rafmacalaba/datause-encoder-data huggingface dataset:
|`(13.184)`|`(13.130)`|`(13.187) (14.812) (16.468)`|
|---|---|---|
|`treat3round4 39.790***`|`39.923***`|`39.862*** 44.774*** 51.440***`|
|`_`<br>`(13.212)`|<br>`(13.158)`|<br>`(13.125) (13.861) (15.883)`|
|`treat4_round4 32.235**`|`30.928**`|`30.764** 34.240** 34.892***`|
|`(13.084)`|`(12.691)`|`(12.676) (13.524) (11.953)`|
|`treat5_round4 47.875***`|`48.539***`|`48.382*** 50.344*** 49.817***`|
|`(14.058)`|`(14.300)`|`(14.233) (14.553) (14.393)`|
|`treat6_round4 45.796***`|`45.992***`|`44.833*** 57.601*** 72.253***`|
|`(13.832)`|`(13.800)`|`(13.888) (16.446) (17.634)`|
|`age_resp`|`-0.047`|`-0.054 -0.079 -0.017`|
||`(0.050)`|`(0.050) (0.055) (0.048)`|
|`sex_res`|`-6.525**`|`-7.069** -7.951*** -9.717***`|
||`(2.697)`|`(2.868) (2.840) (2.968)`|
|`Education`|`1.810`|`1.475 -0.318 -2.208`|
||`(1.782)`|`(1.767) (1.704) (1.510)`|
|`Marital`|`4.319*`|`4.222* 5.885** 5.056**`|
||`(2.335)`|`(2.344) (2.460) (2.410)`|
|`Ade_TOT_HH`|`0.637`|`0.713 -0.034 -0.095`|
||`(0.669)`|`(0.653) (0.719) (0.688)`|
|`religion_dummy`|`5.309**`|`5.279** -0.478 -1.460`|
||`(2.348)`|`(2.327) (1.847) (2.917)`|
|`Livestock_Tot_HH`|`0.453`|`0.444 0.169 0.488`|
||`(0.296)`|`(0.296) (0.302) (0.331)`|
|`Children_15`|`-0.139`|`-0.200 0.055 -0.234`|
||`(0.595)`|`(0.618) (0.608) (0.558)`|
|`Dist_road`|`0.018**`|`0.019** 0.016* 0.025**`|
||`(0.008)`|`(0.008) (0.009) (0.012)`|
|`particpation`||`-2.159 -2.680 -2.417`|
|||`(2.150) (2.006) (1.805)`|
|`av_equivalent`||`0.271 0.329 0.689`|
|||`(0.785) (0.780) (0.658)`|
|`Average_temp`||`-0.652 -2.176*`|
|||`(0.509) (1.248)`|
|`Injera_no`||`0.347*** -0.052`|
|||`(0.117) (0.103)`|
|`Stove_use`||`4.887* 1.972`|
|||`(2.831) (2.605)`|
|`Qunatity_flour`||`0.775 0.615`|
|||`(0.475) (0.405)`|
|`Flour_kind2`||`0.894 -2.833`|
|||`(2.631) (2.603)`|
|`Stove_install`||`0.886 2.250`|
|||`(1.778) (1.762)`|
|`Main_fuel`||`-7.118*** 3.681`|
|||`(2.127) (3.105)`|
|`Constant -30.597**`|`-37.404***`|`-36.830***-24.592** 4.142`|
|`(13.079)`|`(13.421)`|`(13.037) (9.948) (26.065)`|
|`N 1139`|`1115`|`1103 1079 1079`|

Note: *** = Significant at 1%, ** = Significant at 5%, * = Significant at 10%, District dummies are included in model 5 but are not reported for the sake of economizing space.

The results for the six sets of hypotheses presented in Appendix B are briefly discussed below. The first set of hypotheses focus on the fuel saving implications of paying for the stove

**29 |** P a g e
- 42 -

# Table 16: Annual Percentage Ctiange in Head-count Ratio for Rural Poor Growth.lnequality and Total Effects

|States||73-74 t|o 77-78|77-78|to 83||83 to|86-87||
|---|---|---|---|---|---|---|---|---|---|
||Growth|'Ineq|Total|Growth|'Ineq|Total|Growth|'Ineq|Total|
|ANDHRA PRADES|-5.9|-0.3|-5.3|-6.2|0.3|-6.2|-5.3|2.9|-2.4|
|ASSAM|2.5|-0.1|3.2|-7.2|0.3|-6.3|-3.5|1.1|-1.8|
|BIHAR|-0.4|-0.4|-0.8|0.2|0|0.2|-6.9|-0.1|-6.7|
|GUJRAT|-5.7|1.8|-2.8|-5.2|-1.2|-7.4|-1.6|4|2.8|
|Haryana|-1.9|-2.2|-5|-4.4|-1.2|-6.9|-7.2|8.2|2.8|
|Jammu-Kashmir|-2.3|-0.3|-3.8|-9.6|0.6|-10.2|-10.8|8.1|1.6|
|KARNATAKA|-3.7|0.8|-2.7|-3.4|-1.1|-4.8|1.5|-0.4|0.6|
|KERALA|-5.3|0.6|-3.6|-3.9|-0.2|-4.8|-4.8|0.4|-3.8|
|MADHYA PRADES|-1.7|1.4|-0.3|-2.8|-0.8|-4.1|-2.9|1.9|-1|
|MAHARASHTRA|1.2|0.6|2.1|-5.8|0|-5.8|-1.4|0.5|-0.7|
|ORISSA|-2.4|0.8|-1.3|-0.7|-0.4|-4.4|-2.2|5.8|-0.8|
|Punjab|-15.8|3.7|-10.8|0.1|-3.2|-3.2|-2.1|1.8|-0.2|
|RAJISTHAN|-7.4|1.9|-4.5|0|1.4|-1.3|0|-3.6|-3.6|
|TAMIL NADU|-1.6|1.5|0|-1.2|-0.1|-1.5|-4.4|-1.8|-6.1|
|UTTAR PRADESH|-6.7|2|-3.8|-0.2|-0.7|-0.8|-8.2|1.3|-6.6|
|WEST BENGAL|-1.6|0.1|-1.5|-1.3|-0.3|-1.7|-5.7|-0.7|-6.8|
|ALL INDIA|-3|0.8|-1.8|-2.2|-0.3|-2.9|-4.6|0.8|-3.9|

Table 17 :Annual Percentage Change in Head Count Ratio for Rural Ultra Poor:Growth.lnequality and Total Effects

|States<br>|73-74 t<br>|_______<br>Growth|o 77-78<br>______<br>'Ineq|<br>_<br>Total|77-78 t<br>Growth|o 83<br>'Ineq|Total|83 to 86<br>Growth|-87<br>'Ineq|Total|
|---|---|---|---|---|---|---|---|---|---|
|ANDHRA PRADES|-8|1.8|-5.6|-8.3|-0.3|-9.5|-7|3.5|-3.9|
|ASSAM|6.7|-1.6|4.1|-11.6|1.7|-9.8|-5.7|5.6|1.3|
|BIHAR|-0.6|-0.9|-1.4|0.3|0.1|0.5|-10.5|0.3|-9.1|
|GUJRAT|-8.3|6.5|-0.4|-7|-4.2|-14.2|-2.3|17.3|15.8|
|Haryana|-3.8|-1.3|-7.1|-6.4|-2|-8.9|-10.6|9.7|-1.7|
|Jammu-Kashmir|-4.4|-1.7|-7.2|-15.4|-0.8|-15.6|-15.4|19.3|8.5|
|KARNATAKA|-6.2|1.8|-3.3|-5.3|-1.4|-6.1|1.5|-3|-1|
|KERALA|-7.8|2.7|-3.7|-5.9|-1.5|-8.6|-7|0.3|-6.7|
|MADHYA PRADES|-3|2|-0.7|-4.6|-1.8|-7.1|-4.5|2.1|-2.6|
|MAHARASHTRA|2.6|2|4.2|-8.8|0|-9|-2.4|2.9|0.5|
|ORISSA|-3.7|1|-2.8|-1.1|-1.6|-7.6|-2.8|10.4|-0.7|
|Punjab|-29.5|5.7|-14.5|0|-4.1|-4.1|-2.4|-2.9|-5.4|
|RAJISTHAN|-8.9|4.7|-3.2|0|2.1|-1.5|0|-7.5|-7.5|
|TAMIL NADU|-2.4|4.2|2|-1.8|-0.1|-1.9|-7|-2.4|-8.6|
|UTTAR PRADESH|-12.6|3.9|-5.6|-0.2|-0.7|-0.9|-11.6|2.1|-9.5|
|WEST BENGAL|-2.6|0.4|-2.2|-2.3|-0.7|-3.1|-7.8|-3.3|-13.9|
|ALL INDIA|-4.4|1.9|-2|-3.1|-0.7|-4.4|-6.8|1.3|-6|
conditional on being more than 11 hours away, by applying the inverse distance weight. This ensures that within the set of states beyond 11 hours, those relatively closer to the state in question have a greater weight.<sup>21</sup> other oth We then replace the _pjst_ in equation (2) with pjst to obtain the instrument CIst . We also do the same for _PI_ . Note that the rest of equation (2) is unchanged, as the goods _j_ are the same in equations (5) and (2) since the price data come from the same source (Nigeria Bureau of Statistics). That is, of course, the case for both the narrow and the broad matching of goods between the price and the survey data. It is also the case for the production indices defined in equation (3).

Following Cameron and Trivedi (2013), we use these instruments to extract the endogenous component of _CI_ and _PI_ through the first stage regressions. We use the instruments in separate regressions: oth oth and retrieve the respective estimated residual components of the price indices μsrt and νsrt from intl intl equations (7) and (8) or μsrt and νsrt from equations (7’) and (8’). These should contain the endogenous component of _CIsrt_ (Cameron and Trivedi, 2013). We then add these endogenous components of _CI_ and _PI_ to equation (4), which becomes (when using the domestic price indices of faraway states as instruments): The coefficients of CI and PI should not suffer from endogenity bias as the residuals from the first stage will purge the endogenous component of the price indices. This formulation ensures the computation of consistent standard errors (Cameron and Trivedi, 2013). The signs of the residuals (not reported in the tables but available upon request) confirm the direction of the endogeneity bias, that is negative for _CI_ and positive for _PI_ . In other words, by reducing the demand for products, conflict reduces the prices of consumed goods and thus it generates a spurious negative correlation between _CI_ and conflict. The opposite is true for _PI_ . Therefore in both cases the endogeneity biases the _CI_ and _PI_ coefficient towards zero.

As it turns out, the international price indices have a relatively weak predictive power for _CI_ and _PI_ because of the different composition of the items’ basket and the limited transmission of

> 21 The results do not change without weights. We also experiment with different distance thresholds, i.e. 3, 4, 6 and 7 hours obtaining similar results (results available upon request).

17
web-based management information system, so that the _cogestores_ are able to update profiles during the counseling. Originally the whole intervention, from the co-responsibility agreement to the moment families exit the service, was expected to last for a period of no longer than five years. Instead, the accompaniment is continuous until a family leaves poverty, as indicated by a multi-dimensional poverty measure and an income-based measure of extreme poverty.<sup>3</sup> As a complement to the family counseling, the community counseling is intended to foster interaction between families with common challenges. At these sessions, _Red Unidos_ provides information regarding the 45 welfare conditions that ought to be achieved (Table 4). Families are able to share their experiences concerning their family plans and achievements. Additionally, the community sessions allow families to make joint decisions towards collective actions that contribute to the accomplishment of any of the welfare conditions.

2. A **supply of social services component** . This component seeks to coordinate the preferential access of beneficiaries to social services (national and regional) by providing information and technical assistance to both families and regional authorities, and, at the national level, by coordinating the processes of selection of beneficiaries. This component has shown some challenges and results have shown strong context dependence: for example, in many cases, the information provided by local authorities about available services is not accurate (Acción Social 2008; World Bank 2013).

3. A **social private investment component** . This component is designed to connect beneficiaries with the private sector and social organizations like NGOs, academic institutions and private firms able to provide services benefitting _Red Unidos_ beneficiaries. _Red Unidos_ partners with private firms to support the profiling and family counseling processes in three different forms: philanthropy, social responsibility projects, and inclusive businesses.

4. A **social innovation component** . This fourth component is intended to promote national and international networks to develop knowledge and new practices for poverty reduction strategies (World Bank 2013). The ultimate objective of the

> 3 A household is considered to be multidimensional poor if it does not comply with 5 or more of the 15 dimensions of the index.

22
ˆ _similarly, let_  (  _m_ ,  _n_ ) _be the (k-2)x(k-2) partial correlation matrix given_ <sup>ˆ</sup> _m and_ <sup>ˆ</sup> _n with the_ ˆ ˆ ˆ _off-diagonal entries_  ˆ _dst_ . _mn_  <u></u> 1  _<u>dst</u>_  <u>.</u> _<u>m</u>_ ˆ _dsn_ 2  . <u></u> _m_ _<u>dsn</u>_ 1. _<u>m</u>_  _<u>dtn</u>_ ˆ _dtn_ 2. _<u>m</u>_ . _m for s, t= 1,..., k and s_ , _t_  _m_ , _n . The_ _with V_ (  ˆ _m_ ) _being the asymptotic covariance-variance matrix for the estimated coefficients obtained from the corresponding equation in (18) and V_ (  ˆ  _m_ ) _being approximated by_ (8 _N_  7)  ˆ  2 _m_ (4 _N_  3)2 _._

# **III.4. Mobility between Different Consumption Groups**

The results in Proposition 3 can be straightforwardly extended to settings with more than two consumption groups as follows.

22
Overall, the results from Table 7 and Appendix B show that, informal financing is associated with greater tax evasion, especially so in low and lower middle income countries. By contrast, bank financed firms are subject to more scrutiny and thus are less likely to evade taxes.

It must be noted though that we are unable to fully address endogeneity concerns. We can make partial progress, so that, for example, if we were to instrument for just Innovator in panel A using time spent with government officials other than those in the tax inspectorate, we find significant results that Innovators are more likely to be Victims and Retaliators and less likely to be Abiders and Perpetrators. However, the complexity of the relations between the multiple endogenous variables – innovation and financing and the categorical dependent variable makes it tough to find multiple instruments at the firm-level in our survey data that affect innovation and financing but not firm participation in illegal behavior. Hence we leave the identification issues in this area for future work.

# **6. Robustness**

# **_6.1 Bribe Payments and Tax Evasion - Robustness Checks using BEEPS_**

In this section we perform robustness checks of our main results using the BEEPS surveys that surveyed 27 transition countries across Europe and Central Asia in 2005. The data from the smaller sub-sample of countries have the following additional features that make it very attractive for robustness checks: First, as noted earlier, we have data on profit margins for about 6700 firms, where profit margin is defined as the margin by which sales price exceeds operating costs. We also have a dummy variable indicating whether or not the firm was profitable in 2003.

Second, in developing countries, firms may try to evade taxes in other ways other than under-reporting income. In addition to the raw measure of tax evasion that is used in the previous tables, we have information on **Tax Evasion (Wagebill)** and **Tax Evasion (Labor)** . Tax Evasion (Wagebill) is constructed as 100 - firm responses to the survey question “Recognizing the difficulties that many firms face in fully complying with labor regulations, what percentage of the actual wage bill would you estimate the typical firm in your area of business reports for tax purposes?” Tax Evasion (Labor) is constructed as 100 - firm responses to the survey question “Recognizing the difficulties that many firms face in fully complying with labor regulations, 30
If it is assumed that there will be no further losses in market share if certification is not adopted then, the incremental revenues due to certification total $62 million -- the equivalent of 0.6% increment of the total value of tropical timber product export revenues of developing countries. If the market segments ("niches") affected by certification are totally lost in the absence of certification, the potential benefits associated to certification rise to $428 million; the equivalent of 4% of export timber revenues of developing countries.

**Table 5: A Scenario for Timber Certification**

|Source of revenue|Commercial benefits<br>available, US$ million|
|---|---|
|(i) through (iii) Incremental revenue from European and<br>American market "niches"|62|
|(iv) Aversion of additional potential losses in the absence<br>of certification minus additional revenues derived from<br>related trade diversion|366|
|Total commercial benefits due to TC|428|
|Revenue as a % of export timber revenues of developing<br>countries a/|4%|

Note: Figures are based on 1991 trade values and flows.

_a/_ The group of developing countries is defined as in FAO and excludes China, Argentina, Chile and the countries of the Near East.

As already noted, in these calculations we did not calculate the recapturing of markets already lost due to environmental concerns. However, previously we calculated that the value of tropical timber imports in Europe has dropped by about US$149 million (after adjusting for price increases). Thus, even if we assume that all of it was due to environmental concerns and all of it could be recaptured with TC, the total potential commercial benefits of Table 5 would increase to US$577 million, or 5.4% of the total timber export revenues of developing countries.

27
**Table 4: Comparison of Status between Survey and SSBs**

1. Yesterday (Any Outage)

|||||**S**|**SBs**|||
|---|---|---|---|---|---|---|---|
||Obs.|on<br>o|ff|Total|Fraction|on|off|
||on|7993|2613|10606|on|0.615|0.201|
|**Survey**|off|1259|1123|2382|off|0.097|<br>0.086|
||Total|9252|3736|12988|Svy Unde|r report|-10.4%|
|. Day Be|fore Yest|erday (Any|Outage)|**S**|**SBs**|||
||Obs.|on<br>o|ff|Total|Fraction|on|off|
||on|7900|2560|10460|on|0.611|0.198|
|**Survey**|off|1362|1118|2480|off|0.105|<br>0.086|
||Total|9262|3678|12940|Svy Unde|r report|-9.3%|
|. Past 10|Days (A|ny Outage)||**S**|**SBs**|||
||Obs.|on<br>o|ff|Total|Fraction|on|off|
||on|3749|5479|9228|on|0.289|0.423|
|**Survey**|off|1405|2321|3726|off|0.108|<br>0.179|
||Total|5154|7800|12954|Svy Unde|r report|-31.4%|

2. Day Before Yesterday (Any Outage) 3. Past 10 Days (Any Outage)

# III – Accounting for fixed effects and time trends

A complimentary approach to document systematic differences between survey-based data and SSBs uses a regression framework to establish average cluster-level differences between survey-based reported outages and actual outages, while accounting for temporal fixed effects: where Ygt is the outcome variable of interest (such as average outage duration, or number of outages) aggregated in PSU _g_ for time _t_ . The variable Survg is a dummy variable indicating whether the observation was obtained using data collected from the survey (==1) or from the automated system (==0). The term τt is a vector of calendar date dummies and εgt is a random error term.

The first set of results provided in Table 5 demonstrate statistically significant differences between the data collection methods. The significantly negative differences between survey responses and SSB results confirm the expectations given the summary statistics in Tables 3 and 4. Survey-based estimates of outages and outage duration were systematically lower than automated measures. In terms of magnitudes, the largest errors were present in comparison 8, where, over a 10-day reference period of having “any outage”, survey estimates are off by about 4 days out of ten, on average.

11
The last four variables relate to trade costs and regulatory variables. The first one is the all-inclusive measure of policy- and non-policy-related trade costs due to Miroudot et al. (Forthcoming).<sup>10</sup> It displays a strongly negative and 1% statistically significant correlation with our exporter tradability index. The next two indicators cover a narrower range of trade costs, namely the restrictiveness of administrative regulations and domestic regulations as measured by the OECD’s PMR Database. They correlate negatively with tradability, which is in line with expectations. In other words, a more restrictive regulatory environment makes it more difficult to export services. However, only one of the two variables—administrative regulation—has a statistically significant correlation coefficient. The final regulatory variable is the cost of starting a business, as measured by the World Bank’s Doing Business project. Helpman et al. (2008) have shown that it can be used as a proxy for the fixed costs of market entry. As expected, we find that the entry cost data are negatively and 1% significantly correlated with the exporter tradability index.

Table 8 considers a reduced range of correlations for the importer tradability index. We exclude exporter-specific factors, such as endowments and institutions, which are not as relevant on the import side. Results are qualitatively identical in all cases. In addition, we examine the correlation between the importer tradability index and the World Bank’s Services Trade Restrictiveness Index (Borchert et al., 2012a and 2012b), as an indicator of policy restrictions affecting foreign suppliers. We use the overall index value for Mode 1 trade. In line with expectations, the correlation coefficient is negative and 1% statistically significant: countries with more restrictive services import regimes tend to exhibit lower levels of tradability on the import side.

Based on the descriptive evidence presented in this section, we conclude that our indicators are largely associated in the expected way with important outcome and input measures related to the concept of

> 10 See Miroudot et al. (Forthcoming) for a full explanation of their methodology and results. They rely on an inversion of the standard gravity model to infer model-based trade costs for services sectors. Trade costs in this definition include all factors that drive a wedge between export and import prices. International trade costs (between countries) are expressed relative to intranational trade costs (within countries), and are converted to ad valorem equivalents.

27
retaining labor as an objective of the enterprise. If providing employment is not a goal of the enterprise, policies can be designed to remove redundant labor.77/ The introduction of ccmpetition (Step 2) and firm budget constraints (Step 3) may convince organized labor that laborintensive practices diminish the competitiveness of the enterprise and may result in the loss of all jobs through dissolution of the enterprise. Studies of rival firms (Step 2) or firms in similar services in other regions or countries may provide information to assess labor requirements. Increased wages paid to individual workers to increase productivity must be accompanied by nformation to evaluate performance, combined with the possibility of reduction of benefits or dismissal. The success of such a program depends on managers who have incentives to perform and who exert effort to monitor, motivate, and evaluate their workers (Step 4).

77/ See Op Cit, Galenson, p. 33-52.

33
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**_31_**