rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 13: Country-specific public input constraints, firm growth and firm characteristics**

Notes: “Pos” and green indicates a positive coefficient; “Neg” and red indicates a negative coefficient.

*=significant at the 5% level; **=significant at the 1% level.  Standard errors are robust to heteroskedasticity.

|Country|Indicator|Size (log L)|Different?|New firm|Different?|Expanding L|Different?|Services|Different?|Foreign|Different?|Exporter|Different?|Importer|Different?|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|All|Electricity|-0.053**|n.a.|0.004|n.a.|0.081**|n.a.|-0.164**|n.a.|-0.089|n.a.|0.079*|n.a.|0.023|n.a.|
|All|Telecoms|0.041**|n.a.|0.092**|n.a.|0.060**|n.a.|0.142**|n.a.|-0.094|n.a.|-0.055|n.a.|0.146**|n.a.|
|All|Transport|0.018|n.a.|0.070*|n.a.|0.140**|n.a.|-0.057|n.a.|0.052|n.a.|-0.030|n.a.|0.270**|n.a.|
|All|Customs|0.101**|n.a.|0.077*|n.a.|0.112**|n.a.|-0.016|n.a.|0.028|n.a.|0.025|n.a.|0.294**|n.a.|
|All|Competit|0.017|n.a.|0.063|n.a.|0.040|n.a.|-0.111*|n.a.|0.047|n.a.|-0.172**|n.a.|0.126**|n.a.|
|All|AccessLand|-0.045**|n.a.|0.080*|n.a.|0.049*|n.a.|0.062|n.a.|0.000|n.a.|0.072*|n.a.|0.041|n.a.|
|All|TaxRates|0.058**|n.a.|-0.094**|n.a.|0.114**|n.a.|-0.061|n.a.|-0.184*|n.a.|-0.079*|n.a.|0.066|n.a.|
|All|TaxAdministration|0.082**|n.a.|-0.059|n.a.|0.170**|n.a.|0.003|n.a.|-0.143|n.a.|-0.051|n.a.|0.058|n.a.|
|All|BusLicensing|0.024*|n.a.|-0.031|n.a.|0.078**|n.a.|0.052|n.a.|0.023|n.a.|0.095**|n.a.|0.099**|n.a.|
|All|PoliticalInstability|0.011|n.a.|-0.008|n.a.|0.040|n.a.|0.120*|n.a.|0.221*|n.a.|0.323**|n.a.|0.067|n.a.|
|All|Corruption|0.038**|n.a.|-0.088*|n.a.|0.216**|n.a.|-0.032|n.a.|-0.050|n.a.|0.038|n.a.|0.163**|n.a.|
|All|Courts|0.049**|n.a.|-0.032|n.a.|0.098**|n.a.|0.129*|n.a.|0.116|n.a.|0.174**|n.a.|0.031|n.a.|
|All|AccessFinancing|0.000|n.a.|0.081*|n.a.|0.012|n.a.|-0.317**|n.a.|-0.308**|n.a.|-0.111**|n.a.|0.016|n.a.|
|All|LaborReg|0.084**|n.a.|-0.032|n.a.|0.054*|n.a.|-0.094*|n.a.|-0.123|n.a.|0.083**|n.a.|0.023|n.a.|
|All|InadEducLabor|0.045**|n.a.|0.016|n.a.|0.131**|n.a.|0.085|n.a.|-0.107|n.a.|-0.010|n.a.|0.029|n.a.|
|All|GovPolicyUnc|0.051**|n.a.|-0.079|n.a.|0.114**|n.a.|-0.020|n.a.|0.181|n.a.|-0.047|n.a.|0.145**|n.a.|
|All|MacroInstability|0.050**|n.a.|-0.046|n.a.|0.109**|n.a.|-0.013|n.a.|0.083|n.a.|0.086*|n.a.|0.204**|n.a.|
|All|bribe|0.010*|n.a.|-0.001|n.a.|0.035**|n.a.|-0.018|n.a.|-0.056*|n.a.|0.029*|n.a.|0.040**|n.a.|
|All|mng_time|0.327**|n.a.|0.446|n.a.|-0.252|n.a.|0.518|n.a.|-0.176|n.a.|1.221**|n.a.|0.070|n.a.|
|All|generator|0.131**|n.a.|-0.012|n.a.|0.020*|n.a.|0.114**|n.a.|-0.039|n.a.|0.111**|n.a.|0.071**|n.a.|
|All|insufficient_water|0.011**|n.a.|0.015|n.a.|0.035**|n.a.|0.085**|n.a.|0.002|n.a.|-0.008|n.a.|0.038**|n.a.|
|All|num_insp|2.568**|n.a.|0.099|n.a.|2.078**|n.a.|2.322|n.a.|1.496|n.a.|1.977|n.a.|2.848**|n.a.|
|All|days_customs|0.593**|n.a.|-0.492|n.a.|-0.495|n.a.|0.406|n.a.|-2.921**|n.a.|2.261**|n.a.|-1.465*|n.a.|
|All|m_power_out|-0.031**|n.a.|0.000|n.a.|0.052**|n.a.|-0.045**|n.a.|-0.045|n.a.|0.015|n.a.|0.027*|n.a.|

49
# **5 Results**

## **5.1 Completers vs non-completers with individual fixed effects**

We first discuss the main difference-in-differences results, with coefficients from the estimation of equation 1 on the firm-requested-course registrants presented in Table 5. We find a 0.023 percentage-point (or 0.046 standard deviation) increase in the probability of employment for course completers, on average, in the year following course completion. This effect can then be decomposed, across panels B and C, into effects emanating from employment at requesting firms versus employment outside requesting firms, respectively. We find that a small fraction of this effect comes from employment at requesting firms, while the majority comes from higher employment probability at non-requesting firms. In column 2, we condition the estimation just on the sample of firm-supplied registrants, and find that their overall employment effect (0.031) comes largely through increased employment at requesting firms (0.020).<sup>8</sup> We estimate sizable negative effects of course completion on employment among UB registrants (column 3), and small but significant positive effects among all other registrants who find employment at requesting firms (Panel B, column 4).

Event study estimates from the estimation of equation 2 are presented in Appendix Tables 4 to 5, with results plotted graphically in Figures 2 to 10, respectively. In the graphs, series for completers and non-completers correspond to equation 2 vectors _α_ and ( _α_ + _β_ ), respectively. The event study analysis provides some immediately apparent patterns regarding employment and participation in worker training in this context that would otherwise not be apparent from the main analysis. Figure 2 shows how the employment rate of program participants in the month in which they start their course is, for the overall sample, at its – lowest point in the preceding months, confirming that a substantial share more than 40%

> 8It is important to note that many of the registrants already worked for requesting firms prior to the training course, so this estimate is necessarily net of pre-course employment at these firms.

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a high tax revenue/high quality of public investment equilibrium. Given the alternative, such transfers are incentive-compatible as far as the government’s objectives are concerned.

- _Poverty trap:_ When stuck in a credibility trap and without sufficient resources to effectively deter itself from being corrupt, the government is then caught in a poverty trap, whereby no taxes are paid and no project gets funded.

Our findings hinge on two assumptions: (i) the government is unable to enforce full tax compliance and (ii) citizens are not atomistic when it comes to the payment of taxes. The first assumption finds ample empirical support (see, e.g., Bachas and Soto 2020, Lobel et al. 2021, and Londono-Velez and Avila-Mehecha 2021 for recent evidence on tax evasion). The non-atomicity assumption is necessary to avoid free-riding so that citizens pay taxes in the first place. It finds some support with evidence on “tax morale”, whereby citizens pay taxes based on social norms of compliance. Luttmer and Singhal (2014) provide examples from laboratory studies, natural experiments, and randomized control trials that suggest that tax morale, as opposed to tax enforcement, plays an important role in compliance decisions. Besley, Jensen, and Persson (2019) formalize this tension between extrinsic and intrinsic incentives to pay taxes. To be sure, tax compliance is also influenced by the availability of third-party information and investments in tax capacity (Pomeranz 2015) but these are under-supplied in resource-rich countries. Finally, in our model, the taxpayers or citizens need not be a set of non-atomistic individuals. Instead of cash transfers made to atomistic citizens, we could consider giving budgetary authority to the parliament (as in 17th Century England) or transferring resource revenues to an independent sovereign wealth fund. These institutions would play the role of the representative citizen to hold the government accountable by threatening to withhold resources. The independence of such institutions from the government is however not a given (as we have seen in the case of sovereign wealth funds [Frynas 2017]), so the choice of accountability-enforcing institution is not clear-cut.

Our paper relates to the literature on state capacity, in particular the capacity to raise taxes. Closest to our paper is Besley (2020) who links state capacity to social norms (or “civic culture”) and highlights reciprocity, whereby citizens’ moral obligation to pay taxes is 5
mostly by marketing costs, _δx_ . Since we want to separate the effect of prices from the effects of export marketing costs, we need to control for the price offered by the intermediaries in a district, _p_ (and not for the reported _p − δx_ , which varies at the household level). To do this, we approximate the price _p_ with the price faced by the farmer with the lowest possible marketing cost ( _x_ = 0 in the model), that is, the farmer with the highest observed unit value. For robustness against outliers, we use the 75th percentile of the distribution of household unit values in each district as a measure of _p_ . Finally, since we are pooling together different export crops (coffee, cotton, tea, passion fruits and pineapples), the export price index is a weighted average of the 75th percentile log unit values of each of these crops, with weights given by the average share of land allocated to each crop in the district.

Notice that export prices and the weights attached to them play another important function. As shown in Figure 3, farmers in different regions tend to specialize in different export crops. Since these crops sell at different prices, specialization in different export crops can lead to different poverty impacts. These regional differences in profitability across export crops can be controlled for with the weights used in the construction of the export price, that is, the shares of each export crop.

Even after controlling for all these confounding factors, the major econometric concern with regression (10) is the reverse causality between poverty and market density. Poverty is lower when marking costs are lower, and markets may develop in richer districts. We need instruments that are exogenous determinants of market density and that vary by district. The model of section 2 suggests that good candidates are the costs of intermediation activity, such as the out-of-district transportation cost from each district to international markets, represented in the model by _d_ and _D_ . Out-of-district transportation costs indicate how difficult it is for intermediaries to reach export destinations. It is a fundamental determinant of the profitability of intermediation activities in export crops, either in the form of intermediaries in the strict sense, market stalls, or plantations, and thus a determinant of market density, our endogenous variable.

In Uganda, a landlocked country, most international shipments must go through the capital, Kampala. We proxy the out-of-district transportation costs with district level data 15
of secondary school and below. In particular, both women and men with professional degrees have the highest rate of underreporting, with men having at least a marginally higher rate of underreporting than women in this category. For the least educated group with educational attainment of secondary school and below, female respondents far exceed their male counterparts in terms of underreporting domestic violence. This is also true for the education category of high school and diploma. There is no clear pattern in the case of physical harassment on public or private buses, but males with professional degrees have the highest rate of underreporting.

In terms of religion, respondents who identified themselves as Hindus have the highest rates of underreporting followed by Muslims and then Christians for domestic violence (Table 5). Females have higher rates of underreporting of domestic violence compared to males for all religions (Table 5), with the highest underreporting rates for Hindu women respondents followed by Muslim and Christian female respondents respectively. There is no clear pattern regarding this for physical harassment on public or private buses.

# **V. Discussion**

The incidence of lifetime domestic violence in Kerala, as measured by the list randomization method, is 15 percent, while it is only 5.6 percent from direct questioning, which indicates a 9.4 percentage point rate of underreporting for domestic violence. In terms of physical harassment on public/private buses, the analysis finds an incidence rate closer to 1 percent and an over-reporting rate of 1.8 percentage points. This finding is of particular interest, as the incidence of physical harassment on buses has not been estimated in the past (as mentioned in the literature review), nor have past studies used the list randomization method to study underreporting of gender-based violence. The difference between the results for domestic violence and physical harassment in public/private buses may be due to the fact that the former is perpetuated by intimate partners and relatives while the latter is perpetuated by strangers. Women may be more likely to report harassment by strangers than violence by people on whom they are economically dependent. Hence, this result is not surprising.

16
(2018) use electricity transmission and distribution (T&D) losses to measure the quality index for electricity infrastructure. However, T&D loss is not an indicator of electricity service quality; it simply represents energy lost in the transmission and distribution system; power outages and voltage fluctuations are the quality indicators for the electricity supply system. Power sector reliability is a critical factor to be considered to represent the quality of the power supply system. Calderon and Serven (2010) use waiting time for the installation of main telephone lines as a quality index for telecommunication infrastructure. This is also not the right quality indicator; instead of waiting for installation time, the study should have taken the frequency and duration of service disruptions. Studies that can identify appropriate quality indicators and put more effort into collecting data on those indicators are needed. Otherwise, the findings could be misleading. For example, findings of Kodongo & Ojah (2016) and Chakamera and Alagidede (2018) that suggest no relationship between infrastructure quality and economic growth in Sub-Saharan Africa could be a result of selecting wrong indicators for infrastructure quality.

# **_6.3 Marginal Returns of Infrastructure Investment_**

Some studies show that infrastructure development has an inverted U-shape relationship with economic growth. For example, Bougheas et al. (2000) find an inverted U-shaped relationship between infrastructural development and economic growth for the United States. The threshold infrastructure development level above which infrastructure investment has negative marginal returns will vary across countries depending on several factors, including climate, land area, and spatial distribution of inhabitants across the country. Wylie (1996) illustrates this with a finding that Canada needs a much higher level of infrastructure as compared to the United States due to its colder climate and highly scattered inhabitants across the country. The nature of the threshold in turn, affects how the marginal return to infrastructure investment changes as infrastructure grows toward the threshold. However, more studies needed to understand the peak infrastructure level, after which the marginal impacts of infrastructure on economic growth is declining. Does the threshold level vary across countries? What does historical experience or data demonstrate? Is it true that the relationship between infrastructure and economic growth in today’s developed countries was stronger while they were achieving the ‘developed’ status? Is this true for newly emerging economies – China, Brazil? What about the Eastern European and Former Soviet 30
percentage values for participants with odd-numbered IDs. In contrast, the complex table and simple table with 10 institutions were presented in peso amounts for participants with odd-numbered IDs and in percentage terms for participants with even-numbered IDs. The simplified KFS designed by us with CONDUSEF always displayed the total amount to be paid or earned in peso amount. This way, the same individual was presented with the total amount to be paid (credit products) or earned (savings products) in peso amounts or percentages depending on the format. Finally, a glossary explaining key financial terms was distributed in half of the sessions. Table 1 reports the number of participants who received the glossary and the messages or calls. Given these interventions, we use the following specification for Mexico: BestChoiceijk = αj + β1Simplifiedjk + β2Complexjk + β3Simple5jk + β4Simple10jk + β5AmountPesosijk + β6Glossaryik + β7LiveCallik + β8SMSik + β9LowProfileik + Xik′γ + εijk (2) where Simplifiedjk, Complexjk, Simple5jk and Simple10jk are dummies that take value 1 if participants received the respective treatment in round _j_ of session _k_ . The omitted treatment is again the one that uses marketing materials such as brochures. AmountPesosijk is a dummy variable that takes the value 1 if participant _i_ in round _j_ of session _k_ saw the total amount to be paid (credit product) or earned (savings product) displayed in pesos and 0 if in percentages. Glossaryik is a dummy indicating that participant _i_ was provided with a glossary of terms during session _k_ and 0 otherwise and LiveCallik / SMSik takes the value of 1 if participant _i_ received a call / SMS prior to session _k_ and 0 otherwise. We finally pool the data from both Peru and Mexico and run the following regression: In this pooled specification, we include country fixed effects and keep only the treatments that are common in both countries, namely the simplified format and brochures.

13
**Figure 8: District level adult female literacy rates and the district level sex ratio of children**

<!-- Start of picture text --> Correlation between literacy level and sex ratio<br>District Means for Pakistan<br>Faisalabad<br>Attock<br>Rahim Yar Khan<br>1.2 1.4 1.6 1.8 2<br>Sex Ratio<br>95% CI Fitted Data<br>PSLM 2004<br>.6<br>.4<br>.2<br>0<br>Fraction of adult females who can read -.2<br><!-- End of picture text --> 42
These results on maternal and child time allocation paint a picture of the learning environment that is very different in households with mothers who have some education. Of interest is that, while time spent directly on children’s educational needs does increase for mothers with some education, child study time increases even more dramatically. Mothers with some education create a space whereby their children are able to spend an extra hour and fifteen minutes each day studying and preparing for school.

# **Learning Outcomes**

To assess whether maternal education has an impact on learning outcomes, we matched children in our household survey to those who were tested in school through our study in Grade 3, eventually yielding a sample of 676 children for whom we have both test-scores and household survey data. The key econometric issue that this poses, in addition to that arising from the selection into maternal education, is that the children for whom we observe test-score data may systematically differ from children for whom test-score data are not available. This arises both because some children are not enrolled, but also because children may be absent on the day of the test (10 percent of all children in the relevant grade were not administered the test due to absenteeism). Therefore, IV specifications followed for other outcome data may be biased if such selection is not accounted for. Following Angrist (1995), the test-score equation is determined through a linear equation conditional on the existence of a test-score observation and a censoring equation indicating whether the test score is missing. Thus, although presence of a school is a valid instrument for maternal education, it is not a valid instrument in equation for selection into the test scores.

There are two potential solutions. One approach is to follow Heckman (1978). If we assume that errors are jointly normally distributed, homoskedastic and independent of the instrument, we obtain the familiar "Mills-ratio" as the relevant expectation function conditional on participation. This Mills ratio is then directly included in test score equation as the appropriate selection-correction. An alternative approach, proposed by Heckman and Robb (1986) and developed by Ahn and Powell (1993), uses the "controlfunction" approach, where we condition on the predicted probability in the test score equation. In essence, this method proposes to estimate the effect of maternal education by using pair-wise differences across children for whom the non-parametric probability of participation is very close. The approach is implemented 19
- 18 -

**Figure 15. Contract size and winning bid per km**

<!-- Start of picture text --> 7<br>ln( winning bid per km ) = 0.998 - 0.837 ln(road length)<br>6 R2 = 0.1693<br>5<br>4<br>3<br>2<br>1<br>0<br>0 10 20 30 40<br>Road length (km)<br>Winning bid per km (million Rs)<br><!-- End of picture text --> Efficiency in road contracting in Nepal may be favorably compared to other low-income countries, especially in Africa, where the average time required to award a contract after the bid opening ranges from 2-3 months to nearly a year (Table 2). In Nepal, the bid evaluation takes on average about 65 days. But it varies across districts, ranging from 30 days in Nawalparasi and Nuwakot to 100 days in Mahottari, Palpa and Rautahat (Figure 16). The difference may depend on technical conditions, such as the complexity of works, and the number of bids to be assessed. But the lengthy evaluation process is clearly a matter of concern, because it would inflate the procurement costs and reduce the transparency of the process.

**Table 2. Average time required to sign a road contract after the bid opening in low-income countries in Africa**

||Avg. time<br>(days)|
|---|---|
|Congo, Democratic Rep. of|71.2|
|Madagascar|88.8|
|Ghana|153.1|
|Zambia|187.9|
|Ethiopia|207.4|
|Mozambique|220.6|
|Tanzania|253.7|
|Malawi|261.5|
|Uganda|326.3|
|Kenya|348.0|

Source: Alexeeva, Padam, and Queiroz, 2008.
dimension of the availability of information and information quality is the information sharing that occurs between lenders through credit bureaus. Information sharing via credit bureaus has been shown to increase the competitiveness in the credit market (Vives, 1990), increase in the volume and efficiency of lending (Pagano and Jappelli, 1993) and lower default rates.

The empirical evidence overwhelming supports the positive impact of having credit bureaus. Jappelli and Pagano (2002) and <mark>Djankov, McLiesh, and Shleifer (2007) c</mark> ollect data on the existence and operation of <mark>credit bureaus around the world and find that bank lending is higher and credit risk is lower in countries where lenders share information. Brown, Jappelli, and Pagano (2009) use firm-level data in the transition countries of Eastern Europe and former Soviet Union to show that information sharing is associated with improved availability and lower cost of credit to firms, especially for more opaque firms and for firms in countries with weak legal environments.</mark> Most studies distinguish between private and public credit registries, which have different policy prescriptions for governments wanting to reduce information asymmetry problems in access to finance for firms. Credit bureaus may operate on the principle of reciprocity collecting and distributing the information supplied by its members (private credit bureaus) or may be managed by country central banks that mandate the reporting of data on borrowers (public credit registries). Jappelli and Pagano (2002) <mark>find that public intervention is more likely where private arrangements have not arisen spontaneously and creditor rights are poorly protected. Other studies point to the differing impact of private versus public credit registries. Love and Mylenko (2003) find that existence of private credit registries is associated with lower financing constraints and higher share of bank financing, while the existence of public credit registries is not. However, Djankov, McLiesh, and Shleifer (2007) find that public registries are associated with more private credit but only in poorer countries while private credit bureaus are associated with more private credit, in both a subsample of poorer countries and the full sample. Overall, the evidence seems to suggest that private credit bureaus are particularly important in facilitating access to credit although public credit registries fulfill that role in markets where private information providers have not stepped in due to cost considerations.</mark> 33