rafmacalaba/datause-encoder-data huggingface dataset:
**Table A2 – Share of households living in usufruct or free of charge in Italy, 1989-2020** _(percentages)_

||**Age**<br>**U t**|**of the refe**|**rencepers**|**on**<sup>**(*)**</sup><br>**M**|**Geo**|**graphical**|**area**<br>**Sth**||
|---|---|---|---|---|---|---|---|---|
|**Period**|**p o**<br>**40**<br>**years**|**41-50**<br>**years**|**51-65**<br>**years**|**ore**<br>**than 65**<br>**years**|**North**|**Center**|**ou**<br>**and**<br>**Islands**|**Total**|
||<br>|||<br>Usufr|uct<br>||||
|**1989 -1998**|2.8|1.4|2.0|7.5|3.6|4.1|3.0|3.5|
|**2000-2008**|2.1|2.2|1.5|4.6|3.2|1.9|2.6|2.7|
|**2010-2020**|3.1|2.1|1.4|4.7|3.2|2.0|3.2|3.0|
|**Total**|2.7|1.9|1.6|5.6|3.3|2.7|2.9|3.1|
|||||Free of c<br>|harge<br>||||
|**1989 -1998**|11.9|4.9|2.8|6.8|6.2|7.7|7.7|7.0|
|**2000-2008**|11.6|8.8|3.9|5.1|6.1|8.4|8.4|7.3|
|**2010-2020**|12.1|10.3|5.4|4.4|5.6|8.1|9.7|7.5|
|**Total**|11.8|8.0|4.0|5.5|6.0|8.1|8.6|7.2|

Source: Our elaboration on SHIW data.

(*) The reference person is defined as the member with the highest income.

**Table A3 – Ownership of houses occupied in usufruct or free of charge in Italy, 2002-2016** _(percentages)_

|**Period**|**Parents**|**Sons or**<br>**Daughters**|**Other**<br>**relatives**|**Other**<br>**households**|**Other**|**Total**|
|---|---|---|---|---|---|---|
|||<br>|Us<br>|ufruct<br>|||
|**2002-2008**|31.5|46.2|14.4|3.0|5.0|100.0|
|**2010-2016**|33.3|45.9|12.4|2.4|6.1|100.0|
|**Total**|32.4|46.0|13.4|2.7|5.5|100.0|
||||Free o|f charge|||
|**2002-2008**|51.4|13.1|17.2|10.1|2.3|100.0|
|**2010-2016**|48.2|12.7|16.3|13.9|2.7|100.0|
|**Total**|49.8|12.9|16.8|12.0|2.5|100.0|

Source: Our elaboration on SHIW data. Data for 2020 and before than 2000 are missing.
34 Appendix Table A'-2: Pardal Correlation of Labor Force Growth on Growth Rate of Output per Worker, for Various Samples of Countries, 1960-1990

|Control<br>variables:|None|Ya|YO.BM?,LLY,XS|_N_|
|---|---|---|---|---|
|All|-.10<br>(.37)|-.12<br>(.28)|0.04<br>(.73)|79|
|OECD|-.50<br>(.02)|-.08<br>(.07)|-.04<br>(.89)|22|
|Developing<br>______________|0.15<br>(.25)|0.16<br>(.24)|0.10<br>(.48)|56|
|Africa|-.01<br>(.98)|0.02<br>(.93j|-.09<br>(.73)|19|
|Latin America<br>______________|0.32<br>(.20)|0.33<br>(.19)|0.50<br>(.07)|18|
|Asa/Pacific|0.60<br>(.04)|0.66<br>(.03)|0.75<br>(.03)|12|
|Low Income|0.05<br>(.83)|0.07<br>(.75)|_-.05_<br>(.82)|24|
|Middle Income<br>i_____________|-.12<br>(51)|-.12<br>(.53)|-.02<br>(.94)|32|
|Land Poor w/o<br>SGP|_-.05_<br>(.77)|-.03<br>*0.21<br>(.85)<br>-(.24)||39|
|LaundRich|-. 1-.29<br>(.20)|(-07)|--10<br>(.57)|40|

Notes: 1) Number in parenhesis is dh p-levd of the ts the-correlation is zero.

2) Control variables: YO is initial income, BMP is the black market premim LLY is the ratio of liquid Liabiiies to GDP, XS is the export to GDP ratio.
for the two interest rates, with the main difference being the lower average effective interest rate as compared to the lending rate. We choose to proceed with the lending rate, as it better reflects the cost of borrowing to the private firms. While this rate does not capture the cost of borrowing internationally for the domestic firms, the fact that it has very similar dynamics to the interest rate on government debt (both domestic and foreign) gives us some confidence in its appropriateness for our analysis.

We begin by reporting the key properties of the variables of interest. Table 1 presents the GDP growth rates for the OECS countries, the average real interest rate as well as the average investment and government expenditures as a share of GDP. We also report the net foreign asset (NFA) position of the OECS member countries (we will use it later in the calibration). The OECS countries were growing at an average rate of 3% per year over the 1980-2014 period. The average interest rate was at 7.22%, with the lowest average interest rate observed in Barbados at 5.72% and the highest average rate – in Antigua and Barbuda at 8.39%. Investment stood at 27% of GDP on average, while government expenditures at 30% of GDP across this group of countries over our sample period. All OECS countries had positive NFA, with the average NFA-to-GDP ratio equal to 12.6%. Thus, all these countries were net lenders to the rest of the world during 1980-2014, on average.

**<u>Table 1. Averages values for selected key macroeconomics variables: 1980-2014</u>**

||**GDPgrowth**|**Int rate**|**Inv/GDP**|**Gov exp/GDP**|**NFA/GDP**|
|---|---|---|---|---|---|
|**Antigua and Barbuda**|3.23|8.39|33.73|26.35|9.16|
|**Barbados**|0.84|5.72|15.63|37.13|8.61|
|**Dominica**|2.71|6.94|20.20|32.34|14.82|
|**Grenada**|3.37|6.98|29.87|27.65|11.20|
|**St. Kitts and Nevis**|3.80|7.19|35.65|30.32|23.96|
|**St. Lucia**|3.52|7.94|24.85|26.24|2.60|
|**St. Vincent and the**<br>**Grenadines**|3.50|7.34|25.70|27.79|18.07|
|**Average**|3.00|7.22|26.52|29.69|12.63|

_Notes_ : Int rate is the real interest rate computed as the lending rate minus inflation rate. _Source_ : World Economic Outlook.

Next, we turn to the business cycle statistics for the OECS countries. All series (except trade balance, terms of trade and real interest rate) were log-transformed. To obtain the cyclical components of the key variables, they were Hodrick-Prescott (HP) filtered with the smoothing parameter of 100. Table 2 reports the percentage standard deviation of the key variables, as well as the relative standard deviation. It shows that all variables are quite volatile with the average real GDP volatility equal to 3.72%. This is quite high compared to developed economies and even compared to a few other developing countries (see Neumeyer and Perri, 2005, Table 1). The highest GDP volatility is observed in Antigua and Barbuda and in Grenada. Interest rate is also quite volatile in these countries but the magnitudes are similar across them, with the exception of St. Lucia. The volatility of trade balance and terms of trade shows much more dispersion across 7
Breen, Richard, and John H. Goldthorpe. 1997. "Explaining educational differentials: Towards a formal rational action theory." _Rationality and Society_ 9 (3): 275-305.

Central Statistical Organization (CSO), UNDP and World Bank. 2020. _Myanmar Living Conditions Survey 2017: Socio-economic Report._ Nay Pyi Taw and Yangon, Myanmar: Ministry of Planning, Finance and Industry, UNDP and WB.

Department of Labour. 2016. _Report on child labour in Myanmar._ Geneva, Switzerland: International Labour Organization.

Edmonds, Eric V. 2007. _Child labor._ Discussion Paper No. 2606. Bonn, Germany: IZA.

Emerson, Patrick M., Vladimir Ponczek, and Andre Portela Souza. 2014. _Child labor and learning._ Policy and Research Working Papers 6094. Washington, D.C.: World Bank Group.

Goulart, Pedro, and Arjun Bedi. 2008. "Child labour and educational success in Portugal." _Economics of Education Review_ 27 (5): 575-587.

Guarcello, Lorenzo, Scott Lyon, and Furio Camillo Rosati. 2006. "Child labour and education for all: An issue paper." Working Paper. http://ssrn.com/abstract=1780257.

Heady, Christopher. 2003. "The effect of child labor on learning achievement." _World Development_ 31 (2): 385-398.

No, Fata, Chanphirun Sam, and Yukiko Hirakaw. 2012. "Revisiting primary school dropout in rural Cambodia." _Asia Pacific Education Review_ 13: 573-581.

Patrinos, Harry Anthony, and George Psacharopoulous. 1997. "Family size, schooling and child labor in Peru – an empirical analysis." _Journal of Population Economics_ 10 (1997): 387–405.

Phoumin, Han, and Seiichi Fukui. 2006. _Hours supplied of Cambodian child labor and school enrollment: Simple theoretical and empirical evidence for policy implications._ GSICS Working Paper Series. Kobe, Japan: Graduate School of International Cooperation Studies.

Putnick, Diane L., and Marc H. Bornstein. 2015. "Is child labor a barrier to school enrollment in low- and middle-income countries?" _International Journal of Educational Development_ 41: 112-120.

Ravallion, Martin, and Quentin Wodon. 2000. "Does child labour displace schooling? Evidence on behavioural responess to an enrollment subsidy." _Economic Journal_ 110: C158-C175.

24
Conversely, we do not find empirical support for the H4 hypothesis on the existence of an upper voluntary immobility threshold: we do not observe any reduction in the probability of migration for wealthier households, which would have signaled that sending migrants in anticipation of or in response to shocks is no longer necessary. Therefore, this finding is at odds with the expected result for this hypothesis (negative effects for households with high asset levels). The absence of an inflection point may indicate either that this upper threshold just does not exist for farming households whose income is inextricably linked to biological processes dependent on weather (e.g., agronomic conditions), or that we simply do not observe such level of wealth in our sample.<sup>26</sup> The relationship of the treatment effects with respect to past weather conditions is concave and nonlinear: previous exposure to drier-than-normal conditions is associated with a negative and – sizable (up to 20%) effect of current weather shocks on the probability of being a migrant-sending household. This nonlinear relationship is consistent with hypothesis H2 and the notion advanced in previous literature (Cattaneo et al., 2019) that, more than the occasional exposure to weather shocks, what matters is the recurrent, cumulative exposure to climate stress and severe shocks. Specifically, living in areas recurrently plagued by anomalous weather seems to be conducive to resource-constrained immobility and may give rise to geographic immobility pockets. This result partially contradicts findings from other recent work (Di Falco et al., 2023), which instead found that cumulative drought exposure leads to a significant increase in the probability of migration.

Other than assets and previous shocks, there is a steep negative gradient with respect to _in situ_ adaptive capacity: households with a low adaptive capacity score tend to send more migrants due to rainfall scarcity (with an effect close to +15%), whereas the impact turns close to zero for households with higher adaptive capacity, until becoming negative for the most adaptive households. This suggests that migration and local adaptation are substitutes rather than 26 In our sample, even better-endowed farming households are relatively poor: a back-of-the-envelope calculation reveals that, after accounting for Purchasing Power Parity (2017) and converting local currency to US dollars, the average daily per capita consumption for households in the highest asset index decile is $4.22. While this figure is above the international line of $2.15 (based on 2017 PPPs) for extreme poverty currently used by the World Bank, and also slightly above the $3.65 poverty line set for lower-middle-income countries such as Nigeria, it is below the $6.85 per day poverty line of upper-middle-income-countries. For more information on poverty lines, see here.

24
# Table VII: Heterogeneity of Withholding Rate Impact

||(1)<br>Payment|(2)<br>Liability|(3)<br>Payment|(4)<br>Liability|(5)<br>Payment|(6)<br>Liability|
|---|---|---|---|---|---|---|
|Treat|0.290<sup>_∗∗∗_</sup>|0.137<sup>_∗∗∗_</sup>|0.328<sup>_∗∗∗_</sup>|0.188<sup>_∗∗∗_</sup>|0.231<sup>_∗∗∗_</sup>|0.116<sup>_∗∗∗_</sup>|
||(0.021)|(0.018)|(0.021)|(0.017)|(0.018)|(0.015)|
|Treat x Self-Employed|0.554<sup>_∗∗∗_</sup><br>(0.042)|0.256<sup>_∗∗∗_</sup><br>(0.042)|||||
|Treat x Never-Reclaimer|||0.261<sup>_∗∗∗_</sup><br>(0.078)|-0.059<br>(0.072)|||
|Treat x From-Zero WHR Increase|||||1.681<sup>_∗∗∗_</sup><br>(0.167)|0.400<sup>_∗∗∗_</sup><br>(0.135)|
|Observations|1069680|1069680|1069680|1069680|1010120|1010120|
|_R_<sup>2</sup>|0.757|0.743|0.730|0.737|0.750|0.743|

Notes: This table displays OLS estimates of the impact of the withholding rate increase on rms in the treatment group (rms with a predicted rate increase at reform time), as per equation 2. The columns denote the outcome variables, which are winsorized at the 99th percentile. Payment is total tax payment (constructed) and liability is gross tax liability, as in table VI. All amounts are in '000 CRC, deated with base in 01/2015 (1USD=530CRC). The treatment and control group are specied as in table VI. NeverReclaimers are treated rms that do not make any reclaim, compensation request or refund request during the post-reform period. The label From-Zero Withholding Rate Increase indicates that rms experience an increase of the predicted withholding rate from zero to a positive rate at reform time, as opposed to other treated rms that were already subject to a non-zero withholding rate prior to reform time. We exclude from the former group all rms that experience that maximum rate increase of six percentage points, to ensure that the average rate increase in this group is not larger than the average rate increase among other treated rms.

37
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12

# TABLE 3: TEST RESULTS OF WAGNER'S HYPOTHESIS

|----------|-----<br>**Countries --------**|**---------**|
|---|---|---|
|**Statistically**<br>**Significant Value of**S<br>IND|MIE<br>LIE|ALL|
||**(percent)**||
|**Less**than or**equal**to 1.00<br>53|45<br>21|42|
|Larger than 1.00<br>47|55<br>79|58|
|**Larger**than 1.00|||
|**irrespective of**<br>**significance**<br>53|71<br>93|70|
|Number of Countries<br>19|38<br>14|71|
|Median Value for6<br>1.04|1.63<br>2.23|1.60|

Note: Significance level used - 0.05 Although the results seem to support Wagner's<sup>Law--58 percent of the</sup> countries have income elasticity of government<sup>expenditure that is</sup> significantly larger than one and if one disregards<sup>the variances of the</sup> estimated coefficien,ts, the percentage of favorable cases goes up<sup>to 70</sup> percent--the more interesting aspect of this exercise<sup>is revealed on examining</sup> the breakdown of these results. Both the average<sup>coefficients for the three</sup> groups as well as the relative frequency of<sup>coefficient values that are</sup> significantly larger than one indicate that the<sup>rate at which real government</sup> size expands with increasing income decreases<sup>as countries develop. The median</sup> income elasticity of government expenditure, for<sup>instance, declines from 2.2</sup> for low-income economies to 1.6 for middle-income<sup>economies to around 1 for</sup> industrial market economies. Similarly, the<sup>percent of countries that show a</sup> significantly larger-than-one value for b drops from almost 80 percent in the LIE to 55 percent in the MIE to 47 percent in<sup>the IND. In other words, the</sup> relationship between income and government expenditure<sup>in the long-run takes</sup> on a form that is almost a mirror image of the<sup>movement of relative prices; as</sup>
EA is controlled for.<sup>23</sup> Hence, neither in Figure 10 nor in the regressions in Table 7, do we see any evidence of spillover effects.<sup>24</sup>

# **5. CONCLUSION**

We present evidence from one of the few experimental evaluations of CCT programs in SubSaharan Africa. To the best of our knowledge, this study is a first in examining the impact of simultaneous and experimental contract variation over conditionality, transfer size, intra-household transfer allocation, and treatment saturation. We find strong one-year schooling impacts for the entire sample, both among students who had already dropped out of school at baseline and for those who were still in school. Among the _baseline dropouts_ – who are older, more sexually active, and come from poorer households that are more likely to be female-headed – not only school attendance, but also self-reported literacy in English improved significantly.

Generally speaking, schooling outcomes are surprisingly insensitive to the rich variation in contract parameters provided by our study design. We cannot reject the hypothesis that, among _baseline schoolgirls_ , the price (or substitution) effect is zero, even though we find relatively large income effects. Nor can we reject the hypothesis that the marginal impact of an increase in the total transfer size on school enrollment is zero. These imply, as can be seen in Figure 9, that a $5/month transfer to a HH made _unconditionally_ had roughly the same impact on schooling outcomes as a $15/month transfer made _conditional on school attendance_ . The only variation in schooling outcomes with respect to the contract parameters comes from the identity of the HH member receiving the transfer: one-year impacts on school

> 23 To draw this figure, we first calculate average schooling rates by number of treated girls in an EA, and then run a weighted regression to remove the effects of the overall number of girls per EA. Figure 10 plots the residuals from this regression.

> 24 Barrera-Osorio et al. (2008) find large peer effects from a CCT program in Colombia among friends registered in the same school and grade, but don’t find any effects for the number of students treated in a student’s school-gradegender cohort and suggest that the latter might significantly underestimate the magnitude of peer effects. We have similar data on the network of friends for each of our study participants and will be able to analyze these data in the future.

22
database from the Microfinance Information Exchange. The two other data sets have information on a wider range of institutions, forming a total of 2600 institutions world-wide and serving 94 million borrowers, but the data are largely self-reported and unadjusted. For the most part, the comparison reinforces our conclusions.

# **Eight Questions**

The data allow us to focus on eight basic questions about the microfinance “industry”: Who are the lenders? How widespread is profitability? Are loans in fact repaid at the high rates advertised? Who are the customers? Why are interest rates so high? Are profits high enough to attract profit-maximizing investors? How important are subsidies? How robust are the financial data? The answers then take us back to reconsider the initial questions of subsidy, profit, and social impact in microfinance.

# _Who are the lenders?_

The clash between Grameen Bank and Banco Compartamos described at the start reflects the variety of institutions huddled under the microfinance umbrella. The first column of Table 1 shows the composition of our sample of leading institutions. Three-quarters of the institutions are either nongovernmental organizations (NGOs) or “non-bank financial institutions.” Just 10 percent are microfinance banks. (The “rural banks” are state-run banks, and since there are only a handful of them, they are not the focus here.) The groups turn out to be quite distinct. Microfinance banks, and to a lesser extent credit unions, are likely to have for-profit status. Nongovernmental organizations have non-profit status. Non-bank financial institutions are in a broad category that includes both for-profits and non-profits such as nongovernmental organizations that are specially regulated in return for being allowed to assume additional roles, including, for some, taking deposits. From the economics standpoint, the main difference between for-profit and non-profit status is the ability to distribute profits(Glaeser and Shleifer 2001). If non-profits earn revenues greater than costs, they have to plough them back in to the business to further social missions. For-profit institutions, in contrast, can do what they wish with after-tax profits. But, as we show below, important differences emerge in the outreach and scale of the institutions.

9
time, commensurate with a rising carbon price (Hotelling, 1931), before declining again to the steadystate on long time horizons. This dynamic gives rise to the bell-shaped investment pathway mentioned in Lemma 2.1. These considerations will be important when we discuss the sectoral allocation of abatement investment in § 3.3.

## **2.1.2 “Strawman” model**

We formulate our “strawman” model (referred to as the MAC model throughout) as an analogous problem to that of (2.1) without adjustment costs and abatement capital stock accumulation. In this formulation, the social planner controls the abatement rate, rather than abatement investment; this approach is analogous to conventional approaches in climate-economic integrated assessment models (Nordhaus, 2017; Barrage and Nordhaus, 2024) and has been used as a counterfactual case to isolate the effects of adjustment costs on decarbonization policy (see, e.g., Campiglio et al., 2022). Rather than using investment costs, the social planner uses an abatement cost function in each sector _i ∈I_ , _γi_ : R<sup>+</sup> _→_ R<sup>+</sup> , that maps abatement to monetary cost. The abatement cost functions _γi_ are increasing and convex functions of abatement. The remaining constraints are identical to those of the INV model.

We can formulate this problem as a cost-minimizing optimization problem such that, This model is also treated in Vogt-Schilb et al. (2018) and we refer interested readers to their paper for a full analytical treatment.

# **2.2 Adding delayed learning to the INV and MAC models**

## **2.2.1 Abatement investment model with delayed learning**

We now add delayed learning about the RCB to the model structure. Consider the INV model outlined by (2.1), and allow the remaining carbon budget _B_ to be uncertain. We use a recent study (Dvorak et al., 2022) that estimates the RCB probability distribution using a state-of-the-art, simplified climate emulator, FaIR (Smith et al., 2018; Leach et al., 2021), which was also used by the IPCC for the same purpose (Intergovernmental Panel on Climate Change, 2021). Their RCB distribution is approximately Gaussian<sup>5</sup> , and we sample the 1<sup>st</sup> _−_ 99<sup>th</sup> percentile range of their distribution using a truncated normal

> 5Note the distribution of the RCB is approximately Gaussian owing to the underlying distribution of the transient climate response to emissions being approximately normal (Intergovernmental Panel on Climate Change, 2021; Matthews et al., 2021; Dvorak et al., 2022).

6