rafmacalaba/datause-encoder-data huggingface dataset:
Table S1.11: MEDIATION ANALYSIS ON NET TRANSFERS RECEIVED

||Extensive<br>margin<br>(Yes=1)|Intensive<br>margin<br>(BDT)|
|---|---|---|
||(1)|(2)|
|1. Baseline ITT impact|-0.131***|-151.721**|
||(0.019)|(74.478)|
|2. Unrestricted Estimate (includes mediators as controls)|-0.104***|167.599|
||(0.024)|(184.197)|
|3. Total Mediated Effect (difference between ITT and unrestricted effect)|-0.027**<br>(0.013)|-319.319*<br>(188.647)|
|4. Mediator: received enterprise support from BRAC|-0.043**|-425.316**|
||(0.016)|(211.314)|
|5. Mediator: received sanitary latrine or tubewell from BRAC|0.008<br>(0.009)|4.063<br>(56.664)|
|6. Mediator: received health support from BRAC|-0.001|136.057**|
||(0.006)|(55.779)|
|7. Mediator: received education support from BRAC|-0.001|4.104|
||(0.001)|(4.715)|
|8. Mediator: received other support from BRAC|0.007|25.580|
||(0.006)|(37.423)|
|9. Mediator: received help from GDBC committee|0.005|-3.210|
||(0.003)|(17.737)|
|10. Mediator: received microcredit from BRAC|-0.002|-60.597**|
||(0.004)|(29.875)|

**Source:** Author’s analysis based on original survey data.

**Notes:** The table shows the results from mediation analysis following Gelbach (2016). In each panel, the first row shows the ITT impact based on specification (1) estimated with OLS, corresponding to the estimates in Table 3 (in the paper) columns 5 and 6. The second row shows the ITT impact once the mediators are controlled for?these are having received enterprise support (asset, training and cash transfer) from BRAC, having received any other type of support from BRAC (e.g. health or education support, sanitary latrine, tubewell), having received sanitary latrinne or tubewell from BRAC, having received health support from BRAC (for medical care of any household member), having received any help with school expenses from BRAC, having received any help with transportation costs from BRAC, having received any support from the GDBC (Village Poverty Alleviation) committee and having received any microcredit from BRAC. Row 3 shows the total mediated effect, and rows 4 - 10 show the contribution of each mediator. The sample includes observations from ultra-poor households surveyed at the midline and endline surveys. All specifications control for the baseline level of the outcome, survey wave and subdistrict (strata) fixed effects. Standard errors are clustered at the BRAC branch office level (unit of randomization). All monetary values are measured in Bangladeshi Takas, deflated to 2007 prices using the annual CPI index published by the Bank of Bangladesh. In 2007, 1USD=69BDT nominal and 1USD=18.5BDT at PPP. *** (**) (*) indicates significance at the 1% (5%) (10%) level. The dependent variable in column 1 is a dummy variable (DV) equal to 1 if the respondent’s household was a net transfer receiver in the past 12 months; in column 2 it is the monetary value of transfers received minus transfers made by the respondent’s household in the past 12 months.

11
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<u>f / f</u> f if ;
**Table 18 OLS: Village fixed effects regression of teacher effort, KA** (Dependent variable has four observations per teacher: Attendance=1 if teacher present, 0 otherwise: Activity=1 if teacher actively <u>engaged in teaching, 0 otherwise)</u>

||**Attendance**|**Attendance**|**Activity**|**Activity**|
|---|---|---|---|---|
|Age|0.00|0.00|0.003|0.004|
||(0.53)|(0.13)|(0.03)*|(0.05)*|
|Caste (1 if non-SC/ST)|0.00|-0.01|-0.01|-0.01|
||(0.68)|(0.60)|(0.32)|(0.27)|
|Gender(1 if male)|-0.04|-0.04|-0.06|-0.06|
||(0.01)**|(0.01)**|(0.01)*|(0.01)*|
|College degree|0.03|0.03|0.07|0.07|
||(0.11)|(0.10)|(0.01)**|(0.01)**|
|Graduate degree|-0.03|-0.04|-0.14|-0.15|
||(0.52)|(0.39)|(0.04)*|(0.03)*|
|Pre service training|-|-0.01|-|0.02|
|||(0.84)||(0.61)|
|Years ofservice|-|0.00|-|0.00|
|||(0.22)||(0.50)|
|Days of inservice training|-|0.002|-|0.00|
|||(0.03)*||(0.30)|
|Village/school fixed effect<br>(210 villages)|YES|YES|YES|YES|
|R<sup>2</sup>|.15|.15|.14|.14|
|Observations|3374|3374|3374|3374|
|Robust p valuesinparentheses|clustered at teac|her level|||
|*significant at 5%;**signific|ant at1%||||

32
Policy Research Working Paper 10664

# **Abstract**

In addition to the conventional determinants of labor market participation and the choice between wage employment and self-employment, there is a growing interest of the significance of gender role attitudes and personality traits. This study uses data from the 2022 Ghana Informal Sector Measurement Study to investigate the influence of these factors on employment outcomes in the Northern and Ashanti regions of Ghana. The findings are based on a series of analyses, including descriptive, multinomial logistic, and linear probability model regressions. The empirical results show the critical role played by both gender role attitudes and personality traits in shaping individuals’ decisions on labor market participation and employment choices.

Notably, personality traits emerge as significant drivers of observed employment outcomes. However, the impact of these personality traits is often mitigated or even reversed in the presence of heightened traditionalism. Furthermore, the gender-disaggregated analysis reveals that possessing at least a secondary education level is a pivotal factor in the selection of men into formal employment, whereas this criterion holds less significance for women. Conversely, once the decision to participate in the labor market has been made, having at least a secondary education becomes relevant for securing wage employment, regardless of an individual’s gender.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at aamankwah@worldbank.org. A verified reproducibility package for this paper is available at http:// reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
11

|**Mechanism**|**Advantages**|**Potentialproblems**|
|---|---|---|
|Introduce a temporary stabilization<br>fund (Chile, Peru), temporary tax<br>reduction (diesel in Thailand)|Deal with large price shocks while<br>limiting the period of artificially low<br>prices|Political pressure to repeatedly extend<br>the phaseout date (Chile, Peru,<br>Thailand), resulting in a growing<br>budgetary outlay|
|Switch to rule-based pricing when<br>world prices are low (China in Jan<br>2009)|No large price increases needed at the<br>time of switching|When world prices begin to rise, the<br>political will to adhere to rule-based<br>pricing may weaken (as in China); a<br>period of very low prices may not<br>return in the future for governments to<br>follow this approach|
|Adjust when world prices change<br>significantly and subsidies become<br>too costly to bear (Bolivia, Islamic<br>Republic of Iran, Jordan, gasoline<br>in Nigeria)|Stable prices between changes|Price changes are large when<br>adjustments are finally made,<br>adjustments almost always mean price<br>increases, tendency to delay price<br>increases, lack of predictability,<br>possibility of growing subsidies,<br>politicization of price increases,<br>hoarding in response to rumors of<br>imminent price increases and leading<br>to fuel shortages|
|Adjust when world prices change<br>by more than ±X% (Malawi,<br>Togo)|Stability within the price band|If X is relatively large, potentially<br>large changes when adjustments are<br>made; possibility of losses exceeding<br>savings within the price band|
|Float prices within a price band,<br>smooth changes outside (Chile for<br>small and medium consumers,<br>Peru)|Avoid large price changes|Can lead to large subsidies unless<br>price bands are frequently adjusted|
|Set different rules depending on<br>world oil price (China)|Limit subsidies to times of high world<br>prices|Unless price bands are adjusted from<br>time to time, if world prices remain<br>high, subsidies could grow|
|Agree on the total subsidy<br>envelope for the fiscal year and<br>adjust prices, volume, or both<br>accordingly|Limit the total subsidy bill.|Politically difficult to raise prices<br>when money runs out (Indonesia)|
|Adjust based on world prices<br>averaged over past 3–6 months (no<br>example in this study)|Prices change gradually|World and domestic prices could be<br>moving in the opposite direction,<br>inviting political backlash; could lead<br>to large losses if world prices are<br>rising over time.|
|Adjust regularly based on world<br>prices averaged over 1–4 weeks<br>(Dominican Republic, South<br>Africa)|Tracking world prices well|<br>World price volatility quickly<br>transmitted.|
|<br>Deregulate, subject to anti-trust<br>regulations (Philippines, Turkey)|Market based, no subsidies|Downstream petroleum sector needs<br>to be competitive or else consumers<br>may be charged high prices; world<br>price volatility immediately<br>transmitted|

_Source_ : Author and Table A1.1.
# **Table 3. Connection Rates, 2004**

(In percent of all households)

|||Energ<br>|yProduct<br>||
|---|---|---|---|---|
|Quintile|Central Gas|LPG|Electricity|Central Heat|
||(1)|(2)|(3)|(4)|
|**_A. All households_**|||||
|Poorest|22.5|68.5|97.3|9.1|
|Second|30.2|62.1|98.9|16.2|
|Third|31.3|61.4|99.6|20.8|
|Fourth|37.5|55.5|99.6|28.3|
|Richest|47.8|38.7|99.8|44.5|
|_All_|35.2|55.7|99.2|25.7|
|Of which:|||||
|**_A1. Large Cities_**|||||
|Poorest|67.0|0.0|97.2|97.8|
|Second|66.0|3.4|97.3|97.8|
|Third|69.5|1.9|99.4|98.2|
|Fourth|76.2|0.7|99.7|97.8|
|Richest|69.6|1.6|99.8|95.3|
|_All_|70.8|1.5|99.4|96.7|
|**_A2. Small towns_**|||||
|Poorest|67.8|29.9|95.8|19.2|
|Second|73.7|25.0|99.6|20.0|
|Third|76.0|23.7|99.6|21.2|
|Fourth|74.2|25.4|99.6|13.3|
|Richest|78.4|21.6|100.0|12.6|
|_All_|74.1|25.0|99.0|17.4|
|**_A3. Rural areas_**|||||
|Poorest|7.3|83.7|97.8|...|
|Second|11.5|83.2|98.9|...|
|Third|9.1|87.4|99.7|...|
|Fourth|10.6|88.3|99.6|...|
|Richest|15.4|83.4|99.8|...|
|_All_|10.7|85.4|99.2|...|

Source: HBS and Bank staff calculations. Note: "..." indicate insufficient observations.

8
<u>Table 2. Crop income, cultivated area and productivity by crop and gender</u>

||Gross|cropincome|(Rs.)|Cultiv|ated Area|(acre)||Logof|Productivit|y<sup>b</sup>|
|---|---|---|---|---|---|---|---|---|---|---|
|||||||||||_t_-test|
||Total|Male|Female|Total|Male|Female|Total|Male|Female|(_p_-value)|
|Paddy|9,951|10,435|6,513|1.9|1.99|1.31|8.40|8.41|8.34|0.010***|
|Chilies|9,616|7,995|18,252|0.58|0.61|0.42|9.13|9.09|9.38|0.497|
|Onions|18,254|18,204|18,777|0.59|0.6|0.57|10.16|10.16|10.16|0.805|
|Yams|19,287|20,972|10,771|0.82|0.88|0.54|9.77|9.81|9.61|0.762|
|Vegetables|10,961|12,724|4,935|0.65|0.73|0.37|9.45|9.51|9.23|0.020**|
|Tea|20,190|23,825|14,507|0.82|0.98|0.58|9.90|9.93|9.85|0.019**|
|Rubber|20,702|20,757|20,539|1.36|1.37|1.36|9.43|9.47|9.28|0.119|
|Coconut|13,747|12,829|16,324|1.48|1.46|1.52|9.01|9.01|9.00|0.832|
|Coffee, pepper, betel|18,814|21,501|11,236|0.73|0.8|0.52|9.81|9.82|9.77|0.856|
|Banana/fruits|18,230|19,177|14,751|0.98|1.09|0.5|9.42|9.47|9.21|0.194|
|Total<sup>a</sup>|15,487|16,970|11,086|1.7238|1.9452|1.0438|9.14|9.09|9.30|0.000***|

Source: Authors’ calculation using HIES 2016. Notes:<sup>a</sup> Sri Lankan farmers cultivated 1.32 crops on average. b Productivity is measured by yield, i.e., gross real monthly output divided by the area cultivated (Rs./acre).

*, **, and *** indicate that the male-female difference is significant at the 10%, 5%, and 1% level, respectively.

13
A couple of remarks are in order. First, the model variance will be smaller the better fit we have for our regressions in equations (1) and (2). And the larger the sample sizes used for prediction, the further the sampling variance can be reduced; thus, this points to the advantages of cross sections over panel data when the former has much larger sample sizes than the latter. A natural extension of this would be to pool estimates from the two cross sections for a larger sample size to reduce the sampling errors even more.<sup>17</sup> Whether the model variance or the sampling variance is the dominant component would depend on the dynamics of the underlying regression relationship and the overall precision of our theoretical models. As shown later, our estimation results also indicate that the sampling variance is significantly larger than the model variance, which is consistent with findings in the small area estimation literature (see, e.g., Rao (2003, p. 35)). But more importantly, when the postulated estimation model is correct, it follows that the model-based variances (synthetic panel estimates in our case) are usually smaller than the design-based variances (weighted estimates based on actual panel data) (Matloff, 1981; Binder and Roberts, 2009).<sup>18</sup> Second, the formulae in Proposition 3 are general and can be used to obtain our estimates using data either from the first or the second survey round, where the subscript _j_ in<sup>_x_</sup> _ij_<sup>should</sup> be adjusted accordingly to indicate data for the corresponding survey round. Estimates using either survey rounds are theoretically equivalent since the following identity always holds

> 17 Since we are mostly interested in estimating the means, assuming that the sample sizes of the cross sections are similar, we can simply use the corresponding population weight for each cross section. For estimates of other quantities such as totals, the population weights when pooling two cross sections can be adjusted by dividing by half; see, for example, Botman and Jack (1995) for a related discussion with the National Health Interview Surveys, and Kish (1999, 2002) for overviews on combining surveys.

> 18 Notably, this decomposition of the error terms is similar in spirit to the decomposition in Elbers, Lanjouw, and Lanjouw (ELL) (2003), which is related to the familiar formula in sampling statistics that the mean squared error of the estimate is composed of the variance of the estimate plus its bias squared. However, the key difference between that study and ours is that ELL impute data from a survey into a census, but we are imputing data from a survey to a survey. Thus while ELL do not need to take into account the sampling errors, we have to model these sampling errors explicitly. An alternative to this analytical error would be the bootstrap error.

17
goods, or by using aggregate deflators, and contaminating inference about the relationship between these variables and growth or policy.<sup>9</sup>

**<u>Figure 4. Annual inflation by percentile</u>**

<!-- Start of picture text --> Brazil (1988-1996) Colom bia (1997-2003)<br>734.0 11.6<br>733.0<br>11.4<br>732.0<br>731.0<br>11.2<br>730.0<br>729.0 11.0<br>728.0<br>727.0 10.8<br>726.0<br>10.6<br>725.0<br>724.0 10.4<br>5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100<br>Pih 1996 Weighted (L) Pi Pih 1997 Weighted (L) Pi<br>Mexico (1996-2002) Peru (2001-2003)<br>12.8 2.0<br>12.7 1.9<br>12.6 1.8<br>12.5 1.7<br>12.4 1.6<br>12.3 1.5<br>12.2 1.4<br>12.1 1.3<br>12.0 1.2<br>11.9 1.1<br>11.8 1.0<br>5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100<br>Pih 2002 Weighted (L) Pi Pih 1995 Weighted (L) Pi<br><!-- End of picture text --> Source: Goñi, Lopez and Serven (2005) To explore these issues Figure 4 shows the “actual” inflation rate suffered by different viniventiles of the income distribution for Brazil (1988-1996), Colombia (1997-2003), Mexico (1996-2002) and Peru (2001-2003). These estimates have been constructed on the basis of the consumption baskets of different population groups and on the different evolution of the prices of the different components of those baskets. The Figure also plots the average inflation rate (the horizontal bar).<sup>10</sup> 9 A previous literature has addressed this issue for some economies: Busso et al. (2000) estimate the difference between the standard _cpi_ -index and a democratic version for Argentina over the period 19891998; Ruiz-Castillo et. al. (2002) study the Spanish experience while Hobijn and Lagakos (2003) have analyzed the effective inflation rate suffered by different groups in U.S, just to mention some of them. Beyond literature, Hong Kong Census and Statistics Department computes, in addition to an overall _cpi_ , consumer price indexes by income bracket and Colombia has begun to follow this steps. Within this strand, our interest is to dig in LAC data to see if differences in consumption patterns among quintiles and differences in price variations among products are strong enough to validate _cpi_ ’s disarticulation 10 Goñi, Lopez, and Serven (2005) show that these patterns persist even after adjusting for quality change bias and after recomputing Paasche indices to control for potential substitution effects.

7
the comparison to politically connected and unconnected firms operating within the same 2-digit sectors (Column two of Table 2). The advantages of politically connected firms make them more profitable relative to other firms operating in the same sector. Thus, the performance differences are not specific to the (broader) sectors in which connected firms operate.

**Table 2: Within-sector differences, politically connected (of any type) and other firms**

||PC vs. other<br>establishments|PC vs. other<br>est., within<br>2-digit<br>sector|No. of<br>Politically<br>connected<br>establishments|No. of other<br>establishments|
|---|---|---|---|---|
|Ln(Employment)|1.40**<br>(15.88)|1.02**<br>(12.39)|436|19,375|
|Ln(Revenues)|1.61**<br>(6.46)|1.59**<br>(6.27)|67|611|
|Ln(Profits)|1.43**<br>(1.95)|1.37*<br>(1.73)|49|239|
|Ln(Profits/Assets)|1.88**|2.17**|47|236|
||(3.03)|(3.29)|||

Source: Orbis establishment database. Note: The establishment data are pooled across years (2003-2011). The first column reports the coefficient and _t_ -statistic on the politically connected dummy variable from an OLS regression of the performance variable (e.g., ln(employment)) on the dummy variable, which is equal to 1 for politically connected establishments and 0 otherwise. In the second (third) column, we control for 2(4)-digit sector dummies. *, ** indicates that the coefficients are significant at the 5%, 10% level.

While we do not conduct a formal event study, we can still ask whether the profit advantage of connected firms varies with the strength of the Mubarak regime. This should be the case if connected firms were more profitable because of their connections, rather than some unobserved features of their entrepreneurial skill or products; and if our measures of the performance advantages of connected firms are reasonable. In fact, Figure 1 illustrates the predicted variation.

It compares the profits of firms with political connections relative to other (large) firms in the Orbis data and shows that the difference between the two was systematically related to the survival of the regime. The differential in profits was more than two times larger between 2005 and 2010 (significant at the 5 percent level). After the fall of the Mubarak regime on February 11, 2011, the large differential suddenly disappeared.<sup>13</sup> The fact that the profit differential between connected and unconnected firms disappears shortly after the fall of Mubarak also corroborates the quality of our empirical measurement of politically connectedness in Egypt.<sup>14</sup>

> 13 Unfortunately, longer time series data for profits are not available in Orbis. The precision of estimated profit differential in 2003 and 2004 is low due to the few available observations.

> 14 We note that most of the regulatory privileges favoring connected firms (e.g., energy subsidies to industry or trade protection) are still in place until today. Thus, the decline in the profit differential for connected firms immediately after

10
- 40 -

Data on subsidy rates are taken from Table<sup>5, which we reproduced</sup> from Kim (1988). Since 1980 is the most<sup>recent year for which detailed</sup> information on subsidy rates from this<sup>source is available, we use the 1980</sup> data and assume it to be roughly equivalent<sup>to the rates in both 1981 and</sup> 1982.

Table 6 reports some summary statistics<sup>from the two data sets we</sup> have constructed. The rapid expansion in<sup>the economy between 1962 and 1982</sup> is evident, as is the change in the industrial<sup>composition of employment</sup> and output, and the changes in importance<sup>of trade to the economy. What</sup> remains to be established is how significant<sup>tax policies were in promoting</sup> outward orientation and how great a contribution<sup>they made to Korea's</sup> strong growth performance.

# V. RESULTS

We have used the general equilibrium<sup>model described above to</sup> assess the contribution of tax policies<sup>to Korean growth as part of the</sup> outward oriented growth strategies<sup>used in recent decades.</sup> The counterfactual policy exercises we have<sup>performed involved a series of</sup> counterfactual experiments in which the<sup>base year (1962 or 1982) policies</sup> are removed, and a new equilibrium<sup>for the model is computed and compared</sup> to the benchmark equilibrium. This<sup>comparison yields estimates of</sup> quantitative changes in all the endogenous model variables<sup>under the policy</sup> change. Further counterfactual experiments<sup>are then performed in which</sup> outward oriented tax policies during<sup>each year of the specified time</sup> periods (1963-82, 1963-72 or 1981-62)<sup>are sequentially introduced. For</sup> each of these policy changes, a new counterfactual equilibrium is computed and compared with the same no policy equilibrium.