rafmacalaba/datause-encoder-data huggingface dataset:
model based on rational inattention does not capture these cross-domain effects in terms of available cognitive bandwidth is therefore less of a concern.<sup>20</sup> In sum, standard modeling approaches, e.g. based on learning or credit constraints, have difficulties explaining the combination of low levels of demand for extension and a (positive) reaction of demand for extension to non-agricultural shocks. On the other hand, our model with costly attention would be consistent with non-agricultural shocks explaining changes in demand for extension and we verify this prediction of the model below. Because it is conceivable that non-agricultural shocks are linked to demand for extension through other channels (e.g., due to non-separability), the empirical part below also discusses the evidence in light of some alternative theories.

# **4 Empirical evidence**

In this section, we first show that there is statistically strong and robust evidence that, in line with the model, non-agricultural shocks are indeed associated with increased demand for extension. In addition, we assess the plausibility of some alternative explanations which might also account for the observed link. Our model is based on the assumption that agricultural advice has positive effects on farmers’ production. To complement the existing evidence, cited in the introduction, we therefore also investigate the relationship between agricultural advice, farm decisions, and outcomes in our data (see Appendix D). While our data allow us to improve upon prior literature that also uses observational data to study the effects of extension services, we stress that the observational nature of the data limits our ability to make causal claims.

## **4.1 Data and descriptive statistics**

The empirical work is based on survey data from three countries in Sub-Saharan Africa (Malawi, Nigeria, and Uganda), which were collected as part of the World Bank’s Living Standard Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA).<sup>21</sup> In each country, households are selected in a nationally representative way and tracked over time, such that we can use a panel dataset consisting of three rounds of surveys in Malawi (2010-11, 2013-14, 2016-17) and Nigeria (2010-11, 2012-13, 2015-16), and four rounds in Uganda (2009-10, 2010-11, 2011-12, 2013-14).<sup>22</sup> We focus on households that engage in

> 20The same argument applies to the concern that farmers’ demand for advice could also be explained based on a similar model using time rather than attention as the limiting factor, where for some types of non-agricultural shocks (e.g., illness of a family member) the opportunity cost of time would increase and thus demand for agricultural advice would tend to decline.

> 21The same data are used by Deininger et al. (2017), Gollin and Udry (2017), and Sheahan and Barrett (2017).

> 22Detailed information about the design and implementation of the surveys in each country can be found at World Bank (2018). While the LSMS-ISA database covers more countries, only the data from the three countries we use here allow for separately identifying extension contacts that were actively

14
and the exporter at time _t_ .<sup>4</sup> Following Anderson and Van Wincoop (2003) as well as Feenstra (2002),<sup>_d_</sup> _i_ are importing country dummies, _d j_ are exporting country dummies and<sup>_d_</sup> _t_ are time dummies.

Thus, the average impact of an importers’ growing GDP on exports is captured by the parameter α . In order to capture the impact associated with growing demand in China (or India), we augment the model in equation (1) by including the interaction of a dummy variable that takes the value 1 when China or India is the importer with the GDP of the importer, _Yit_ . Also, because economic and factor endowment differences can be important within LAC, we will also interact this variable with four dummy variables that take the value 1 when the exporter belongs to one of the four sub-groups we considered (Andean countries, Caribbean countries, Central America, and the Southern Cone). The same logic applies for the GDP of the exporter to measure the differential impact of the growth of different LAC sub-regions on exports to China (or India), as well as with the Linder effect.5 The final specification that captures the impact on bilateral imports is: where α + α _R_ capture the impact of the growth of China on exports of region _R_ to China, and β + β _R_ capture the impact of growth of region _R_ on exports to China.

> 4 The Linder variable is often used in gravity specifications to capture the effect of similarities between importers and exporters in their levels of development on bilateral trade (see for example Thursby and Thursby, 1987). However, this captures intra-industry trade effects, whereas most of the trade between LAC and China and India is inter-industry. In 2005, LAC’s trade deficit in manufactured products with China represented 277% percent of LAC exports to China, while its trade surplus for agriculture and mining was 92 percent of exports. The numbers for trade with India are 108 and 46 percent respectively. We nevertheless follow the traditional specification and include it as a control variable. In practice, the inclusion of this variable does not affect the parameters of interest for this paper.

> 5 We also examined the differential effects on LAC imports from China and India, but we omit them from the presentation here for ease of exposition. The results on imports of LAC from China and other third markets are discussed in the Appendix.

6
# **Table 8. Bargaining Power**

|||**Midlin**<br>Control<br>Group|**e Survey**<br>Treatmen|t - Control||**Endlin**<br>Control<br>Group|**e Survey**<br>Treatmen|t - Control|
|---|---|---|---|---|---|---|---|---|
||Obs|Mean|Coeff.|_p-value_|Obs|Mean|Coeff.|_p-value_|
|**Expenditures last 30 days (FDJ)**|||||||||
|Husband clothes and shoes|914|277|6|_0.931_|882|345|15|_0.881_|
|Beneficiary Woman clothes and shoes|914|468|162 **|_0.044_|882|499|111|_0.322_|
|Khat and Tobacco for male adults|844|5,250|-87|_0.888_|792|5,908|585|_0.600_|
|**Woman's participation in HH decisions**|||||||||
|Index 1: Woman participated in HH decisions|n.a.|n.a.|n.a.|n.a.|882|0.000|0.116 *|_0.059_|
|Index 2: Woman took decisions alone|n.a.|n.a.|n.a.|n.a.|882|0.000|0.078|_0.255_|

Notes _:_ Coefficients from an OLS regression of the left-hand side variable on a treatment dummy, controlling for strata dummies, surveytime dummies, and baseline traits (see Table 3 note). ***, **, * indicate significance at 1, 5 and 10 percent.

47
records of eligible households that were compiled from the list of farmers kept by the village extension officer, when available. This list of eligible farmers recorded the name and gender of the household head as well as the area of land under cultivation of maize and rice. The VVC checked the farmer list against the eligibility criteria to come up with the list of eligible farmers.

The baseline data collection took place in January and February of 2011 immediately after the implementation of the village-level interventions in the three regions of Arusha and Kilimanjaro in the North, and Morogoro Region in the South.

In Arusha we collected baseline data for 460 households. Since the intervention was only successfully implemented in the 46 Arusha villages, at endline we doubled the number of observations for the eligible household survey, resulting in a sample of 920 households. The criteria used to draw the additional sample were the same as in the baseline eligible survey: eligible beneficiaries were defined as those that began participating in NAIVS during the 2010/11 planting season, and eligible nonbeneficiaries were defined as those that were eligible to participate in 2010/11 but were not selected as beneficiaries. Table OA4 in the Online Appendix suggests that the baseline sample and the additional sample are comparable. Endline data were collected in July and August of 2012. The households that participated in the baseline villager survey were also revisited.

Table 1 reports the mean of village level characteristics in Panel A from the village survey, VVC member characteristics in Panel B from the VVC survey and household member characteristics in Panel C from the villager survey. Column 1 of Table 1 reports the data source, column 2 reports mean characteristics across all villages, columns 3 and 4 report the means in lottery and non-lottery villages, respectively, and columns 6 and 7 report the means in meeting and no-meeting villages. Columns 5 and 8 show balance across experimental arms by reporting the p-values associated with a t-test that the differences in characteristics are different from zero.<sup>5</sup> According to column 2 of Panel A in Table 1, there are on average 407 eligible farmers per village and about 67 percent of them were beneficiaries in 2010. Only 9 percent of households have access to electricity. About 59 percent of villages have a health clinic or hospital and almost all have a primary

> 5 The p-values reported account for clustering at the village level. Since the number of clusters (villages) is 47, we use the t- asymptotic wild cluster bootstrap procedure described in Cameron et al. (2008).

11
would certainly push trade volumes higher, but it is difficult to predict by how much. Thus, the estimates below should be considered conservative.

Method 1 uses the minimum and maximum estimates of the shares of transport costs in the unit values of specific commodities given by traders and the estimates of transport cost reduction from realigning routes by transportation companies. Hence, the estimates of transport savings are derived. If _si_ is the share of transport cost in unit value, _ei_ is the potential economy in transport costs, and _Vi_ is the value of trade (imports or exports) of commodity i, then total economy equals _2i (Vi s i e d._ Method 2 is based on the information about transport costs per unit of cargo (such as container, truckload, rail car) provided by transport companies and trade volumes in physical units compiled by the statistics agencies. This method will also gives an interval estimate because actual loading factors (i.e., how many tons on average are loaded in a container or in a truck) are unknown and we have to assume minimal and maximum loading factors. If _ti_ is transport cost per unit of cargo, _ei_ is the potential economy in transport costs, and _H_ is the physical volume of trade, then total economy equals _Li (Hi t i e d._ Table 10 shows an example of such calculations using Method 1.Transport savings arising from opening borders presented below are obtained with these methods.

# Armenia

Armenia would benefit from straightening two circuitous routes - with Turkey and Iran - and from using Turkish ports for transshipments. Arrnenia's use of the Georgian territory for transit will significantly decrease. Trucking costs between Armenia and Turkey would at least halve as a result. This estimate does not include general cargo, most of which would continue to go via sea and railway rather than by road. A percentage of general cargo might be sent via road for crossborder and short distance trade. However, since no current estimates are available, we did not attempt to gauge the magnitude of this flow and so kept the estimates on the conservative side. The transport savings for Turkish imports into Armenia vary between US$0.6 and 0.8 million, based on the 1999 data. However, if the higher, 1998 trade volumes are assumed, the savings will rise to US$0.9 to 1.2 million. Armenian exports to Turkey are currently very small, so no significant savings could be achieved at these low volumes.

In addition to savings in money, the direct link between Armenian and Turkish road systems would increase the availability, predictability, and reliability of shipping services. These features, currently unavailable for South Caucasus exporters/importers, are as important as transportation costs - if not more so. The benefits will be shared by Armenia and its Caucasus neighbors. In this case, Armenia would become a transit country rather than "the end of the line".

29
Variable #109 | Question #26 | **ienfbgov** | percentage of water quality enforcement decision making at the government level before the creation of RBO | percentage| float

Variable #110 | Question #26 | **istdsblocal** | percentage of the setting of water quality standards decision making at the local level before the creation of RBO | percentage| float

Variable #111 | Question #26 | **istdsbbasin** | percentage of the setting of water quality standards decision making at the basin level before the creation of RBO | percentage| float

Variable #112 | Question #26 | **istdsbstate** | percentage of the setting of water quality standards decision making at the state level before the creation of RBO | percentage| float

Variable #113 | Question #26 | **istdsbgov** | percentage of the setting of water quality standards decision making at the government level before the creation of RBO | percentage| float

Variable #114 | Question #26 | **iotherblocal26** | percentage of decision making for other responsibilities at the local level before the creation of RBO | percentage| float

Variable #115 | Question #26 | **iotherbbasin26** | percentage of decision making for other responsibilities at the basin level before the creation of RBO | percentage| float

Variable #116 | Question #26 | **iotherbstate26** | percentage of decision making for other responsibilities at the state level before the creation of RBO | percentage| float

Variable #117 | Question #26 | **iotherbgov26** | percentage of decision making for other responsibilities at the government level before the creation of RBO | percentage| float

Variable #118 | Question #26 | **iadmalocal** | percentage of water administration decision making at the local level after the creation of RBO | percentage| float

Variable #119 | Question #26 | **iadmabasin** | percentage of water administration decision making at the basin level after the creation of RBO | percentage| float

Variable #120 | Question #26 | **iadmastate** | percentage of water administration decision making at the state level after the creation of RBO | percentage| float

Variable #121 | Question #26 | **iadmagov** | percentage of water administration decision making at the government level after the creation of RBO | percentage| float

Variable #122 | Question #26 | **ifinalocal** | percentage of water administration decision making at the local level after the creation of RBO | percentage| float

60
|||All fa|ctors|||Global|factor|||Group|factor||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Info|ws|Outf|ows|Info|ws|Outf|ows|Inf|ows|Outf|ows|
|Country|value|2S.E.|value|2S.E.|value|2S.E.|value|2S.E.|value|2S.E.|value|2S.E.|
|MEX|0.34|0.30|0.14|0.20|0.00|0.07|0.14|0.20|0.34|0.30|0.00|0.00|
|PER|0.17|0.27|0.27|0.22|0.01|0.11|0.27|0.22|0.16|0.27|0.00|0.00|
|URY|0.26|0.28|0.02|0.09|0.01|0.07|0.02|0.09|0.25|0.27|0.00|0.00|
|VEN|0.08|0.21|0.17|0.19|0.00|0.09|0.17|0.19|0.07|0.19|0.00|0.00|
|CYP|0.10|0.49|0.09|0.52|0.10|0.50|0.09|0.52|0.00|0.05|0.00|0.00|
|ISR|0.37|0.29|0.50|0.24|0.01|0.12|0.50|0.24|0.36|0.29|0.00|0.00|
|JOR|0.18|0.25|0.01|0.10|0.15|0.23|0.01|0.10|0.03|0.12|0.00|0.00|
|KWT|0.12|0.24|0.14|0.37|0.11|0.22|0.14|0.37|0.01|0.12|0.00|0.00|
|OMN|0.28|0.29|0.36|0.30|0.26|0.29|0.36|0.30|0.03|0.15|0.00|0.00|
|SAU|0.25|0.31|0.34|0.40|0.16|0.25|0.34|0.40|0.09|0.24|0.00|0.00|
|EGY|0.11|0.29|0.02|0.17|0.01|0.10|0.02|0.17|0.10|0.28|0.00|0.00|
|IND|0.72|0.17|0.67|0.17|0.70|0.19|0.67|0.17|0.02|0.09|0.00|0.00|
|KOR|0.50|0.23|0.41|0.30|0.00|0.06|0.41|0.30|0.50|0.23|0.00|0.00|
|MYS|0.33|0.24|0.35|0.21|0.01|0.06|0.35|0.21|0.32|0.25|0.00|0.00|
|PAK|0.14|0.30|0.00|0.08|0.02|0.15|0.00|0.08|0.12|0.29|0.00|0.00|
|PHL|0.59|0.25|0.25|0.22|0.04|0.12|0.25|0.22|0.54|0.26|0.00|0.00|
|SGP|0.29|0.32|0.28|0.34|0.15|0.22|0.28|0.34|0.14|0.23|0.00|0.00|
|THA|0.17|0.32|0.40|0.28|0.00|0.13|0.40|0.28|0.17|0.31|0.00|0.00|
|MAR|0.30|0.34|0.09|0.21|0.02|0.15|0.09|0.21|0.29|0.35|0.00|0.00|
|CHN|0.35|0.31|0.79|0.12|0.35|0.32|0.79|0.12|0.00|0.11|0.00|0.00|
|POL|0.52|0.25|0.29|0.24|0.50|0.26|0.29|0.24|0.02|0.10|0.00|0.00|
|ROM|0.55|0.27|0.29|0.27|0.53|0.28|0.29|0.27|0.02|0.11|0.00|0.00|

Table A.6: Fraction of the variance explained by the different factors.

54
income, and lower-middle income.<sup>11</sup> Similarly, we use WB’s regional classi...cation to divide countries into regional groups of East Asia and Paci...c (EAP), Europe and Central Asia (ECA), Latin America and the Caribbean (LAC), Middle East and North Africa (MNA), South Asia (SAR), and Sub-Saharan Africa (AFR).<sup>12</sup> We will discuss the division of countries into groups according to their levels of indebtedness directly in section 3.3.

# 3.1 Comparison across Income Groups

Figure (1) shows the percentage of countries in our sample that have a public debt management strategy when looking across di¤erent income groups. Further, panel B of Figure (1) shows what percentage of strategies is made public, and what percentage of strategies is expressed in terms of a strategic benchmark as opposed to strategic guidelines when looking across di¤erent income groups.

Consider ...rst panel A of Figure (1). As expected middle-income countries (MICs) in general fall behind the high-income countries regarding the percentage of countries which have a debt management strategy. This would support the hypothesis that more comprehensive management of public ...nances comes with a higher stage of economic development. However, it is interesting to observe that across the two subgroups of MICs the pattern does not hold with the same signi...cance, i.e. that countries in the lower MIC group show similar probability of having a debt management strategy as countries in the higher MIC group. A tentative explanation might be that while MICs pursue implementation of more stable (robust) macroeconomic policies, a relatively higher improvement in this respect can be observed in the lower MICs. Nevertheless, the pattern observed in Figure (1) can be to some extent an artifact of the selected conventional income ranges to group the countries for the purpose of constructing a histogram. We explore the relationship between income levels (economic development) and the probability of a country having a strategy using regression analysis later on in this paper.

> 11Recall that low income countries are not included in this analysis.

> 12Footnote 8 provides a link to WB’s website containing the list of countries in each income group and region.

8
- **SME share of employment** : this variable measures the size of the SME sector in an economy, which should be reflected in overall SME demand for finance. The expected sign of this variable, is however, difficult to determine _a priori_ . On the one hand, a large SME sector can contribute to diversify banks’ loan composition and therefore be associated with more credit supply; on the other hand, a large SME sector may be prevalent in developing economies with relatively underdeveloped and constrained financial markets that limit access to finance by SMEs.

- **FCS** : this is a dummy variable that is meant to capture FCS countries, defined as those with an FSI above 90. To check the robustness of our results, we also employ the World Bank Group classification of FCS countries. As shown, SMEs in FCS countries have generally lower access and usage of financial services than SMEs in non-FCS countries. This dummy variable is therefore expected to be negatively correlated with the SME financial inclusion index.

Additional variables ( Zi,t ) included in the augmented model are as follows:

# _Macroeconomic environment_

- **Income level** : this is a categorical variable to capture the level of economic development of a country. It takes value of 0 if a country is classified as low income according to the World Bank Group classification; value of 1 if a country is listed as middle income, and value of 2 if a country is high income. SME financial inclusion is expected to improve as countries develop so this variable is expected to be positively correlated with our dependent variable. We do not include GDP per capita to reduce risks of collinearity with the other variables in the model.

- **Informality** : this variable proxies the degree of informality in an economy as measured by the estimated size of the shadow economy (as a share of official GDP). Economies with higher informality are likely to impose tighter constraints on SME financial inclusion due to opacity and traceability issues, hence the sign of this variable is expected to be negative.

- **Trade openness** : this is proxied by the sum of exports and imports of goods and services measured as a share of GDP. A more open economy is expected to be more favorable to SME development and growth thus facilitating their access and usage of financial services. This variable is therefore expected to be positively correlated with the SME financial inclusion index.

- **Natural resources rents** : In some countries, earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings come in the form of economic rents, i.e. revenues above the cost of extracting the resources. Natural resources give rise to economic rents and as a result to a relatively large public sector that centralizes and controls them. Bank lending may then be

8
STCs submitted to the TBT Committee per annum more than quadrupled (from 17 to 76) in the period 2000-2011. 14 new STCs were raised in the SPS Committee in 2011 about half by developing countries. Some of these concerns were left unresolved and have led to cases going to WTOs dispute settlement although the number of cases in dispute settlement which specifically refer to SPS and TBT is relatively small (WTO 2012, p.111).

In light of the increased use of SPS and TBT measures, it has been argued that all of the measures imposed on these grounds could be considered as NTMs similar to quantitative restrictions and trade remedies (Gourdon and Nicita, 2012). If such a definition were used, then SPS and TBT measures would constitute the bulk of developed (and developing country) NTMs. On average countries apply TBT measures on approximately 30 of products and trade and SPS on 15 percent of products and trade. Taken together these measures would cover almost half of a typical country’s trade. And they are likely to be much more extensive than core non-tariff barriers such as quotas, trade remedies and non-automatic licenses.

But it would not be useful to lump together TBT and SPS measures together with other nontariff barriers for two reasons: first, it is quite clear that there are very legitimate reasons for introducing such measures for health and safety reasons; and when they are abused there is a process for seeking redress—even though this process poses some constraints for developing countries. Second, it is possible that the establishment of standards can actually benefit developing country exporters. A study of Chinese exports of agricultural products showed that the adoption of more stringent standards had a positive effect as the signaling of enhanced food safety and quality in foreign markets apparently surpassed compliance costs (Manzelsdorf, Perez and Wilson 2012). Similarly a recent study by Xiong and Beghin (2011) found that the predictions by Otsuki _et al._ of a dire effect of the introduction of EU regulations on aflatoxin content on West 57