rafmacalaba/datause-encoder-data huggingface dataset:
**Table S1.8: Robustness: Time of the day**

||DP|FDC|NRM|UPC|
|---|---|---|---|---|
|Enumerator gender (male)|-0.560***|-0.400**|-0.108***|-0.511***|
||(0.150)|(0.159)|(0.034)|(0.116)|
|Enumerator age|-0.021|-0.006|0.002|-0.034**|
||(0.021)|(0.020)|(0.004)|(0.016)|
|Enumerator education|0.108|0.131|-0.075**|0.060|
||(0.109)|(0.121)|(0.035)|(0.110)|
|Enumerator from urban locality|0.454**|0.567***|0.001|0.427***|
||(0.185)|(0.204)|(0.039)|(0.149)|
|Afternoon|-0.005|-0.041|0.035|0.006|
||(0.052)|(0.054)|(0.030)|(0.052)|
|Observations|6,240|6,328|6,547|6,315|
|R-squared|0.485|0.458|0.234|0.478|

_Source_ : Data for the study comes from a survey of farmers in northern Uganda. The survey was conducted by Innovations for Poverty Action.

Note: This table presents OLS regression results for equation (1). For each column, the outcome variable is (an increasing) categorical variable (1-4) which measures the openness to vote for a political party, namely, the Democratic Party (DP), the Forum for Democratic Change (FDC), the National Resistance Movement (NRM), and the Uganda People’s Congress (UPC). Afternoon is a dummy that takes value 1 if the interview was conducted after 1 pm and 0 otherwise. Each regression includes community fixed effects and a constant. Robust standard errors in parenthesis are clustered at the enumerator level. *** p<0.01, ** p<0.05, * p<0.10.

41
literacy. Life expectancy is measured in years and literacy is an index value ranging from 0 to 100. We choose education and health as both are direct determinants of capabilities and influence the freedom to choose the kind of life one likes. Education as well as health raises productivity and the ability to convert income and resources into the favored way of life (Sen 2003: 55). The third dimension of human development, namely income, is not of interest for this paper, since there is much work on the relation between democracy and economic development already available. Our data on political institutions, especially democracy comes from the Polity IV Project of the Center for International Development and Conflict Management at the University of Maryland. This dataset includes the Polity2 score ranging from 10 (highly democratic) to -10 (highly autocratic), while a zero score indicates a state between autocracy and democracy which we consider as not democratic.<sup>13</sup> Following Besley and Kudamatsu (2006) we calculate the fraction of democratic years over the past five years as our measure for democracy. The consideration of a period of five years captures the effect of democratic experience and reduces the uncertainty concerning the length of the delay until a change in the political system affects human development.

Other variables that we expect to have an impact on human development or that describe possible conditions under which democracy affects human development are the following: GDP per capita PPP in constant prices<sup>14</sup> from the Penn World Tables 6.2; Gini coefficients<sup>15</sup> from the WIDER dataset with improvements in terms of compara-

> 13 According to the Polity2 measure, a system can be classified as democratic if three interdependent elements exists: 1) competitiveness of participation, institutions and procedures allow citizens to express their political preferences; 2) openness and competitiveness of executive recruitment and constraints on the chief executive, so that the executive power is institutionally constraint; 3) civil liberties. The last element as well as rule of law, system of checks and balances, freedom of the press etc. is not coded in the index as the latter are performance indicators of democratic regimes. Autocracies are defined vice versa. For more details see Marshall and Jaggers (2005: 13f.).

> 14 US$, base year: 2000. 15 Gini coefficients are not available for every year. We therefore use a simple moving average between available observations to complete the dataset.

16
**Table S4.7.** ITT effects on number of adopted ISFM technologies and integrated adoption of the full ISFM package, FREG- and non-FREG samples separately; using kernel matching weights in the outcome regressions.

|||OL<br>|Numb<br>S<br>|er of ISFM te<br>|chnologies a<br>|dopted<br>Pois<br>|son<br>||Int<br>|egrated adop<br>|tion of full pa<br>|ckage<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Non-FRE|G sample|FREG|sample|Non-FRE|G sample|FREG|sample|Non-FR|EG sample|FREG|sample|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|T1 (extension)|0.304|0.269***|1.165***|1.141***|0.310|0.277***|1.165***|1.147***|0.036|0.036|0.260***|0.256***|
||(0.189)|(0.077)|(0.192)|(0.118)|(0.190)|(0.084)|(0.184)|(0.122)|(0.047)|(0.024)|(0.060)|(0.049)|
|p-value|0.111|0.001|0.000|0.000|0.102|0.001|0.000|0.000|0.447|0.138|0.000|0.000|
|T2 (extension + video)|0.532***|0.471***|1.047***|1.037***|0.522***|0.459***|1.064***|1.064***|0.085*|0.085***|0.211***|0.205***|
||(0.177)|(0.080)|(0.174)|(0.121)|(0.169)|(0.083)|(0.171)|(0.131)|(0.045)|(0.025)|(0.067)|(0.041)|
|p-value|0.003|0.000|0.000|0.000|0.002|0.000|0.000|0.000|0.061|0.001|0.002|0.000|
|Test T1 >= T2 (p-value)|0.145|0.010|0.711|0.778|0.144|0.016|0.711|0.742|0.176|0.042|0.745|0.830|
|Endline control mean|2.4|63|2.8|68|2.4|63|2.8|68|0|.163|0.|251|
|Additional controls|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|
|(Pseudo)R-squared|0.029|0.495|0.196|0.520|||||0.008|0.289|0.049|0.282|
|Observations|1,958|1,958|1,352|1,352|1,958|1,958|1,352|1,352|1,958|1,958|1,352|1,352|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note:_ Poisson and probit models (columns (5) to (12)) show average marginal effects (AME). Number of ISFM technologies adopted ranges from 0 to 5. Integrated adoption of full ISFM package is a dummy variable. FREG stands for ‘Farmer research and extension group’. FREG- and non-FREG samples consist of actual FREG respectively non-FREG farmers in treatment mws, and control observations weighed with Kernel matching weights according to their similarity with FREGs and non-FREGs, respectively. Additional control variables identical to those listed in notes of Table 2. One-sided equality tests of T1 and T2 are F-tests or Wald tests. Robust standard errors in parentheses, clustered at the mws level. Significance levels indicated as following: *** p<0.01, ** p<0.05, * p<0.1.

15
41 Table (4.1) MAXIMUM LI}ELIHOOD PROBIT ESTIMATES OF THE STATUS PARTICIPATION EQUATION Low income countries

|Variable|Coefficient|Std. Error|t-Stat|2-Tail Sig.|
|---|---|---|---|---|
|GDP2|-18.073|10.984|-1.645|0.109|
|GD121|_-11.455_|5.295|-2.164|0.037|
|POL2|-0.230|0.132|-1.736|0.091|
|DEBT21|3.024|1.617|1.869|0.070|
|EXT21|-2.111|1.719|-1.228|0.228|
|INFL2|2.000|1.125|1.777|0.084|
|X2|-2.456|2.213|-1.110|0.275|
|DEF21|8.235|7.604|1.083|0.286|
|LAC|-6.002|3.893|-1.541|6.132|
|ASIA|0.979|0.964|1.015|0.317|

Note: Log likelihood = -15.497; pseudo R squared - 0.48
important effect on the performance of these countries. We next ask whether this has been the case empirically.

# _Empirical Assessment_

In this sub-section we assess the empirical relevance of the saving based poverty trap models discussed above. We first ask whether the cross-sectional and time-series behaviour of saving is consistent with models of poverty traps. We then ask whether there is any evidence that in the past aid has helped countries to escape from saving-based poverty traps.

Consider first the nonlinear relationship between saving and the level of development that is required for a poverty trap to exist in the Solow model. In the left-hand panels of **Figure 5** we plot the empirical counterpart of this relationship. In each panel we have gross domestic investment rates on the vertical axis, and on the horizontal axis we have the capital stock.<sup>13</sup> The top panel plots data for all countries, the second only for low-income countries, and the third only for countries in Sub-Saharan Africa. In all these graphs we have fitted a third-order polynomial relationship to pick up any non-linearities that might be present in the data. These non-linearities do appear to be present in the cross-sectional relationship between investment rates and the level of development. In all three cases the patterns are broadly similar, with sharp increases in saving at very low capital stocks, then a fairly flat or even declining section, and then finally again an increasing section. Note however that these turning points occur at very different levels of the capital stock per capita, as the scale for the top graph is very different from the bottom two panels. In particular, for the low-income and Sub-Saharan Africa samples the turning points all occur at capital stocks less than $1500 per capita. Interestingly also, some of these nonlinearities are statistically significant: the quadratic and cubic terms in the first and third panels are statistically significant at the 5 percent level. At the same time, however, a visual inspection of the three graphs suggests that the shape of these nonlinearities is likely to be quite sensitive to extreme observations.

13 Throughout the paper we use data on investment rates as a proxy for saving rates. We do this mainly for reasons of data availability, as gross domestic investment rates are available for many more country-year observations than is gross national saving, reflecting gaps in data on current accounts. While many poor countries of course receive substantial recorded capital inflows, many also have substantial unrecorded capital outflows and so it is not obvious a priori the extent to which our findings are affected by this approximation.

20
(column 1), and sensitivity analyses with an additional set of controls (column 2), non‐linear expenditures (column 3), and the interaction term between household expenditures and electricity technology.

**Table 7: WTP as a share of the total household expenditure**

||**Burkina Faso**|**Rwanda**|**Senegal**|
|---|---|---|---|
|Overall|0.12|0.15|0.13|
|— Solar lamp consumption level|0.06|0.12|0.11|
|— Pico‐PV/ SHS consumption level|0.10|0.15|0.11|
|— Electricity grid consumption level|0.23|0.19|0.16|

_Sources:_ Senegal (2011), Burkina Faso (2010), Rwanda (2011) Consistent with the theoretical model’s predictions, we see that higher household income is associated with higher WTP for electricity access. In the base model specification, for example, a 1 percent increase in expenditures increases the WTP by around 0.2 percent (column 1). Estimated coefficients for country dummies show that the WTP is the highest in Senegal, followed by Rwanda. The household WTP is the lowest in Burkina Faso (the reference case). This result is consistent with the descriptive analysis in Section 4.1. Also consistent with the descriptive analysis in Section 4.1, the WTP generally increases with higher household electricity consumption levels. Furthermore, we can see that the WTP is clearly affected by the values of the initial starting bid.<sup>14</sup> The higher this initial bid, the higher the households’ WTP. Most of the other household characteristics do not appear to have a clear influence on the estimated WTP (column 2). Only two household characteristics ‐ the educational level of the head of household, and access to finance (measured by the availability of bank account) increase the WTP substantially. We also see that the addition of these controls has a very small effect on the size of the estimated coefficient for household expenditures. To account for potential

> 14 Remember that in each country, three alternative initial starting bids were randomly assigned to the households.

22
**Table 4: Robustness for price makers** **<u>and conflict data source</u>**

|Conflict data|(1)<br>PRIO|(2)<br>PRIO|(3)<br>PRIO|(4)<br>PRIO|(5)<br>PRIO<br>|(6)<br>COW<br>|
|---|---|---|---|---|---|---|
|Dep. Var.|any onset|any onset|any onset|any onset|Major<br>(1,000)|Major<br>(1,000)|
|P t|0.028*|0.040**|0.040**|0.043**|0.006|0.018**|
|x ()|(0.016)|(0.018)|(0.016)|(0.016)|(0.010)|(0.009)|
|P t1|-0.012|-0.023|-0.022|-0.025|-0.019*|-0.025**|
|x (-)|(0.020)|(0.022)|(0.020)|(0.021)|(0.011)|(0.011)|
|P 2|0.001|0.007|0.005|0.008|0.004|0.011|
|x (t-)|(0.017)|(0.019)|(0.017)|(0.017)|(0.008)|(0.009)|
|P t|-0.006|0.002|-0.001|0.009|-0.007|0.037**|
|m ()|(0.024)|(0.027)|(0.024)|(0.025)|(0.017)|(0.017)|
|P 1|-0.001|-0.006|-0.004|-0.009|0.005|-0.017|
|m (t-)|(0.026)|(0.031)|(0.026)|(0.028)|(0.017)|(0.021)|
|P 2|-0.008|-0.017|-0.008|-0.013|-0.008|-0.007|
|m (t-)|(0.022)|(0.027)|(0.022)|(0.024)|(0.015)|(0.014)|
|MP t|-0.004|0.001|-0.007|-0.001|0.005|0.001|
|()|(0.023)|(0.024)|(0.023)|(0.023)|(0.012)|(0.009)|
|MP (t1)|-0.042|-0.062|-0.038|-0.035|-0.032|-0.030|
|-|(0.054)|(0.059)|(0.054)|(0.056)|(0.046)|(0.043)|
|MP 2|0.061|0.081|0.059|0.049|0.043|0.057|
|(t-)|(0.065)|(0.070)|(0.066)|(0.067)|(0.057)|(0.061)|
|Trade with|-0.034|-0.053|-0.033|-0.025|-0.037|0.003|
|neigh. (t-1)|(0.053)|(0.055)|(0.053)|(0.053)|(0.034)|(0.036)|
|Observations|3,428|2,491|3,428|2,992|4,001|3,834|
|R-sq. (within)|0.070|0.084|0.071|0.079|0.024|0.036|
|Countries|114|85|114|98|115|115|
|Threshold|10%|10%|20%|20%|None|None|
|Exclude|Commod.|Countries|Commod.|Countries|None|None|
|Sum Px|0.017*|0.024**|0.023**|0.025**|-0.009|0.004|
|Sum Pm|-0.015|-0.022|-0.014|-0.013|-0.010|0.013|
|Sum MP|0.015|0.020|0.015|0.013|0.016|0.028|

_Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; all regressions include country fixed effects, year effects and controls (any conflict since 1946 and war border (t-1)); trade shock variables are weighted using the slow-moving averages (no country-specific time trends)._ 29
the equity market is liberalized based on the chronology in Bekaert and Harvey (2000). First sign is a broader measure that takes the value of one if there is either an official liberalization or ADR and country fund introduction.<sup>20</sup> Investability is the ratio of capitalization of the IFC 'Investable" to the "global" stocks in a country as in Edison and Warnock (2001). For balance-of-payments restrictions we consider 4 types of restrictions recorded **by** the International Monetary Fund. They are (i) restrictions on payments for capital transactions, (ii) restrictions on repatriation off oreign investment earnings, (iii) presence of multiple exchange rates, and (iv) restrictions on current account transactions. For each of these categories, the IMF records a score of **1** when restrictions apply, and **0** otherwise. Our proxy is the sum of these scores for a given country and year.

Each of these liberalization variables is country specific while our globalization measure applies for groups of countries. To come up with a proxy for the extent of liberalization in a particular group of countries we compute the weighted average liberalization level for each variable where the weights are the countries' gross domestic product divided **by** the sum of outputs across all countries.

In table **6** we report the results of regressing the globalization measure on the 4 different indices of liberalization. As expected, the signs of the coefficients are positive for the first three measures and negative for balance of payments restrictions (a **high** value for balance of payments restrictions indicates low liberalization). Most coefficients are significant at the **1** percent significance level and the R-squares from the regressions are very high (ranging from **.5** to **.7** for developing countries and are about **.16** for industrial countries except when using capital restrictions where the R-square is **.6)** indicating that a substantial portion of the time series variation of our globalization measure is accounted for **by** the rise of world market integration. Interestingly, the increased relevance of global factors is much more related to increased liberalization- in developing countries than in industrialc ountries.

We view the results presented in table **6** as evidence that increased financial and

> 20 For developed countries first sign turns out to be identical to official liberalization.

22
after baseline).

# **3.4 Assignment to Treatment**

We implemented a temporarily double blind, individual-level randomization at the end of the baseline survey. The enumerator asked each woman to draw a colored candy from a paper bag. Women received a matching colored paper with information about the time, date, and venue of the first training session. Whereas time and date were the same for both groups, the venue differed depending on treatment status. We did not reveal to participants which venue corresponded to each treatment until the first day of training when they were at the venue. We changed the color of the candies corresponding to each group in each new location and never revealed the correspondence to enumerators.<sup>14</sup> The randomization is only temporarily double blind in the sense that women knew after the first day of training which group they had been assigned to, and all program coaches knew which group they were coaching.

While the control group did not receive any training during the RCT, women in this group took part in a placebo activity during the very first day of the program where we invited them to a designated venue to get to know each other and ask questions to the research staff regarding when they would be eligible to participate in the program. The placebo activity assisted with treatment compliance and allowed us to re-explain the process of randomization so that we addressed any concerns from women in the control group before the program was already underway.

Program coaches took careful attendance to ensure compliance with treatment, particularly during the first month. Monitoring from the coaches largely succeeded in limiting non-compliance. Of participants in the control group, 1.7% entered the treatment. Our main results show average treatment effects based on the randomly assigned treatments, but instrumenting for each participant’s actual group with their treatment assignment yields qualitatively similar results (see Appendix D). Of women assigned to treatment, 6.5% never

> 14Note that the number of women in treatment and control is not precisely even as a result of random chance.

14
whether a household has a plan to open a new non-farm enterprise in the 12 months immediately after the data collection in each round of the survey.

I capture societal cooperation using the extent of cooperation within the customary laborsharing arrangements, which are known by various names, such as _debbo_ , _wenfel_ , and so on, across different parts of the country. In particular, I use two individual-level variables to capture the extent of cooperation in such arrangements in the 12 months immediately before the data collection in each round of the survey: a binary variable indicating whether an individual has undertaken unpaid work for any other household and the number of other households for which an individual has undertaken such work.

I further use three household-level binary variables each indicating whether a household is negatively affected by a fall in prices of food items, a rise in prices of food items, or a rise in prices of agricultural inputs (seed and fertilizer) in the 12 months immediately before the data collection in each round of the survey.

In the main empirical analyses, I use the standard (natural) logarithmic transformation of the continuous outcome variables that take only positive values (total and food consumption expenditure per capita) while I use the “started log” of the continuous outcome variables that take non-positive values ( : wealth score, education expenditure per capita, and other non-food consumption expenditure per capita), i.e., , where is the negative of the minimum of xx plus 0.001, ensuring that for all possible values of each of the variables. The rest are ln(xx+ zz) zz xx either binary or count variables which are used in levels. xx+ zz> 0 _Sample Construction_ **.** The ESS contains GPS coordinates of the entire sample EAs,<sup>19</sup> enabling usage of observations from all 433 EAs, spread across 317 woredas, the fourth biggest administrative region. Figure 3 presents the locations of these sample EAs. In the empirical analyses, I weight every sample unit by the inverse of its probability of inclusion in the ESS2. This implies that 193 sample households that were surveyed only in the ESS1 are excluded from the analyses. Further, I excluded 67 households from the ESS3, since they moved outside their original woreda between data collections for the ESS2 and ESS3. Having done so, the final number of household-round observations becomes 13,925. For the individual-level analyses, I further restrict

> 19These coordinates are randomly shifted to maintain the anonymity of sample households (and EAs) by 0-2 kilometers in urban EAs, 0-5 kilometers in 99% of rural EAs, and 0-10 kilometers in 1% of rural EAs.

12
|Ghana|SSA|||||||2.4|52|
|---|---|---|---|---|---|---|---|---|---|
|Namibia|SSA|||||1.1|44|2.2|53|
|Rwanda|SSA|||||-0.8|64|2.2|54|
|Honduras|LAC|0.5|29|0.1|36|1.4|39|2.2|55|
|Lesotho|SSA|||-1.5|46|1.9|35|2.2|56|
|France|OECD|4.9|8|-0.8|42|0.3|50|1.8|57|
|Israel|OECD|6.1|4|-0.3|40|1.0|46|1.7|58|
|Bolivia|LAC|||-1.4|45|-0.3|59|1.6|59|
|Lao PDR|EAP|||6.6|3|1.8|36|1.6|60|
|Grenada|LAC|-2.4|31|0.6|33|||1.6|61|
|Togo|SSA|||||||1.5|62|
|Jordan|MNA|||||1.1|43|1.4|63|
|Kenya|SSA|||0.1|38|-1.2|67|1.2|64|
|Panama|LAC|||||-2.3|71|1.2|65|
|Gambia, The|SSA|||||||0.9|66|
|Paraguay|LAC|3.6|16|0.9|25|||0.8|67|
|Netherlands|OECD|||0.0|39|2.7|30|0.6|68|
|Austria|OECD|||2.0|15|6.1|17|-0.2|69|
|St. Lucia|LAC|||2.1|13|||-0.2|70|
|Thailand|EAP|0.7|27|1.7|18|2.3|32|-0.4|71|
|New Zealand|OECD|-1.9|30|6.2|4|4.0|23|-1.0|72|
|Japan|OECD|||6.1|5|-3.4|72|-2.8|73|
|Dominica|LAC|2.8|20|1.2|22|-56.2|78|-2.9|74|
|United States|OECD|4.5|10|3.0|10|3.1|29|-3.0|75|
|Spain|OECD|||||||-3.6|76|
|Samoa|EAP|||||1.0|47|-3.7|77|
|Swaziland|SSA|||1.3|21|8.5|11|-5.4|78|
|Portugal|OECD|7.7|3|2.1|14|2.3|31|-6.0|79|
|Greece|OECD|6.0|5|1.4|19|-2.0|69|-7.2|80|
|Ireland|OECD|||||13.6|6|-7.4|81|
|Slovak Republic|OECD|||||||-28.2|82|
|Georgia|ECA|||||0.1|54|||
|Armenia|ECA|||||||||
|Kyrgyz Republic|ECA|||||19.6|1|||
|Czech Republic|OECD|||||1.6|38|||
|Estonia|OECD|||||-2.2|70|||
|Iceland|OECD|||||1.1|42|||
|Poland|OECD|||||||||
|Slovenia|OECD|||||||||
|Uruguay|LAC|||8.3|2|12.9|7|||
|<br>Seychelles|SSA|||28.3|1|||||
|Tanzania|SSA|||||6.9|14|||
|Uganda|SSA|1.7|24|-0.5|41|7.4|13|||

Note: Housing supply elasticity to urban growth is calculated as the ratio of % change in housing supply to urban growth.

34
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