rafmacalaba/datause-encoder-data huggingface dataset:
**Table 18.3 Approval of women’s work under different contexts, males vs. females**

||Fem<br>Mea<br>|ales<br>n/(SE)<br>|M<br>|ales Mean/(SE)<br>|Pairwi<br>differen<br>|se t-test Mean<br>ce<br>|
|---|---|---|---|---|---|---|
||**Individual**<br>**approval**<br>**(PNB)**|**Perceived**<br>**community**<br>**approval**<br>**(SNE)**|**Individual**<br>**approval**<br>**(PNB)**|**Perceived**<br>**community**<br>**approval**<br>**(SNE)**|**Individual**<br>**approval**<br>**(PNB)**|**Perceived**<br>**community**<br>**approval**<br>**(SNE)**|
|**A woman's charact**<br>|**eristics and ro**<br>|**les**<br>|||||
|She is unable to<br>keep up with all<br>household tasks<br>without help<br>**A woman's jobs' ch**|0.46<br>(0.02)<br>**aracteristics**|0.34<br>(0.01)|0.52<br>(0.02)|0.41<br>(0.02)|0.05**|0.07***|
|She returns home|0.87|0.73|0.88|0.77|0.01|0.04**|
|from work after 5<br>pm|(0.01)|(0.01)|(0.01)|(0.01)|||
|Her workplace is<br>not close to her<br>home|0.79<br>(0.01)|0.60<br>(0.01)|0.83<br>(0.01)|0.64<br>(0.02)|0.04**|0.04*|
|Observations|1,|150|8|50|2,|000|

_Note:_ Only select contexts that have a significant difference are reported, other contexts explored in the survey not reported for brevity.

56
# **3. RECENT LABOR MARKET DYNAMICS**

We start with an analysis of labor flows between the three possible labor states, employment, unemployment, and nonparticipation (sub-sections A, B, and C). For those employed, we study mobility between different sectors. Light is shed on the process of economic restructuring by relating the processes of job reallocation, job creation, and job destruction. The groups who experience difficulty in the labor market are identified via analysis of factors influencing the probability of losing and finding jobs, and exiting to nonparticipation. Subsections D and E examine the determinants of job search methods by the unemployed, as well as the determinants of individual earnings, focusing on the impact of education, age, gender, and regional factors.

## **A. FLOWS BETWEEN EMPLOYMENT, UNEMPLOYMENT, AND INACTIVITY**

The high level of outflows from jobs in Latvia is suggested by the fact that a substantial proportion of those employed in any year are not employed one year later. However, this fraction has declined from 10 to 8 percent over the last 5 years. The net inflow into employment is now positive in Latvia. Inflows to employment were smaller in 1997-1999 but larger starting from 2001, so that an initial trend towards declining employment rates has been reversed. This is shown in Figure 14 (see Appendix Table 14 for details). The Russian financial crisis of 1998 resulted in big net outflow between 1998 and 1999.

**Figure 14.  Flows from and to Employment, 1997-2002**

(as proportion of employment in the first of the two periods) <!-- Start of picture text --> 0.120<br>0.110<br>0.100<br>outflow<br>0.090<br>inflow<br>0.080<br>0.070<br>0.060<br>1997-98 1998-99 1999-00 2000-01 2001-02<br><!-- End of picture text --> _Notes_ : 1997-2001: flows between Mays of corresponding years; 2001-2002 – annual average. Flows exclude the impact of migration, mortality, and new entrants who were younger than 15 in the first of the two periods. In this way, the impact of economic change is identified. _Source:_ LFS data and calculations (see Appendix Table 14).

While in 1997-1999 the larger part of the outflow from employment went to unemployment, in 20002002 flows to unemployment and nonparticipation were roughly equal. From 1999 to 2000, however, flows out of employment were predominantly to inactivity, likely because many job-seekers were discouraged and did not seek new jobs. Other reasons for non-activity, such as child-rearing and retirement may also play a role.

**The persistence of unemployment has declined but is still substantial.** As discussed in the previous chapter, the proportion of unemployed who find themselves in the same situation one year later is very substantial. Still, it has declined from 58 to 46 percent in five years (see Appendix Table 6, middle panel). As Figure 15 shows, the declining persistence of unemployment is due to increasing flows both to

20
Masset, E., M. Gaarder, P. Beynon, and C. Chapoy. 2013. “What Is the Impact of a Policy Brief? Results of an Experiment in Research Dissemination.” _Journal of Development Effectiveness_ 5 (1): 50–63.

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http://siteresources.worldbank.org/EXT2009ARDE/Resources/arde09_web.pdf.

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World Bank. 2012b _World Bank Group Impact Evaluations. Relevance and Effectiveness.._ Washington, DC: IEG, World Bank. http://ieg.worldbank.org/Data/reports/impact_eval_report.pdf World Bank. 2014a. “World Bank Corporate Scorecard, April 2014.” Brochure, World Bank, Washington, DC.

http://siteresources.worldbank.org/CSCARDEXT/Resources/2014_WBG_corporate_scorecard_eversion.pdf.

World Bank. 2014b. “Review of the World Bank FY13 Portfolio and Update on Actions to Assure Quality.” World Bank, forthcoming

# **FIGURES AND TABLES**

21
# **3 Data and Empirical Strategy**

## **3.1 Data**

To estimate the innovation premium in Brazil, we use the RAIS database (Rela ̧c ̃ao Anual de Informa ̧c ̃oes Sociais) from 2014 to 2017. This is an administrative database from the Brazilian Ministry of Economy considered a high-quality census of the Brazilian formal labor market. The census includes all establishments nationwide with at least one registered worker. We use data on over 40 million employees per year, matched with firm and establishment information, including location and industry. At the individual level, the database includes information on workers’ gender, age, education, employment status, type of contract, tenure, data of hiring, among others. We excluded observations where the data were clearly miscoded or missing, as well as individuals younger than 18 or older than 65 years old. We also restricted our sample to one observation per worker-year. We impose this restriction by choosing the highest-paying in any given year. Finally, we have opted for using data at the level to account for characteristics.

Due to the large size of the database, we focus on the period 2014 to 2017. This period represents a period of stagnation in the economy with recessions in 2015 and 2016 when GDP growth dropped 3.5% and 3.2%, respectively. Thus, this is a period of stagnation of wages, and it is possible that since the wage premium oscillates with the business cycle, the estimates for the period provide a lower bound of the premium.

## **3.2 Classification of occupations**

The RAIS database also includes information on the occupation of each worker, coded according to the Brazilian Code of Occupations (CBO). To improve comparability, we matched CBO with two other codes, the International Standard Classification of Occupations – ISCO08 and a match between the National Qualification Framework (NQF) from the United Kingdom and the Standard Occupation Code (SOC). These two codes resulted in two different classifications of skills. We follow Autor and Dorn (2013) and Dorn (2009) and classify occupations according to Table 1.

For robustness of the analysis we also follow Aghion et al. (2019), who use the NQF from the United Kingdom. The NQF defines 8 levels of skill based on the required qualification from PhD (level 8) to Entry level (less than GCSE grade D-G). The current UK immigration rules use 6 groups (see Table A1). One crucial distinction between these classifications is related to the definition of medium-skill workers. While the previous classification includes most of production and clerical support workers as medium-skill, many manufacturing and 7
mainly the adoption of technologies developed in other sectors. In the words of one of the leading researchers in the area, “the service sector is a composite of many subsectors, each with different levels of technological input and whose characteristics are very different. There is no unique input-output process, end even the very measurement of input or output is difficult” (Pires et al (2008)).

The characteristics and heterogeneity of the services sector have clearly influenced the conceptualization and analysis of innovation within this sector. In fact, the view of innovation in services has significantly evolved in a relatively short period of time. The initial ‘neglectful’ perspective considered the service sector a laggard in terms of innovation, merely “serving” important sectors in the economy. This was followed by an ‘assimilation’ view which assumed the inputs and outputs of innovation are the same as, and should be measured and evaluated in the same way as, manufacturing (technological) innovation. The `independence’ or ‘demarcation approach’ held that innovation is so different in services that it has to be understood and measured in a completely independent way from manufacturing (Djellal and Gallouj (2001) and Gallouj and Weinstein (1997) are good examples of this approach). Today, we have arrived at some sort of `synthesis’ wherein it is recognized that there is a lot of common ground between manufacturing and services but also particularities of the services sector which affect the way innovation takes place (Nijssen et al. (2006), Tether and Howells (2007), Drejer (2004) , Howells (2000)).

Even though the latter view is more prevalent in recent studies, it has not yet been fully reflected in data collection strategies. These are still heavily biased towards measuring innovation through a “manufacturing lens”, although in the most recent surveys, questions about organizational and marketing innovation are now being asked.<sup>5</sup> Furthermore, some empirical studies which exclusively address the service sector innovation have elaborated service sector specific surveys which focus on 5
negative externalities caused by production technologies, either in the way of environmental contamination or threats to human health (e.g., the Clean Air Act in the United States). While the traditional policies of inducing cleaner technologies are effective in reducing contamination, our results show that the Internet might be a complementary way to reduce these negative externalities. In this context, identifying and examining the main Internet adoption drivers becomes important (see Andres et al. 2010).

# **References**

Andres L, Cuberes, M, Diouf A and Serebrisky T. 2010. The diffusion of the Internet: A cross-country analysis. Telecommunications Policy, 334: 323-340.

Baudin, A. and Lundberg, L. 1987. A world model of the demand for paper and paperboard. Forest Science, Vol. 33, No. 1, pp.185–196.

Buongiorno, J. 1978. Income and price elasticities in the world demand for paper and paperboard. Forest Science, Vol. 24, No. 2, pp.231–246.

Chas-Amil, M.L., Buongiorno, J. 2000. The demand for paper and paperboard: econometric models for the European Union, Applied Economics, Vol. 32, No. 8, pp.987–999.

Energy Information Administration, United States, 2009. Emissions of Greenhouse Gases in the United States 2008.

Ensis/CSIRO (Australia) joint research "Frequently Asked Questions on Kraft Pulp Mills" http://www.ensisjv.com/Portals/0/QuestionsonKraftPulp0503082.pdf Filistrucchi L. 2005. The Impact of Internet on the Market for Daily Newspapers in Italy, EUI Working Paper ECO No. 2005/ 12 Gentzkow M. 2007. Valuing New Goods in a Model with Complementarities: Online Newspapers. American Economic Review. 97 (3). June 2007.

Hujala, M. 2011. The role of information and communication technologies in paper consumption. Int. J. Business Information Systems, Vol. 7, No. 2, pp.121–135.

24
**<u>Table 1. Baseline Balance (weighted)—Mean and Normalized Mean Differences of Selected Variables</u>**

||**Control**|**Arm 1**|**Arm 2**|**Arm 3**||**Nor**|**malized Mea**|**n Differenc**|**es**||
|---|---|---|---|---|---|---|---|---|---|---|
|Number of GPs<br>Number of households|80<br>1,872|60<br>1,406|60<br>1,394|60<br>1,395|**Control-**<br>**Arm 1**|**Control-**<br>**Arm 2**|**Control-**<br>**Arm 3**|**Arm 1-**<br>**Arm 2**|**Arm 1-**<br>**Arm 3**|**Arm 2-**<br>**Arm 3**|
|**A. Household characteristics**|||||||||||
|**Number of members in the household**|||||||||||
|All|5.31|5.236|5.298|5.19|.0256|.004|.0432|-.0219|.0172|.0397|
|Children less than 5 years|.6453|.5897|.6826|.6663|.0522|-.0336|-.0191|-.0861|-.0718|.0147|
|**Caste/social category of the household**|||||||||||
|General|.3286|.2913|.3443|.3143|.057|-.0235|.0217|-.0805|-.0353|.0451|
|Scheduled caste/scheduled tribe|.5039|.5282|.4641|.5459|-.0344|.0564|-.0595|.0909|-.0251|-.1161|
|**Economic category**|||||||||||
|Below poverty line (BPL)|.5042|.5166|.5499|.5136|-.0175|-.0647|-.0132|-.0472|.0043|.0515|
|Above poverty line (APL)|.3404|.2677|.2444|.2489|.1122|.1501|.1427|.0378|.0304|-.0073|
|**Asset ownership**|||||||||||
|Mobile phone|.9724|.9621|.9695|.9683|.0408|.012|.017|-.0289|-.0239|.005|
|Toilet|.7989|.7296|.7461|.7377|.1159|.0892|.1028|-.0266|-.013|.0135|
|**Education of the head of household**|||||||||||
|Secondary|.2316|.1919|.1968|.2199|.0687|.06|.0197|-.0086|-.0489|-.0403|
|College/graduate/postgraduate|.1061|.1087|.1077|.1034|-.006|-.0037|.0062|.0023|.0122|.0099|
|Illiterate|.317|.3504|.3513|.3377|-.0501|-.0514|-.0312|-.0013|.0189|.0202|
|**Primary occupation of the head of household**|||||||||||
|Cultivation|.1723|.1661|.2181|.1928|.0117|-.0817|-.0376|-.0934|-.0492|.0442|
|Agricultural labor|.08015|.1248|.1135|.08566|-.1043|-.0799|-.0141|.0245|.0903|.0659|
|Nonagricultural labor|.3154|.344|.3341|.3294|-.043|-.0283|-.0213|.0148|.0218|.007|
|Salaried work|.1526|.136|.121|.1186|.0335|.0651|.0704|.0316|.0369|.0054|
|Business|.0907|.0829|.06481|.09183|.0196|.0684|-.0028|.0489|-.0224|-.0712|

19
information (and perhaps in the ability to produce viable applications) and in civic engagement, rather than the high costs or low perceived benefits of applying.

We found that a simple predetermined formula to allocate funds from the center to the districts was the main reason for the program‟s mild success in reaching the poor – reversing the regressive nature of the application pool. In this case, the heterogeneity in applications turned out to be almost entirely across-district, meaning that a district-level funding formula is mechanically effective at unwinding this regressivity. In a different context with stronger localized variation in applications, the formula would have been less effective and the final funding allocations would have likely reflected the regressive application pattern. We conclude that program funds will be more likely to reach the poor to the extent that the center can directly target smaller administrative units – or induce local authorities to perform better by providing the necessary information and/or incentives.<sup>16</sup> The initial propagation of information about the program and who are deemed eligible to benefit appear to be particularly important activities over which to maintain centralized control.

Any effort to quantify the incidence of spending in CDD projects must confront the question of what attributes are in fact optimal to target. Such programs may have other goals that clash with a narrow focus on targeting those who are consumption poor – especially in the short run. When central governments use quantified metrics of poverty to channel funds, then central government provision almost tautologically improves poverty targeting if these same metrics are used to analyze spending incidence. However, not only the priorities of local communities may be different than those of the center, but so can their definition of who is poor, vulnerable, or deserving of social assistance (Alatas et al., 2011). One of the benefits of decentralized decisionmaking and localized participatory development is to take advantage of idiosyncratic information at the local level (e.g. Alderman, 2002). Other goals include improving accountability, democratization of local politics by giving voice to previously marginalized groups, increasing social capital, and improving governance (Mansuri and Rao, forthcoming). These are broader goals that may well lead to a substantially improved allocation of social assistance funds in the

> 16 Ravallion (2000) finds that the improvement in the targeting performance of Trabajar II was in part due to the incentives provided by the center to the provinces for reaching poor areas. Baker and Grosh (1994) and Elbers et al. (2007) both find that targeting smaller administrative units, by using “poverty maps” or similar data, would produce large gains in poverty reduction.

21
reflects population movements (which are potentially endogenous to economic prospects as alluded to in section 5.2). To address this concern, column 4 estimates a specification in which we hold population fixed in our market access measure such that all variation in market access results from changes in the configuration and severity of obstacles. The resulting coefficient estimate is nearly identical to the one in our preferred specification. Column 5 repeats this exercise but now using _θ_ = 80; again, the results are very similar to those reported in column 3. Variation in market access is thus primarily driven by changes in obstacles rather than migration and/or differential population growth.

Third, column 6 presents estimates for an augmented measure of market access in which access to external markets is proxied for by including Tel Aviv as an additional destination. We hold the population fixed (so that we don’t have to take a stance on the evolution of the size of the external market over time) and use a distance decay parameter of 80 such that our measure of market access is especially sensitive to changes in the accessibility of foreign markets. The resulting coefficient estimate, however, is very similar to the one presented in column 5 which only considers domestic markets. This suggests that our findings are robust to controlling for changes in external market access. Inflating the size of the external market does not alter this conclusion, as is shown in Appendix Table 6.

Table 4 presents estimates of the change in the log of lights emissions between 2006 and 2010 on the change in market access between 2006 and 2010 as well as the change in our proxies for political violence, separately using (the more conventional) top-coded data (columns 1 and 2) and bottom-coded data (columns 3 and 4). Changes in market access are a strong and significant predictor of changes in lights, irrespective of which datasource is used; ceteris paribus, localities with bigger improvements in market access also experienced larger increases in lights per capita. Interestingly, the estimated coefficients are larger than in the specifications that exploit annual variation, perhaps reflecting that longer time lags have a higher signal to noise ratio. The estimated impact of improving market access is slightly lower when using bottom-coded data, hinting at the possibility that less developed localities (whose natural light emissions are best captured using top coded data) benefit more from improvements in market access than more prosperous towns (whose light emissions are best captured by bottom coded data).

33
to lead to quite di¤erent e¤ects on exporters in di¤erent origins.

As we suggested in Section 2, the distinction between countries according the level of development may correspond to the analytical distinction we make between countries on the basis of their costs of meeting standards. We generate a dummy variable, i.e. developing, which is equal to 1 for a developing country exporter and 0 for a developed country exporter. We consider all OECD countries as developed and the rest of the sample countries as developing. This dummy variable is used to interact with the three variables which capture the impact on imports from third countries: HAR_M , MRA_RO_M , and MRA_NRO_M . The regression results are reported in column I of Table 3.

While regional harmonization promotes imports from developed third countries by 34%, there is actually a 16% reduction in imports from the excluded developing world (the coe¢ cient on HAR_M for developing country exporters is -0.1675 and e<sup>�0:1675</sup> = 0:84). These results are consistent with the assumption, which drives the predictions in Proposition 1 in Section 2, that developing countries tend to ...nd it more costly to meet stricter standards, and reap smaller bene...ts from economies of scale generated by integrated markets (i.e. ck < cl and Fk > Fl).

The impact of MRAs on developing and developed countries turns out to depend on whether they include rules of origin. If they do, they hurt developing country exports (39% decline) more than they hurt developed country exports (28% decline). But MRAs without rules of origin enhance developing country exports even more than they enhance developed country exports, revealed by the positive coe¢ cient on the interacting term MRA_NRO_M � developing. A possible explanation is o¤ered by the intuition behind Proposition 4 in Section 2: the greater reduction in compliance costs for developing country ...rms may o¤set the smaller bene...ts they derive from economies of scale.

Finally, we directly test for the hypotheses in Proposition 1 by comparing respectively the four cases: the importer had more/less stringent initial standards while the exporter is a developed/developing country. Column II of Table 3 shows that with harmonization, exports to countries with stricter initial standards of both developed and developing countries increase, but the increase in the former (57%) is much greater than the increase in the latter (5%). Developed third countries’exports to countries that raise their stan21
_Role of NGOs in Health Delivery Afnca Health Policy Study_

-14-

# Table 2. Proportion of Health Services Provided by Non-Governmental Organizations in Selected African Countries

|Country|Estimate of Volume of Services|Source|
|---|---|---|
|Cameroon|Church missions provide 40%<br>of facilities.|Vogel 1989|
|Ghana|Mission hospitals have > 25%<br>of beds and provide nearly 50%<br>of outpatient care.|Bradley 1989|
|Kenya|NGOs deliver up to 35% of<br>health care services.|Bratton 1988<br>Sheffield 1987|
|Lesotho|Private Health Asociation of<br>Lesotho (PHAL) is responsible<br>for 50% of the country's<br>hospitals and 60% of Its dinics.|Vogel 1989|
|Malawi|Private Health Associadon<br>of Malawi (PHAM) is responsible for<br>for 40% of all health services in<br>Malawi.|PHAM documents|
|Uganda|NGOs provide approximately 40%<br>of all health care.|Owor 1988|
|Zambia|Missions offer health care to<br>50 per cent of the rural population<br>and 35 percent of total population.|WHO PHC Review|