rafmacalaba/datause-encoder-data huggingface dataset:
- l.39 -

worker product. Sconario 4 **consilers the** effects of a policy of food self sufficiency on cocoa supply.

# Scenario 1 - Supply Response Under Fixed Resource Supplies

This Is a reference case, not a realistic sconario; however, It strips the situation to Its essentials, the use of land and labor In the production of cocoa, other perennials crops and annual crops. Except for the export crop, cocoa, all other crops are produced to supply domestic demand; and domestic supply and demand are equilibrated by an endogenous domestic price. The crops maize **and** rice, which compoet with Imports, are protected by a 100 percent tariff; and thol pricos are ondogenous only when less than twice the world price. The cocoa supply response for the fixed endowment case Is presented In Figure l.C.1 for a set of prico experW nts over a horizon of 50 years. Note that the Initial conditions are the same for each experiment--the stock of cocoa acreage by vintage and variety, cocoa production **and** the Ghana Cocoa Board buying price at the midpoint of the years 1985-89. Measuring the surply response as the average of the five-year average supplies over horizons of 20, 30 and 50 years and computing arc elasticities of supply yields tho estimates for this scenario presented in Table l.C.6. Clearly, the resporse Is Inelastic Irrespective of horizon except for the 120-140 cedls price Interval; and the estimates are consistent In the sense that they do not exhibit a large coefficient of variation. The clear implication Is that reducing the buying prico below 140 1987 cedis will significantly reduce supply, at least In a context of fixed resources.

Note the 30-year cycle In cocoa supply response which corresponds with the productive life of a cocoa tree. Since the tial conditons for cocoa tree stocks are relatively iow for historical reasons, heavy plantig of now trees Is profitable in the early periods when a buying prico that at least covers cost prevails, In additlon, It Is profitabie to scrap the stocks of traditional varieties in the hintlal periods since these are dominated by the hybrid varieties, In this respect, Ghana Is still going through the
Table 3: Roll-Out of Intervention

||Number of Villages|
|---|---|
|No training in year 1|136|
|Training for VH only|69|
|Training for VH + CL|65|

The civil society leaders invited to attend the sessions were required to be from the “non-traditional” sector, and included village health workers, representatives of farmers’ groups, church leaders, representatives of NGOs and school development committee members.<sup>13</sup> These same sets of leaders have often been represented on VIDCOs and tasked with implementing reforms via that institution (Munro, 1998, p. 308-314, 340-324). The expectation was that these leaders would provide a constituency for reform because they had less stake in the traditional system than the village head. Indeed, table 4 compares data from our surveys of village heads and our surveys of a representative sample of civil society leaders in these villages to show that – on average – civil society leaders are more pro-reform than village heads.<sup>14</sup> Specifically, the top panel of table 4 shows that civil society leaders are often from demographic groups who would benefit from the reform of village-level governance. For example, a majority of civil society leaders in these village are female. In addition, they are younger and less likely to be visible supporters of the ruling party than the village head (as measured by whether they had a sign displayed outside their home during our interviews, which were shortly after the 2013 elections). At the same time, the profile of the average civil society leader suggests low power differentials between them and the village head. They are significantly more educated than village heads, and they report higher levels of income and wealth. These findings suggest civil society leaders often have both an interest in and the ability to check the village head’s power. We also find that 57 percent of all civil society leaders have a family relationship to the village head.

> 13The exact breakdown of the civil society leaders who were selected to participate in the sessions is indicated in table 6 of the appendix.

14We exclude dare members, who were included in the survey but not eligible for the training, from the sample of civil society leaders described in this table.

16
to these two questions, and classi...ed as “high capture”...rm owners with scores of the median of 8 or above –that is if on average they agree with both statements.

Then within each strata, we ranked ...rms according to January 2009 reported pro...ts (collected in the second round survey), and formed matched quadruplets of ...rms. We used wave 2 rather than baseline pro...ts for the match since 9 percent of the ...rms did not report round 1 pro...ts. Within the quadruplet one ...rm was then randomly chosen to receive the cash treatment, one to receive the in-kind treatment, and two to be control ...rms. We then randomly selected which quadruplets would receive their treatments after each round. In the end this resulted in the 793 ...rms being matched into 195 groups, of which 4 groups ranged in size from 5 to 8 ...rms and the remainder were quadruplets.

This randomization design was based on the analysis in Bruhn and McKenzie (2009) who showed the potential for signi...cant increases in power and baseline balance from matched pairs (with a single treatment group) and strati...cation compared to simple randomization. The variables used for strati...cation were motivated by the results in de Mel et al. (2009a). In particular, we strati...ed by gender and industry since the ex post heterogeneity analysis in that paper found strong di¤erences by gender, and some suggestion of di¤erences according to whether women were working in female-dominated versus mixed industries. The choice of “high capture” as a stratifying variable is motivated by the literature referenced earlier that has suggested that many individuals who succeed in raising their incomes face large demands to share it from others. In addition, there was some evidence in Sri Lanka that a reason for the low returns to women is pressure from household members to redistribute resources. Strati...cation on baseline capital stock was done both because this was believed to be a variable which would be correlated with future pro...ts, and to allow for testing potential heterogeneity in treatment e¤ects for smaller and larger microenterprises. Matching of quadruplets on pro...ts was done to achieve greater balance on the pre-treatment value of the main outcome of interest as well as to investigate treatment heterogeneity in this dimension. It also enables us to eliminate quadruplets with outlier values of pre-treatment pro...ts and still be assured of balance and random allocation to treatments and control among the remaining sample.

# 3.3 Data collection and description of ...rms

The two pre-treatment survey rounds were followed up by four additional survey waves in May 2009, August 2009, November 2009, and February 2010. Of the 793 ...rms which completed the 17
adult children. In sharp contrast, until the 2000s almost all rural residents had no access to state-provided pension or health insurance, unless they held an urban hukou as a teacher or were government ocials (which were rare). In our model, a greater reliance on adult children for old age support can be interpreted as rural parents putting more weight on the couple's sharable production relative to the son's welfare (i.e. higher _γ_ ). This implies greater agency cost under parental matching, as the wife candidates ltered by parents will demonstrate more attributes preferable by the parents for their old age support ( _∂h_<sup>_∗∗_</sup> _f_<sup>_/∂γ>_0</sup> mathematically).

Strong implications on what types of wife the son would want are also provided by BMS (2015), which modies the implications of our model signicantly. So far we assume that the son cares only about his own welfare while parents are altruistic, following the standard assumption in Becker's Rotten Kids Theorem (Becker 1981). BMS (2015) argue that parents have incentives to manipulate the son's preference when he was young so that he is more altruistic towards the parents' old age support when he grows up. This is consistent with the traditional value of Chinese families, which emphasizes that it is the son's duty to continue the surname by having children and to take care of parents when they are old. If the son's altruism is incorporated in our model, the son's preference will be more aligned with the parent's preference (e.g. allow _β_ to increase with _γ_ ). Not only does this reduce the agency cost of parental matchmaking (thus leading to more parental matchmaking), but it also encourages the son to choose a wife closer to the parents' preferences even if he decides to search by himself. BMS (2015) thus recongure the forces underlying the costs and benets of parent matchmaking so that the benets now loom larger, and parents' demand for certain traitssay, submissiveness of the daughter-in-lawis more likely to win out in the end. It also renders the son to be more willing to delegate the search to parents.

Another key dierence between rural and urban residents is that rural couples have more freedom in choosing how many children to have, and this freedom aects the impact of parent matchmaking on the number of children for the couple. Though both rural and urban China adopted one-child policy since early 1980s, enforcement is looser in rural areas. One reason is that rural labor is an important input for agricultural production, and without access to pension and health insurance, having a greater number of children is an important way to ensure old age support. Furthermore, enforcement is more dicult in rural areas: while urban employers, especially state-owned enterprises and government units, can threaten (and often do threaten) to demote, ne, or even re those who attempt to have more than one child, such threats are not credible in rural areas. Thus, rural residents, if they want, can have more children than urban couples.

How would parent matchmaking aect the number of children for the rural couple then?

11
comparatively high number of children actively seek information about family planning and birth control (to minimize the likelihood of having more children) and hence are more likely to be exposed to mass media, driving the spurious correlation between exposure to mass media and fertility. Finally, we find that the increase in educational attainment of household heads (who are mostly men) has been significantly associated with increased fertility, a widely found pattern in empirical studies on fertility in Africa (Schultz, 1997). The size of the association is however small ( _0.005_ ).

The second column of Table 4 disaggregates women’s work in two types: agricultural and nonagricultural work. We expect that non-agricultural employment is related more strongly to fertility than traditional farm work through a higher opportunity value of time for women engaged in nonfarm work. This is confirmed empirically: the increased incidence of non-agricultural work has been a lot more important for the fertility decline than agricultural work. While the increased engagement in nonagricultural activities explains over 8 percent of the total drop in fertility, increased agricultural employment explains less than one percent. Even though this does not represent a causal relationship, the progressive shift to non-agricultural employment in Rwanda over the past decade has been strongly associated with the decline in fertility. Improved female education remains the main contributor, accounting for over one-fifth of the total decline in fertility. The increase in household living standards remains the second main contributor, accounting for an additional 12.5 percent.

In the third column of Table 4 we drop the potential endogenous variables (women’s labor force participation and exposure to mass media). Results on the key variables do not change much: Improvements in female education still account for slightly over one-fifth of the overall decline in fertility, while improved living standards at the household level explain an additional 14 percent. In this parsimonious model 28 percent of the total decline in fertility is explained by changes in covariates, while the remaining 72 percent is left unexplained.

20
# **Table 9**

# **Tests of selective communication**

||Market Par|ticipation|First Yea|r Returns|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Experience Rate|0.284|0.281|-0.314|-0.316|
||(0.619)|(0.612)|(-0.467)|(-0.478)|
|Talks|-0.012|-0.007|0.090|0.058|
||(-0.435)|(-0.248)|(0.905)|(0.592)|
|Peer Returns|-0.069**||0.503||
||(-2.409)||(0.375)||
|Experience Rate x Talks|-0.401|-0.436|0.227|0.220|
||(-0.863)|(-0.941)|(0.346)|(0.300)|
|Peer Returns x Talks|0.084***||-0.417||
||(2.763)||(-0.345)||
|Max(0, Peer Returns)||-0.022*||3.649|
|||(-1.763)||(1.072)|
|Max(0, Peer Returns) x Talks||0.050**||-2.737|
|||(2.357)||(-0.899)|
|Min(0, Peer Returns)||-0.062||-1.118|
|||(-1.051)||(-0.574)|
|Min(0, Peer Returns) x Talks||0.015||-0.416|
|||(1.060)||(-0.353)|
|Constant|-0.267|-0.280|-0.843|-1.017|
||(-0.587)|(-0.602)|(-0.570)|(-0.718)|
|Observations|760|760|63|63|
|R-squared|0.281|0.284|0.879|0.901|
|Controls|Yes|Yes|Yes|Yes|
|Time FE|Yes|Yes|Yes|Yes|
|City FE|Yes|Yes|Yes|Yes|
|Curriculum FE|Yes|Yes|Yes|Yes|

This table shows the results of regressions on market participation (columns 1 and 2) and first year returns of new investors (columns 3 and 4) from a sample of students who completed an electronic survey. For market participation, the dependent variable is set to one for inexperienced students who made at least one stock purchase in a 12-month window after the start of a course and is zero otherwise. Experience Rate is defined as the percentage of students in a class with trading background (i.e. students with at least one stock purchase either 1 year prior to the begin of the course). Peer Returns is the average returns calculated in a 6-month window among students with trading background in a given course. _Talks_ is a dummy variable equal to one for students that reported having investment conversations with classmates. Control variables are described in the text. The OLS regressions include time (year-month), city, and course curriculum fixed effects. T-statistics in parentheses, calculated from clustering standard errors at the course level. * denotes significance at the 10% level; ** at 5%; and *** at 1%.

49
**Table A1: African Development Bank Country Policy and Institutional Assessments (ADB)**

|**Data Provider**<br>African Development Bank.<br> <br>|||||||||
|---|---|---|---|---|---|---|---|---|
|**Description**<br>Multilateral development bank|headquart|ered in|Abidjan|, Cote|D'Ivoire||||
|**Website**<br>www.afdb.org|||||||||
|**Data Source**<br>Country Policy and Institutiona|l Assessm|ents|||||||
|**Type**<br>Expert Assessment|||||||||
|**Respondents**<br>African Development Bank<br>comparability<br>|country|econom|ists, s|ubject|to cen|tralized|review|for|
|**Frequency**<br>Annual since 1998|||||||||
|**Country Coverage**<br>African Development Bank clie<br>|nt countri|es|||||||
|**Public Access**<br>Since 2005|||||||||
|**Description**<br>Indicators on 16 dimensions of<br>on a 6-point scale. CPIA indic<br>Development Bank.|policy an<br>ators are|d institu<br>used to|tional<br>allocate|perform<br>conces|ance.<br>sional|Respons<br>loans by|es are c<br>the Af|oded<br>rican|
||2006|2005|2004|2003|2002|2000|1998|1996|
|**Voice and Accountability**|||||||||
|NA|..|..|..|..|..|..|..|..|
|**Political Stability**|||||||||
|NA|..|..|..|..|..|..|..|..|
|**Government Effectiveness**|||||||||
|Policies to improve efficiency of public sector|X|X|X|X|X|X|X|..|
|Budget Management|X|X|X|X|X|X|X|..|
|Efficiency of Public Expenditures|X|X|X|X|X|X|X|..|
|Management of public debt|X|X|X|X|X|X|X|..|
|**Regulatory Quality**|||||||||
|<br>Trade policy|X|X|X|X|X|X|X|..|
|Competitive environment|X|X|X|X|X|X|X|..|
|Labor Market Policies|X|X|X|X|X|X|X|..|
|**Rule of Law**|||||||||
|Property rights|X|X|X|X|X|X|X|..|
|**Control of Corruption**|||||||||
|Anti-corruption policies|..|..|X|X|X|X|X|..|
|<br>Transparency / corruption|X|X|X|X|X|X|X|..|
|**Country Coverage**|**52**|**52**|**52**|**50**|**50**|**51**|**51**|**..**|
|**Year of Publication**|**2005**|**2005**|**2004**|**2003**|**2002**|**2000**|**1998**|**..**|

39
Among the empirical studies that have tackled directly the inequality impact of infrastructure are those of López (2004) and Calderón and Servén (2010), both of which use cross-country panel data. In both cases, the finding is that, other things equal, infrastructure development is associated with reduced income inequality. Combined with the finding that infrastructure also appears to raise growth, the implication is that, in the right conditions, infrastructure development can be a powerful tool for poverty reduction.

# **3.1 Measuring aggregate infrastructure**

In the remainder of this section, we provide an empirical illustration of the contribution of infrastructure development to growth across Latin America and other world regions. A preliminary question that needs to be addressed is what kind of measure of infrastructure should be used for this task. Infrastructure is a multi-dimensional concept, and both the availability and quality of infrastructure services matter for growth.<sup>14</sup> Yet most empirical studies measure infrastructure in terms of the quantity of assets in a particular infrastructure sector (most often main telephone lines). This is partly due to (i) the difficulty of capturing the multiple dimensions of infrastructure in a simple way, and (ii) the high correlation often found among indicators of availability of different types of infrastructure assets.<sup>15</sup> To overcome this problem, while accounting for the multi-dimensionality of infrastructure, we use principal component analysis to build synthetic indices summarizing information on the quantity of different types of infrastructure assets as well as the quality of services in different infrastructure sectors.<sup>16</sup> These synthetic indices combine information on three core infrastructure sectors —telecommunications, power, and roads— and help address the problem of high co-linearity among their individual indicators. We denote the synthetic quantity and quality indices that result from this procedure **IK** and **IQ** , respectively. The indices can be expressed as linear combinations of the underlying sector-specific indicators, and hence their use in a regression context is

> 14 The universality of access is more relevant for issues of equity.

> 15 For instance, in our sample the correlation between standard measures of telephone density and power generation capacity (measured respectively by a country’s total number of telephone lines, and its total power generation capacity, in both cases relative to the number of workers) exceeds 0.90, which makes it hard to disentangle in a regression framework the separate roles of the two types of assets.

> 16 Alesina and Perotti (1996) used principal component analysis to create a measure of political instability, while Sanchez-Robles (1998) employed it to build an aggregate index of infrastructure stocks.

14
Table 2: Internet and transnational jihadist recruitment

|Dependent variable|||Num<br>(inv|ber of D<br>erse hyp|aesh rec<br>erbolic s|ruits<br>ine)|||
|---|---|---|---|---|---|---|---|---|
||||O<br>|LS<br>|||2S<br>|LS<br>|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|_Panel A: OLS and 2SLS second sta_|_ge_||||||||
|Internet access (rate)|0.105|0.105|0.074|0.075|0.060|0.072|0.318|0.557|
||(0.017)|(0.016)|(0.025)|(0.025)|(0.025)|(0.028)|(0.159)|(0.214)|
|Population (log)|0.026|0.026|0.013|0.013|0.007|-0.002|0.002|-0.014|
||(0.004)|(0.003)|(0.004)|(0.004)|(0.003)|(0.004)|(0.008)|(0.010)|
|Unemployment (percentage)|||0.012|0.012|0.002|0.014|0.056|0.096|
||||(0.039)|(0.039)|(0.038)|(0.037)|(0.053)|(0.060)|
|Married (percentage)|||-0.033|-0.032|-0.012|0.027|0.001|0.064|
||||(0.040)|(0.041)|(0.042)|(0.042)|(0.048)|(0.057)|
|Delegation area (log)|||-0.009|-0.009|-0.003||-0.002|0.009|
||||(0.003)|(0.003)|(0.002)||(0.005)|(0.006)|
|1995-2010 nighttime lights (log)|||-0.003|-0.003|-0.006||-0.002|-0.001|
||||(0.005)|(0.005)|(0.005)||(0.005)|(0.006)|
|1995-2010 rainfall (log)|||-0.009|-0.010|-0.009||0.005|0.013|
||||(0.006)|(0.007)|(0.007)||(0.012)|(0.014)|
|Islamist vote (share)||||-0.005|-0.025|||-0.081|
|||||(0.026)|(0.026)|||(0.036)|
|Jihadist lectures (dummy)|||||0.067|||0.055|
||||||(0.010)|||(0.010)|
|Convicted terrorists|||||0.010|||0.011|
||||||(0.004)|||(0.004)|
|Protests and riots|||||0.000|||0.000|
||||||(0.000)|||(0.000)|
|_Panel B: 2SLS frst stage_|||||||||
|Lightning strikes (log)|||||||-0.047<br>(0.006)|-0.043<br> (0.006)|
|Age group (dummies)|No|No|Yes|Yes|Yes|Yes|Yes|Yes|
|Education group (dummies)|No|No|Yes|Yes|Yes|Yes|Yes|Yes|
|Delegation FE|No|No|No|No|No|Yes|No|No|
|Std. err. clustered by delegation|Yes|No|No|No|No|No|No|No|
|Std. err. correction (Conley, 1999)|No|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Kleibergen-Paap Test|||||||66.183|51.521|
|N|7878|7878|7878|7878|7878|7878|7878|7878|
|R2|0.032|0.032|0.074|0.074|0.090|0.228|0.061|0.036|

_Notes:_ Standard errors are reported in parentheses, and are clustered by delegation (column 1) or corrected for spatial correlation following Conley (1999) using a 50 km distance cutoff (columns 2-8). OLS estimates are shown in columns 1-6, and 2SLS estimates in columns 7-8. Panel A presents the estimates for the OLS and the second stage of 2SLS. Panel B reports only the coefficient for the excluded variable. Variable definitions are available in Appendix Table A1 and summary statistics in Table 1.

12
Figure 1: Nominal Rate of Assistance and Trade and Welfare Reduction Indexes for covered tradable farm products, world, 1960 to 2007 (percent) <!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>1960-64 1970-74 1980-84 1990-94 2000-04<br>NRA WRI TRI<br><!-- End of picture text --> Source: Authors’ calculations based on NRAs and CTEs in Anderson and Valenzuela (2008).
|**[4]**|-1.830**<br>(0.741)<br>-0.935<br>(0.661)<br>0.722***<br>(0.031)<br>-0.050***<br>(0.017)<br>632 / 58<br>0.927<br>8.852<br>4.345||
|---|---|---|
|**[2]**<br>**[3]**<br>**(t+1)**<br>**Unemployment Rate (in %)**|-0.439**<br>(0.203)<br>0.244<br>(0.191)<br>-2.912***<br>(0.771)<br>-1.946***<br>(0.749)<br>0.745***<br>0.738***<br>(0.034)<br>(0.035)<br>-0.013<br>(0.008)<br>-0.1<br>(0.194)<br>-0.021<br>(0.021)<br>-0.079<br>(0.064)<br>-0.036*<br>(0.019)<br>591 / 56<br>523 / 46<br>0.925<br>0.922<br>8.398<br>8.284<br>4.312<br>4.129|he variable_y_.<br>er which the dependent variable is measured.|
|**Table 9**<br>_Sample of 1974-2004_<br>_Dependent Variable: Unemployment Rate_<br>**Variables**<br>**[1]**|int1. Index (t)<br>int1. Index (t-1)<br>int2. Index (t)<br>int2. Index (t-1)<br>_Control Variable_<br>-0.414*<br>(0.221)<br>0.067<br>(0.211)<br>_s_<br>Lagged dep. va<br>∆Log(ToT)<br>Population grow<br>Financial accou<br>Trading partners<br>∆%GDP (t)<br>Observations / C<br>R-squared<br>Mean Dep. var.<br>St Dev Dep. va<br>r.<br>0.760***<br>(0.037)<br>-0.009<br>(0.008)<br>th<br>0.009<br>(0.194)<br>nt to GDP<br>0.009<br>(0.022)<br>growth<br>-0.112<br>(0.070)<br>ountries<br>491 / 44<br>0.915<br>7.734<br>r.<br>3.985|Note:_y_(t, t+s) corres<br>Except otherwise ind<br>All regressions includ<br>Robust standard erro<br>* significant at 10%;<br>ponds to the average over the period t to t+s of t<br>icated, all controls are averages for the period ov<br>ed country fixed effects and Time dummies<br>rs in parentheses<br>** significant at 5%; *** significant at 1%|