on policy design or implementation, capture rents. In addition, capture – where it exists – may not necessarily be very large. Lastly, elites may have leadership skills and knowledge that the general population does not. Therefore, the desirability to address elite capture depends on the magnitude of capture compared with the efficiency costs associated with circumventing elites.

Typically, unions have played an important role, historically in aggregating the preferences of workers and helping both structure their demands and negotiate with employers, the most successful example being the solidaristic bargaining in Northern Europe (Moene and Wallerstein 2006). Just like more generally in the case of elite capture, whether public sector workers capture rents is context-specific. Moreover, circumventing elites – in an effort to avoid capture – can also have costs in terms of the quality of service delivery.

In the broader case of elite capture, there is indeed variation in its extent and it depends on local context and project design. Khwaja (2009) attempts to estimate the benefits of local leadership involvement in development projects. Based on data from 132 community maintained infrastructure projects in 99 communities in Northern Pakistan, he estimates how project maintenance is related with a number of community and project features.

Khwaja (2009) finds that both community and project characteristics matter for project maintenance. In particular, social heterogeneity affects maintenance negatively, while there is a U-shaped relationship between land inequality and maintenance. Importantly, he finds that presence of a leader is significantly positively related with better maintenance and that this effect increases substantially with project complexity. In line with the theory, community participation is beneficial in non-technical decisions, but has a negative effect on maintenance for technical ones.

11
- 12 -

This stabilization and adjustment program was remarkably successful. Between 1983 and 1988, the rate of growth of real GNP averaged 10.2 percent, while domestic inflation (GNP deflator) averaged 3.8 percent (compared to 20.8 percent during the period 1973-79). The current account balance continued to improve throughout the 1980s and reached a record surplus of $14.3 billion by 1988.11 In 1989, however, the Korean economy produced yet another downturn in growth performance, and there is now growing concern that it is heading into a further crisis.1<sup>2</sup> Estimates for 1989 indicate that real GNP growth fell from 12 to 6.7 percent, the current account surplus fell from $14.2 billion to $5 billion, the inflation rate rose to six percent and export volumes declined by 6.5 percent, the first such decline since the early 1960s.<sup>13</sup> The Koreans believe that these dramatic changes are the result primarily of a sharp deterioration in Korea's export competitiveness, caulsed by the appreciation of the won over the past three years, and social and political moves toward democratization since 1987 which have prompted large wage increases.14

# B. Korean Tax Policy during the Growth Process

Disentangling the contribution of tax policy to this strong growth is difficult, not only because of the changes in tax policies that have occurred, but because of the many other factors that have influenced Korean growth.

- _11/_ **This** data is from Pyo (1989), Table 7 and Oum (1989), Table 1.

- **_12/_** _See_ Park (1989), p. 2.

- **13/** This data is _from_ Park (1989), p. 34, and Oum (1989), Table 1.

- 14/ See **Oun** (1989), p. 13.
_Low-Income Countries’ Access to Private Debt Markets_ issues in 14 of the past 18 years (table 3).<sup>7</sup> Pakistan issued a series of external bonds in the mid-1990s, prior to its debt crisis in 1998-99, and re-established access in 2004.<sup>8</sup>

**Table 3 Frequency of access to private debt markets, 1990-2007**

_Number of developing countries that received a syndicated loan or issued an international bond_

|Number of years|0|1-9|10-17|18|
|---|---|---|---|---|
|Syndicated loans|||||
|Low income|12|24|10|2|
|Lower middle income|6|20|14|7|
|Upper middle income|3|12|11|9|
|Bond issuance|||||
|Low income|41|7|0|0|
|Lower middle income|21|17|9|0|
|Upper middle income|6|11|16|2|

Source: Dealogic DCM Analytics More recently, Vietnam gained access in 2005, followed by Mongolia, Ghana and Nigeria in 2007. Vietnam issued a $750 million sovereign Eurobond in 2005, followed by a $187 million issue (denominated in domestic currency) in 2007 by a publicly owned corporation. The Trade & Development Bank of Mongolia, a public company, issued a $75 million Eurobond. A publicly-owned Nigerian bank (GTB Finance BV) issued a $350 million Eurobond in January, followed by a $175 million issue by a privately-owned bank (the First Bank of Nigeria PLC) in March. Ghana became the first heavily indebted poor country (HIPC) to issue an external bond, offering a $750 million Eurobond issue in September. The bond issue was oversubscribed several times, despite being launched in the midst of the turmoil in international financial markets.

In most cases countries issued sovereign bonds for the first time after having graduated from lowincome status. Only five countries launched their first sovereign bond when their GNI per capita was below the low-income cut-off at the time; ten countries had incomes over $2000 above the low-income cut-off (in constant dollars) (figure 8). Income per capita of countries making a first-time sovereign bond issue has varied considerably, suggesting that there is no threshold level required to gain access to the international bond market. The same is true for first-time corporate bond issuance.

> 7 India made the transition from a low- to middle-income country in 2007 with a GNI per capita of $950, just above the lowincome cut-off of $935.

> 8 Some low-income countries have launched “one-off” bond issues. Examples include the following. In 1984 Papua New Guinea issued a seven-year $20 million sovereign bond (private placement). In 1994 the Republic of Congo issued a five-year $600 million sovereign Eurobond.  In 1995 the Bank of Ceylon (a public bank in Sri Lanka) issued a three-year $12 million bond (private placement), followed by a five-year $100 million bond issue (private placement) by a private corporation (Sri Lanka Telecom) in 2004 and a $500 billion sovereign issue in 2007.

11