rafmacalaba/datause-encoder-data huggingface dataset:
<mark>Bertrand, Marianne, and Kevin F. Hallock (2001). "The gender gap in top corporate jobs."</mark> _<mark>ILR Review</mark>_ <mark>55 (1): 3-21.</mark> Blalock, Garrick, Paul J. Gertler, David I. Levine (2008). “Financial constraints on investment in an emerging market crisis” _Journal of Monetary Economics_ , 55(3): 568-591, <mark>Blanchflower, David, Levine, Phillip and Zimmerman, David (2003). “Discrimination in the Small-Business Credit Market”,</mark> _<mark>The Review of Economics and Statistics</mark>_ <mark>, 85(4): 930-943.</mark> Blau, F.D., and L. M. Kahn (2017). “The Gender Wage Gap: Extent, Trends, and Explanations,” _Journal of Economic Literature_ , 55(3): 789–865.

Blinder, A. S. and L. J. Maccini (1991). “The Resurgence of Inventory Research: What Have We Learned?” _Journal of Economic Surveys_ 5(4): 291–328.

Blum, T. C., D. L. Fields, and J. S. Goodman (1994). “Organizational-level determinants of women in management.” _The Academy of Management Journal_ , 37(2) :241–268.

<mark>Brennan, M., V. Maksimovic, and J. Zechner (1988). “Vendor Financing,”</mark> _<mark>Journal of Finance</mark>_ <mark>43(5): 1127-1141.</mark> <mark>Buccirossi, P., L. Ciari, T. Duso, G. Spagnolo, and C. Vitale (2013). Competition Policy and Productivity Growth: An Empirical Assessment,</mark> _<mark>The Review of Economics and Statistics</mark>_ <mark>, 95(4): 1324-1336. https://doi.org/10.1162/REST_a_00304</mark> <mark>Bussemakers, Carlijn, Kars van Oosterhout, Gerbert Kraaykamp, and Niels Spierings (2017). Women’s worldwide education–employment connection: A multilevel analysis of the moderating impact of economic, political, and cultural contexts.</mark> _<mark>World Development</mark>_ <mark>99: 28-41.</mark> <mark>Cavalluzzo, Ken S., Linda C. Cavalluzzo, and John D. Wolken (2002). “Competition, Small Business Financing, and Discrimination: Evidence from a New Survey.”</mark> _<mark>The Journal of Business</mark>_ <mark>75(4): 641–79.</mark> <u><mark>https://doi.org/10.1086/341638.</mark></u> Chaudhuri, K., S. Sasidharan, and R. S. N. Raj (2018). “Gender, small firm ownership, and credit access: Some insights from India.” _Small Business Economics_ 54: 1165-1181. https:// doi.org/10.1007/s11187-018-0124-3.

Chodorow-Reich, G. (2014). “The Employment Effects of Credit Market Disruptions: Firm-level Evidence from the 2008–09 Financial Crisis.” _Quarterly Journal of Economics_ 129(1): 1–59.

Cooke, Dudley, Ana P. Fernandes, Priscila Ferreira (2019), “Product Market Competition and Gender Discrimination.” _Journal of Economic Behavior & Organization_ 157(Jan): 496-522.

<mark>Coric, Bruno (2018). “The glass ceiling puzzle, legal institutions, and the shadow economy.”</mark> _<mark>Feminist Economics</mark>_ <mark>24(4): 56-82. DOI: 10.1080/13545701.2018.1456672</mark> 29
# _3.6. Robustness and Interpretation_

We perform multiple checks for robustness of both the linear and non-linear models. For the linear models, we search over multiple possible linear specifications by adding and removing controls and adjusting lags and observe the effects on significance measures and coefficients for exogenous variables.<sup>13</sup> . We also test for orthogonality between _X_ and _W_ in equation 1, to test for the possibility that _W_ is a function in part of past _X_ and hence that the residual in equation 2 is prematurely purged of the influences of _X_ , weakening the association unintentionally. In other words, we test that associations between past aid and present controls are not muddying the results. The non-linear models are all tested using the standard practice of K-fold cross-validation.<sup>14</sup> We interpret the models in part using standard methods specific to model types, such as coefficient magnitudes and significance for linear models and impurity-based feature importance in decision-tree ensembles, supplemented by Shapley Additive Explanations (SHAP values, see Lundberg and Lee 2017). SHAP values use a game theoretic approach to explain the output of a model by attributing contributions to the final prediction to model features, analogously to attributing the contribution of individual players within a team to the final result of a game.

# **4. Results**

- _4.1. Sectoral Aid Effects: Can any general statements be made about the impact of development aid on sector outcomes?_

The linear model regression results are reported in Appendix A. In each case, the volume of aid per capita had a statistically significant effect on sector outcomes, appropriately lagged and smoothed. The coefficients were, though, modest in each sector:

- Doubling per capita education aid is associated with an 8 percentage point increase in net primary school enrolment.

- Doubling per capita health aid is associated with a 2 percentage point reduction in the infant mortality rate.

- A 1 percentage point increase in WASH aid as percentage of GDP is associated with between 1-5 percentage point increase in rural access to water and sanitation.

> 13In other words, we introduce causal perturbation, following the practice in the DoWhy library (Sharma and Kiciman 2020).

> 14The training set is itself divided five times into a “hold-out” (or validation) set and a training set proper, with a candidate model trained on the training set proper and scored on the validation set. After the five runs both the scores and the models themselves are averaged, and excessive variance between each “fold” is examined.

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**Follow-up surveys** Our next three rounds of follow-up surveys preserved and expanded the baseline sample to add 496 farmer group members across the remaining 32 villages in our experimental sample, and an additional 246 farmer group members across the original 78 villages. Regressions that leverage baseline information such as social network relationships will not include these additional 32 villages and 742 group members; in Appendix Tables A19 and A20 we show that including or dropping these villages does not meaningfully affect our main results beyond the precision of our estimates.

In our first follow-up survey, just at the end of the demo year, we surveyed a much smaller sample of households and oversampled households in treatment arms with more demonstrators. We use the demo year household survey in two tables and one figure. In Table 5, we include demo year data for additional power when estimating the impacts of the new seeds on yields; these results are robust to excluding demo year data. In Appendix Table A1, we use this survey to construct measures of demonstration year profits for demonstrators. In Figure 4a, we show the dynamics of adoption of improved seeds across our four years of data.

In our second follow-up survey, one year post-demonstration, we additionally asked households about their knowledge of each of the new varieties of pulses, including characteristics of the new varieties. We construct an indicator that the household reported one of the new varieties for a given crop is a high-yielding variety; building on our conceptual framework, we interpret this as a binary proxy for households’ cropspecific beliefs about the returns to the new seeds, and we incorporate this measure of beliefs into analysis of mechanisms in Appendix C.

**Attrition** Our main analysis focuses on impacts of demonstration one and two years post-demonstration. We attempted to revisit our full baseline sample in each of these waves; we reached between 81% and 89% of households across treatment arms during these two survey waves, and find no evidence of differential attrition across treatment arms (Appendix Tables A7 and A8).

**Demonstration monitoring** In addition to the household survey, we gathered operational data during the demonstration year (November 2012 – March 2013) through detailed communication with each of the extension agents. For each village, we identified the full set of crops for which demonstration packages were distributed, and

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- 21 -

# 4.2 An Overview of the Results

Tables 2-A, 2-B, 3-A and 3-B show the OLS estimates of models I, II, III and IV, respectively. The results were obtained from the use of panel data for Morocco, Portugal, Tunisia, Turkey and Yugoslavia, over the period 1977-89. Therefore, the sample comprises 65 observations. The exclusion of Algeria improved considerably the results across all the specifications considered. Indeed, it seems that the pattern of remittances in Algeria is different from the other countries because of the much longer history of migration and its highly centralized economy.

The results shown in tables 2-A, 2-B, 3-A and 3-B are generally very good. The coefficients of all of the variables have the predicted signs and are usually statistically significant. In addition, the estimates that were obtained with the stock of migrant workers are remarkably comparable to the ones based on the stock of total migrant population. Regarding the demographic and income variables, the results are consistent with previous studies (e.g. Glyteos (1988), Katseli and Glytsos (1986), Rocha (1989) and Swamy (1981) to mention a few examples). In addition, we found strong and systematic evidence on the relevance of macroeconomic and portfolio factors to the long-run determination of official remittances.

The imposition of a unitary elasticity relative to the stock of workers (or population), does not result in any noticeable change in the estimated coefficients, as shown in tables 2-B and 3-B. Indeed, the increased robustness of the estimated coefficients lends strong support to the per capita models, despite the loss of the explanatory power compared to case of the unrestricted levels models. Such feature does not obtain in previous literature.
# **_Table 4: Attrition in End‐line Survey_**

Dependent Variable: Interviewed in Endline Survey

||(1)|
|---|---|
|Movie|‐0.014<br>(0.02)|
|MFB|‐0.032|
||(0.02)|
|Movie + MFB|‐0.021<br>(0.02)|
|Pure Control|‐0.069**<br>(0.02)|
|N. of Obs.|2437|
|R‐squared|0|
|P‐value of F model|0.6|
|* p<0.05, ** p<0.01, *** p<0.001||

29
# by 2014 in Costa Rica and Peru.

# **Regional Averages in figures 1-5**

All regional and sub-regional averages in figures 1-5 are unweighted averages of 13 countries: Argentina, Bolivia, Brazil, Chile, Costa Rica, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, El Salvador, and Uruguay. Colombia, Ecuador, and Guatemala were excluded from the regional averages because there is no comparable data for 1995-2002. In the construction of figures 1-5, and to ensure a consistent average of the 13 countries that are included in the sample over time, the data were linearly interpolated at the country level when the survey data were missing for a specific year.

# **Variable Definitions and Sample Restrictions**

Every survey asks individuals about their labor earnings in the main occupation during the last month. Hourly wages are constructed by dividing the monthly wage by the total hours worked during the reference period. Nominal hourly wages are converted into real terms using national consumer price index (CPI) deflators, and they are expressed in dollars using 2005 purchasing power parity (PPP).

The SEDLAC project homogenizes the household surveys’ information on educational systems to make it comparable across countries. The variable for educational attainment consists of the following five categories: i) completed primary or less, ii) high school dropout, iii) high school completed, iv) college incomplete, and v) college completed or more. Although educational attainment is classified in five groups, the paper often reports aggregates for the grouping: i) high school dropouts or less, ii) high school completed, and iii) college completed. The level of potential experience of workers is calculated as age minus years of completed education minus six.

Using the International Standard Classification of Occupations (ISCO-08), a homogeneous one-digit-level classification is obtained: i) directors and senior officials, ii) employed workers in administrative occupations or at an intermediate level, iii) employed workers in agricultural occupations, iv) employed workers in other occupations (not classified), v) employed workers in service occupations, vi) employed workers in the military, vii) non-agricultural workers and operators, viii) technical and professional workers, ix) traders, and x) retail workers. However, the analysis in figure 6 presents changes in employment and wages for detailed occupations, as discussed in the paper.

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- 16-

international achievement tests. 17 Figure 8 shows while the former indicator tends to rise with the level of aggregate spending, the latter is flat against spending per pupil. There is, moreover, substantial cross-country variation even among those with similar levels of spending.

Figure 8: Relation between schooling outcomes and input of public resources, circa 1993 <!-- Start of picture text --> (A) School life expeCtMy (B) Stdet Leang<br>0 60<br>0<br>15 o a O 0 0% 0<br>oo ° 0 oc 0 0 o<br>0~~~~~<br>0 10 000 0 8 0 0<br>0 0 0 000 0~0 5 0 o° 0 0<br>I 0 OO 0 0 0<br>o 0<br>0 o ~~~~~~~~000 10 5 0.0 0.1 0. 0<br>5 000~~~C 0 0 40 0<br>0 4 00<br>0 0<br>0 0 0<br>00 0 0<br>0 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _30 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _<br>0 5 l0 0.0 0.1 0.2 0.3 0.4<br>Public spending on education as V of GNP Public spending per p*-ary puqil/per capit GNP<br><!-- End of picture text --> Deviations from the regression line in the figure provide one measure of efficiency in resource use: positive deviations signal better than average efficiency because more is produced for the same level of spending, and negative deviations signal the opposite. For both outcome indicators, the deviations relate positively to per capita GNP, implying that the education systems of richer countries tend to be more efficient than those of poorer countries. As before, however, the general tendency masks large variations across countries: the R<sup>2</sup> statistic for the relevant regressions range between 0.10 and 0.50, suggesting that country wealth is not the only, or even the most important, factor in determining how efficiently an education system operates.

# 2.5 Equity in access **and resource allocation**

We examine here two aspects of equity in education: gender disparities and the distribution of public spending on education.

> 7 Because private financing of education differs across countries and are probably influenced by policies toward private education (including the level of public spending itself) the indicator actually refers to the efficiency of public spending on education working directly through government provision of services, as well as through its catalytic effect in leveraging private resources for education.
Nigeria and South Africa having only slight changes in the ratio, while Ethiopia, Rwanda, Zimbabwe, and Uganda having more substantial changes (Table 1).

**Table 1: Total dependency ratios in 2011 and 2030**

||**2011**|**2030**|**Difference**|
|---|---|---|---|
||**I**|**II**|**III**|
|**Burkina Faso**|0.94|0.76|-0.18|
|**Cameroon**|0.87|0.71|-0.16|
|**Côte d’Ivoire**|0.81|0.72|-0.09|
|**Ghana**|0.73|0.58|-0.15|
|**Nigeria**|0.88|0.81|-0.07|
|**Senegal**|0.87|0.72|-0.16|
|**Ethiopia**|0.89|0.62|-0.28|
|**Kenya**|0.82|0.67|-0.15|
|**Madagascar**|0.85|0.72|-0.13|
|**Malawi**|0.96|0.79|-0.16|
|**Mozambique**|0.95|0.81|-0.14|
|**Rwanda**|0.88|0.66|-0.22|
|**Tanzania**|0.92|0.80|-0.13|
|**Uganda**|1.05|0.85|-0.20|
|**Zambia**|0.98|0.86|-0.12|
|**Zimbabwe**|0.80|0.59|-0.21|
|**Botswana**|0.60|0.51|-0.09|
|**South Africa**|0.54|0.49|-0.04|
|**Rest of SSA**|0.90|0.77|-0.13|

Source: Authors’ calculations from United Nations (2013) While LINKAGE provides the economy-wide effects of demographic change over time, the GIDD microsimulation framework of Bussolo et al. (2014) will be used to generate income distributions under the various scenarios. GIDD draws on household level survey data benchmarked to 2007 for over 128 countries to estimate income distributions by country that account for demographics, household characteristics (e.g. age, gender, and education of different members), sector of employment, skill premia on wages, and income. Using the simulated income and employment under future scenarios from LINKAGE, and accounting for the demographic shifts characterized in the United Nations (2013), GIDD is able to generate income distributions by country that are consistent with both the more ‘aggregated’ changes under the CGE simulations and also what is known about households from survey data. In addition to incorporating the changes in key variables from the LINKAGE scenario results, the GIDD methodology updates the household survey data for the terminal year of the simulation. This is done by reweighting the population characterized by the base year household surveys using nonparametric cross-entropy methods, but keeping it consistent with the United Nations’ population projections.

To be consistent with the GIDD, the LINKAGE is modified to adopt the former’s skilled-unskilled labor definition, whereby a skilled worker is anybody with more than nine years of education, and an unskilled worker is anybody with less than nine years of education. This redefinition necessitates an adjustment of the GTAP data on value added by labor type in production, such that the number of workers of a given skill type in a given sector is consistent in the 2007 benchmark year across the two modeling frameworks.

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# **Figure 3**

# **Pension System Holdings as a Share of GDP**

This figure shows the size of total assets of pension funds across all PFAs relative to Chile's GDP by fund type for the entire sample period (July 1996 to December 2005), as of December of each year. The fund types reflect different risk profiles, from the riskiest fund (Fund A) to the most conservative fund (Fund E). The nominal values for December of each year are deflated using the GDP deflator.

<!-- Start of picture text --> 80%<br>70% Fund E<br>Fund D<br>60%<br>Fund C<br>50%<br>40%<br>30%<br>20% Fund B<br>10%<br>Fund A<br>0%<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br>Total Pension System Assets as a Share of GDP<br><!-- End of picture text -->
# **Table  13d:       Piecemeal Sensitivity Analysis: Impact on Rwanda of EACU Deep Integration and  Multilateral Liberalization**

|in Equivalent Variation (E|V) as a percentage of consum|ption||**Equiv**|**alent V**|**ariatio**|**n**||
|---|---|---|---|---|---|---|---|---|
||**Parameter Valu**|**e**|**EACU D**|**eep Integ**|**ration**|**EACU**|**Liberal**|**ization**|
|Parameter|Lower<br>Central|Upper|Lower|Central|Upper|Lower|Central|Upper|
|σ(qi, qj) – services sectors|2<br>3|4|1.40|1.40|1.40|4.91|4.95|4.99|
|σ(qi, qj) – goods sectors|see below||1.32|1.40|1.44|5.32|4.95|4.90|
|σ(va, bs)|0.625<br>1.25|1.875|1.42|1.40|1.39|4.88|4.95|5.03|
|σ(D, M)|see below||1.41|1.40|1.40|4.95|4.95|4.95|
|σ(M, M)|GTAP values<br>30||1.32|1.40|NA|4.87|4.95|NA|
|σ(L, K)|0.5<br>1|1.5|1.41|1.40|1.40|4.94|4.95|4.95|
|σ(A1,...An)|0<br>0|0.25|NA|1.40|1.40|NA|4.95|4.95|
|εEACU,εCOMESA,εSADC|Lower (upper) values are 0.5 (1.|5) central|1.46|1.40|1.36|4.45|4.95|5.27|
|εEU ,εROW,εUSA,εCHINA|values. See table 7 for central va|lues.|||||||
|θr|0<br>0|1|NA|1.40|1.18|NA|4.95|1.66|
|θm|0.025<br>0.05|0.075|1.40|1.40|1.40|4.95|4.95|4.95|
|**σ(qi, qj) – IRTS goods**|**Parameter Valu**|**e**|**σ(D, M)-**|**-CRTS s**<br>|**ectors**<br>|**Par**|**ameter V**|**alue**|
|chemicals and metals|3.4<br>6.8|10.2||||Lower|Central|Upper|
|energy and minerals|5.8<br>11.6|17.4|agricultur|e and for|estry|1.3|2.5|3.8|
|food products|2.6<br>5.1|7.7|other serv|ices||0.8|1.5|2.3|
|petroleum and coal prod.|2.1<br>4.2|6.3|trade|||0.8|1.5|2.3|
|other manufacturing|3.9<br>7.7|11.6|utilities|||1.4|2.8|4.2|
|textiles, apparel and leather|3.8<br>7.6|11.4|||||||
|wood andpaperproducts|3.2<br>6.3|9.5|||||||

## **Key:**

σ(qi, qj): Elasticity of substitution between firm varieties in imperfectly competitive sectors σ(va, bs): Elasticity of substitution between value-added and business services σ(D, M): Elasticity of substitution between domestic goods and imports in CRTS sectors σ(M, M): Elasticity of substitution between imports from different regions in CRTS sectors σ(L, K): Elasticity of substitution between primary factors of production in value added σ(A1,...An): Elasticity of substitution in intermediate production between composite Armington aggregate goods εROW, εEU, εCHINA, εUSA εEACU, εCOMESA, εSADC: Vectors of elasticities of imperfectly competitive firms' supply in the Rest World, EU, China. USA, EACU, COMESA and SADC with respect to the price of their outputs.

θr: Share of rents in services sectors captured by domestic agents θm: Shares of value added in multinational firms due to specialized primary factor imports Source: Authors’ estimates.

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compounded by the cost of maintaining a high-value stock. Though it need not be the case, packers often export, and importers from the three main importing countries ―the USA, France, and Germany― keep close and lasting marketing contacts with exporters.

Several of a bean’s quality characteristics are unobservable and largely determined by growing conditions, time of harvest, and the curing process. This asymmetric information can make it tempting for individual growers and/or curers to free-ride, in particular by early harvest, giving rise to a prisoner’s dilemma.<sup>4</sup> The resulting market failure could in principle be addressed by a variety of market mechanisms, including vertical integration, branding, or industry standards.

As a matter of fact, vertical integration between farming and processing is virtually nonexistent. If incentives to vertically integrate between curers and packers are stronger to overcome information asymmetries, they are still limited because the activities require specific skills. In the absence of vertical integration, the industry has developed weaker mechanisms to alleviate adverse-selection issues, such as the introduction of identification marks that remain visible after curing.

Virtually all packers have traditionally been Malagasy companies, some owned by ethnic Chinese. Although in the mid-1990s there were about 45 packers, five of them largely controlled the business (Blarel and Dolinsky, 1995), and the top three exporters accounted for two thirds of Madagascar’s exports. This high concentration arguably resulted as much from government policies and rentseeking as from economic rationality (marketing externalities and the like).

# **2.1.2 Vanilla farming**

We now turn to a characterization of vanilla farms on the basis of Madagascar’s four household surveys (EPM under their French acronym standinf for “ _Enquêtes Permanentes de Ménages_ ”): 1993, 1997, 1999 and 2001. Although they are not

> 4 Five months after flowering, vanilla beans have reached their optimal size but contain less than 1% of vanillin. In order to reach a vanillin content of around 2% (the norm for Bourbon vanilla) the beans must be harvested at least eight months after flowering. Fringe traders can thus be tempted to compete on collection dates. That is, if trader _i_ collects at date _t_ , trader _j_ has an incentive to collect at date _t-1_ . Depending on his discount rate, the farmer can be induced, in equilibrium, to sell his vanilla too early—though this competition can only take place over a short period-- leading to rent dissipation pretty much like in patent races.

8