rafmacalaba/datause-encoder-data huggingface dataset:
|Adjusting or converting their<br>production or services|0.108|-0.035|0.330|2.029***|-0.111|0.967|-0.196|0.528|
|---|---|---|---|---|---|---|---|---|
||(0.104)|(0.078)|(0.472)|(0.583)|(0.333)|(0.737)|(0.304)|(0.539)|
|Started or increased online<br>business activity|-0.050|-0.269***|0.231|-0.927*|-0.540*|-0.832**|-0.603|-1.157|
||(0.131)|(0.086)|(0.441)|(0.515)|(0.316)|(0.383)|(0.397)|(0.757)|
|Started or increased delivery of<br>goods, services or carryout|-0.053|0.147**|0.065|0.483|0.338|0.712|-0.208|0.309|
||(0.065)|(0.071)|(0.367)|(0.485)|(0.280)|(0.483)|(0.516)|(0.688)|
|Started or increased remote work|0.228*|-0.031|-0.843*|-1.351*|-0.030|-0.077|0.012|0.050|
||(0.120)|(0.103)|(0.453)|(0.734)|(0.616)|(0.654)|(0.291)|(0.445)|
|Exporting directly (at least 10% of<br>sales)|-0.063|-0.096|-0.326|-0.132|-1.044|-0.718|0.275|1.905***|
||(0.075)|(0.100)|(0.517)|(0.667)|(0.671)|(0.850)|(0.734)|(0.711)|
|Constant|-1.171*|0.127|-2.333***|-2.514|0.642|-0.119|-0.958|-2.544***|
||(0.642)|(0.251)|(0.558)|(1.623)|(0.398)|(0.951)|(0.854)|(0.968)|
|Number of observations|630|262|551|223|641|264|630|261|
|r2_w|0.349|0.386|||||||
|r2_b|0.036|0.003|||||||
|r2_o|0.012|0.022|||||||
|rho|0.763|0.823|||||||

Notes: This table reports panel regressions with individual firm fixed effects for the El Salvador COV-ES data. Size information used as the interaction terms is taken from the total number of full-time equivalent workers in the baseline ES. Columns 3 through 8 report marginal effects from logistic regressions. Sampling weights from the ES are used. Standard errors are shown in parenthesis. All standard errors are clustered at survey strata, and *, **, *** denotes the significance level at 10%, 5%, and 1%, respectively. See Section 6 for more details.

53
|India:|- 39-<br>Rural- localities with population 5,000 or loss.<br>Economically active- no age cutoff; apparently includes<br>workers of all ages who worked the major part of the<br>previous year. Women's participation<br>-<br>15 percent<br>versus 51 percent for men. Unlike other countries, this<br>is calculated as a percentage of total rather than<br>working age population.|
|---|---|
|South Ko|rea:<br>Rural- unincorporated<br>rural areas (myeons). Implicitly<br>includes settlements<br>under 20,000. Economically active<br>-<br>all persons over 14. Women's participation rate-<br>33<br>percent versus 79 percent for men.|
|Taiwan:|<br>Rural- undefined. Breakdown here follows Shih (1983)<br>who defines rural as total population minus the five<br>largest cities and three metropolitan countries.<br>Effectively excludes all cities over 250,000 as well as<br>two suburban counties surrounding<br>Taipei. Economically<br>active- all persons over 15. Women's participation<br>-<br>33 percent versus 79 percent for men.|
|Thailand|:<br>Rural- sanitary districts and non-municipal areas i.e.,<br>all non-urban areas. Urban-<br>cities over 50,000; towns<br>over 10.000 and density over 3,000 per square kilometer<br>or which contain an administrative<br>seat; and communes<br>established as appropriate and without regard to size.<br>Economically active- all persons over 11. Women's<br>participation<br>-<br>65 percent versus 77 percent for men.|
|_Av_|This conception of the rural economic transformation<br>draws on a<br>wealth of antecedent views, most explicitly expressed by Johnston<br>and Kilby (1975), but also drawing on Anderson and Leiserson<br>(1980), Anthony et al. (1979) and Binswanger (1983), Byerlee and<br>Eicher (1974), Liedholm (1973), and Vyas and Nathai (1978).|
||African rural nonfarm entrepreneurs<br>commonly hire or apprentice 50<br>to 70 percent of their workers from outside the family (Aluko,<br>1972 and 1973; Malawi, 1980; Milimo and Fisseha, 1986; Mozambique,<br>1983; Rwanda, 1978; Tanzania, 1982; Wilcock and Chuta, 1982;<br>Williams and McClintock, 1981; all data reported in Haggblade,<br>Hazell and Brown, 1987, Table 13). In agriculture, nonfamily<br>labor usage averages closer to 15-20 percent of total farm<br>employment (Eicher and Baker, 1982; Byerlee, 1980; Anthony et al.,<br>1979; Cleave, 1974; Collier and Lal, 1986; Collier, Radwan and<br>Wargwe, 1986; Ghai and Radwan, 1983b; Natlon et al., 1979; Norman,<br>1972; Norman, Pryor and Gibbs, 1979; Oates, 1984; Robertson and<br>Hughs, 1978; Spencer and Byerlee, 1976; and Weinrich, 1975).<br>Combining these estimates with the labor force data in Table 1, we<br>estimate that about 20 percent of Africa's rural labor force is<br>channeled through labor markets (60 percent of nonfarm workersxa<br>15 percent employment share+15 percent of agricultural laborx<br>an 85 percent rural employment share-<br>22 percent of rural<br>employment). Further evidence indicates that exchange labor<br>rarely exceeds five percent of labor flows (Hill, 1977; ILO 1985b<br>and 1985c; Norman, Pryor and Gibbs, 1979; Oates, 1984; Weinrich,<br>1975).|
**Table A.6. E-commerce development and various categories of household expenditure per capita by region (Extended Model 3)**

|VARIABLES|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||All|Urban|Rural|East|Central|West|
|Total household expenditure|0.210***|0.151***|0.623***|0.207***|0.189|0.538***|
||(0.061)|(0.054)|(0.103)|(0.067)|(0.124)|(0.157)|
|Observations|10,171|4,740|5,431|4,049|3,185|3,163|
|Adjusted R square|0.366|0.345|0.395|0.374|0.347|0.338|
|Cosmetics and beauty|0.328***|0.256**|0.845***|0.387***|0.221|0.879***|
||(0.110)|(0.107)|(0.226)|(0.128)|(0.165)|(0.320)|
|Observations|10,069|4,695|5,374|4,006|3,157|2,906|
|Adjusted R square|0.349|0.376|0.326|0.338|0.362|0.367|
|Food|0.268***|0.205***|0.718***|0.280***|0.218*|0.444**|
||(0.059)|(0.052)|(0.124)|(0.070)|(0.113)|(0.218)|
|Observations|10,037|4,673|5,364|3,982|3,155|2,900|
|Adjusted R square|0.358|0.360|0.367|0.373|0.337|0.384|
|Of which: Food at home|0.289***|0.245***|0.639***|0.289***|0.240**|0.362*|
||(0.063)|(0.055)|(0.165)|(0.086)|(0.107)|(0.214)|
|Observations|10,034|4,671|5,363|3,980|3,154|2,900|
|Adjusted R square|0.399|0.416|0.390|0.340|0.410|0.470|
|Of which: Dining out|0.330**|0.171|1.192***|0.207|0.444*|1.092**|
||(0.134)|(0.127)|(0.266)|(0.151)|(0.241)|(0.493)|
|Observations|10,176|4,742|5,434|4,052|3,187|2,937|
|Adjusted R square|0.337|0.328|0.370|0.326|0.333|0.362|
|Clothes|0.137|0.079|0.513***|0.128|0.155|0.304|
||(0.087)|(0.082)|(0.169)|(0.115)|(0.131)|(0.254)|
|Observations|9,924|4,603|5,321|3,926|3,104|2,894|
|Adjusted R square|0.338|0.359|0.327|0.346|0.359|0.301|
|Utilities|0.130***|0.130***|0.191|0.174***|0.037|0.408|
||(0.034)|(0.037)|(0.141)|(0.041)|(0.042)|(0.307)|
|Observations|9,972|4,623|5,349|3,979|3,125|2,868|
|Adjusted R square|0.305|0.302|0.311|0.313|0.303|0.319|
|Communications|0.156***|0.117***|0.481***|0.126**|0.175**|0.334|
||(0.041)|(0.034)|(0.080)|(0.049)|(0.066)|(0.200)|
|Observations|9,868|4,582|5,286|3,915|3,108|2,845|
|Adjusted R square|0.348|0.373|0.338|0.322|0.407|0.311|
|Local transport|0.292***|0.235***|0.527***|0.223**|0.319***|0.137|
||(0.075)|(0.068)|(0.160)|(0.102)|(0.090)|(0.289)|
|Observations|9,812|4,546|5,266|3,886|3,087|2,839|
|Adjusted R square|0.319|0.335|0.309|0.305|0.335|0.327|
|Travel|0.579***|0.395***|1.270***|0.518**|0.489***|1.697***|
||(0.164)|(0.146)|(0.200)|(0.239)|(0.153)|(0.484)|

·· 39
Table 2. Descriptive Statistics of Covariates used in the Pooled OLS Models

|**Variable**|**Obs**|**Mean**|**Sd. D.**|**Min**|**Max**|
|---|---|---|---|---|---|
|Age of respondent|1436|42.134|13.159|20|90|
|Sex of respondent 1 if male,0 if female|1436|0.880|0.325|0|1|
|Marital of respondent 1 if married,0 otherwise|1440|0.900|0.300|0|1|
|Education- 1 if illiterate,0 if literate|1440|0.383|0.486|0|1|
|FamilySize in adult equivalent|1440|4.888|1.859|0|10.88|
|Religion -1 if Christian,0 Muslim|1436|0.710|0.454|0|1|
|Livestock in tropical livestock units(TLU)|1424|5.077|3.711|0|26.23|
|Children under 15|1316|3.036|1.548|1|11|
|Walking Distance from household to nearest road<br>(two-way)in minutes|1436|63.214|105.565|0|840|
|Participation in controlled cookingtest,1 ifyes,0 if no|1440|0.300|0.458|0|1|
|Risk coefficient|1420|3.815|1.212|0.08|6.58|
|Average temperature in<sup>0</sup>C|1409|24.982|3.296|12|32.5|
|Average number of injeras baked at a time|1436|19.721|10.292|0|55|
|Use the of improved stove for any other purpose other<br>than baking1 ifyes,0 if No|1440|0.333|0.472|0|1|
|The averagequantityof flour in kgusedper cooking|1428|4.677|2.375|0.75|15|
|Type of flour, teff=1,mixed or no teff=0|1440|0.156|0.363|0|1|
|Place of stove installed-1 if inside the house, 0<br>separate kitchen|1432|0.360|0.480|0|1|
|Main fuel used for baking- 1 if fuel wood,0 otherwise|1440|0.747|0.435|0|1|

In these models we adjust for key household level variables that may confound our interpretation of the treatment effects. These variables are given in Table 2 above. Most variables are self-explanatory, except the risk coefficient. Risk preferences are measured by eliciting participants’ preferences between lotteries and a list of sure amounts between the low and high amounts of the lottery. The prize to be won at the lottery was fixed at 40 Birr and the low amount at 0, with the sure amounts increasing from 0 Birr to 40 Birr in steps of 1 Birr.

The point at which a participant switched from preferring the lottery to preferring the sure amount of money was encoded as the certainty equivalent for the participant, i.e. the amount for which the participant is indifferent between the sure amount and playing the lottery. There were in total seven such choice tasks, with the probability of winning the prize ranging from 1/8 to 7/8 and the tasks administered in random order. The measure used in this paper is simply the average

**16 |** P a g e
India and discussions in Pakistan about the National Assessment and Educational System), a uniform and easily accessible methodology would allow both official and unofficial bodies to produce consistent data over time and regions.

A systematic assessment would enable comparisons across households, children, schools and geographical locations. The findings from Pakistan show large differences across government schools in the same village. Typically studies test children from only one school in every village. While this is valid when there are a small number of schools, administrative data on schoolsupply in Pakistan suggest that even in rural Sindh, a province with one of the worst educational outcomes, there are close to _six_ government schools in every village. To enable meaningful comparisons over time, we suggest that periodic tests satisfy three requirements.

- First, design the test to be norm, rather than criterion-based (that is, the test is informative about where children are on an overall scale, rather than designed to check whether children have achieved one type of competency). One problem with tests in low-income countries is that they are designed around a curriculum suitable for the grade-level the child is in. Typically, children are three to four grade-levels below the curriculum. Consequently, tests are relatively uninformative about what children actually know. Norm based tests, with a list of questions available for test-designers, allow meaningful comparisons of what children know even when they perform below their grade-levels.

- Second, collect basic child characteristics along with the test. At the minimum, data on parental education, some indicator of wealth (children in third grade were able to accurately answer questions about household assets such as radios or televisions), child gender and age should be available to researchers so that gaps across these observable dimensions can be monitored carefully.

- Finally, allow for variations across districts _and_ across schools in the same village in the sampling strategy. Our findings suggest it is necessary to include at least two to three schools in every village, two to three villages in every district and a large number of districts. The Pakistan study is the first that tests children in multiple public and private schools within the same village, but suffers from the small number of study-districts; conversely, tests in India assess learning across villages but seldom sample more than one school in every village.

On a substantive note, large differences across schools are consistent with two (broad) explana-

- 24 -
Along these lines we approach our definition of social cohesion as an aptitude. One encounters in the literature a great number of different measures, both direct and indirect. Among the direct measures are: Measures on memberships rates of organizations and participation in organizations: Social relations have been measured in developing countries by Deepa Narayan and her collaborators (e.g., Narayan and Pritchett, 1999), but mostly on a micro (community) scale. At that level they are shown to be significant predictors of an aptitude for cooperation. Robert Putnam's (1993) important work uses membership of organizations as a measure of social cohesion (or more accurately, what he calls "social capital.") There are sharp differences, however, between his assessment of the US and that of many European countries, where indeed social cohesion went up as measured in this way. Is this variance in "social cohesion"-as measured by a richness of participatory processes in organizations-related to strong variance in social and economic policy reform? I do not think so. Hence I question whether Putnam's measure is that relevant for social cohesion as I have defined it. The results presented by Steve Knack (2000) seem to support this. He finds that the trust variable does contribute to the explanation of economic growth, but the membership of organization variable is not influencing growth in a statistical significant way. For reasons and because of the relative small available sample, we will not use the membership variable.

# - Measures on trust

A typical measure on trust (from the World Value Survey) is the aggregate of the answers to the question "do you think people can be trusted?" for a random sample of respondents.

There are cross-country measures of "trust" (see Knack and Keefer 1997). The new surveys being conducted around the world, including OECD countries such as Australia and members of the European Union, promise to yield significant new insights, and will allow us to address these issues with much greater confidence. Work in the transition economies is in its infancy, though some early promising work is starting to appear (e.g. Rose, 1998).

12
is some such variation. Of the 8 villages that received such a new connection between January 2007 and June 2009, two enjoyed an operational connection throughout a full season for the first time in _kharif_ 2007. Another village followed starting in _rabi_ 2008, two more in _kharif_ 2008, and three in _rabi_ 2009. This clustering (on the _tj −_ 1) will make the standard error of the estimate of _βr_ correspondingly that much larger. More importantly, the variation in the date of first provision introduces a second problem: it could be that changes in the unobserved, time-varying components in (1) are correlated with such timing. By assumption, _wt_ is common to all villages; but a shock to a specific village might affect its chances of getting a road over the interval in question.

The ninth village’s road became fully operational only in October 2009, and the household data collected therein followed the pattern for those villages with no allweather road in that _kharif_ season. This village is, however, included in the ‘before and after’ comparisons arising from the focus group interviews, which were conducted in September 2010, by which time the respondents would have been able to form a more reliable – or at least settled – view of the changes wrought by their new road. A careful inspection of the household data also revealed that these villagers had been making unauthorised use of the road before its official completion, which led us to classify it as having an all-weather connection for the purposes of cross-section analysis in Sections 3 and 5.

To close this section, it should be remarked that the great majority of these villages are comprised of a collection of hamlets, scattered in varying degrees. Even within a village, therefore, the distance from the house or field to a passable road or track – or even the village primary school – can vary quite substantially. Such variation makes it unlikely that, for many of the variates that concern us here, the intra-cluster correlation coefficient is very high.

# **3 Trade**

Transport costs matter in most spheres of economic activity, but they are especially burdensome to remote rural communities that lack all-weather roads. As producers, farming families must then pay stiffer prices for inputs like chemical fertilizers, certified seeds and diesel fuel, and they obtain correspondingly lower net prices for their marketed surpluses, whether they sell at the farm gate or organize transport to the market of their choice. The gross prices farmers pay for their inputs and the net prices they receive for their outputs also depend on the structure of competition in the markets 8
We shall examine how the effect of disclosure depends on the quality of a country’s institutional environment. To this end, we rely on an index from the Heritage Foundation: property rights ( _PropertyRights_ ). There is a wide variation in _PropertyRights_ across our sample countries (see Panel A, Table 1).

As the basic macroeconomic environment also influences firm performance, we control for key macro indicators including GDP, GDP growth rate, GDP per capita, and inflation (as in Beck Demirgüç-Kunt and Maksimovic, 2005; Knack and Xu, 2015). We in addition control for country, industry, and year fixed effects in all of our multivariate regressions. The country dummy holds constant all country-specific factors such as geography, culture, and the basic legal system. The inclusion of industry and year dummies further holds constant all industry-specific heterogeneity and worldwide common shocks. We thus have pushed quite far in reducing the extent of omitted variable bias.

# **C. Univariate Tests**

We first present univariate test results for our key variables between audited and unaudited firms. These two types of firms exhibit a similar level of corruption obstacles and similar sales growth rates. Relative to unaudited firms, audited ones face a significantly lower level of financial obstacles; they are larger, have been in business longer, and their top managers are more experienced; they are more likely to have foreign or government ownership stakes, to be exporters, and are less likely to have to compete with competitors in the informal sector. Thus, audited and unaudited firms differ greatly in basic characteristics, and it is important to control for such key characteristics.

# **D. Determinants of Audit**

11
# **<<B>>** _Other Explanatory Variables (X)_

Hicke, Lobell, and Asner (2004) note that NPP can change in response to shifts in types of crops, changes in crop management practices (for example, fertilization, irrigation, pest management), and climate-related factors. In this regard, estimating net changes in NPP due to changes in crop management as a result of dam construction is the goal of our exercise, although the results may need to be interpreted in a broader sense if irrigation benefits result in more productive pest management and fertilization. In contrast, failing to take account of crop type shifts may result in overestimating the net benefits of dams in using NPP as a proxy of cropland productivity if, as a result of dam construction, farmers were to switch to crops that benefit more from irrigation. Unfortunately, we know of no time-varying data on crop types at the basin level that would allow us to control for such changes.

In terms of controlling for climatic factors, we use weather data extracted from the CRU TS 2.1 data set (Mitchell and Jones 2005), which provide monthly precipitation and temperature measures at the 0.5 degree level over the entire 20th century. We use these to derive two specific annual river-basin-wide measures of climate, namely, precipitation and reference evapotranspiration. To capture the effect of precipitation, we calculate the local standardized precipitation index, which has been argued to be particularly good at capturing the cumulative effect of reduced rainfall over time in a chosen locality.<sup>11</sup> To capture the effect of temperature on cropland productivity, we calculate reference evapotranspiration, which measures the evaporative demand of the air within a basin, by following Hargreaves and Samani (1985).

24
opinions. Many of these problems are obvious, but some of their manifestations are not. For example, a simple "yes/no" question can accidentally be manipulative or suggest to the respondent that there is a correct response by not including all possible responses in the question itself. A question that simply asks, "Do you do x?" may make the respondent more likely to answer "yes" since the alternative was not presented. One possibility is to ask instead, "Do you do x, or not?" (Fowler 1995). An induced response is more of a concern when one answer to a question is more socially acceptable than another. We discuss issues in asking sensitive questions below.

Asking multipurpose questions-questions that ask about more than one issue or along more than one dimension-turns out to be unfortunately simple and common. For example, the World Business Environment Survey (World Bank 1998) asks respondents to "rate the overall quality, integrity, and efficiency of services delivered by the following public agencies or services." Although quality, integrity, and efficiency are probably highly correlated, they are three separate issues and should be asked separately. Sometimes, however, the problem is not so obvious. "Agree/disagree" questions, for example, usually ask the respondent to rate how much she agrees or disagrees with a statement. The survey asks the respondent whether she "strongly agrees, agrees, disagrees, or strongly disagrees." The problem with this scale is that it combines an emotional component ("strongly") with the more important cognitive component. Instead, the survey should ask whether the respondent "completely agrees, generally agrees, generally disagrees, or completely disagrees" (Fowler 1995).

Careful survey writing can minimize problems of the sort described above. Other problems are more complicated. Small differences in question wording, for example, can generate large differences in responses. A particularly well-known example is from a 1941 experiment in which matched samples of respondents were asked one of two questions (Sudman, Bradburn, and Schwarz 1996). The first group was asked whether they thought the United States "should allow public speeches against Democracy," while the second group was asked whether they thought the United States "should forbid speeches against Democracy." Among the first group, 21 percent favored free speech. Among the second group, 39 percent favored free speech. Either question may be useful in tracking changes in perceptions, as long as the same question is asked consistently across people and over time. That is, whichever question is asked, the exact 25