rafmacalaba/datause-encoder-data huggingface dataset:
||[0.033]***|[0.173]***|[0.010]*|[0.030]|[0.032]***|[0.173]**|
|---|---|---|---|---|---|---|
|Observations|1120|669|1120|669|1120|669|
|R-squared|0.170|0.371|0.032|0.605|0.150|0.247|

Source: Own estimations using IES Mozambique and Zambia. Notes: Linear probability models estimated by Ordinary Least Squares. Demographic controls include: owner age dummies, gender, and education dummies. Firm controls include: firm age dummies, sector, location of business, and use of bank account, and if the firm is among the top 10% in productivity. Robust standard errors in brackets. * significant at 10%, ** 5%, *** 1%.

**Table 5: Urban Infrastructure and Services Correlates with Urban Informal Types of** **<u>Workers</u>**

||Unpaid w|orkers|Low-edu<br>emp|cated Self-<br>loyed|Informal Wa|ge employees|
|---|---|---|---|---|---|---|
|VARIABLES|(1)|(2)|(1)|(2)|(1)|(2)|
|Access to|-0.056|-0.001|-0.061|-0.024|0.010|0.013|
|electricity|[0.005]***|[0.006]|[0.008]***|[0.007]***|[0.007]|[0.007]*|
|Has access to|-0.010|-0.007|-0.112|-0.024|0.014|0.020|
|internet|[0.004]***|[0.005]|[0.006]***|[0.006]***|[0.006]**|[0.007]***|
|Use computer<br>Access to|-0.021<br>[0.004]***<br>-0.003|-0.015<br>[0.006]***<br>-0.005|-0.177<br>[0.007]***<br>-0.002|-0.009<br>[0.007]<br>0.004|-0.042<br>[0.008]***<br>-0.010|-0.041<br>[0.009]***<br>-0.004|
|water|[0.004]|[0.004]|[0.006]|[0.006]|[0.006]*|[0.006]|
|Non-poor|-0.102<br>[0.005]***|-0.035<br>[0.005]***|-0.050<br>[0.007]***|-0.009<br>[0.007]|0.049<br>[0.006]***|0.026<br>[0.006]***|
|Demographic<br>controls|no|yes|no|yes|no|yes|
|Jobs controls|no|yes|no|yes|no|yes|
|Constant|0.244<br>[0.045]***|0.477<br>[0.014]***|0.621<br>[0.118]***|0.398<br>[0.018]***|0.105<br>[0.019]***|0.005<br>[0.021]|
|Number of<br>countries|17|14|17|14|17|14|
|Observations|87,008|53,958|87,008|53,958|83,510|53,958|
|R-squared|0.054|0.34|0.087|0.379|0.032|0.162|

Source: Own estimations using GMD.

Notes: Linear probability models estimated by Ordinary Least Squares. Demographic controls are: age dummies, education dummies, gender, and relationship to household head. Job controls are: sector of employment, occupational skill level. Robust standard errors in brackets. significant at *10%, ** 5%, *** 1%.

Page **41** of **60**
1980 to 96 g per capita in 2009, and that about two thirds of this growth came from increases in animal product consumption.

The changing dynamics of food consumption shown above affect supply and demand balances for food directly and indirectly. In particular, whereas _direct_ demand for food grains such as wheat and rice, tends to decrease as income rises, the same driving force (the rise in per capita income) is likely to lead to an increase in _indirect_ demand for feed grains as more grain and other feeds are needed for animal production. Figure 2a shows the self-sufficiency ratios for the key staple foods (rice, wheat, maize and soybeans combined) for the period 1960-2012 for Asian countries. Most strikingly, the self-sufficiency ratios declined sharply in higher income Asian countries, from around 75 to 27 percent for Japan, from about 88 to 21 percent for the Republic of Korea and from about 86 to 13 percent for Taiwan, China, during the period 1960/1961-2012/2013 (Production, Supply and Distribution (PSD) data, the United States Department of Agriculture (USDA)).<sup>5</sup> Figure 2a also shows that whereas China tended to achieve self-sufficiency for grains for the 1960s, 1970s, 1980s and most of the 1990s, its selfsufficiency ratio has been declining in recent years.

Figure 2b reports the evolution of the demand and supply gap by major grains for China. It is clear that China’s recent declining self-sufficiency ratio for grains is predominantly attributable to a large increase in soybean imports. The expansion of the livestock sector which increased the demand for protein meal, along with the rise in consumers’ demand for vegetable oils, was a major factor leading to the growing demand.<sup>6</sup> The Chinese government appears to

> 5 However, further disaggregation of the data reveals that the self-sufficiency ratios for Japan, the Republic of Korea and Taiwan, China, vary by grain: whereas imports of corn contributed most to the widening gap for three countries, they have been relatively self-sufficient in terms of rice.

> 6 In 2009, out of 59 million tons of domestic soybean consumption, about 9 million tons was consumed as food. 49 million tons of soybean was crushed and made into 9 million tons of vegetable oil and 39 million tons of soymeal (PSD, USDA).

6
31

**Graph 5 Real State Prices**

<!-- Start of picture text --> Argentina<br>1.1-<br>10.<br>0.94<br>0.8-<br>0.7-<br>0.6.<br>0.5-<br>0.4 i 8 8 S g0.0<br>80 82 84868890 92 94 96 98 00<br><!-- End of picture text --> <!-- Start of picture text --> Chile<br>3.0<br>2.5<br>2.0-<br>1.0-<br>0.5.<br>7 7<br>75 76 77 78 79 80 81 82<br><!-- End of picture text --> <!-- Start of picture text --> Source: Conley and Maloney (1995)<br><!-- End of picture text -->

**Colombia**

<!-- Start of picture text -->
1.02-<br>1.00<br>0.98.<br>0.96<br>0.94-<br>0.92-<br>0.90  8<br>0.88.<br>0.868<br>80 828486  88 9092  949698  00<br><!-- End of picture text -->

<!-- Start of picture text --> Mexico<br>1.1<br>10<br>0.9.<br>0.8-<br>07,<br>0.6<br>0. 5<br>80 8284 866889092 9496 980<br><!-- End of picture text -->
while for the health houses we find a significant impact only on the evaluation of facilities. We do not find impacts on the internal administration of the facilities. The program did not affect the frequency of internal evaluations of staff, the number of staff meetings, or solicitation of patient opinions. We also do not find impacts on turnover as measured by whether staff left the facilities. Changes in staffing were measured over the 12 months preceding the follow-up survey. As the program had been ongoing for more than three years at the time of the follow-up survey, we cannot rule out impacts on staff retention earlier in the implementation period.

Next, we turn to examine the impacts of the PBF program on outcomes related to the health providers in the primary health facilities (Table 4). The analysis was conducted on a pooled sample of providers at the RHCs and HHs. The PBF program increased the monthly income of providers by 438 Tajik Somonis (about 48.6 USD at the time of the follow-up survey) (p<0.001). This difference corresponds to 62 percent of the income of the control group providers. We do not find an impact on self-reported absenteeism or number of hours worked. Providers in PBF facilities reported an average increase of 4.9 patients seen in the past day (p = 0.065).

Providers in the PBF districts reported significantly higher satisfaction. In the baseline survey, providers reported high levels of satisfaction with respect to the facility management, and relationship with the staff at the facility, district and ministry level. However, providers were mostly unsatisfied with respect to the physical condition of the facilities and the equipment and supplies available (Table A4 in the appendix). Of a list of 13 aspects related to their work and perception of the facility, providers in the PBF facilities were especially more likely to report satisfaction in the follow-up survey with respect to availability of medicine, equipment and other supplies in the facilities.

In Panel B of Table 4, we present impacts of the intervention on provider competence in case management of scenarios for children and adults, measured by clinical vignettes. For each of the 6 standardized scenarios, we coded whether the provider correctly diagnosed the case, the proportion of a recommended set of questions asked during clinical history taking, and the proportion of recommended examination procedures they would conduct. The definitions of these variables are presented in Appendix 5. We find an overall improvement in provider competence as a result of the project. Providers were more likely to correctly diagnose a case of high cardiovascular risk by 27 percentage points. For 4 of the 6 vignettes, the proportion of 12
Dix-Carneiro and Kovak Trade Reform and Regional Dynamics Figure 16: Individual Probability of Formal Employment Away from the Worker’s Initial Region - Tradable Sector Worker Sample - 1990-2009 <!-- Start of picture text --> 0.8<br>0.6<br>0.4<br>0.2<br>0<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br>-0.2<br>-0.4<br>-0.6<br>-0.8<br><!-- End of picture text --> Each point reflects an individual regression coefficient where the dependent variable is an indicator for being observed in a different region in the year listed on the x-axis than in 1989, when the sample was drawn, calculated using RAIS. For all years, the independent variable is the regional tariff change reflecting tariff changes from 1990-1995 (described in the text), with various controls (described in the text). Positive estimates imply a lower probability of being employed away from the initial region for workers initially employed in regions facing larger tariff declines. Vertical bar indicates that liberalization was complete in 1995. Dashed lines show 95 percent confidence intervals. Standard errors adjusted for 112 mesoregion clusters.

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<!-- Start of picture text --> a. GDP b. Employment rate<br>2 2<br>Sg ee. *e °<br>doc ag ee e 2° ee.” e° o e = e “eee ° e<br>‘® 8 e y+ e e<br>Ps4° g<br>x=oc! i<br>D6<br>Pe =}<br>0 20 40 60 80 100 0 20 40 60 80 100<br>ite) c. Inactivity rate its) d. $5.5 Poverty headcount rate<br>ot =<br>S ° °<br>“2 2<br>ava le ° e<br>£ e e bd<br>ge eco . ° e ° ° . ®<br>(S) o . ogSe eee of . oe8 A x oO §¢ e e% e ne<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Job protection measures, % of SP response budget Job protection measures, % of SP response budget<br><!-- End of picture text -->
**Table** 7. **OLS Regression - Subjective Life Expectancy of Men and Women**

||M|en|Wo|men|
|---|---|---|---|---|
|SLIFEX|Coefficient|Std.Err.|Coefficient|Std.Err.|
|LIFETAB|0.854|(0.034)***|0.879|(0.031)***|
|YEDU|0.281|(0.087)***|0.197|(0.086)**|
|SMOKER|-1.113|(0.306)***|-1.051|(0.335)***|
|HOSP|-2.151|(1.051)**|-0.124|(0.717)|
|PSICK|-32.196|(12.951)***|-25.040|(11.839)**|
|DEATH|-2.613|(1.813)|-1.025|(1.577)|
|SICK|0.607|(0.932)|-1.231|(0.852)|
|DISAB|0.103|(0.114)|-0.221|(0.115)**|
|PERLIVE|5.953|(2.473)**|6.689|(2.231)***|
|AGEPERD|0.065|(0.037)*|0.100|(0.034)***|
|Intercept|-0.817|(3.086)|-5.116|(2.747)|
|Numberofobs|1068||1210||
|F(10,1057)|156.420|F(10,1199)|200.370||
|Prob >F|0.000||0.000||
|R<sup>2</sup>|0.597||0.626||
|AdjR<sup>2</sup>|0.593||0.623||

"*** statistically significant at 1%; "**" at 5%; and "*" at 10% 46
cohort study to prepare for future HIV prevention clinical trials in Kisumu, Kenya', _J Infect Dev Ctries_ , 6: 870-80.

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Commission, Ghana AIDS. 2007. "2007 HIV Sentinel Survey Report." In.

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72
comparable and well-balanced at baseline. As can be seen from the table, the share of available baseline data is not perfectly balanced (see footnote 18), but the response rate at follow-up is well-balanced, which is what matters most since it is the basis for empirical estimation.

Table A4 highlights that most firms offering apprenticeship positions are informal micro and small enterprises. 84 percent have no formal legal status and 68 percent do not keep books. Firms have 6.3 permanent employees on average (counting the owner), of which 3.3 are apprentices. Traditional apprentices therefore constitute more than half the workforce in these micro and small firms. Traditional apprentices are mostly hired through private channels, 82 percent based on a request from their parents. About half of the apprentices in firms at baseline pay fees to the master craftsmen. These traditional apprenticeships are expected to last six years on average. Table A4 documents that the experiment led to good baseline balance between the treatment and control firms: the few significant differences are marginal and of small magnitude.

The follow-up survey was collected by phone for youth in the treatment and control groups. The most important variables for the analysis are youth activities at the moment of the survey, as well as hours and earnings in those activities. The survey contains a detailed employment module covering primary and secondary activities. We can thus describe the portfolio of youth activities, distinguishing between occupations as apprentice (formal or traditional), wage worker and self-employed. Other important variables relate to youth human capital investments, including participation in apprenticeship (formal or traditional), vocational training and schooling.

The follow-up firm survey in the treatment and control groups took place in person. It collected data on the characteristics of the firm, its workforce, as well as its sales and profits. It also compiled a listing of all apprentices who entered or left the firm since the start of the experiment (i.e. on or after the randomization date in each locality), and collected additional information on each of these apprentices, both from enterprise owners and from apprentices themselves. This employer-employee type of data enables us to accurately measure the flows of apprentices in and out of firms, as well as their contribution to firm activity. For example, we are able to compute the number of apprentices working in firms at the moment of the survey, but also various interesting flows: the number of apprentices who entered firms since 14
**Figure 2: Provider Absence Rate in Education (%)**

<!-- Start of picture text --> Papua New Guinea 15.1<br>11<br>Honduras 14<br>14<br>Zambia 17<br>27<br>Senegal 30<br>8.1<br>Ghana 19.4<br>18<br>Lao PDR 8<br>19<br>India 25<br>16<br>Bangladesh 16<br>0 5 10 15 20 25 30 35<br><!-- End of picture text --> SOURCE: ROGERS AND KOZIOL (2011) But, more generally, developing countries have shown grave difficulties in improving the quality of service. Take the case of primary education. A majority of developing countries show appalling performance in a variety of learning assessments. Just as an example, in rural India 41% of children in class 5 cannot read at class 2 level, 22% of children in class 2 cannot recognize words and 58% of children in class 5 cannot divide 3 digits by 1 digit (Pratham 2008). It is not an exaggeration to say that school systems in many developing countries are becoming large factories producing what will be, de-facto, illiterate young people.

Given this background, it is hardly surprising that service delivery reform has become almost a rallying cry among development practitioners. A quick review of the international experience shows that over the last two decades developing countries have 3
able social clubs and carries over to public-good-providing civic participation later (perhaps much later) . The third type of program (self-help groups) is closer to the Putnam model. Siuch programs create groups that produce club goods, however even in these programs the benefits of membership are clearly more economic (better access to capital, better prices on produce) than social (making friends by singing or bowling together) and so even self-help groups do not strictly fit the Putnam mechanism.

In summary we identify three possible mechanisms by which CDD programs may affect outcomes. They may reduce the costs of collective action as Casey, Glennerster and Miguel (2012) model or they may create denser social networks or they may increase the benefits of pro-social behavior to individuals by making their preferences more pro-social. Our networks survey and laboratory measurements allow us to zero in on whether these latter two factors are changing. Thus, the theory of change we are testing is whether participation in CDD programming activities and civic education created denser social networks and increased participants’ preferences to engage in pro-social action.

Due to the control we were able to exercise in the lab, our subjects’ behavior should be unaffected by any increased efficiency or lower costs of collective action in the community. Any difference in the laboratory behavior of members of treated and control communities are, by the construction of our experiments, not attributable to different costs, which are constant across the treated and control communities. Similarly, while our networks survey allows us to test if CDD programs create denser social networks, such networks and the enforcement/incentivizing power they may possess, cannot explain subjects’ behavior in the lab, where subjects’ actions were unknown to any one else in the community. Thus what we measure in the lab is the subjects’ willingness to behave pro-socially even when there were no punishments for not doing so. Our laboratory activities measure subjects’ primitive preferences to engage in pro-social action—less prosaically, they measure subjects’ civic 13
Concerning labor market characteristics, public sector workers are relatively more likely to be employed as regular wage workers. This status applies to 94.1 percent of public sector workers but to only 16.5 percent of private sector workers. Conversely, public sector employees are substantially less likely to be contract or piece paid workers or, unsurprisingly, own-account workers. Sociodemographic variables also show notable differences between the two sectors. In particular, statistically significant differences exist with respect to workers’ average location, gender and experience. For instance, 67.9 percent of public sector workers live in urban parts of Bhutan but only 27.0 percent of private sector workers are city dwellers. Moreover, public sector workers are less likely to be female and on average have less potential experience. At the same time, while according to the BLFS 2014 the proportion of Bhutanese nationals is very high in both sectors it does not significantly differ between them.<sup>8</sup> The average educational attainment of workers differs markedly between the two sectors. Whereas less than a quarter (22.7 percent) of public sector employees have no or no formal education this is the case for almost two-thirds of private sector workers (64.8 percent). On the other hand, 3.5 percent of public sector workers but only 0.5 percent of private sector workers have higher tertiary education, defined here as the completion of at least a Master’s degree. Finally, industrial structures also differ between the two sectors. For instance, public sector workers are dramatically less likely to work in agriculture, hunting and forestry ( _nil_ vs. 46.7 percent) but much more likely to work in public administration and defense (52.5 vs. _nil_ percent). Again, these differences are statistically significant at the 1 percent level. In general, the picture that emerges from Table 2 is that in Bhutan wages, jobs and workers all differ considerably between the public and the private sector.

Fringe benefits that are paid in addition to the base salary are an important aspect of overall worker compensation for which the BLFS 2014 contains relatively detailed information. According to Bhutan’s Labor and Employment Act, the majority of workers should _de jure_ be entitled to the following eight distinct fringe benefits: gratuity (not in the sense of tips but as an additional retirement benefit after a certain length of service), provident fund payments (another retirement benefit), overtime payments, paid annual leave, paid sick leave, paid casual leave, paid maternity/paternity leave

> 8 Administrative data from the MoLHR and the World Bank’s Bhutan Enterprise Survey 2015 document much higher proportions of non-Bhutanese workers than the BLFS 2014. This hints at a general under-reporting of nonBhutanese individuals in the BLFS 2014.

- 16 -