rafmacalaba/datause-encoder-data huggingface dataset:
individuals choosing between consumption and investment in their business will choose a production level such that the marginal return on investment equals the marginal rate of substitution between consumption today and consumption in the future. This gives rise to two steady-state production points given by A and B above, and to the S-shaped wealth dynamics in Figure 4 below. Individuals who begin with too little wealth will be stuck in the poverty trap at A – returns to capital are too low for them to bootstrap their way out of poverty, while borrowing constraints prevent them reaching the scale needed for the entrepreneurial activity.

**Figure 4: Poverty trap corresponding to dual technology choice**

<!-- Start of picture text --> Wealtht+1<br>B<br>A<br><!-- End of picture text --> Wealtht So how realistic is this model? The closest practical analog to this theoretical case can be found in studies of pastoralists in rural Ethiopia conducted by Travis Lybbert et al. (2004) and Paulo Santos and Christopher Barrett (2011), and by Barrett et al. (2006) among rural pastoralists in Kenya. Raising cattle is the only production activity for these individuals, and looking at cattle herd size dynamics, these studies find evidence for a process like that in Figure 4, whereby individuals who start with a small herd size appear trapped around a very low level (A in the figure), whereas there is also a much higher level equilibrium (B). They suggest that a minimum herd size is required in order to be able to undertake the 14
separating FHH into those with a resident adult male member (FM) and those without one (FNM), on the assumption that this is a key distinction between male and female headed households and may explain variations in well-being. The expectation is that marital status of the head is another important factor to take into account. Parts of the analysis are therefore also conducted for the above sub-groups and by whether the head is married or not.

# **4. The frequency and characteristics of female headed households in Africa**

It is useful to begin with an overview of the frequency and attributes of FHHs and to emphasize how sharply some of their attributes differ from those of MHHs across Africa and its macro-regions. Table 1 presents statistics on the mean incidence of FHHs by country (total, urban and rural), as well as regional aggregates, using the latest available DHSs for 35 countries.<sup>15,16</sup> Africa-wide, 26% of all households are currently headed by women, comprising 21% of Africa’s population. A pronounced variance across countries and regions is apparent. West Africa exhibits the lowest incidence with one out of 5 households headed by a woman and accounting for 15% of the population. This likely reflects the continuing practice of polygamy, together with high widow and divorcee remarriage rates that continue to be widespread in majority Muslim countries (van de Walle 1990). Southern Africa has the highest rate at 43% of both households and of the region’s population living in FHHs. We also note the lower variance within the region here relative to that found elsewhere, particularly in the western and middle regions. With the exception of southern Africa, FHHs are more common in urban areas.

A few key differences in the characteristics of male and female headed households are worth emphasizing (Tables 2 and 3).<sup>17</sup> On average FHHs have older heads (reflecting the many widowed heads), with fewer years of education (4.1 versus 5.6). These households tend to be smaller (3.9 versus 5.1), and have higher dependency ratios (1.2 versus 1.0). Mirroring the latter, female heads are 27 times more likely to live in households in which they are the only adult living with one or more children. In contrast, almost three quarters of MHHs are composed of two or more adults and children, compared to only 44% of FHHs (Table 2). FHHs are also more likely to be single adult households (16 versus 10%).

Another striking difference between these households relates to the head’s marital status (Table 2). While the vast majority of male heads are married (88%), this is only true of a third of female heads. The others are primarily widows (40%) and divorcees (17%). The large gender

> 15 We group countries following UN (2012), except that we place Zimbabwe in Southern rather than East Africa.

> 16 Note that we get very similar FHH prevalence rates when we use the household consumption surveys instead. 17 This paragraph and the next draw on Milazzo and van de Walle (2015).

9
THE IMPACT OF INFRASTRUCTURE SPENDING IN SUB-SAHARAN AFRICA

# **Benin > Telecom investments > Macro results**

||||Reduction in<br>th bli|T|elecom<br>It|Fi|I|
|---|---|---|---|---|---|---|---|
|||||||||
|Variable|Definition|Reference|<br>expenditure|VAT|<br>duties|<br>aid|<br>tax|
|Ym|Aggregate household income|1,250,000.00|0.36|0.48|0.48|0.49|0.50|
|EV|Equivalent variation||0.36|0.44|0.46|0.49|–1.68|
|s|Wage|1.00|–3.65|3.56|3.88|4.15|4.59|
|yg|Government income|214,000.00|1.45|12.79|12.79|12.79|12.79|
|g|Total government expenditure|132,000.00|–9.60|8.85|8.85|8.85|8.85|
|ye|Firm income|135,000.00|1.03|–0.15|–0.21|–0.25|–0.32|
|It|Total private investment|463,000.00|0.55|0.43|0.24|–0.05|–1.28|
|GDP|GDP|1,320,000.00|0.42|0.42|0.42|0.42|0.42|
|e|Nominal exchange rate|1.00|0.78|1.45|–0.95|–4.96|0.49|

# **Benin > Telecom investments > Sectoral results**

||||Reduction in|T|elecom|||
|---|---|---|---|---|---|---|---|
||||other ublic||Imort|Forein|Income|
|Variable|Sector|Base|p<br>expenditure|VAT|p<br>duties|g<br>aid|<br>tax|
||Crop agriculture|461,000.00|0.49|0.28|0.26|0.26|0.20|
|Va|Export agriculture|95,000.00|0.62|0.36|0.31|0.34|0.29|
|Value added or|Industries|190,000.00|1.35|0.21|–0.07|0.51|0.12|
|output|Private services|477,000.00|1.45|–0.34|–0.18|–0.56|–0.19|
||Public services|93,400.00|–7.22|5.50|5.41|6.07|5.39|
||Crop agriculture|1.01|0.27|0.29|0.26|–0.30|–1.61|
||Export agriculture|1.00|1.61|–0.06|–0.96|–0.55|–1.71|
|pq (Market<br>prices)|Industries|1.10|0.84|2.21|2.94|–1.64|0.63|
||Private services|1.06|–0.83|2.28|0.35|–0.48|–0.55|
||Public services|1.00|–2.57|3.18|3.27|2.62|3.29|
||Crop agriculture|1.00|0.74|0.50|0.02|0.04|–1.48|
|r<br>|Export agriculture|1.00|2.36|–0.14|–1.65|–0.38|–1.80|
|Rental rate of<br>capital|Industries|1.00|2.65|3.17|1.76|5.60|3.67|
||Private services|1.00|1.06|–2.32|–1.05|–3.02|–0.46|

31
# Table 1.6.

# Turkey: Unemployment* by Urban and Rural Areas<sup>and Completed</sup> Education

# (Labor Force Surveys, 1985, Age 12 and<sup>Above,</sup> Oct.-Nov.)

|||Turkey|||Urban|||Rural**<br>||
|---|---|---|---|---|---|---|---|---|---|
|u|nemployment<br>rate,<br>percent<br>u|percent<br>of<br>nemploye'|percent<br>of<br>lab.forc|unemployment<br>rate,<br>c<sup>percent</sup>|percent<br>of<br><sup>unemploye</sup>|percent<br>of<br><sup>d</sup><br>lab.force|unemployment<br>rate,<br>percent <br>|percent<br>of<br><sup>unemploye</sup><br>|percent<br>of<br><sup>d</sup><br>lab.force<br>|
||(1)|(2)|(3)|(-4)<br>|(5)<br>|(6)<br>|(7)<br>|(8)<br>|(9)<br>294|
|No formal educatior|8.8|14.5|20.9<br>|19.8<br>|13.2<br>|10.2<br>|5.8<br>|16.0<br>|.<br>61|
|Primary|12.0|52.7|55.6|14.0|45.7<br>|49.9<br>|10.7<br>|60.5<br>|0.<br>|
|High school|22.5|24.4|13.8|20.3|28.6|21.6<br>|27.7<br>|19.6<br>|7.6<br>|
|Vocational|15.4|5.7|4.7|14.5<br>|7.9<br>|8.4<br>|18.6<br>|3.3<br>|1.9<br>10|
|University,<br>etc.|7.0|2.7|4.9|7.1|4.6|9.9|6.1|0.6|.|
|Total|12.7|100|100|15.3|100|100|10.7|100|100|

***** Both unemployed and<sup>economically</sup> active population including inactive unemployed, unemployed<sup>not seeking employment,</sup> and seasonal workers not seeking<sup>job.</sup>

** Computed as difference between national LFS 1985 and urban<sup>places LFS 1985.</sup> Source: Statistical Yearbook of Turkey,<sup>1987, Tables 137 and 146.</sup>
An alternative choice of numeraire could also be an “artificial” high performer. The advantage of this approach is that it is stable across subjects and over time by construction. For example, if we were to anchor _LAYS_ to a Singapore in 2015 and another country, say the Republic of Korea, outperforms Singapore in the next round, we would then have to convert _LAYS_ from Singaporeequivalent years to Korean-equivalent years, making a comparison difficult. In contrast, an artificial top performer with a fixed score would continue to serve as the anchor in this scenario. One reasonable performance benchmark would be the “advanced” benchmark of 625 on TIMSS and PIRLS, which is constant across subjects, grade levels and assessment rounds.<sup>17</sup>

# **5. Consistency of** **_LAYS_ with other evidence**

On the surface, the _LAYS_ measure has some plausibility. It makes sense that we should value education systems (and, more broadly, societies) differently based on the amount of learning they deliver. And as we have seen, cross-country comparisons using the standard _LAYS_ approach that use mean test scores from international assessments—which are admittedly based on a somewhat arbitrary scale—are similar to those based on the share of students reaching a particular level of learning proficiency (which are not scale-dependent).

This section evaluates whether other evidence is consistent with the assumptions and implications of the _LAYS_ approach. Specifically, it explores: (1) whether students’ learning gains across years exhibit local linearity, as assumed in the _LAYS_ calculations; (2) whether observed returns to schooling are consistent with the quality adjustments implied by _LAYS_ ; (3) whether the _LAYS_ learning adjustments are consistent with other test-score-based quality adjustments in the literature; and (4) how the findings from the _LAYS_ approach, which relies on a multiplicative combination of quantity and quality, would compare with those of a linear approach that has been used on subnational data. The section concludes by comparing what these different approaches—existing and potential—would imply for the size of cross-country human capital gaps.

> 17 The value of learning achievement in the numeraire country L� (the Grade 8 average mathematics score for Singapore) used in Figures 2.1 and 3.4 is 621, so replacing that by 625 would barely change the results presented.

25
|**Table 21: Impacts on Number of Firms f**<br>**No initial rent capture case**|**rom EU FTA**<br>**(% chan**|**ge from ben**|**chmark)**||
|---|---|---|---|---|
||Kenya|European<br>Union|Africa|Rest of the<br>World|
|**Business Services**|||||
|Communication|-1.4|6.9||-5.6|
|Insurance|-0.4|42.0||-0.8|
|Banking and other financial services|0.5|0.5|0.5|0.9|
|Professional business services|0.2|46.5|0.2|0.7|
|Road services|1.7|44.3|0.7|1.4|
|Railway transport||||2.5|
|Maritime transport|-7.0|127.4|-12.2|-33.1|
|Pipeline transport|1.4||1.0|3.8|
|Airline transport|1.2|6.2|0.3|1.1|
|**Dixit-Stiglitz Goods**|||||
|Beverages & tobacco|0.4|56.5|-1.3|-2.1|
|Grain milling|0.4|59.5|-1.4|-2.8|
|Sugar & bakery & confectionary|0.3|53.9|-3.0|-5.7|
|Petroleum|3.0|27.1|-0.6|-1.4|
|Chemicals|-0.4|33.2|-3.5|-11.2|
|Metals and machines|-3.2|96.8|-6.7|-33.9|
|Non metallicproducts<br>Source: Authors' estimates.|-0.7|53.7|-2.2|-5.6|

65
million, (ii) keeping constant the working age population ratio (67.6 percent), yielding a working age population of about 58 million, (iii) keeping constant the employment rate (66.5 percent), putting total employment at about 38.7 million, and (iv) keeping constant the sectoral employment shares, therefore changing the amount of labor in each sector proportionally. These changes do not change the results in any considerable way, which is rather reassuring.<sup>17</sup> Table 10 provides information on the sectoral contribution to structural transformation – the between-sector component. The negative signs in the first column indicate that the employment shares of agriculture, mining and electricity & water were lower in 2011 than in 1996. In the case of agriculture, this actually generated a positive momentum for structural change, since it is the sector with the lowest productivity. In the other two cases, the outcome was a fairly small negative impact. Moreover, construction, trade, transport & communications and other services have provided solid contributions to structural change.

<u>Table 10: Sectoral Contributions to Inter-Sectoral Shifts (1996-2011)</u>

||Direction of<br>Employment<br>Share Shift<br>|Contribution to<br>Inter-sectoral<br>Shifts(%)|
|---|---|---|
|Agriculture|–|19.94|
|Mining and Quarrying|–|-5.45|
|Manufacturing|+|3.70|
|Electricity and Water|–|-1.48|
|Construction|+|16.68|
|Trade|+|20.48|
|Transport and Comms|+|20.85|
|Other Services|+|25.28|
|_Total_||_100.00_|

Table 11 repeats the same exercise for its sub-periods. We have already described some of these estimates, so we will focus on the decomposition of output per worker. The decline in labor productivity that occurred in the 1996-1999 period was due to both within- and between-sector productivity decreases – especially the former. Productivity levels recovered in the 1999-2005 period, although mostly due to within-sector improvements. Since 2005, productivity growth has accelerated, to which both within- and between-sector productivity contributed. Although productivity improvements within economic sectors have provided the main contribution to total productivity, structural change appears to be gaining an important momentum. In fact, the gap between the contributions of within- and between-sector productivity is narrowing.

<u>Table 11: Sectoral Decomposition of GVA per capita (Several Periods)</u>

||Within-Sector<br>Productivity|% Contributionof<br>Changes in<br>Employment|<br>Inter-sectoral<br>Productivity|Demographic<br>Component (%)|Change in GVA<br>per capita (%)|
|---|---|---|---|---|---|
|1996-2011|79.50|9.72|11.17|-0.38|103.11|
|1996-1999|-491.20|987.55|-164.85|-231.49|1.06|
|1999-2005|62.51|39.61|5.40|-7.51|29.85|
|2005-2011|102.86|-33.27|20.71|9.71|54.77|

> 17 In addition, we re-calculated the decomposition for the period 2005-2011 using the HICES 2004/05 instead of the LFS 2005. The conclusions are robust to this change, even though the structural change component increases from 21 to 27 per cent.

22
traditional cookstove technologies and are not willing to pay much for new cookstoves. They point to the need to design nontraditional cookstoves with features, such as reduced operating costs, that may be valued highly by users. Very little work has been done that includes credible estimates on whether improved stoves reduce carbon emissions, though Johnson et al. (2009; 2010) is a notable exception.

Possible approaches to measure how much fuelwood is used per unit of time include randomized kitchen performance (KPT), controlled cooking (CCT) and water boiling (WBT) tests. Each has its advantages and drawbacks (Lee et al. 2013). The KPT measures total fuelwood use in a household several times before and after an ICS intervention. The advantage is that it can account for the technology combinations that imply leakage. KPTs, however, have the disadvantage that little is known about the actual cooking use and mechanisms after adoption. It also requires virtually complete on-site measurement of fuelwood use, otherwise measurement errors can be large. Because of the intensive nature of the required measurement, sample sizes tend to be small. This can be a very serious problem if ICS have multiple uses, because in regression models it may not be possible to effectively adjust for potential confounders. Alternatively, and less effectively, experimental subjects can keep fuelwood use logs pre and post intervention. Such an approach has the risk of serious reporting errors that can perhaps only be obviated through direct measurement.

The other two tests focus on differences in fuelwood consumption per unit of time. The WBT measures the fuelwood and time needed to boil a fixed quantity of water. It is the simplest test and assures that cooking tasks are exactly the same for traditional and intervention stoves, but it also has little to do with actual cooking and typically is done in labs. The CCT also focuses on fuelwood consumption per unit of time, but unlike the WBT it examines the cooking of a standardized meal cooked in the home prepared by actual cooks. CCTs provide data on the quantity of fuelwood used per unit of food cooked and per unit of time. Though conducted _in situ_ , the CCT only measures wood used and saved to cook typical meals. CCTs are therefore not very appropriate when actual meals deviate substantially from the “standardized” meal. Under such circumstances the measured savings may not be any more related to actual _in situ_ cooking than the WBT.

Using a CCT-based method that is similar to the one used in this paper, Burwen and Levine (2012) evaluate a locally made and designed wood burning cookstove in Ghana. They find that the stoves on average reduce fuelwood to cook a standardized meal by 12%. Using electronic stove use monitors they also find that in general the stoves were used very frequently.

**5 |** P a g e
# **4.4 Robustness Checks**

To assess whether the treatment effect heterogeneity of T2 holds for smaller intervals, I estimate the model using deciles. The results are reported in the Online Appendix. The same treatment effect pattern persists: respondents holding correct beliefs are clearly the subgroup who reacts most to treatment.

As a more general robustness check of my findings, I re-estimate the above models without imputing missing baseline covariate values. I also drop the respondents who did not find the provided information credible (1.5 percent) or not very credible (9.1 percent). I next drop respondents who take unusually little time to respond to the survey (less than 20 minutes, 5.5 percent). My main findings also hold for these smaller subsamples. Moreover, I construct an index of respondents’ score on the Crowne and Marlowe (1960) propensity to give socially desirable answers scale and re-estimate my main models on the subsample of those who score in the bottom 6 deciles of the distribution. The positive treatment effect of T1 on the ideal bequest is smaller and no longer statistically significant but still positive. This suggests that some of the treatment effect may be driven by social desirability bias. I estimate all models without imputing missing baseline covariate values. All these results are reported in the Online Appendix.

Finally, to address potential selection bias created by survey non-response, I follow Valliant and Dever (2018) by estimating the pseudo-inclusion probabilities into my survey sample: I treat the Tunisian Labor Market Panel Survey (TLMPS), the most recent face-to-face panel survey (ERF, 2014), as reference probability sample, which I code as “0”. I then append it to my survey data, which I code as “1”. Using a logit model, I estimate _Pr_ ( _y_ = 1 _|X, β_ ), where _X_ is a set of covariates with common support in both samples.<sup>16</sup> This allows me to estimate pseudo-weights, defined as the inverse of the predicted probabilities. This strategy addresses the imbalance with respect to educational attainment which I observe across treatment arms. Moreover, as I show in the Online Appendix, my sample of respondents is highly representative of Tunisia in terms of the geographical spread, but on average younger. Weighting allows me to address this potential concern.

Weighting does not significantly affect my conclusions. Regression results are reported

> 16The set of covariates _X_ includes categories of age, education and occupation, household size, assets, and rural-urban status. I do not impute missing values in ERF (2014). Weighting down highly educated respondents also addresses baseline imbalance in the first treatment arm.

36
households, which shrank from 60 to 25 percent of total marketable debt. The main increase was recorded by foreign investors, who raised their share from 6 to 41 percent, and investment funds.

**Table 2.10: Holders of Central Government Marketable Securities (1990-2000)**

|Holders|1990|1994|2000|
|---|---|---|---|
|Bank of Italy|6.4%|11.7%|5.6%|
|Banks|15.6%|19.5%|15.1%|
|Households|67.2%|50.5%|24.9%|
|Non Residents|4.0%|9.9%|41.2%|
|Investment Funds|2.3%|3.2%|11.3%|
|Others|4.5%|5.1%|7.3%|
|Total|100.0%|100.0%|100.0%|

Source: Bank of Italy, Annual Reports Foreign investors were attracted to Italian securities by the prospects of large capital gains that were expected in fixed-rate long-term instruments at a time of accelerating convergence of Italian interest rates to Euro zone levels. The simplified tax regime for foreign investors and the development of liquid instruments and efficient trading and settlement infrastructure removed obstacles to their participation in Italian markets.

Investment (mutual) funds were launched in Italy at the end of 1984. Their assets grew rapidly in the 1990s, reaching 39 percent of GDP in 2000. Their average size is still quite large by international standards and public securities represent the most important component of mutual fund assets. Furthermore, the majority of mutual funds are specialized, or semispecialized in bonds, and hence in public securities.

# **2.5 Conclusions**

Italy is an interesting case of a country that moved successfully to a market-based mechanism of public debt management and funding, while at the same time fostering the development of debt markets. Faced with a very large and rapidly growing public debt that was mainly financed by the banking sector, the Italian authorities took several measures in the late 1970s and the 1980s to target debt instruments at the household sector. Several new instruments were issued as a result of extensive innovation and experimentation. Some of these proved shortlived, but others had a lasting impact on the composition of public debt.

However, even reliance on households and variable-cost instruments had its own limitations, causing a large exposure to interest rate and currency risks, while probably also involving a high cost in terms of spreads and commissions paid to intermediaries. The funding and servicing problems caused by two crises, one in 1987 when rising interest rates raised the interest cost of public debt and the other in 1992 when a currency crisis resulted in a large increase in spreads over international rates, induced the authorities to foster the development of liquid and efficient secondary markets and develop long-term fixed-rate instruments that had a greater appeal to international institutional investors and also lowered the interest and exchange rate risks of public debt.

The growing success of economic convergence policies facilitated access to the emerging euro financial markets. Public debt management was no longer constrained by the weakness of 22