rafmacalaba/datause-encoder-data huggingface dataset:
randomization. Table 5 reports the results. All coefficients of interest but one (No Credit x Post in the Maize regression), are insignificant, thus, we conclude that there are no differences between unit prices perceived by members of Treatment and Control SHGs even if Treatment group is split into Credit and No-credit groups. The point estimates of Treatment x Post in column (3) and Credit x Post and No credit x Post in column (4) are all negative and insignificant, indicating that treatment groups did _not_ receive on average higher prices for French beans. The DrumNet administrative data show an average net transaction price in 2005 of Ksh 25 per Kg, compared to a lower mean transaction price for French beans in 2005 of Ksh 19.5 per Kg. Thus, while transactions with DrumNet were possibly more profitable than with middlemen, the average price of French beans in the treatment group fails to show it. Notice in contrast that the Post coefficient of French beans, maize and coffee is positive and significant, indicating that on average, the price of these crops was higher in 2005 than in 2004. Figure 3 plots the Kenya-wide price index of the same crops, taking year 2001 as the base year.<sup>9</sup> Consistent with the Post coefficient of Table 5, Figure 3 shows an increase in prices from 2004 to 2005 for the same crops.

Finally, we interviewed the few local input suppliers that serve Gichugu and we found anecdotally that the price of inputs (fertilizer, pesticides and seeds) was not affected either by the presence of DrumNet. This is not surprising, since in aggregate DrumNet was fairly small compared to the market as a whole.

> 9 Price data for French beans and bananas come from the Horticultural Crops Development Authority (HCDA), for maize and beans come from the Regional Agricultural Trade Intelligence Network (RATIN) and finally prices for coffee come from the Nairobi Coffee Exchange.

18
Table 9: Parental Investment by Proxies of Community-Level Collaboration

||Engage at School|Manage School|Engage With Child|Engagement Index|
|---|---|---|---|---|
|API Plus_×_ No Confict|0.161<br>[0.176]|0.159<br>[0.103]|0.370<br>[0.000]|0.364<br>[0.001]|
|API Plus_×_ Confict|0.926<br>[0.000]|0.281<br>[0.183]|0.592<br>[0.005]|0.914<br>[0.000]|
|Confict=No Confict|[0.000]|[0.596]|[0.329]|[0.020]|
|Mean Control (Confict)|-0.006|-0.059|0.152|0.011|
|Mean Control (No Confict)|-0.023|0.013|-0.013|0.003|
|Number of clusters|224|224|224|224|
|Observations|1045|1045|1045|1045|

_Notes_ : This table shows OLS estimates on parental outcomes measured after two academic years of exposure to the API program under the second experiment designed and implemented by the authors in collaboration with the government. The variable Conflict takes the value of one if least on hostile event related to land property, religion, elections, crime, or drug addiction is reported at the locality level in the population census (2010). For a detailed descriptions of the variables used in this table, see Appendices A.1–A.2. The dependent variables are standardized with respect to their means and the standard deviations in the control group. All _p_ -values account for clustering at the school level. Asymptotic _p_ -values reported in brackets are clustered at the school level.

As reported by the mentors, parents follow up with their children on homework and other pedagogical material whenever the mentor is busy attending tasks outside of the community: “ _Parents used to provide support with homework whenever mentors are visiting other communities ensuring pedagogical support, so that upon the return of the mentors they are able to make progress in the schooling activities without setbacks._ ” We next empirically investigate the second prediction of the model, as depicted in Figure 2 and discussed above. We take advantage of information from the 2010 locality-level census on the degree of social hostility in the community (see Appendix A.1), which is based on hostile event related to land property, religion, elections, crime, or drug addiction. This measure proxies the degree of collaboration in the community. In the 224 communities that form part of the second experiment, we construct an indicator variable for the presence of a conflict in the community if at least one of these hostile events is reported. We then interact this variable with the experimental API _Plus_ assignment and regress the various survey-based measures of parental engagement collected during the second experiment on these interaction terms.

31
<!-- Start of picture text --> 140<br>e<br>120 ¥<br>?<br>100<br>80<br>60 § i ¥<br>40 at |#Pa Hitee# $ * ee 1K<br>8<br>20 * ¥<br>x<br>0<br>AL AP CE ES MA MS PA PE PR RN RR SC_ SP<br>AC AM BA DF GO MG MT PB PI RI RO RS SE TO<br>BB Total CO2 Emission (t) per Million Income (R$)<br>BB Total CO2e Emission (t) per Million Income (RS)<br><!-- End of picture text -->

<!-- Start of picture text --> 140<br>120 s° e ° hd of fg 0° 0° 0° of<br>ee °°<br>100<br>eo ee e<br>80 °e °<br>60<br>40<br>ee<br>20 ~e eo °° oo ee ee ee ge ce Se<br>)<br>2011 2013 2015 2017 2019<br>2010 2012 2014 2016 2018 2020<br>Hi Total CO2 Emission (t) per Million Income (R$)<br>HB Total CO2e Emission (t) per Million Income (RS)<br><!-- End of picture text -->
first part of this sub-section we explore if these high returns are persistent and if being a superstar is associated with superior performance. To this end, we construct five non-overlapping panels: 1990-1995, 1995-2000, 2000-2005, 2005-2010, and 2010-2015 and examine if being a superstar is associated with higher average performance in the subsequent five year period. Specifically, the regression we estimate is as follows: _Performanceijt_ = _α_ 0 + _β_ 1 _× Log_ ( _Assets_ ) _it−_ 5 + _β_ 2 _× Log_ ( _Age_ ) _it−_ 5 + _β_ 3 _× HHIjt−_ 5 + _β_ 4 _× Market Shareit−_ 5 + _β_ 5 _× Markupsit−_ 5 + _β_ 6 _× ROIC_<sup>_TOT_</sup> _Starit−_ 5 We look at the following five performance measures: 5-year average _ROIC_<sup>_TOT_</sup> , _Sales growth_ computed as the five year log difference in sales divided by 5, _Employment growth_ computed as the five year log difference in employment divided by 5, 5-year average _Labor Productivity_ , and 5-year average Tobin’s _Q_<sup>_TOT_</sup> over these periods. Using stacked panel regressions, we examine the association between each of these measures and star firms identified at the beginning of each panel. We also control for size, age, market share, HHI, and markups at the beginning of each panel. All regressions also include industry and year fixed effects.

Column 1 of Table 5 shows that while higher _ROIC_<sup>_TOT_</sup> is on average positively associated with higher average _ROIC_<sup>_TOT_</sup> in the subsequent five year period, star firms have lower _ROIC_<sup>_TOT_</sup> suggesting a regression to the mean. The predicted value of average 5-year _ROIC_<sup>_TOT_</sup> for firms that were superstars five years ago is 55.3 compared to 20.5 for firms that were not superstars five years ago. Column 2 shows that while past _ROIC_<sup>_TOT_</sup> is not associated with sales growth, past _ROIC_<sup>_TOT_</sup> stars have higher sales growth in the future but not necessarily higher employment growth in column 3. In column 4, _ROIC_<sup>_TOT_</sup> is associated with higher average labor productivity whereas star firms are not necessarily more productive than other firms in the economy. Column 5 shows that firms that are classified as _ROIC_<sup>_TOT_</sup> stars five years ago have high average Tobin’s Q in the 5 years hence.

Overall these results suggest that star status is associated with higher future performance and there is a fair degree of persistence in star status as firms that were ROIC stars five years ago have 21
||**Table 8**|**. Inflation by population quintile**||
|---|---|---|---|
|**Period**|**Quintile**|**Quintile Inflation**<br>**Inflation Differential**|**Growth**|
|**1988-1996**|**1**|725.47<br>4.48|2.68|
||**2**|726.43<br>3.51|1.95|
|**azil**|**3**|727.59<br>2.36|2.09|
|**Br**|**4**|729.06<br>0.89|1.89|
||**5**|732.02<br>-2.08|1.65|
||**All**|729.95|1.84|
|**1997-2003**|**1**|10.53<br>0.37|1.50|
|**ia**|**2**|10.50<br>0.40|0.38|
|**mb**|**3**|10.61<br>0.29|0.94|
|**olo**|**4**|10.76<br>0.14|0.38|
|**C**|**5**|11.20<br>-0.30|-0.81|
||**All**|10.90|0.00|
|**1984-1989**|**1**|77.58<br>0.30|3.76|
||**2**|77.60<br>0.29|2.83|
||**3**|77.65<br>0.23|3.63|
||**4**|77.73<br>0.16|1.54|
||**5**|78.15<br>-0.27|2.71|
||**All**|77.88|2.68|
|**1989-1994**|**1**|15.94<br>0.83|4.33|
||**2**|16.17<br>0.59|4.88|
||**3**|16.42<br>0.34|3.74|
||**4**|16.51<br>0.26|4.66|
|**o**|**5**|17.26<br>-0.49|4.32|
|**xic**|**All**|16.77|4.37|
|**1994-1996**<br>**Me**|**1**|35.12<br>-1.34|-0.89|
||**2**|34.92<br>-1.14|-3.08|
||**3**|34.47<br>-0.69|-2.22|
||**4**|34.17<br>-0.39|-3.27|
||**5**|32.92<br>0.86|-7.41|
||**All**|33.78|-4.85|
|**1996-2002**|**1**|12.08<br>0.39|4.08|
||**2**|12.17<br>0.30|4.09|
||**3**|12.30<br>0.17|3.73|
||**4**|12.38<br>0.09|3.67|
||**5**|12.69<br>-0.22|5.21|
||**All**|12.47|4.45|
|**1995-1999**|**1**|7.79<br>0.37|-6.45|
||**2**|7.86<br>0.30|-1.33|
||**3**|7.98<br>0.18|0.66|
||**4**|8.13<br>0.03|-0.37|
||**5**|8.41<br>-0.25|3.29|
||**All**|8.16|0.79|
|**1999-2001**|**1**|3.75<br>0.87|7.60|
||**2**|3.89<br>0.73|4.80|
|**ru**|**3**|4.16<br>0.46|6.59|
|**Pe**|**4**|4.52<br>0.10|7.42|
||**5**|5.24<br>-0.62|-5.73|
||**All**|4.62|1.25|
|**2001-2003**|**1**|1.28<br>0.32|3.35|
||**2**|1.35<br>0.25|4.38|
||**3**|1.46<br>0.14|2.57|
||**4**|1.58<br>0.02|0.69|
||**5**|1.81<br>-0.20|4.52|
||**All**|1.60|3.16|

Note: The table reports the annual inflation rate by population quintile of the expenditure distribution, the difference between the inflation rate corresponding to each quintile and the average inflation rate, and the estimated annual growth rate in real consumption of each quintile.

24
stationarity of the price differential.

Using the South African maize price, we obtain estimates for _β_ in equation (1) that are much closer to unity (the median across the 18 markets is 0.9) than those based on the U.S. Gulf price. All are significantly different from zero at the 1 percent level but the median R-square is 0.60 — lower than with the U.S. Gulf price. Furthermore, about half of the unit root statistics of equation (1) do not confirm a long-run relationship at the 5 percent level. The evidence based on the price spread is more supportive of the existence of a long-run relationship between the Tanzanian markets and the South African.

Using the Nairobi maize price, we find that all the parameter estimates are highly significant and much closer to unity (the median across 18 markets is 0.97) than their counterparts based on either U.S. Gulf or South African prices, and with a median R-square of 0.78 — much higher than with the other two prices. The unit root statistics of equation (1) confirm co-integration. The price differential is stationary as well, in most cases at the 1 percent level (the only exception is the ADF test for Bukoba): this is expected since the co-integration parameter is very close to unity. Based on these results, we choose the price

# **Quantifying Price Relationships**

Table 5 reports parameter estimates consistent with the error correction specification (6). This model explains about 30 percent of domestic price changes, with the R-square ranging from 0.20 in Songea, a surplus market in the Southern zone, to 0.37 in Arusha, a deficit market in the Northern zone. The error correction term, _γ_ 1, is significant in 17 of the 18 markets: in 15 markets at the 1 percent level and in 2 markets at the 5 percent level (Mbeya the exception). However, the estimates vary widely, from a low of 0.11 in Iringa (t-statistic = 2.64) to a high of 0.31 in Bukoba (t-statistic = 4.60). The error correction term averages a little more than 0.13 across all 18 markets, implying that, on average, 13 percent of the price gap between Tanzania and Nairobi prices will be eliminated in the second (and every subsequent) period.<sup>17</sup> Results on the short-run impact of the external price shocks are even more heterogeneous. The parameter estimate for _γ_ 2 differs significantly from zero at the 5 percent level in 13 markets. The short-run effect in these 13 markets averages 0.25, implying that only one quarter of the adjustment to external price shocks is transmitted instantaneously.

To better understand the adjustment process, we combined the short-run and feedback effects into a single summary statistic.<sup>18</sup> Figure 8 depicts the cumulative adjustment for

> 17Figure 7 shows prices in Dar es Salaam, Songea (lowest average real prices for our sample) and Mwanza (highest average real prices). Although domestic prices move together, there are differences in terms of price levels, the influence of harvest cycles as well as anomalous movements (also see Table 3).

> 18The cumulative adjustment is calculated as follows. Let, _k_ be the amount of adjustment that takes place

10
|Marital status|1.3**|0.9*|Marital status|0.1|0.1|
|---|---|---|---|---|---|
|Region|0.004|0.009|Region|0.03|0.1|
|Ethnicity|-0.006|-0.023|Ethnicity|0.004|0.02|
|**Coefficients**|**-3.4*****|**-4.4*****|**Coefficients**|**-10.2*****|**-10.7*****|
|_Of which:_|||_Of which:_|||
|Age|0.6|5.8**|Age|-23.6***|-13.2**|
|Ratio of children to household<br>members|-0.5***|-0.5***|Ratio of children to household members|-0.6**|-0.4|
|Ratio of working adults to|-0.8|-1.1|Ratio of working adults to household|2.4|2.8**|
|household members|||members|||
|Household non-labor income|-0.1|-0.05|Household non-labor income|0.1|0.1|
|Educational attainment|-0.4|0.1|Educational attainment|-3.3***|-4.0***|
|Marital status|-1.3*|-3.6***|Marital status|-2.2|-0.9|
|Region|-0.4**|-0.6***|Region|-0.5|-0.2|
|Ethnicity|0.4|0.4|Ethnicity|-0.9|0.2|
|Constant|-1.0|-4.9|Constant|18.4**|5.0|
|**Interaction**|**-0.5***|**0.1**|**Interaction**|**-0.3**|**-0.3**|

_Note: the gap is the difference between the men’s probabilities and the women’s probabilities, taking the men distribution as non-discriminatory. The probabilities are calculated within the total working age population (aged 15+). *** means statistically significant at the 1% level, ** at 5% and, * at 10%. Data source: VHLSS 2006/2008_

# _6.2 Gender wage differentials_

Gender wage differentials in Vietnam are largely unexplained and linked to the different returns to endowments, and to the differentiated treatments given specific circumstances that exist between men and women in Vietnam.

Raw wage differentials vary by individuals’ characteristics and circumstances. On average, women were paid 75 percent of men’s wage in 2009 (LFS). This average hides differences across occupations (Table 22): women in higher level occupations had smaller wage differentials with men, while women in manual or unskilled occupations had greater wage differentials. The raw gender wage differential is explained by a number of factors that are best analyzed through multivariate regression. This average also hides differences in the type of employment; the gender wage differential is much smaller among wage workers than among self-employed workers. Using LFS data, we find that whilst the raw gender gap in monthly earnings of wage workers was 16 percent in 2009, up from 12 percent in 2007, it was 22 percent for self-employed workers in 2009 (down from 25 percent in 2006). This is confirmed in the VHLSS data, which show that at the mean hourly wage, female wage earners earn 90 percent of their male counterparts’ wages and at the median hourly wage they earn 83 percent of men’s wage (Figure 1).

33
The contributions of this study are twofold. First, we establish the relationship between imports costs and the use of foreign inputs and estimate its size. Second, we use cross-country comparable firm level data sets based on common sampling methodology. We hope that the results of the paper will aid policy makers in boosting foreign inputs usage and thereby growth.

# **2. Data and main variables**

Our main data source is a stratified random sample of about 14,000 manufacturing firms across a repeated cross-section of 26 developing countries provided by the World Bank’s Enterprise Surveys (ES) between 2004 and 2011.<sup>2</sup> These surveys are representative of the private nonagricultural economy and they are comparable across countries as well as over time.

The dependent variable is the percentage of a firm’s material inputs that are of foreign origin ( _Imports_ ) which is obtained from ES. The sample mean value of _Imports_ equals 36.4 and the standard deviation is 37.4. Averaged across the two time periods, use of imported inputs is highest in Albania (64.8 percent) and lowest in Mexico (15.1 percent). Previous work using the same dependent variable includes for example, Amin and Islam (2014).

Our main explanatory variable is a measure of cost to import provided by Doing Business, World Bank. The variable is defined at the country level and captures costs (in USD and for a standardized container) associated with all procedures required to import goods, including documents, administrative fees for customs clearance and technical control, customs broker fees, terminal handling charges and inland transport ( _Import Cost_ ). The mean value of _Import Cost_ is 1,733 and the standard deviation is 1,000. As figure 1 shows, there is substantial variation over time in _Imports_ and _Import Cost_ to allow for proper estimation.

> 2 For each country in our sample, data are available for two rounds conducted at two different points of time. For the first round, data were collected in 2004 for 11 countries, 2005 for 11 countries and 2006 for 4 countries. The second round was conducted in 2008 for 11 countries, 2009 for 11 countries, 2010 for 3 countries and 2011 for 1 country.

3
are more easily exposed to street crimes and more likely to be attacked by criminals (Langenderfer and Shimp, 2001; Holtfreter et al., 2008; Pratt et al., 2010), we use the following model to examine the determinants of approach propensity. A 0 − (3) Here is a dummy variable indicating whether the household ii ii ii cc ii _i_ was approached AAFFccFFvvh = δδ + δδββFFββFFv vvc ss + φφXX + θθ + ηη by fraudsters last year. ii refers to the same set of control variables as defined in equation FFA FFccFFvvh (1). County fixed effects (ii ) are included. The OLS estimation results using the urban XX sample are reported in Table 6. cc θθ Households are clearly not randomly approached by fraudsters. Households with higher income, worse health conditions, and better education are more likely to be approached by fraudsters. Compared to household heads that are younger and older, those with middle-aged household heads are more likely to be approached. This is perhaps because middle-aged-headed households have a higher level of income, which makes them “better” targets. And middle-aged household heads have more family responsibilities, and thus may engage more in social interactions with potential leaking of private information (i.e., adding strangers as WeChat contacts or follow WeChat public accounts for children’s extra-curriculum activities or elder parents’ old-age support).

To address the potential selection bias in household approaching, and the possibility that common factors determining both household approaching and fraud victimization, we employ the Heckman selection model (Heckman, 1979). This model first employs the probit model to predict the participation odds, and then introduces an adjustment term to the outcome model to eliminate potential omitted variable bias. To properly implement the Heckman model, the selection model should exploit exogenous variations that influence the participation decision (i.e., being approached) but that are independent of the outcome (i.e., fraud victimization).

We employ the E-commerce coverage at the community level as the exogenous variation that determines the probability of being approached by fraudsters. As mentioned previously, via internet and big data technology, fraudsters now can target individuals more precisely based on leaked private information from telecom operators or online shopping websites. Thus, individuals with frequent online shopping behavior would face a higher vulnerability to fraud schemes (Holtfreter et al., 2008; Reisig and Holtfreter, 2013). In 19
from the mean.<sup>19</sup> Tables 7 and 8 list the results for schooling **--** only secondary school regressions are reported for the sake of brevity -- and health regressions using pooled data for 1965 and 1987 (1985 for per capita income). The sample size was smaller (72) since it consists of countries that have the required data for both 1965 and 1987.

Secondary school enrollment regressions provide strong support for the predictions of the theory. The services sector's share has a larger

> 19 For details, see Binswanger, Khandker and Rosenzweig (1989). Lot D **it** be the set of variables that proxy the production structure in country j at time t (sectoral shares of industry and services in employment/GDP), and Dt - Dt ( t) (20) where U is the effect of unmeasured country factors, and e is a timespecific error term. Let C be the level of the ith human capital variable in country j at time t. Then

jt jt **it it** 9t J S t) (21) where Y and Pjt are per capita income and degree of urbanization in country j at time t The simultaneity between C and Dj arising from their joint dependence on unobserved country-level variables U can be overcome if an additive model is estimated C c0 + c Y + c D + c P + c U + e (22) **it** 0 **1 it 2 it** e **s t** 4 **5 5t** The relationship for country means over time is C - c + cIY + c2DJ + cPJ +c4uJ **+iJ** (23) **5. 0 1** 5. **2 i. S 5.** 4 5 5. (3 Subtracting equation (23) from (22) we get (C -a ) -c (Y - t) +c(D - 6) + c(P ~+ (e **-)** (24) **t** J. **1** i+t J. **2** jt J. **S jt** S. **jt** J. If cj is random and uncorrelated with Dj , then this relationship can be e.timated using ordinary least squares.

31