rafmacalaba/datause-encoder-data huggingface dataset:
- [24] Eckstein, Z., and K. Wolpin (1985) “Endogenous fertility and optimal population size,” Journal of Public Economics 27, 93—106.

- [25] Ege, S. (1997) “The Promised Land: The Amhara Land Redistribution of 1997.” SMU-rapport 5/97, Norwegian University of Science and Technology, Centre for Environment and Development, Dragvoll, Norway.

- [26] Ege, S. (2002) “Peasant Participation in Land Reform. The Amhara Land Redistribution of 1997.”In: B. Zewde and S. Pausewang (Eds.) Ethiopia– The Challenge of Democracy from Below, Nordiska Afrikainstitutet: Uppsala, Sweden.

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- [28] Gelaye, G. (1999) “Peasant Poetics and State Discourse in Ethiopia: Amharic Oral Poetry as a Response to the 1996—97 Land Redistribution Policy,” Northeast African Studies 6(1—2), 171—206.

- [29] Gizachew, A. (2010) “Development-Induced Displacement and Impoverishment to some Affected Villages of Wag Hemra Zone, North East Ethiopia.” Mimeo.

- [30] Gray, L., and M. Kevane (1999) “Diminished access, diverted exclusion: Women and land tenure in sub-Saharan Africa,” African Studies Review 42(2), 15—39.

- [31] Guyer, J. (1986) “Beti Widow Inheritance and Marriage Law: A Social History.” In: B. Potash (Ed.) Widows in African Societies: Choices and Constraints, Stanford University Press: Palo Alto, CA.

- [32] Hallward-Driemeier, M., and O. Gajigo (2013) “Strengthening Economic Rights and Women’s Occupational Choice: The Impact of Reforming Ethiopia’s Family Law,” World Development 70, 260—73.

- [33] Hoben, A. (1973) Land Tenure Among the Amhara of Ethiopia; The Dynamics of Cognatic Descent. University of Chicago Press: Chicago.

- [34] Maddison, A. (2006) The World Economy. A Millennial Perspective (Vol. 1). Historical Statistics (Vol. 2), OECD: Paris.

- [35] Mamdani, M. (1972). The Myth of Population Control: Family Caste and Class in an Indian Village, Monthly Review Press: New York.

- [36] McQueston, K., R. Silverman, and A. Glassman (2012) “Adolescent Fertility in Low- and Middle-Income Countries: Effects and Solutions.”CGD Working Paper 295, Washington, D.C.: Center for Global Development.

- [37] Milligan, K. (2005) “Subsidizing the Stork: New Evidence on Tax Incentives and Fertility,”The Review of Economics and Statistics 87(3), 539—55.

21
robust program management or ‘Mission Office’ greatly assists the delivery of the reforms, so that great designs are not lost by poor implementation.

# **2. Using long‐run pension outcomes to guide decision‐making on private pensions**

Before introducing the key outcomes, it is necessary to identify the different parts of a pension system and highlight the parts or pillars and the risks to which they are exposed. Public pension provision comes broadly in the form of ‘zero pillars’ ‐ poverty alleviating payments paid out of government revenues that do not require contributions ‐ and ‘first’ pillars that are typically mandatory and can have the full range of benefit options but are often Defined Benefit. Many first pillars are Pay‐As‐You‐Go where current contributions fund current pensions, but there are a number that are at least part funded. Examples range from the CCSS in Costa Rica,<sup>2</sup> the Social Security and National Insurance Trust in Ghana, and Vietnam Social Security (VSS). Such arrangements are very rare in Europe – where many countries are very reliant on large Pay As You Go (PAYG) public pillars with systems that are in great need of diversification (EU White Paper 2012). The NDC debate focusses on this ‘first pillar’. It introduces a mechanism that does not fund the benefits in advance but aims to avoid the build‐up of unsustainable Defined Benefit promises by altering pensions in payment with changes in factors such as longevity (if politics do not intervene).

**Figure 1: Different pensions pillars have different functions and face common and unique risks**

|**Sources of retirement**<br>**consumption**|**Risks affecting payout size**|
|---|---|
|Zero Pillar –poverty prevention|Fiscal,Intergenerational,Longevity|
|First Pillar – public contributory –<br>consumption smoothing|Fiscal, Intergenerational / Political,<br>Longevity, Labor Market, GDP|
|Second Pillar– mandatory private<br>contributoryDB,DC or hybrid|Capital Market (Inv returns/costs),<br>Laborparticipation,Longevity|
|Third Pillar- private contributory<br>DB,DC or hybrid|Capital Market: Investment/Costs<br>Labor market/individual myopia|
|Fourth Pillar: Financial assets|Economic Growth, Instability|
|Fourth Pillar: Familytransfers|Familysize/wealth/culture/location|
|Fourth Pillar: Housing / Physical|Housing market, labor income|
|Labor Income & Own-<br>consumption|Labor Market, Agricultural market|
|Longevityand infl|ation risk ispervasive|

## **Source: Author**

> 2 Caja Costarricense de Seguro Social.

3
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16
between the treatment and control group, where the treatment group shows earnings that are approximately 10 percent lower than the earnings of the control group. Even though the difference is marginally statistically significant (p-value of 0.076), all our estimations include this variable as covariate. Moreover, when we conduct a test of joint significance of differences of all pre-treatment characteristics, we do not reject the null hypothesis that all characteristics between treatment and control group are the same (F-test=0.68 with a p-value of 0.835).

Given that Table 3 uses only data from our surveys, the set of variables we show there is richer than the one described in Table 2, particularly for variables regarding education and skills. A few additional things are worth mentioning about our sample characteristics that were not described in section 3.2. We observe that about 30 percent of our sample has specialized on a STEM degree<sup>20</sup> during their education. The proportion of the sample that holds a degree in Computer Science is significantly lower: only 5.5 percent. About a third of our sample has taken a coding training course before the bootcamp. Importantly, all these variables that measure skills as well as the Big 5 social skill index are perfectly balanced between treatment and control groups in our sample.

As mentioned in section 3.1, we repeat the balance tests for the sample that responds the follow-up online survey. Results are shown in columns 4-6 in Table 2. Three features are worth mentioning. First, characteristics of the overall sample remain the same between the whole sample and the sample of non-attriters. Second, as shown in Table 3, the sample of non-attriters continues to show balance between treatment and control groups.<sup>21</sup> Last, the F-test of overall significance keeps a similar magnitude and statistical significance. Given this, our main estimates do not adjust for this attrition. Moreover, in Table 10 we show that our main estimates hold even after adjusting for differential attrition by implementing the bounds by Behaghel et al. (2015).

> 20STEM degrees are Biology, Biochemistry, Computer Science, Math, Physics, and any specialization of Engineering.

> 21Indeed, the only imbalance present in the whole sample disappears in the sample of follow-up nonattriters.

13
Comorbidities have been identified as significantly raising the COVID-19 fatality, but they are unlikely to solve the puzzle of the low developing-country share. A global meta-analysis of the role of prevalence of comorbidities highlights the importance of hypertension (21 percent), diabetes (10 percent), cardiovascular disease (8 percent) and respiratory system disease (1 percent) in severely infected patients (Yang and others, 2020). These comorbidities should play an important role in the developing world, too. For example, of the 1.1 billion people with hypertension, twothirds live in developing countries (WHO, 2019). Over the last decade, the number of cases and prevalence of diabetes has risen most quickly in the developing world (WHO, 2016b). Limited access to quality health care in developing countries would also mean that many ailments would be left untreated or undertreated, heightening vulnerability.

There are environmental factors extrinsic to hosts such as population density and informality, which will complicate physical distancing, raise the population-level infection rate and steepen the curve. Informal and self-employment will pose one challenge; urban density another one (La Porta and Shleifer, 2014). The latest estimates suggest that respectively 65 and 27 percent of urban populations in LICs and MICs live in slums. Flattening the curve will therefore be more difficult in many developing countries, meaning that pre-existing health capacity constraints will become more binding and result in greater case fatality.

Some have argued that the Bacille Calmette-Guérin (BCG) vaccine as well as weather and climate patterns explain why developing countries have low mortality rates so far. Yet, there is currently no evidence that would support such conclusions.

- While there is experimental evidence from both animal and human studies that the BCG vaccine has non-specific effects on the immune system, these effects have not been well characterized and their clinical relevance is unknown (WHO, 2020b). The WHO concludes that there is no evidence to support to claim that BCG can protect against COVID-19.

- There is currently also no conclusive evidence that either weather (short term variations in meteorological conditions) or climate (long-term averages) have a strong influence on transmission (WHO, 2020c). The SARS-CoV-2 virus which causes COVID-19 disease has been transmitted in all regions of the world, from cold and dry, to hot and humid climates.

The developing world of course exhibits enormous variety. For example, urbanization and urban density will vary considerably across developing countries. The transmission of the disease may be slowed by urban-rural dynamics, particularly in countries where rural areas are disconnected from the possible urban epicenters where the pandemic would be expected to spread more swiftly. The urban-rural disconnect could explain why the pandemic is spreading more slowly in countries that are less urbanized. This would certainly be consistent with the rapid deterioration that is noted in Latin America, which is highly urbanized compared to other developing regions.

20
baseline collusive equilibrium with tax officials, e-filing disrupts that collusive relationship and causes them to pay significantly higher taxes. These high-risk firms are also less likely to sign up for e-filing voluntarily and, when they do (potentially due to perceived compulsion), they are more likely to stop e-filing, indicating a preference to continue interacting directly with tax officials. On the other hand, for firms in a non-collusive equilibrium, e- filing results in lower tax payments as tax inspectors no longer constrain them to pay higher taxes (possibly higher than their true liability). At the same time, low-risk firms benefit from e-filing by becoming less exposed to coercive demands for unofficial payments. These opposing patterns of impact on tax payments close the revenue gap that would otherwise exist between high-risk and low-risk firms.

Over time, e-filing use has continued to expand in Tajikistan. The overall impact on tax revenue from the introduction of e-filing (beyond the encouragement design considered in this study) would depend on the distribution of high-risk and low-risk firms among the total population that adopts e-filing and the relative magnitudes of the respective increase and decrease in tax payment among these two groups. Beyond the impact on tax payments, the tax authority stands to derive significant system-wide efficiency gains. Back-of-theenvelope calculations indicate that, at current e-filing rates, the tax authority frees up over 5,800 tax-official-hours each month that can be reallocated to other activities likely higher in marginal productivity than receiving tax declarations.<sup>34</sup> This study’s findings provide lessons extending beyond e-filing to other technologies designed to increase efficiency and reduce corruption. First, our results highlight the importance of selection among potential users. Those who anticipate negative benefits from the new technology due to expected constraints on their behavior may be the least likely

> 34 This calculation assumes that a tax official spends six minutes on each tax declaration. This is a conservative estimate as the firms in our baseline survey report tax officials spending an average of 18 minutes checking their reports.

35
|**Table (XII)**<br>Impacts of IDP infows in Employment in Informal and Formal Sectors<br>**Dependent Variable**<br>Pr (Employed)<br>**Sector**<br>All Sectors<br>Highly Informal Sectors<br>Highly Formal Sectors<br>(1)<br>(2)<br>(3)<br>**Panel A. Reduced Form**<br>Predicted Infows<br>0.001***<br>0.002***<br>-0.00002<br>(0.0004)<br>(0.0004)<br>(0.00005)<br>R-squared<br>0.328<br>0.398<br>0.017<br>Observations<br>2,797,614<br>2,323,262<br>474,347<br>**Panel B. OLS**<br>Share of IDPs (% Working Age Pop.)<br>0.0003<br>0.0003<br>-0.00001<br>(0.0002)<br>(0.0002)<br>(0.00002)<br>R-squared<br>0.328<br>0.398<br>0.017<br>Observations<br>2,797,614<br>2,323,262<br>474,347<br>**Panel C. 2SLS**<br>Share of IDPs (% Working Age Pop.)<br>0.001**<br>0.002**<br>-0.00003<br>(0.0006)<br>(0.0007)<br>(0.00008)<br>Observations<br>2,797,614<br>2,323,262<br>474,347<br>**Panel C. First Stage**<br>**Dep. Variable: Share of IDPs (% Working Age Pop.)**<br>Predicted Infows<br>0.774***<br>0.794***<br>0.656***<br>(0.177)<br>(0.179)<br>(0.166)<br>First Stage F-statistic<br>16.12<br>16.69<br>13.68<br>Observations<br>2,797,614<br>2,323,262<br>474,347<br>**Controls for all Panels**<br>Year FE<br>Yes<br>Yes<br>Yes<br>Municipality FE<br>Yes<br>Yes<br>Yes<br>Month FE<br>Yes<br>Yes<br>Yes<br>Individuals Covariates<br>Yes<br>Yes<br>Yes<br>Additional Controls<br>Yes<br>Yes<br>Yes<br>_Notes:_ Each coeffcient corresponds to a separate regression. Individuals covariates include gender, marital status, education level and household size, and additional<br>controls include interactions of year dummies and i) armed attacks by illegal armed groups in 1995, ii) fatal victims of attacks in 1995, iii) homicide rates 1995, iv)<br>public expenditures in 1995, v) central government transfers to health, education, and other expenditures in 1995, vi) number of fnancial institutions in 1995, vii)<br>number of tax collection offces in 1995, viii) 2000 GDP share in agriculture, services, and industry, ix) municipal tax income in 1995, and x) night light density in<br>1995. Clustered standard errors at the municipality level are reported in parentheses. _∗∗∗_signifcant at the 1%, _∗∗_signifcant at the 5%, _∗_signifcant at the 10%.|
|---|

43
Only in the pooled OLS estimation is there an effect of aid and it is positive. Previous work based on pure cross-section data, the Kilby (2004) panel data and the Djankov et al. (2005) estimations also find the OLS estimators to be significant. The effects of economic development on economic institutions are positive and statistically significant at 5%. This result confirms that countries’ legal structure is affected by the level of economic development. In all the specifications we observe that the autoregressive coefficient of institutions is positive and statistically significant. Given the discussion of the previous section we interpret only the estimates of the Differenced and the System estimator. Institutions are persistent and the probability of having higher level of institutions today is monotonically increasing in the level of past year’s value of institutions.

Table 10 reports the summary of the System Estimation for our legal structure/property rights variable, regulation and contract intensive money. As expected the estimated coefficients for the lagged dependent variable are always positive and statistically significant at 5%. The coefficient on the level of (the natural log) GDP per capita is positive and significant for legal structure and contract intensive money at 5% while it is not for Regulation. Although, we have considered a very parsimonious specification given the few theoretical models and possible explanations for the evolution of economic institutions, all the tests for specification and instruments validity suggest that the selected model is well specified. For instance, for all three indicators the Sargan/Hansen tests do not reject the null of instrument validity and the ArellanoBond test for autocorrelation rejects the null of first order serial correlation for the AR(1) type, but not for the AR(2) type. All the incremental Sargan tests suggest that the extra assumptions used in the “System-GMM” estimation are valid. Finally, the unit root tests suggest that although institutions are very persistent they do not have a unit root.

32
intermediaries or foreign entrants, and the state of the financial sector infrastructure and enabling environment.

# **_a. The structure and size of the economy and Government borrowing_**

One of the determinants of the extent of bank’s involvement with SMEs relates to the structure and size of the economy. The five countries in which SME finance studies were undertaken differ fundamentally in terms of their economic profile with South Africa being the largest economy in Sub-Saharan Africa, Nigeria depending heavily on the oil and gas sectors, Kenya having a reasonably well diversified economy with a well-established layer of larger and medium-sized companies partly using Kenya as a hub for the East African Community (EAC), and Rwanda and Tanzania having considerably smaller economies. Given the different structures of the economy, commercial banks in Nigeria focus their lending on the oil, gas and telecommunications sectors and the associated value chains.<sup>8</sup> In Rwanda and Tanzania, banks need to lend to SMEs simply because of a lack of alternatives and the high competition in the corporate segment which makes margins relatively low. This encourages banks to be innovative in moving down-market in order to achieve adequate returns.

A further determinant closely related to the structure of the economy is the extent of Government borrowing, which continues to lead to crowding out of the private sector especially in Nigeria, but also in Tanzania where banks continue to hold a sizable proportion of their balance sheets in Government securities.<sup>9</sup> The rise in interest rates on Government securities (see Figure 1 for the development in Treasury bill rates) lowers banks’ appetite for lending to SMEs beyond the established value chains by placing an effective floor for yields (exclusive of risk premium) that needs to be reached to make lending attractive. Particularly in financial systems with weaker legal and regulatory structures and capacity, there appears to be a strong interrelationship between banks’ willingness to lend to relatively risky private enterprises and the availability of “safer” investments opportunities, such as Government securities.

> 8 The oil and gas sector represent about 22 percent of total credit to the private sector.

> 9 Treasury bill rates in Kenya have been fluctuating extensively, mainly driven by inflation rates, but rates have not been structurally high. Rwandan rates have increased recently, but were moderate until mid-2012.

9
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