rafmacalaba/datause-encoder-data huggingface dataset:
# **Figures and Tables**

Figure 1: Distribution of cluster sizes in six partial population experiments <!-- Start of picture text --> 300<br>200<br>100<br>0<br>10 20 30 40 50<br>Cluster (block) size<br>Frequency<br><!-- End of picture text --> <!-- Start of picture text --> (a) This paper; n = 68 , 808; G = 3 , 982; n/G =<br>17 . 3; sd ( ng ) = 8 . 25.<br><!-- End of picture text --> <!-- Start of picture text --> 30<br>20<br>10<br>0<br>0 20 40 60 80<br>Cluster (village) size<br>Frequency<br><!-- End of picture text --> (c) Haushofer and Shapiro (2016); _n_ = 2 _,_ 880; _G_ = 123; _n/G_ = 23 _._ 4; _sd_ ( _ng_ ) = 14 _._ 8.

<!-- Start of picture text --> 50<br>40<br>30<br>20<br>10<br>0<br>0 100 200 300<br>Cluster (local agency) size<br>Frequency<br><!-- End of picture text -->

- (b) Cr ́epon et al. (2013); _n_ = 21 _,_ 431; _G_ = 235; _n/G_ = 91 _._ 2; _sd_ ( _ng_ ) = 42 _._ 2.

<!-- Start of picture text --> 6<br>4<br>2<br>0<br>0 20 40 60 80<br>Cluster (neighborhood) size<br>Frequency<br><!-- End of picture text -->

- (d) Gin ́e and Mansuri (2018); _n_ = 2 _,_ 637; _G_ = 67; _n/G_ = 39 _._ 4; _sd_ ( _ng_ ) = 16 _._ 7.

<!-- Start of picture text --> 4<br>3<br>2<br>1<br>0<br>0 10 20 30 40<br>Cluster (constituency) size<br>Frequency<br><!-- End of picture text --> (e) Ichino and Sch ̈undeln (2012); _n_ = 868; _G_ = 39; _n/G_ = 22 _._ 3; _sd_ ( _ng_ ) = 9 _._ 6.

<!-- Start of picture text --> 80<br>60<br>40<br>20<br>0<br>0 20 40 60 80 100<br>Cluster (village) size<br>Frequency<br><!-- End of picture text --> (f) Imai, Jiang and Malani (2021); _n_ = 11 _,_ 089; _G_ = 437; _n/G_ = 25 _._ 4; _sd_ ( _ng_ ) = 16 _._ 7.

_Notes_ : This figure shows the distribution of cluster sizes in six partial population experiments; _n_ denotes the total sample size; _G_ denotes the number of clusters; _n/G_ denotes the average cluster size; _sd_ ( _ng_ ) denotes the standard deviation of cluster sizes. Average values across studies are _n_ = 7 _,_ 781; _G_ = 180; _n/G_ = 40 _._ 4; _sd_ ( _ng_ ) = 20. Median values across studies are _n_ = 2 _,_ 880; _G_ = 123; _n/G_ = 25 _._ 6; _sd_ ( _ng_ ) = 16 _._ 7. The data source for Cr ́epon et al. (2013) is: DARES (2010) “Enquˆete aupr`es des jeunes ́eligibles `a la prestation d’insertion jeunes diplˆom ́es,” Progedo-Adisp. doi:10.13144/lil-1596.

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widely used over the past few years for fiscal incidence analysis in both developing and developed countries.

The application of this fiscal incidence framework requires both microdata and macrodata. The microdata come from the second wave of the National Survey on Household Living Conditions and Agriculture<sup>6</sup> (ECVMA), and the state budget and national account data provide the macrodata. The ECVMA is a survey analogous to the Living Standards Measurement Study-Integrated Survey on Agriculture (LSMS-ISA), which is a program of the World Bank Group designed to collect nationally representative data at the household and individual levels on a variety of social indicators, such as consumption and agriculture. The LSMS-ISA is a survey conducted every three years. The second wave of the panel survey in Niger was implemented in 2014, covering both urban and rural areas in all regions of the country. The ECVMA sample was chosen through a random two-stage process. In the first stage, a number of enumeration areas were selected with probability proportional to size, using the 2001 General Census of Population and Housing as the base for the sample. In the second stage, 12 (urban) or 18 (rural) households were selected, with equal probability in each enumeration area.

The state budget and national accounts data come from Niger’s BOOST,<sup>7</sup> and the Statistics Book of Niger’s National Institute of Statistics. Government social spending in education and health come from BOOST, which is a World Bank Group project to enhance budget analysis across the globe by improving access to government expenditure data and linking spending to outputs and outcomes to the extent of possibilities. Data on public taxes come from the Statistics Book. The fiscal system analyzed here includes both direct interventions (direct taxes and direct transfers); and indirect interventions, such as value-added tax (VAT), excise tax, and other indirect taxes; and the monetized value of public education and health services. Additional details on the application of the CEQ in Niger can be found in Appendix A.

# **4.1 Fiscal Impoverishment**

In presenting the findings on fiscal losses, we contrast the conclusion Higgins and Lustig (2016) made, with the conclusions we have drawn. Table 2 reports fiscal impoverishment effects as defined by Higgins and Lustig in Niger at the national level as well as at some subgroup levels, for example, gender of the head of household, and area of residence.

> 6 _Enquête nationale sur les Conditions de Vie des Ménages et Agriculture_ (ECVMA).

> 7 A World Bank‐wide collaborative effort launched in 2010 to facilitate access to budget data.

13
We present three main findings. First, we find that firm-level productivity has been stagnant, as suggested by macro-data indicators, and that the aggregate gains have been mostly driven by more productive firms gaining market shares. Indeed, firms’ average productivity has not grown since 2015. Second, we find that in Pakistan, as in many other contexts, internationally linked firms perform better than domestic-owned or domestic-oriented firms. For foreign-owned firms we find suggestive evidence pointing to the productivity premium being explained both by a self-selection element – foreign investors target more productive firms in the first place – but also by foreign-owned firms increasing productivity after they are acquired by foreign investors. For exporting firms, we find suggestive evidence that the productivity premium is mostly explained by increases in productivity after starting exporting. Third, we find that integration with the global marketplace of upstream sectors – both for goods and services – has led to increased productivity in downstream sectors. Tariff reductions upstream helped increase productivity downstream mainly for non-exporters, and relatively smaller exporters – for which the process of claiming import duty exemptions is too cumbersome. FDI in upstream services sectors, instead, tends to benefit the productivity of those firms downstream that are further away from the technological frontier. We also find that the type of FDI encourages investments in intangible assets, pointing to a potential mechanism through which upstream services FDI increases productivity downstream.

Finally, data constraints prevent us shedding more light on the causal links between exporting and foreign ownership and productivity in Pakistan. Exploring this is important both from an analytical and a policy perspective. That should be an important task for future research.

# **References**

Ackerberg, D. A., Caves, K., and Frazer, G. 2015. Identification Properties of Recent Production Function estimators. _Econometrica_ 83(6): 2411-2451.

Ali, S. 2015. Trade Costs and Trade Composition: Firm-level Evidence from Pakistan. IGC Working paper.

Amiti, M., and Konings, J. 2007. Trade Liberalization, Intermediate Inputs, and Productivity: Evidence from Indonesia. _American Economic Review_ 97(5): 1611-1638.

Arnold, J.M., Javorcik, B., Lipscomb, M., and Mattoo, A. 2016. Services Reform and Manufacturing Performance: Evidence from India. _The Economic Journal_ 126(590): 1-39.

Atkin, D., Khandelwal, A.K., and Osman, A. 2017. Exporting and Firm Performance: Evidence From a Randomized Experiment. _The Quarterly Journal of Economics_ 132(2): 551-615.

Bastos, P., Monteiro, N.P., and Straume, O.R. 2018. Foreign Acquisition and Internal Organization. _Journal of International Economics_ 114: 143-163.

Battisti, M., Belloc, F., and Del Gatto, M. 2015. Unbundling Technology Adoption and TFP at the Firm Level: Do Intangibles Matter? _Journal of Economics & Management Strategy_ 24(2): 390-414.

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Table A8: Robustness check using the subsample with tenure being 3 years and less— Main reason for choosing the job

||Good|salary|Good|location|Go<br>co|od work<br>nditions|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Social networks|3.901|-0.870|-3.737|-5.390|-5.892|1.052|
||(2.742)|(3.543)|(6.337)|(8.460)|(5.814)|(8.817)|
|Abilities||X||X||X|
|Firm-occ fxed efect||X||X||X|
|R-squared|0.063|0.034|0.016|0.077|0.016|0.073|
|N|1744|1743|1744|1743|1744|1743|
|Control mean|21.558|21.558|14.974|14.974|32.320|32.320|
||Good pr|ospect for|Releva|nt to my|Good|prestige of|
||career pr|ogression|edu|cation||frm|
||(7)|(8)|(9)|(10)|(11)|(12)|
|Social networks|-9.517<sup>_∗∗∗_</sup>|-5.888|-0.027|-0.062|-0.343|-0.186|
||(3.329)|(5.701)|(0.083)|(0.053)|(1.167)|(1.574)|
|Abilities||X||X||X|
|Firm-occ fxed efect||X||X||X|
|R-squared|0.049|0.055|0.003|0.001|0.084|0.028|
|N|1744|1743|1744|1743|1744|1743|
|Control mean|16.472|16.472|0.102|0.102|4.819|4.819|
||Recomm<br>oth|ended by<br>ers|No oth|er ofers|||
||(13)|(14)|(15)|(16)|||
|Social networks|7.388<sup>_∗∗∗_</sup>|8.589<sup>_∗∗_</sup>|8.227<sup>_∗∗∗_</sup>|2.756|||
||(1.990)|(3.850)|(2.681)|(3.078)|||
|Abilities||X||X|||
|Firm-occ fxed efect||X||X|||
|R-squared|0.039|0.059|0.038|0.055|||
|N|1744|1743|1744|1743|||
|Control mean|1.178|1.178|8.578|8.578|||

_Note_ . This table presents linear probability models analyzing main reasons why employees chose the jobs. The dependent variable is the binary dummy for the main reason being the one indicated in a corresponding column header. The survey asked employees which of listed reasons was the main reason. The row _Control mean_ shows the sample means of the dependent variables among the employees who did not use social networks. The row _Abilities_ indicates whether the observable and unobservable characteristics (years of schooling, age at hire, and father’s and mother’s years of schooling, math and language z-scores, and Big 5 index) are included or not. All specifications include geographical division fixed effects. Standard errors clustered within an establishment are in parentheses. Coefficients, standard errors, and control means are multiplied by 100 and should be interpreted as percentage points. Significance levels:<sup>_∗∗∗_</sup> = 1%,<sup>_∗∗_</sup> = 5%,<sup>_∗_</sup> = 10%.

44
11 Since the aspirations of the women workers is simply to get paid and be independent they can also become passive in facing classic problems of labor, such as low pay, bad working conditions and not receiving their rights such as maternity leave, menstrual leave and so on. These women typically do not make the effort to find out about their rights, let alone to fight for their rights. In research conducted in Central Java, Wolf (1982:6) finds that women workers are often unaware of labor legislation and their rights and are reluctant to create conflicts. The situation is made worse by a lower level of education and lack of adequate mechanisms to provide necessary information. The passive attitude of women workers continues to contribute to their general reluctance to form a union (United Nation 1995). According to Scott (1986) the observation that women are at the bottom of the job structure indicates a marginalization process. In a broad context, marginalization is defined as a shift in power away from a party until it loses access to land, water, investment, work, education etc. to another party which will have a monopoly of these resources. Women are more likely to experience marginalization even though it is not exclusively a female phenomenon (Grijn, 1993). For women workers marginalization ensures them less chance to work, and less than adequate job related services, including health benefits. According to Scott (1986), lack of ambition is only one of the contributing factors to marginalization, and the main factor is the whole process of supply side industrial capitalism.

# _1I.3 Migration and Job Mobility_

Migration is an important indicator of the availability of work in an area. In general, the steady move of people from the rural areas to the city is an indication of economic growth (Boserup 1970:174). A survey of migration in Indonesia has concluded that migration has been dominated by men and that women are either following their husband or are avoiding economic hardship in their home town (Khoo, 1986:6). However, it has also been noted that there is an inclination for women to move to another place, especially to Jakarta, by themselves. Hugo (1989:215) finds that there are more women moving to Jakarta than men, whereas the traditional pattern of more men than women migration prevails in Lampung.

Several studies indicate that the majority of migrants are young, between the ages of 1525 years old (Todaro 1976). This general trend probably holds true for Indonesia as indicated by a survey on the pattern of migration to Pasar Rebo district in Jakarta, 28 per cent of the people are 15-19 years old (Aswatini 1986). People who are not married are also more inclined to move
<mark>the other hand, had a more shallow, procedural understanding of the nature of the phenomenon. The authors proffered that teachers should (i) encourage a deep learning approach in students by providing prompts and contextualized scaffolding, and (ii) encourage students to ask questions, predict, and explain the phenomenon during activities.</mark>

# _3. The student asks open-ended questions or performs thinking tasks_

Evidence of successful transfer of critical thinking skills from teachers to students is reflected in whether students ask open-ended questions or perform thinking tasks. Particularly effective for student learning and knowledge consolidation is whether students engage in self-explanation, or engage in higher-order thinking (Taylor et al., 2003). This occurs when the individual tries to explain how new information is related to known information or explained in the steps taken during problem solving (Dunlosky et al., 2013). For example, students who probe particular concepts by asking “Why” (e.g., “Why would humans be interested in exploring space?”) will enhance their knowledge of the topic and their ability to summarize key points and how such new information can be integrated with existing knowledge.

# **3.4 Socioemotional Skills**

One common problem with research on effective teaching is that an artificial duality exists between the development of academic skills versus socioemotional skills, whereby people that assume a high level of support for one outcome implies a low level of support for the other. Effective classroom environments produce rigorous academic experiences in a socially supportive manner, thus promoting both academic and socioemotional development (Ettekal, Kochenderfer-Ladd & Ladd, 2015; Lee & Smith, 1999).

Increasing evidence shows that socioemotional skills development plays an important role in academic achievement (Korpershoek et al., 2016). In a recent meta-analytic study assessing the efficacy of social and emotional (SEL) programs in kindergarten children through high-school students (N=270,034), students experiencing programs designed to enhance socioemotional skills showed an 11-percentile-point increase in academic achievement (Durlak, Weissberg, Dymnicki, Taylor, & Schellinger, 2011). Such programs showed positive consequences for improving student achievement and social and emotional skills even beyond the length of the program (from 6 months up to 18 years after receiving programs) (Taylor, Oberle, Durlak, & Weissberg, 2017). Specifically, the Taylor et al. meta27
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Zachariadis T., 2016. Proposal for a Green Tax Reform in Cyprus. _Cyprus Economic Policy Review_ , Vol. 10, No. 2, pp. 127-139.

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12 positively associated with malnutrition (the interpretation could again be that quintiles with high medical attendance for ARI or diarrhea are those were children are sickest and most malnourished.) For the infrastructure variables, we would expect to find an impact on diarrhea prevalence, given the WHO’s result that 88 percent of diarrheal diseases are due to unsafe water, sanitation and hygiene (WHO 2002.) We find that electricity and a lack of sanitation are indeed important of determinants of the prevalence of diarrhea. For electricity, this supports results found elsewhere that households with electricity are more likely to boil water and properly cook food (see the review in Brenneman and Kerf, 2002.) For sanitation, the result explains a large share of the cross-quintile differences in diarrhea despite the small elasticity. Finally, we still find that water has a significant impact on both child and infant mortality.

# **_4.3. Interaction terms_**

As mentioned in the introduction, the impact of infrastructure is likely to be higher if combined with health and education interventions. This can be tested using our model by introducing interaction terms between variables for infrastructure, health or education. To make this more wieldy, we use principal component indices for the health and infrastructure variables. For education, we continue to rely on the national-level literacy rate, but we transform the illiteracy rate (a “bad”) into a literacy rate (a “good”) so as to have all three variables for health, education, and infrastructure coded in a similar way (i.e., so that “more” is better).

As shown in Table 4, for infant mortality the results don’t suggest that there is much additional explanatory power to be picked up from the interaction terms. For child mortality, the impact of infrastructure increases with education, and vice-versa, since the coefficient of the interaction term is positive and statistically significant. The coefficient on the infrastructure principal component index, however, is no longer statistically significant suggesting that for low levels of education, the impact of infrastructure on child mortality is vanishing. By contrast education continues to reduce infant and child mortality even if infrastructure endowments are low. For malnutrition, both health and education interventions appear to condition the impact of infrastructure. Note however
22 _Border Prices for Diesel (local currency per liter)_

|Country|1980|1981|1982|1983|1984|1985|1986|1987|1988|1989|
|---|---|---|---|---|---|---|---|---|---|---|
|Colcmbia|11.27|13.44|15.63|17.43|<br>23.10|30.13|24.90|31.99|37.87|54.62|
|GhanaO|5.09|5.24|5.16|4.7|8.56|12.05|12.37|22.61|28.34|40.51|
|Maia|1.84|2.44|2.69|2.74|2.49|2.61|1.58|1.76|2.09|2.28|
|Inuocsia|149.69|178.32|184.10|245.20|203.22|217.09|133.69|205.69|228.44|251.45|
|Mdaysia|0.52|0.65|0.65|0.63|0.46|_OA9_|0.27|0.32|0.36|0.38|
|Morocco|0.91|1.46|1.71|1.93|1.93|2.12|1.14|1.13|1.19|1.16|
|Pakisn|2.29|2.79|3.37|3.56|3.08|3.20|2.02|2.33|2.54|2.62|
|Philippius|1.79|2.23|2.38|3.00|3.31|3.64|2.12|2.57|2.86|3.09|
|Thaland|4.89|6.16|6.40|6.20|4.68|5.31|2.74|3.22|3.43|3.65|
|Tuukey|19.18|33.80|49.68|66.63|88.96|121.09|97.48|130.35|236.63|329.86|
|Zimbabwe|0.16|0.21|0.23|0.29|0.29|0.36|0.23|0.24|0.28|32|

*Shadow exchange rate for 1980 through 1983 assumed to be IUS$20 cedis, official exchange rates thereafter.
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16
# **Table 7a: The determinants of training provision and use of temporary workers (Marginal effects from the Bivariate Probit model)**

||Provides training<br>and employs<br>temporary worker|s<br>Does not provide<br>training and<br>employs temporar<br>workers|y<br>Provides traini<br>and does not<br>employ<br>temporary<br>workers|ng<br>Does not provide<br>training and does<br>not employ<br>temporary workers|
|---|---|---|---|---|
|_Perceptions (No obstacle)_|||||
|Minor obstacle|0.027|0.011|-0.016|-0.022|
||(0.018)|(0.009)|(0.012)|(0.016)|
|Moderate obstacle|0.057|-0.006|0.005|-0.055|
||(0.020)**|(0.014)|(0.019)|(0.017)**|
|Major obstacle|0.079|0.010|-0.021|-0.068|
||(0.029)**|(0.024)|(0.029)|(0.023)**|
|_Age (Less than 5 years old)_|||||
|5 to 15 years old|0.035|0.017|-0.023|-0.029|
||(0.011)**|(0.012)|(0.018)|(0.012)*|
|16 or more|0.026<br>(0.031)|0.053<br>(0.022)**|-0.069<br>(0.025)**|-0.010<br>(0.031)|
|Ownership (Public)|0.057|-0.016|0.018|-0.060|
||(0.021)**|(0.021)|(0.029)|(0.019)**|
|Innovator|0.086|-0.038|0.057|-0.105|
||(0.024)**|(0.015)*|(0.025)*|(0.026)**|
|_Size (Less than 20 employees)_|||||
|20 to 100 employees|0.117<br>(0.014)**|-0.052<br>(0.013)**|0.064<br>(0.017)**|-0.130<br>(0.015)**|
|More than 100 employees|0.140|-0.108|0.147|-0.179|
||(0.026)**|(0.018)**|(0.030)**|(0.022)**|
|Sector (Manufacturing)|0.044|0.050|-0.068|-0.026|
||(0.029)|(0.024)*|(0.030)*|(0.031)|
|_Country income level (Low income)_|||||
|Lower Middle income|0.084|-0.036|0.054|-0.101|
||(0.059)|(0.033)|(0.046)|(0.059)+|
|Upper Middle income or high income|0.263<br>(0.072)**|-0.086<br>(0.023)**|0.054<br>(0.048)|-0.231<br>(0.045)**|
|Observations (number of countries)|9676 (38)||||
|Predicted probability at the mean|0.259|0.165|0.309|0.267|
|Wald test for rho = 0|Chi2(1)= 6.612;P|rob > chi2 = 0.010|||

Standard errors in parentheses (and adjusted for clustering on country). Where relevant, base categories of dummies are indicated in parentheses in italics. + = significant at 10%; * = significant at 5%; ** = significant at 1%. Data source: ICS.

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