rafmacalaba/datause-encoder-data huggingface dataset:
beliefs, perception of entrepreneurship as a male activity, social attitudes towards women and employment and family values are highlighted as important institutional influences on female entrepreneurship. We focus on institutional factors that may affect access to capital.

First we note that the literature tends to find that women have less access to capital. For example, in a study from Australia, Watson (2002) shows that women business owners have less start up capital, which explains their lower incomes and profits compared to men. Coleman (2007) found that among small business owners in the US, women were less likely to have a loan of any type (46.5% v. 58.9% of the men) and a significantly lower percentage had loans from banks (27.3% v. 39.3%). Similarly women were significantly less likely to have a line of credit (18.8% v. 32.2%). Finally Carter and Rosa (1998) estimated that in Britain, the average female entrepreneur uses a third less external finance than the average male entrepreneur.

_Is differential access to finance due to discrimination?_ Several studies suggest that raising capital is more difficult for women than men in the US (Brush, 1992: Carter and Cannon, 1992: Carter 2000) and Eastern Europe (Muraveyev et al., 2007), but it is not clear whether it is due to discrimination on the part of the banking sector or the result of other factors.

Some studies infer that discrimination by banks can be a factor. An interesting study by Fay and Williams (1993) tested for discrimination by sending out identical loan applications, whose sole difference was the gender of the hypothetical applicant. They found that the applications were significantly more likely to be declined or restricted if the name was that of a woman than a man. In their study using data from Business Environment and Enterprise Performance Survey (BEEPS) from Europe, Muravyev et al. (2007) find that female managed firms have a 5.4 percent lower probability of securing a bank loan than male managed firms. They also evaluate existence of financial constraints by looking at interest rates and find that female managed firms on average pay 0.6 percent higher interest rates than their male counterparts. Both these factors suggest discrimination against female entrepreneurs and the authors suggest that this discrimination is found to be higher in the least financially developed countries in the region. This is corroborated by Aidis et al. (2007), who - using original survey data from 6
participation of group members in the management of the system; and vii) low frequency of disputes among group members (Mukherji et al., 2009).

We assessed JCLIS projects when they were still quite new. 80 percent of the systems in our sample had been operating for less than two years, and one in four were less than a year old. Therefore, it is difficult for us to assess their performance on some of the indicators listed above or to predict their future success. In this section, we present our conjectures based on the insights from the large existing body of work on irrigation management transfer and from the data we gleaned from our survey of farmers and system operators and managers during semi-structured discussions with them.

Since solar pumps have very low operating costs, many groups in our sample were being lax in setting the irrigation fees and collecting them regularly from farmers. Three of the 50 groups had yet to set an hourly irrigation charge, and another nine had yet to start collecting fees from the farmers. Altogether, collection of irrigation fees had not started for 12 of the 44 solar powered systems in our sample. None of the water user groups or their operators maintained any written records of hours of irrigation service provided to different farmers and the fees collected from them.

Fifteen of the systems in our sample had experienced a breakdown, and in each of these cases water group members promptly arranged the necessary repairs. In 6 of the 15 cases, the machine was still under warranty, and therefore no payments were needed. In other cases, farmers pooled money to pay for the repair or used the irrigation fee they had collected. Members of water user groups that were not collecting any fees told us that they were not keen to start doing so and suggested that they will pool funds when there is a need for repair and maintenance. This _ad hoc_ approach to mobilize resources for repair and maintenance of systems may become hard to sustain with time as systems get older and need more frequent repairs. This system may also be more taxing for the poorer farmers who may not always have the necessary resources with them. Paying regular fees may be easier for them than finding large sums of money for major repairs or replacements.

More than 80 percent of beneficiaries in our sample reported that irrigation from JCLIS was reliable, affordable, fair, and adequate in the Rabi season. Very few of them used any other source of irrigation in the command area of the JCLIS. However, limited availability of water in the dug 27
# **1. Introduction**

Spending on goods and services such as education and health is linked to economic growth, higher social mobility and lower economic inequality (Barro 1996, Owen and Weil 1998, Alesina and La Ferrara 2005). However, that spending can produce outcomes only when the state can deliver these goods and services (Filmer, Hammer, and Pritchett 2000, Rajkumar and Swaroop 2008, Muralidharan, Niehaus, and Sukhtankar 2016). Otherwise, much of the money is lost to inefficiencies and/or corruption (Rotberg 2003, World Bank 2004, Bertrand et al. 2007, Rothstein and Stolle 2008, Arbache, Habyarimana, and Molini 2010). The desire to reduce the gap between spending and outcomes has generated considerable research into measuring a state’s ability to deliver goods and services, and steps that can be taken to improve the delivery (see World Bank 2016, Woolcock 2017). Our paper contributes to this literature using data collected by surveying 13,000 adults in India, using a new questionnaire that was designed to measure individual states’ capacity to deliver goods and services.

This paper is organized as follows: Section 2 discusses existing measures of state capacity, their shortcomings, and the need for a new measure. Section 3 analyzes access to government provided goods and services. Section 4 measures inequality in access to government provided goods and services. Section 5 analyzes the quality of service delivery and Section 6 concludes.

# **2. The Need to Measure State Capacity to Deliver Goods and Services**

Many of the current measures proxy a state’s capacity to deliver goods and services with broader definitions of state capacity (Besley and Persson, 2009). For example, state capacity is broadly defined as the ability of a state to collect taxes (Lieberman 2002, Persson 2008), exercise control within its borders and enforce domestic laws (McAdam, Tarrow, and Tilly 2001, Wang 2003), and deliver public goods and services to residents (Rotberg, 2003). However, the ability to tax, exercise 2
Among the change variables, the most noteworthy differences are in the years of education and in non-farm capital. Years of education rose in urban areas but fell in rural areas, in line with the overall deterioration of basic services observed for rural areas (Grootaert, 1994). These was a huge drop in the revenue from non-farm business in urban areas, exceeding in fact the drop in household expenditure, but in rural areas the average change was zero. Since total income in urban areas fell by even more, the share of non-farm income actually rose. In all likelihood, this reflects the drop in profitability of an average urban small enterprise due to the large number of entrants in the informal sector (as a result of both demographic pressure and the reduction of formal sector employment). The number of durable goods owned, and their value, also declined between 1987 and 1988 in urban areas, while in rural areas there was a small gain. One can hypothesize that some of the declines in urban areas were duress sales.

# **III.** Welfare Changes in **Urban Areas**

Table 2 presents the estimation results of equations (2) and (3) for urban areas. The first column shows the change in welfare in function of only the 1987 level of expenditure per capita. This explains 42% of the variance of the change. The negative coefficient (-0.45) suggests that there is a strong tendency towards the mean: every 1000 CFAF more/less of per capita expenditure in 1987 is associated with a negative/positive change of 450 CFAF between 1987 and 1988. Thus, the higher per capita expenditure is, the more likely the household is to experience a drop in welfare in the following year, and the lower per capita expenditure is, the higher the likelihood is for a rise in welfare. This suggests that there are large transitory components in the expenditure of most households, and this is of course consistent with the high mobility into and out of poverty which was observed in earlier panel analyses.

Skeptical readers may suggest that these are not transitory expenditure components but measurement errors. Clearly, this possibility can never be ruled out entirely in the absence of an independent "true" data source. However, several arguments support the interpretation that what is observed in the CILSS are genuine welfare changes. First and foremost, the average declines in per capita expenditure between 1987 and 1988 were, as we have seen, 15 % and 20%, respectively, in urban and rural areas. This correspQnds quite closely
tricity infrastructure stock _Gt_ , as a consequence of the relatively lower intensity of electricity infrastructure in services and in agricultural output production, shifts the structural transformation process towards these last two sectors. This effect can be observed, for example, in the bottom chart of Figure 4 that depicts the evolution of the sectoral consumption shares. We see that, in the absence of public infrastructure growth, the manufacturing share rises whereas the shares of the other two sectors shift down. The model predictions, in particular, imply that the consumption share rises 0.71 and 1.52 percentage points in agriculture and services, respectively, and declines 2.23 percentage points in manufacturing.

Second, the growth in the electricity infrastructure reduces fixed costs in manufacturing, but increases them in farming and services. This means that, ceteris paribus, the income share of the secondary sector will decrease as the public capital is accumulated as a less rapid increase in fixed costs in manufacturing increases the relative price in the other two sectors. Therefore, both supply-side effects that cause changes in relative prices push down the manufacturing share. This is precisely the qualitative effect that the IV estimates for electricity access in Tables 2 and 3 mainly capture, with the exception of agriculture. Hence, the electricity instrument tends to isolate the effect of the electricity-supply component on the sectoral production capacity and market-entry barriers.<sup>26</sup> Importantly, these findings reinforces the choice of the IV estimates made in the calibration section to assign values to the production-function and fixed-costs parameters.

A growing _Gt_ also contributes to sectoral reallocations through the larger demand for investment goods induced by the grid construction. In this instance, the manufacturing share is the one that gains, because 73 percent of the total investment is supplied by the secondary

> 26In Tables 2 and 3, the two electricity infrastructure proxies, electricity access and generation capacity, enter the regression separately, and electricity access is the only electricity proxy that is instrumented. Therefore, the access variable captures the supply-side channel, which benefits services, whereas the capacity variable captures the demand channel, which benefits manufacturing (as Table 2 suggests). In the simulation exercise, we combine them to get the _Gt_ measure. Estimation results using (and instrumenting) electricity access times generation capacity as a single regressor are qualitatively similar to the ones obtained for electricity access, and in line with the supply-side implications of the production-function estimates in Table 5. The estimated IV coefficients are all strongly significant and imply that agriculture is the sector that experiences the largest gain, followed by services and manufacturing.

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12 for a hospitalization – even for a specific condition – is more complex and depends on a person’s health status prior to hospitalization, the ascertainment of which is extremely hard, irrespective whether administrative or survey data are used, and on the interventions available and their cost-effectiveness. What can be done is to compare the (national) hospitalization rate to a reasonable benchmark, in which case 100 means the intervention rate is at or exceeds the benchmark. This leaves the possibility that different populations may have different needs for hospitalization because of, for example, different age structures and epidemiology. In addition, groups within the population, including different socioeconomic groups, may have a different need for hospitalization. No attempt is made in this paper to adjust for between- and within-population hospitalization differences for need.<sup>8</sup> In addition to capturing need, we want to relate receipt of interventions to a person’s ability-to-pay such that we can penalize countries who, for a given population mean, have lower coverage rates among the poor. This requires the use of household survey data. Assuming we have such data, we can adjust the population mean for inequality in intervention coverage between the poor and better off by switching from the population mean to the ‘achievement’ index (Wagstaff 2002). This is a weighted average of values across the population such that the poorest person is assigned a weight of two and the weights fall linearly

> 8 The existing literature suggests one option for adjusting for within-population differences in need, namely to replace actual hospitalization rates for each group by need-standardized rates, proxying need by self-reported health status (Wagstaff et al. 1991; Wagstaff and van Doorslaer 2000). An implicit assumption in this approach is that hospitalization is no more responsive to ‘need’ among the better-off than among the worse-off. Van de Poel et al. (2012) test and reject this assumption and propose a method to relax it; in their analysis of the Philippines WHS data, they conclude that the pro-poor need-justified inequality in inpatient care is underestimated when it is assumed that all subgroups have the same hospitalization-need relationship.

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group-affiliated. Overall, the statistics indicate that group-affiliated finns are more diversified than are independent firms. This is inconsistent with the substitution hypothesis which predicts less diversification of group-affiliated firms relative to independent firms.

Table 2 provides further comparison between group-affiliated firrns and independent finns using the pooled sample. In terms of segment number, the group-affiliated firrns have an average of 2.35 segments, about the same as that of the independent firms. Consistent with the country-by-country results, over 58 percent of the group-affiliated firms are multisegment, significantly higher than the 54 percent reported for the independent firms. Again, the evidence is not consistent with the substitution hypothesis arguing for a negative relation between group affiliation and firn-level diversification.

Table 2 also compares the structure of ultimate ownership between group-affiliated and independent firms. The mean level of ultimate owners' cash flow rights is 12 percent for affiliated firms, significantly lower than the average 22 percent cash-flow rights associated with the ultimate owners of independent firms. Similarly, the mean control level by the ultimate owners of group-affiliated firms is 16 percent, significantly lower than the average 24 percent control level associated with the ultimate owners of independent firms. For both types of firms, the mean cash flow rights level is lower than the control rights level.

There are also differences across ownership types. Of the group-affiliated firmns, 3.84 percent are controlled by widely-held corporations, 61.09 percent by widely-held financial institutions, 33.49 percent by families, 0.82 percent by the state, and 0.75 percent are widelyheld. Of the independent firms, the corresponding percentages are 22.56, 7.43, 46.91, 16.51, and 6.60. These statistics show that financial institutions dominate other types of ultimate owners in controlling group-affiliated firms. On the other hand, families dominate in controlling independent firms. Lastly, the mean excess value (before taking logs) of groupindustry has fewer than three single-segment furms even defined broadly as in the Campbell study, we use the 10
Gallup World Poll 2020.<sup>17</sup> Overall, the correlation with our data is strong, assuring us that, at least in this vein, our sample is not unrepresentative. The WVS and Gallup World Poll have a lower coverage globally and include fewer countries in Sub-Saharan Africa than the Gender Equality at Home survey on Facebook.<sup>18</sup> The WVS also does not include measures of community norms. However, the Gallup World Poll 2020 data captures both attitudes and norms.

We consider attitudes that address the male breadwinner-female caregiver theme in the WVS and a broad norm using Gallup World Poll 2020 data. We compare the attitudes on the male breadwinner norm with the WVS statement: “If a woman earns more money than her husband, it’s almost certain to cause problems” and the female caregiver norm with the WVS statement: “When a mother works for pay, the children suffer”; and whether women should be allowed to work outside of the home from the Gallup World Poll. In the comparison, we use attitudinal responses disaggregated by gender, and compute averages at the country-level. We standardize the averages and assign a rank to each country within our sample and split into quintiles (i.e. from most progressive=1 to least progressive=5). We compare how the countries differ in rank across the two surveys. For those countries where the difference in rank is positive this indicates that the Facebook sample in that country is potentially more conservative or stereotypical than a respondent in the WVS. Conversely, if the rank is negative this indicates that the sample in the Facebook population is more gender progressive or counter-stereotypical than the more representative WVS. In Appendix A, tables A4 and A5, we show the country level differences in quintile rank. The results vary depending on the norm in question and gender. For example, in Spain (ESP) men are equally gender progressive in both the Gender Equality at Home survey and the WVS (difference in rank is 0); whereas Spanish women are equally progressive in the male breadwinner norm (difference in rank is 0) but are ranked more progressive in our survey measure than the WVS (difference in rank 1). While in most countries there is little difference in the rank across the two surveys for the male breadwinner norm; for the female caregiver norm there are some discrepancies, e.g. Tunisia and Uzbekistan have a difference of 4. Using the Gallup World Poll we present comparisons in Table A6 and show that in the majority of countries there is a less than 10 percentage point difference with our survey measure.

> Specifically, we selected the dataset year that most closely aligned with our collection of the Gender at Home data in 2020. This data is referenced as Haerpfer et al. (2022)

> 17Gallup world data was subtracted at the country level using the Appendix of Bursztyn et al. (2023 _b_ )

> 18The overlap in country coverage is 68% between the WVS and the Gender Equality at Home survey; and approximately 50% with the Gallup World Poll 2020.

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**Table 4. Numbers of firms, products, and countries in estimating sample**

||2007|2008|2009|2010|2011|2012|
|---|---|---|---|---|---|---|
|Total number of importing firms|6,933|7,075|7,220|7,330|7,396|7,303|
|Total number of imported HS 6‐<br>digit products|4,003|4,265|4,021|3,966|3,967|3,912|
|Total number of origin countries|156|155|160|154|161|153|
|Total number of importing firm‐<br>HS 6‐digit product pairs|93,115|100,817|102,928|99,824|97,653|92,577|
|Total number of importing firm‐<br>origin country pairs|17,534|19,210|20,180|21,260|21,629|21,403|
|Total number of HS 6‐digit<br>products‐origin country pairs|25,915|29,266|29,328|30,100|30,228|29,161|
|Total number of importing firm‐|||||||
|HS 6‐digit products‐origin country<br>cells|108,887|120,640|124,660|123,929|121,854|116,255|

# **4.  Estimating Framework**

In this section we describe the IV strategy that we employ to link changes in the probability of inspection to contemporaneous changes in import value and in import composition. The outcomes of interest are measured at the level of an importing firm, HS 6-digit product, origin country, and year. While our raw data are more disaggregated, on the whole our interest is in annual outcomes (e.g., total import value in a year). Analysis at more disaggregated levels would only illustrate the impact of customs reforms on a very specific type of intensive margin, i.e., how does the value of a shipment depend on the probability of allocation to the red channel?<sup>33</sup> In Appendix B we conduct some exercises at much more disaggregated level in an effort to support our argument that the vector of fixed effects included in our specifications is effective in removing non-random variation in inspection behavior. Nonetheless our primary interest remains in outcomes at the firm-product-country level, measured over the period of one year.

In broad terms the sources of plausibly exogenous variation in physical inspection rates are a) random variation in inspection activity that arises because the automated risk management system allocates some shipments to inspection on a random basis, and b) year-to-year reductions in inspection probabilities that are common to all importing firm-HS 6-digit product-country cells, which are exogenous at the level of an individual observation. Our estimation strategy is

> 33 We do study this question along with other questions about the composition of trade in Section 6, but approach this by calculating the average value of shipments rather than the value of individual shipments.

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