rafmacalaba/datause-encoder-data huggingface dataset:
135,000 Naira (US$360), although the mean is higher for outsourcing (207,000 versus 159,000 Naira). These salary levels are in line with our intervention as firms in the outsourcing treatment paid their professionals a mean of 52,224 Naira per month (in exchange for working 1.1 days per week). This amount is similar to what firms in the insourcing treatment paid their hired workers, but the difference is that outsourced professionals also worked with other clients so their total monthly income would be higher.

We emphasize that a further key difference across the interventions is in the expectations of how this outside human capital should be used. With business training, the trainers do not directly carry out any activities for the firm, and instead try to increase the skills of the entrepreneur. In contrast, with an insourced worker or outsourced professional, the functional specialist directly implements tasks for the firm and is responsible for carrying these out on an ongoing basis. Business consulting is a more of a mix: consultants work directly with the entrepreneur (often at the firm), but it is on a time limited basis and typically involves more advising than doing.

# **3. Measuring Impacts**

# **3.1 Data Collection**

Baseline surveys were collected during the induction workshops. Our main data on firm outcomes then come from two rounds of follow-up surveys. The first follow-up survey took place between March and June 2018, which corresponds to a period of about one year since after the interventions started. Thus, the time between an intervention ending and the first follow-up survey is about: 8-9 months for firms in the training treatment; 2-4 months for firms in the consulting treatment; and 2-4 months for firms in the insourcing and outsourcing treatments. The second follow-up survey took place one year later (between February and June 2019), which corresponds to two years after interventions started and 14-21 months after interventions ended depending on treatment group.

The first follow-up survey had a response rate of 88.6%, and the second follow-up survey had a response rate of 86.1%. 93% of firms completed at least one of the follow-up surveys. In addition, we were able to collect data on business closure for many of the firms not answering the survey, so we have operating status for 96.4% of firms in the second follow-up survey. We cannot reject equality of attrition rates across the five experimental groups (Appendix Table 3.1). Further, the sample of survey non-attritors remains balanced on baseline observables (Appendix Table 3.2).

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Behaviour of Firms find little evidence of an increase in part time work being the preferred route. Nor do we find evidence that short time working and involuntary holidays have been that widely used. By 1992.3 under 35% of finns in the sample reported use of short time work and 17% use of involuntary leaves. Under 5% of the total sample labour force was on involuntary leave and nearly 90% were concentrated in machine building and light industry. This estimate appears on the low side comparing with information available from more aggregate data. A far larger Goskomstat suivey in August 1992 covering over 1500 finns across all regions reported nealy a third of employees on rnvoluntary leave or short time 7.

For explicit separations, the survey data indicate that while nearly three-quarters of the sample reported net employment losses for the third quarter of 1992, over 25% posted net employment gains. At the same time, in one quarter alone nearly 3% of the labour force experienced some labour market transidon. Table _5_ contains some interesting information. In the first place, total separations amounted to between 8-10% across the firm size classes and the separation rate was rather evenly distributed. Nfet job losses were much smalller amounting to no more than 5% over the whole sample. This is higher than the national level but reasonably consistent with what we know about the path of employment in the Moscow region. For net job losers, the dispersion is fairly low but in general biassed upwards for smaller firms. Indeed, the largest finns experienced net increases to their labour force over the reference period. Expected job losses over the fourth quarter of 1992 were similarly reported at low frequencies and were inversely associated with firm size.

Table 6 also pins down the principal characestics of the separations process. The dominance of quits is striking and over 50% of all separations can be classed as voluntary. Explicit job reduction decisions display considerable varance and amounted to around 17% of gross job losses for the full sample. Total involuntary separations comprised less than a quarter of reported total separations. The weight of quits in total separations reinforces the view that the Russian labour market remais characterized by rather high turnover at local lev"ia, if not across regions where institutional, housing and other constraints tend to be more binding.

The persistnce in hires raises some interesting questions. Relating output changes to employment changes in the sample is instructive. Perhaps most striking is the absence of a clear and predictable relationship between output and employment movements. Indeed, for the 25% of the sample that reported positive net hires in 1992.3, nearly 70% projected output losses over 1992 with an unweighted mean decline of 15% There is significant dispersion over branches and firm size classes with respect 7 For Moscow the figure was 25% for a sample of 23 firms.
location and corporate structure. Unsurprisingly, most of the observations (more than 90 percent) are recorded in the three most populous regions in Portugal (Norte, Centro and Lisboa), and the overall degree of domestic firm ownership is relatively high (almost 90 percent). Our data refer exclusively to economic activity that takes place in Portugal, which constitutes all economic activity for the vast majority of firms. There are some firms in our data that also have plants located abroad, but these are very few.<sup>12</sup>

## [ Table 1 here ]

Table A3 in the Appendix reports key moments on the distribution of export destinations of Portuguese manufacturing firms in 2006, both in the full customs data and in our estimation sample. The main export destinations are other EU member states that are part of the eurozone (Spain, Germany, France), but also include countries outside the eurozone and/or the EU, notably the United Kingdom, United States, Angola and Singapore. For all destinations, export shares in the estimation sample are remarkably similar to those in the full customs data.

Figure A2 in the Appendix shows the variation of forecasted and actual GDP growth in each of the top 18 destinations for Portuguese exports. We observe significant variation across destinations with regard to both these variables. Since firms initially served different destinations to a varying degree, they were differentially exposed to these unexpected demand shocks across markets.

Figure 1 shows that there exists significant variation across firms in the estimation sample with regard to weighted actual and forecast growth. The range of forecast and actual growth is often greater than 10 percentage points, which is considerably higher than the averages for both these variables. The range of forecast growth is especially wide (and considerably larger than that of actual growth) in the initial years of the sample period, but remains sizeable over the whole period.

## [ Figure 1 here ]

# **5.2 Average effects on firm performance and worker compensation**

We now turn to the main focus of the empirical analysis: the impacts of unexpected demand shocks on worker compensation. Table 2 reports the point estimates on the effects of the weighted forecast error and weighted forecast growth on various measures of firm performance and average labor costs, using data from SCIE. Columns (1) and (2) reveal that unexpected demand shocks

> 12A total of 18 firms, four of which are foreign owned, have plants located outside Portugal.

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# **7 Analysis Using the Panel Data**

In this section, I use the balanced panel dataset constructed from the survey to provide further support for the relation between trade orientation of firms and their evolution and alleviate the endogeneity problem. Using the panel data allows me to control for the unobserved firm fixed effects. I ran several experiments with the panel data. In the first experiment, I regress growth rate between time (t-3) and t and probability of introducing a new variety between time (t-3) and (t) on trading status of the firm in (t-3). As additional firm level controls, I introduce firm’s ownership status, its size, and its age all from time (t-3)<sup>17</sup> . In addition, I include the country, industry, and survey year controls. The results which are given in Table 14 are in accordance with the results presented in Table 10. Trading firms grow faster than non-trading firms. Two-way traders are the fastest growing and most innovative group. Only exporters no longer grow significantly higher than non-traders. For product innovation, all three trading groups are significantly different from non-traders. As before, foreign ownership contributes to growth but is negatively related to product innovation.

In the second experiment, I restrict the sample to the firms who didn’t trade at (t-3) and run the same regressions as above. This experiment shows that among the firms who didn’t trade at time (t3) which group had the highest growth rate over the three year period. The results show that, twoway traders were the fastest growing group. They also were the most innovative group. Following two-way traders, only-importers were the second most innovative group of firms. In the last experiment, I include the firm level fixed effects in the regression<sup>18</sup> . Results show that two-way traders and only exporters grow significantly higher than non-traders. Overall, the results of the analysis from the panel data are in accordance with the results presented in section 5.

> 17 In the panel regressions, indicator variable for past R&D investment was excluded as the inclusion of this variable decreased the sample size by more than half. This led to few observations in each trade group. 18 I run this regression only for employment growth. I also ran the random effect regression and tested which method was appropriate using the Hausman test. The test result strongly rejected the efficiency of random effects model.

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Brooker, S., Inyega, H., Estambale, B., Njagi, K., Juma, E., Jones, C., Goodman, C., & Jukes, M. (2013). _Impact of Malaria Control and Enhanced Literacy Instruction on Educational Outcomes among Kenyan School Children: A Multi-sectoral, Prospective, Randomized Evaluation_ . 3ie Draft Grantee Final Report. The International Initiative for Impact Evaluation.

Bruns _,_ B., & Luque _,_ J. (2015). _Great Teachers: How to Raise Student Learning in Latin America and the Caribbean_ . World Bank, Washington, DC.

Bruns, B., Filmer, D., & Patrinos, H. (2011). _Making Schools Work: New Evidence on Accountability Reforms_ . World Bank, Washington, DC.

Cabezas, V., Cuesta, J. I., & Gallego, F. A. (2011). “Effects of Short-term Tutoring on Cognitive and Noncognitive Skills: Evidence from a Randomized Evaluation in Chile.” Unpublished manuscript, Pontificia Universidad Católica de Chile, Santiago.

Camargo, B., Camelo, R., Firpo, S., & Ponczek, V. (2011). “Test Score Disclosure and School Performance.” Sao Paulo School of Economics Working Paper, Center for Applied Economics, Sao Paulo, Brazil.

Carillo, P., Onofa, M., & Ponce, J. (2010). “Information technology and student achievement: Evidence from a randomized experiment in Ecuador.” IDB Working Paper No. 223, Inter-American Development Bank, Washington, DC.

Conn, K. (2014). “Identifying Effective Education Interventions in Sub-Saharan Africa: A meta-analysis of rigorous impact evaluations.” Unpublished manuscript, Columbia University, New York, NY.

Cristia, J., Ibarrarán, P., Cueto, S., Santiago, A., and Severín, E. (2012). “Technology and child development: Evidence from the One Laptop per Child program” IZA Discussion Paper No. 6401, Forschungsinstitut zur Zukunft der Arbeit GmbH, Bonn, Germany.

Dinkelman, T., & Martínez, A. C. (2013). “Investing in Schooling in Chile: The Role of Information about Financial Aid for Higher Education.” _Review of Economics and Statistics_ , 96, 244-257.

Duflo, E., Dupas, P., & Kremer, M. (2012). “School Governance, Teacher Incentives, and Pupil-Teacher Ratios: Experimental Evidence from Kenyan Primary Schools.” NBER Working Paper No. 17939. National Bureau of Economic Research.

Duflo, E., Hanna, R., & Ryan, S. P. (2012). “Incentives Work: Getting Teachers to Come to School.” _American Economic Review_ , 102(4), 1241–1278.

Evans, D., Kremer, M., & Ngatia, M. (2014). “Schooling Costs, School Participation, and Long-Run Outcomes: Evidence from Kenya.” Unpublished working paper.

Filmer, D., & Fox, L. (2014). _Youth Employment in Sub-Saharan Africa_ . Africa Development Series. World Bank, Washington, DC.

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The greater reduction in variance is more clearly evident when disaggregating poverty measures at lower territorial levels. For example, Table 2 shows standard errors and the coefficient of variation of poverty measures estimated at the governorate level in Tunisia. Since sub-sample sizes are decreasing from regions to governorates, direct standard errors increase exponentially, that is, the coefficients of variation are higher than 10% in one-third of the governorates. On the other hand, standard errors of poverty rates calculated according to the poverty mapping show a very low increase. In fact, the coefficients of variation are all less than 10% except in the case of the Governorate of Ariana.

In the analysis of the poverty mapping measure at lower levels, in particular at the PSU level, direct estimates are affected by too high sampling errors and are therefore no longer significative. On the other hand, estimates from poverty mapping still show very limited standard errors. In this context, we shift our presentation from poverty rate to per capita consumption expenditure. This is done for two main reasons: i) per capita consumption expenditure estimates are usually more precise (see Betti _et al._ , 2018 in general, and INS, 2020 in this context); ii) due to their higher precision, per capita consumption expenditure is the variable used in our proposal for implementing implicit stratification in Section 4.

As previously mentioned, in Tunisia there are about 39,000 clusters used as PSUs. Before presenting results from the poverty mapping at cluster level, it is fundamental to bear in mind that although the PSU-level estimates are not suitable for dissemination and for policy making, they can still be very useful in informing sample design. In order to presents such results, we synthesized measures at the governorate level in Table 3.

# **Table 2. Poverty rates** **<u>by Governorate</u>**

|**Governorate**|**E**<br>**Poverty**<br>**rate**|**BCNV 2015**<br>**Standard**<br>**error**|**cv**|**Poverty m**<br>**Poverty**<br>**rate**|**apping - Cen**<br>**Standard**<br>**error**|**sus 2014**<br>**cv**|
|---|---|---|---|---|---|---|
|**TUNIS**|3.5|0.575|0.164|4.6|0.360|0.078|
|**ARIANA**|5.4|0.698|0.129|7.0|1.024|0.146|
|**BEN AROUS**|4.3|0.595|0.138|5.6|0.288|0.051|
|**MANOUBA**|12.1|1.379|0.114|9.8|0.697|0.071|

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||Energy<br>consumption<br>(kWh/m2/year<br>)|Heating<br>(kWh/m2/ye<br>ar)|Electricity<br>(kWh/m2/<br>year)|Reduct<br>consump<br>Heating<br>(kWh/m2/<br>year)|ion in<br>tion for<br>Electricit<br>y<br>(kWh/m2<br><br>|
|---|---|---|---|---|---|
|**Bulgaria**|||||~~/~~<br>~~)~~|
|Dobrich: Pilot reconstruction<br>project|99.5|n/a|n/a|by 5|3%|
|**Norway**||||||
|Nardo School, Trondheim:<br>Reconstruction of primary<br>school|99|29|43|by 70%|n/a|
|**Austria**||||||
|Kramsach, Tyrol: Construction<br>of energy-efficient kindergarten|n/a|14|n/a|n/a|n/a|
|**China**||||||
|Beijing: Green micro energy<br>consumption kindergarten|68|12|40|n/a|n/a|
|**Denmark**||||||
|Vejtoften, Høje-Taastrup,<br>Denmark: Reconstruction of<br>kindergarten|n/a|69|n/a|by 54%|n/a|
|Ballerup: Reconstruction of<br>Egebjerg School (1997)|109.3|87.3|22|by 50%|by 30%|

_Sources_ : Mørck (2003); Norwegian‐Bulgarian Cooperation Project (2011), and Rose and Thomsen (2012) Given the fact that, in Russia, the government funds ECD institutions, federal and regional policy makers often welcome the opportunity to save public money by using these new technologies so that they may be able to reallocate the saved expenditures to enhance other areas of child development and early learning.

# **Current Design of and Energy Use in Russian Kindergartens**

The traditional approach to kindergarten design in Russia was to create a single room for each activity, which usually transformed a preschool facility into a labyrinth of corridors and separate rooms. This particular spatial arrangement has been reinforced by current sanitary and fire regulations, which are designed to protect children from diseases, to ensure their safety, and to prevent any possible risks. These regulations are based on the “group isolation” principle, which discourages the mixing of different groups within the school (generally, a group consists of 15 to 30 children and one or two teachers).

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participants. The highest intervention impact, however, is measured among recipients of intervention group 4, that is the personal invitation with a conditional cash-like incentive, among whom screening on both tests increased by 31.2 percentage points compared to the control group.

<mark>The rest of this paper is outlined as follows. Section 2 describes the Armenian context, the prevalence of hypertension and diabetes, and rationale for reforms to increase screening rates. Section 3 presents the methods including the intervention design and the experimental methods. Section 4 presents the evaluation results as well as a cost-effectiveness analysis. Section 5 discusses those results and concludes.</mark>

# **2. Context**

Armenia is a landlocked country in Eurasia bounded by Georgia, Azerbaijan, Turkey and the Islamic Republic of Iran. NCDs now account for 93 percent of deaths in Armenia, including cardiovascular diseases (55.2 percent), cancers (20.2 percent), and diabetes mellitus (1.5 percent). Hypertension and diabetes mellitus are significant contributors to the burden of NCDs (Andreasyan et al. 2019; Institute of Health Metrics and Evaluation (IHME), 2020).

In Armenia, general practitioners and family physicians at the primary health care (PHC) level are responsible for health promotion, screening, treatment, monitoring, and referral to specialist care. PHC is provided in polyclinics in urban areas, ambulatory facilities in rural areas, and outpatient health centers. By law, since 2006, all Armenian citizens can access PHC without cost, as the basic benefits package covers PHC and emergency care for the entire population. However, public funding for PHC does not cover the cost of outpatient medicines or expensive diagnostic care, except for socially vulnerable and other selected groups (Lavado et al., 2018; GoA Decree, 2004). Hence, one in five Armenians reports that financial barriers are the reason for forgoing needed care. In addition to these financial barriers, the perception that PHC is of poor quality and gaps in regulation contribute to foregoing of PHC for specialist and emergency care (Andreasyan et al., 2016).

These gaps in service delivery contribute to challenges in detecting and managing NCDs in Armenia, including hypertension and diabetes. In a nationally representative survey, one out of three people above the age of 15 years has undiagnosed hypertension. In addition, only 43.5 percent of the population above 15 years have had their blood pressure measured by a health provider, screening for hypertension. There were higher rates of screening among women compared to men, and in Yerevan compared to the provinces (Andreasyan et al. 2016). Similarly, only 24 percent of individuals aged 15 years and above had their blood glucose levels measured in the past 12 months, of whom 20 percent had been measured by a physician (Andreasyan et al. 2016).

5
sizes are typically too small to yield reliable estimates of poverty at the level of districts or communes. Censuses do not suffer from small sample problems as they cover the entire population. But while censuses also collect valuable information on individual and household characteristics that provide insights into living standards, they rarely include the income or expenditure information needed to measure poverty directly.

Small area estimation techniques have been developed to estimate poverty at the small area level. One popular approach, introduced by Elbers, Lanjouw and Lanjouw (2002, 2003) – henceforth ELL - combines household survey data and census data at the unit record level. The approach exploits the census’s coverage of the entire population and the household survey’s detailed information on income and expenditure. First, an expenditure (or income) model is estimated using the household survey data. The dependent variable is expenditure (or income), and the explanatory variables are a set of household and community characteristics that are comparable and that are available in both the household survey and the census. Subsequently, the parameter estimates from the expenditure model are applied to the census data in order to predict expenditure of all households in the population. From there it is a straightforward procedure to estimate poverty measures in small areas such as communes and districts.

The small area estimation method has been applied in a large number of countries to produce maps of not only poverty measures but also other welfare indicators (see Bedi _et al._ , 2007 for a review of applications). In Vietnam, a number of poverty maps have been developed using the ELL small area estimation method. Minot (2000) combines the 1993 Vietnam Living Standard Survey (VLSS) and the 1994 Agricultural Census to estimate poverty at the local level in rural areas of Vietnam. Minot _et al._ (2003) construct a poverty map using a 1998 VLSS and a 33% sample of the 1999 Population and Housing Census. Nguyen (2009) applies the 2002 VHLSS to the 33% sample of 1999 Population and Housing Census to produce a poverty map for 2002. Nguyen et al. (2009) further update the rural poverty map for 2006 using the 2006 VHLSS and the 2006 Rural Agriculture and Fishery Census.

3