rafmacalaba/datause-encoder-data huggingface dataset:
**-** **<u>Appendix Table 6: First Stage Regressions of Contract Choice: Household Fixed Effects Linear Probability Models</u>**

||**Type of a**<br>**Output sharing only**|**rrangement**<br>**Output and input sharing**|
|---|---|---|
|**Identifying instruments (landlord’s characteristics)**|||
|<br>Distance of the plot from homestead (walking minutes)|0.001***|-0.000***|
||(2.88)|(2.84)|
|Farming head‟s primary activity|-0.723***<br>|-0.068***<br>|
||(39.93)|(4.45)|
|Total owned land by the landlord|0.005|0.013**|
||(0.79)|(2.29)|
|Number of oxen owned|-0.044***<br>|0.020***<br>|
||(7.28)|(3.92)|
|Value of other animals x 10<sup>-3</sup>(Birr)|-0.002|-0.002|
||(1.02)|(1.37)|
|Roof corrugated iron sheet|-0.036***|0.046***|
||(2.78)|(4.22)|
|Number of dependents|0.006*|-0.013***|
||(1.65)|(4.29)|
|Number of male adults|0.001|-0.009**|
||(0.12)|(2.21)|
|Number of female adults|-0.011**|0.013***|
||(2.22)|(3.04)|
|Age of household head (years)|0.000|0.001***|
||(0.97)|(3.83)|
|Female headed household|0.033**|-0.014|
||(2.12)|(1.07)|
|Household head can read and write|0.045***|-0.015|
||(3.93)|(1.59)|
|Farming is landlord‟s main activity (if sharecropped)|0.769***|0.111***|
||(44.44)|(7.57)|
|No. of adult children per ha of rented out land|0.002*|-0.000|
||(1.71)|(0.31)|
|Number of years possessed|-0.002**|-0.003***|
||(2.45)|(5.62)|
|Good soil quality|-0.014|0.015|
||(1.24)|(1.59)|
|Medium soil quality|-0.004<br>|0.007<br>|
||(0.38)|(0.72)|
|Flat land|0.017|-0.029*|
||(0.98)|(1.95)|
|Gently sloped land|<br>0.009|<br>-0.024|
||(0.48)|(1.51)|
|Irrigated land|0.042**<br>|-0.036**<br>|
||(2.19)|(2.22)|
|Teff|-0.012|0.011|
||(0.75)|(0.82)|
|Wheat|<br>-0.011|<br>0.011|
||(0.67)|(0.84)|
|Sorghum|0.008|-0.015|
||<br>(0.49)|<br>(1.09)|
|Pulses|0.001|-0.001|
||(0.07)|(0.06)|
|Constant|<br>0.812***|<br>0.056*|
||(23.61)|(1.92)|
|Number of observations<br>|3745<br>|3745<br>|
|Number of households<br>_R_<sup>2</sup>|637<br>0.834|637<br>0.150|
|Shea Partial R<sup>2</sup><br>|0.600<br>|0.048|
|Partial R<sup>2</sup>|0.611|0.049|
|F-test of excluded instruments F(12,3084)|403.50|13.15|

**Note:** Absolute value of t statistics in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%.

31
# **_Financial literacy affects insurance take-up in lower-income countries_**

Several studies on microinsurance suggest that knowledge of and attitudes towards insurance products play an important role in take-up. Giné et al. (2008) find that after insufficient funds, lack of understanding of the product is the second most commonly cited reason for not purchasing rainfall insurance policies in rural India. However, they find that many fewer households cite lack of understanding in a survey two years later after becoming more familiar with the product.

Similarly, Cohen and Young (2007) note that the main reason for non-renewal of insurance policies issued by the company CALUMNA in Guatemala was lack of understanding. They also observe that Ugandans who understand health insurance are much more willing to buy it, while negative attitudes, such as thinking insurance will be bring ―bad luck‖ or that it’s ―only for the rich‖, tend to reduce take-up. Tran and Yun (2004), an evaluation of a microinsurance project in Vietnam, find that most clients had previously been unexposed to insurance products, and generally lacked a thorough understanding of the concept of insurance and its benefits once they had enrolled in microinsurance. Some clients were subsequently unaware that they were able to make claims upon events such as death or illness.

Indeed, the FinScope surveys indicate that basic awareness of insurance is low in many low-income countries in Africa. Only four percent of Ghanaians have formal insurance, and an even smaller percentage has informal insurance. While two-thirds of those surveyed cited affordability as the main reason for not purchasing insurance, more than a quarter of individuals also reported reasons such as not knowing what insurance is, how it works, or how to buy it. In Malawi, almost 50 percent of adults ―did not know that insurance products can protect you when you have a problem.‖ Many people rely instead on support from their family and community or loans to cover costly medical and burial expenses. Half of those surveyed in both Nigeria and Mozambique had never heard of insurance or insurance products at all.

Interestingly, around half of those in Kenya who did use insurance did not think insurance companies explained their products well, and around half also believed agents to be recruiting clients fraudulently. This suggests that consumer protection measures and other efforts to improve trust in the insurance sector may have an impact on both improving usage rates.

19
33

# VI.7. Total investments

_Table 20. Determinants of Investment Expenditure, 1992-95_

|**Pre-transfer regional revenue**|**1992***<br>(N=86)<br>.125|**1993**<br>(N=78)<br>.162|**1994**<br>(N=85)<br>.147|**1995**<br>(N=73)<br>.146|
|---|---|---|---|---|
||(10.1)|(11.5)|(16.6)|(5.2)|
|**Federal transfers**|.054|.120|.065|.047|
||(3.3)|(6.3)|(4.6)|(2.0)|
|**Wage arrears (1993)**|n.a.|-.533||-.430|
|||(-6.2)||(-4.6)|
|**Real household incomes**||||.011<br>(2.4)|
|**Meat production (1994/1991)**|||-3.26<br>(-4.0)||
|**Hospital beds, per 10,000 (1993**|**data)**||-1.01<br>(-2.1)||
|**Life expectancy (1993 data)**||||7.59<br>(2.4)|
|**Constant**|39.6|21.5|189.4|-529.9|
||(1.9)|(1.3)|(2.7)|(-2.6)|
|**Adjusted R**<sup>**2**</sup>|.58|.64|.81|.68|
|*Excluding<br>Koryaksky<br>AO.|||||

In each year, pre-transfer revenue and transfers are significant determinants of investment, and the relationship between the two is relatively stable.

In 1993 and 1995, wage arrears, a needs factor, demonstrated an inverse relationship with investment expenditures: in these years, holding constant other variables, every additional 1991 ruble of wage arrears resulted in 0.35 rubles less spent on investment. Similarly, in 1994, regions experiencing a greater decline in meat production (used here as a proxy for overall decline in agricultural production) also spent less on investment.

Table 20 suggests that federal transfers to the regions is a statistically significant determinant of regional budget investments. While small in absolute terms (a 0.05-0.08 Rb increase with every ruble increase in federal transfers), Table 20 demonstrates that, even given all the deficiencies of the federal transfer allocation mechanism, it still provides some equalization effect--federal transfers influence positively local, predominantly public, investments, which in turn are crucial for future economic development.

# VI.8. Budget Loans

Budget loans is a growing type of government assistance to the enterprise sector. In fact, in most cases, such loans are implicit subsidies to recipients because they are granted at low,
**Figure 1:** Mandatory pension systems for reformed LAC countries, by type

<!-- Start of picture text --> No Choice Choice<br>FF System PAYG or FF system<br>• Bolivia<br>• Chile • Colombia<br>• Dominican Republic • Peru<br>• El Salvador<br>• Mexico<br>PAYG and FF System PAYG or<br>PAYG and FF System<br>• Costa Rica<br>•<br>• Panama Argentina<br>• Uruguay<br><!-- End of picture text --> Note: FF means fully funded. In Uruguay and Panama, the funded pillar is mandatory for high-income workers. Source: Adapted from Gill et al. (2005) and updated by the authors.

As a safety net, all reformed LAC countries except Peru provide a minimum pension guarantee on the contributory pillars for low-income workers and workers whose retirement benefits turn out to be low due to poor investment returns.<sup>13</sup> A minimum contributory period, usually around 20 to 25 years but as high as 35 years (in Uruguay), is necessary to be eligible for this guarantee. These provisions are inadequate for many workers given the low coverage rates as discussed below, and many countries extend social assistance programs to the elderly poor (Table 1).<sup>14</sup>

# **II.2. Fiscal Impacts**

Overall, the long-run fiscal sustainability of pension systems has significantly increased as a result of structural reforms. According to Zviniene and Packard (2002), Bolivia, Chile, El Salvador, and Mexico showed the largest falls in the implicit pension debt since the reforms closed the PAYG pillar, but the implicit pension debt also declined in countries where a PAYG pillar was retained with the rationalization of the public pillar. More importantly, simulations for the total pension (explicit) debt point out to marked savings after the reforms, most notably in Bolivia, Chile, El Salvador, and Uruguay. For Argentina, such simulations showed a slight increase in the public pension debt as a share of GDP after the reform due to the decline in employer contributions and the public policy of assuming pension liabilities related to provincial civil servants.<sup>15</sup>

> 13 In Peru, it only applies to workers who were born before 1945.

> 14 In Bolivia, all citizens who were 21 or older at the end of 1995 have the right to receive a basic pension when reaching 65 years-old.

> 15 Simulations do not take into account the Peso devaluation of 2001-2002. See Table A1 in Appendix 1.

- 8 -
computerization effects materialize instantaneously or only with some lag. A lag may, for example, be due to banks needing to get acquainted with the information provided online and rewrite loan approval procedures accordingly.

Replacing _Cit_ with _Tit_ , i.e. the log of number of different types of transactions registered in SRO _i_ in year _t_ allows us to undertake similar analysis for transaction volume to explore whether some transactions, in particular mortgages, moved in parallel with credit supply. Though possible complementarities between a 2005 state-wide change in stamp duties and computerization cannot be explored as by 2005 the latter had been completed, it is of interest to compare the impact of computerization to that of an exogenous change in stamp duty rates. This is possible as stamp duty varies between rural and urban areas.

In all cases, fixed effects estimates are used to deal with concerns about unobserved time-invariant factors. Still, our identification strategy relies on the assumption that there are no unobserved factors that contributed to differential trends in growth of credit or registered land transactions between SROs that computerized early and those included in later batches. This is an important concern especially in view of the fact that roll-out of computerization was designed to move from high to low volume SROs. With credit and transaction data that go back to 1997, we can test for this explicitly by considering whether the timing of computerization is correlated with pre-existing levels and trends of credit. To do so we run probit regressions of the timing of computerization. The dependent variable is 1 if the SRO was computerized in 1999 and 0 if the SRO was included later. Independent variables are levels or changes in credit or the number of registered transactions before computerization was introduced.

Results from doing so are reported in appendix table A1 for credit (panel A) and number of transactions (panel B). Panel A suggests that neither levels nor changes in pre-computerization levels of credit had any impact on the timing of computerization. Coefficients on levels as well as changes are small and none statistically significant, giving us no reason to believe that the timing of computerization was correlated with differential pre-existing trends in credit supply. For the 242 SROs for which we have information on number of transactions, panel B points towards high significance and positive sign of coefficients on the pre-computerization level of transactions. This is consistent with the roll-out plan that aimed to focus on high-transaction SROs first. At the same time, the coefficient on the change in transaction volume from 1997 and 1998 is close to zero suggesting that pre existing trends in transaction volume are uncorrelated with the timing of SRO computerization.

# **4. Econometric results**

We find that, once time and SRO fixed effects are controlled for, computerization did not affect credit supply in rural areas but enhanced it, by 15%, in urban ones. This is consistent with the fact that CARD 11
**Figure 2. Share of Students Reporting the Issue Occurs in Every Lesson or in Most Lessons, PISA 2015**

<!-- Start of picture text --> 60.0%<br>50.0%<br>40.0%<br>30.0%<br>20.0%<br>10.0%<br>0.0%<br>Students don't listen There is noise and The teacher waits Students cannot Students don't start<br>to what the teacher disorder long for students to work well. working for a long<br>says. quiet down. time after.<br>Tunisia Algeria Jordan Qatar United Arab Emirates Lebanon Non‐MENA<br><!-- End of picture text --> Source: Analysis using PISA 2015.

School safety drops sharply in MENA countries around and following the Arab Spring by an estimated average of half a standard deviation. Countries with the lowest levels of school safety, Syria, the Republic of Yemen, Tunisia, Morocco and Egypt, experienced, or are still experiencing, the aftershocks of the 2011 Arab Spring. The situation in Egypt, for instance, is striking. In 2007, the school safety index for Egypt was high—0.25 standard deviation above the world average. Egyptian schools were among the safest, even safer than schools in Canada, which are considered to have a fairly safe learning environment.<sup>4</sup> However, in TIMSS 2015, Egypt ranked among the least safe education systems, with a safety index approximately 1.25 standard deviations below the international average. These results coincide with a one standard deviation drop, over eight years, in the classroom disciplinary climate (see Appendix A, Table 6). In fact, trends in school safety indexes are quite consistent with what is observed when measuring classroom disciplinary climate. For instance, school safety indexes are mirrored in the disciplinary climate improvement patterns observed in Jordan and Qatar between 2012 and 2015 and the pattern of significant decline observed in Turkey and Tunisia (Table 1).

**Table 1. Trends in the PISA Disciplinary Climate in Countries Participating in 2009, 2012 and 2015**

||2009-2012|2012-2015|
|---|---|---|
|United Arab Emirates|-0.003|-0.034|
|Jordan|-0.442|0.079|
|Qatar|-0.321|0.231|
|Tunisia|-0.261|-0.052|
|Turkey|-0.096|-0.125|
|**Rest of the world**|**-0.033**|**-0.037**|

Source: Analysis using PISA data, 2009, 2012 and 2015.

**The rapid degeneration of school safety since 2011 indicates that the severity of the problem is recent.** Similar patterns to that witnessed in Egypt are observed in Tunisia, Syria and the Republic of Yemen. School safety was remarkably good in Tunisia in 2007, better than average in the Republic of Yemen, and around average in Syria in 2007. Four years later, in 2011, school safety dropped sharply

> 4 The index for Canada stands at 0.2 standard deviation above the world’s average.

5
U.S. market next year after the quotas have been removed, as is mandated by the agreement.

# **8. The Issue of Rules of Origin**

The granting of clothing benefits is dependent on the country operating an effective visa system, which is a “government-industry process that demonstrates that the goods for which benefits are claimed were in fact produced in a Sub-Saharan Africa country”.<sup>16</sup> Each shipment is accompanied by an original visa stamped on the original invoice. In addition, US Customs is permitted to visit factories, producers and exporters for verification purposes and AGOA countries must provide monthly export data to allow for reconciliation with US import statistics. This highlights that it is these mechanisms which are present to prevent shipments of clothing products from other countries illegally claiming reduced duties under AGOA through false declaration.

Contrary to some beliefs it is not the role of the rules of origin to prevent illegal activity but rather to prevent legal transhipment whereby in the absence of rules of origin products produced in other countries are shipped to beneficiaries where they are subject to only minimal processing and then exported on to the preference granting market. In the case of AGOA the issue is whether assembly of imported fabrics into finished clothing, as is allowed for by the rules of origin currently applied to most beneficiaries of AGOA clothing provisions, is a sufficient process to enable African countries to derive substantial benefits from preferences. This rule entails that clothing producers in Africa can freely choose where to source their fabrics.

The converse issue is whether more restrictive rules of origin would increase or decrease the benefits of AGOA to African countries. This is highly relevant since after October 2004 clothing from African countries will only receive preferences if the fabric is regionally produced or bought from the US. This requires that a much higher degree of processing must be carried out in the combined AGOA-US area but will reduce the

> 16 Africa Growth and Opportunity Act Implementation Guide, October 2000, USTR.

25
experience of controlling behavior by husband/partner by 13.8 percentage points and reduces the probability of women’s household decision-making autonomy by 22.4 percentage points but has no statistically significant effect on women’s earnings decision-making autonomy.

Having a sometimes or often drunk partner is associated with increased risk of women’s experience of controlling behavior from their husbands/partners (11.1 percentage points) while receiving earnings and having more children are associated with decreased risk of women’s experience of controlling behavior (5.2 percentage points for cash only earnings, 11.2 percentage points for earnings in cash and kind and 0.8 percentage points for each additional child). Also associated with increased risk of women’s experience of controlling behavior from husband/partner are having a father who beat his wife/partner, condoning wife beating and practicing Islam as opposed to Christianity (8.3, 13.1 and 3.4 percentage points respectively).

Table 6: Difference-in-differences estimates of the effect of the Boko Haram insurgency on women’s experience of controlling behavior and decision-making autonomy in Nigeria

|**Variables**<br><br><br>|**(1)**<br>Controlling<br>behavior by|**(2)**<br>Women’s household<br>decision- making|**(3)**<br>Women’s earnings<br>decision-making|
|---|---|---|---|
||husband/partner|autonomy|autonomy|
|PPP<br>y<sup>*****PBB</sup><br>l|0.138***<br>(0.034)|-0.224**<br>(0.101)|-0.013<br>(0.008)|
|**Covariates**<br>**Individual characteristics**||||
|Type of earnings (reference category: no<br>earnings)||||
|_Cash only_|-0.052**<br>(0.022)|0.076<br>(0.105)|-|
|_Cash and kind_|-0.112***<br>(0.031)|0.169<br>(0.138)|-|
|_Kind only_|-0.062<br>(0.053)|-0.351<br>(0.097)|-|
|Total number of children born|-0.008***<br>(0.002)|0.033***<br>(0.001)|0.003***<br>(0.001)|
|Married as a child|0.002<br>(0.013)|-0.147***<br>(0.061)|0.001<br>(0.005)|
|**Partner characteristics**||||
|Sometimes or often drunk|0.111***|0.116|-0.010|
||(0.018)|(0.079)|(0.007)|
|**Household characteristics**||||
|Polygamous household|-0.005<br>(0.013)|-0.221***<br>(0.063)|0.009*<br>(0.005)|
|**Attitudes and experiences of IPV**||||
|Father beat mother|0.083***|0.073|-0.010|
||(0.019)|(0.088)|(0.007)|
|Condones wife beating|<br>0.131***|<br>-0.318***|<br>-0.011**|
||(0.014)|(0.006)|(0.005)|
||||_Continued._|

20
- _τ_ ( _ba_ 1) _<_ 0 and

- • _τ_ ( _b_<sup>_LF_</sup> _a_<sup>(</sup><sup>_w_+</sup><sup>_D_))</sup><sup>_≥_0,</sup>

implies that _τ_ ( _ba_ 2) _≥_ 0 because _ba_ 1 _< b_<sup>_LF_</sup> _a_<sup>(</sup><sup>_w_+</sup><sup>_D_)</sup><sup>_≤ba_2.Graphically,bundle</sup> _za_ 2 must be in the non-distortionary budget set _L_ ( _cs_ 2 _,_ 0). Hence, condition (2) is also met, as desired.

<!-- Start of picture text --> hi L ( cs 2 , 0)<br>L ( cs 1 , 0) ( ca 2 − ha 1 , ha 2)<br>Rτ ( za 2)<br>Rb LF a ( w + D ) z k za 2 ( ca 1 + ha 1 , ha 1)<br>za 1<br>ha 1<br>zs 2 ci<br>zs 1<br>b LF a ( w + D )<br><!-- End of picture text --> Figure 14: Position of bundle _za_ 2 when _ba_ 2 _≥ b_<sup>_LF_</sup> _a_<sup>(</sup><sup>_w_+</sup><sup>_D_)(Case1).</sup> Tax _~~τ~~_ is constructed from tax _τ_ and bundle _za_ 2. In a nutshell, _~~τ~~_ provides an exemption up to _b_<sup>_LF_</sup> _a_<sup>(</sup><sup>_w_+</sup><sup>_D_),then</sup> _~~τ~~_ is defined by a constant marginal tax rate such that _~~τ~~_ ~~(~~ _ba_ 2) = _τ_ ( _ba_ 2), and then _~~τ~~_ remains equal to _τ_ for larger bequests. The implied budget sets _B_<sup>_τ_</sup> ( _w_ + _D,_ 0) and _Bτ_ ( _w_ + _D,_ 0) are contrasted in Figure 15. Formally, tax _~~τ~~_ is defined as

- _~~τ~~_ ~~(~~ _b_ ) = 0 for all _b ∈_ [0 _, b_<sup>_LF_</sup> _a_<sup>(</sup><sup>_w_+</sup><sup>_D_)].</sup>

- _~~τ~~_ ~~(~~ _b_ ) = _τ_ ( _b_ ) for all _b ≥ ba_ 2.

Tax _~~τ~~_ is monotonically increasing up to bequest level _ba_ 2 by construction, because its constant marginal tax rate is non-negative because _τ_ ( _ba_ 2) _≥_ 0 (condition 2). This implies that _~~−τ~~_ is single-peaked because _τ_ is monotonically increasing for _b ≥ ba_ 2.<sup>28</sup> We now prove that tax _~~τ~~_ satisfies (i), (ii) and (iii). First, we prove (i), namely that min _i∈_ [0 _,_ 1] _u_<sup>_c_</sup> ~~(~~ _~~z~~ i, ui_ ) = min _i∈_ [0 _,_ 1] _u_<sup>_c_</sup> ( _zi, ui_ ). (Recall that _~~z~~_ denotes the long-run allocation associated to scheme ~~(~~ _~~τ,~~ D_ ), where _D_ is the maximal sustainable demogrant for tax _τ_ .) Under tax _τ_ we have min _i∈_ [0 _,_ 1] _u_<sup>_c_</sup> ( _zi, ui_ ) = _u_<sup>_c_</sup> ( _zs_ 1 _, u_<sup>**s**</sup> ) = _u_<sup>_c_</sup> ( _za_ 1 _, u_<sup>_∗_</sup> _a_<sup>)=</sup><sup>_w_+</sup><sup>_D_.Undertax</sup> _~~τ~~_ we have that _~~z~~ a_ 1 = _z_<sup>_k_</sup> , which

> 28Tax _τ_ is monotonically increasing for _b ≥ ba_ 2 because _−τ_ is single-peaked, _τ_ ( _ba_ 1) _<_ 0 and _τ_ ( _ba_ 2) _≥_ 0.

18