rafmacalaba/datause-encoder-data huggingface dataset:
half a month, and associated monetary costs are negligible. But even some transition economies – which in general tend to be on the high end in terms of the costs of doing business – do considerably better than Moldova. For example, in Latvia opening a business is much less costly: there is fewer procedures, completing them takes less time, and associated expenses are lower.

**Table 5 Entry regulations indicators in selected transition and EU economies, 2002**

|Country|Number of<br>Procedures|Duration<br>(days)|Cost (% of<br>GNI per<br>capita)|Min. Capital<br>(% of GNI<br>per capita)|
|---|---|---|---|---|
|**Transition economies**|||||
|Bulgaria|10|30|9|150|
|Czech R.|10|89|5|50|
|Hungary|5|65|65|222|
|Latvia|7|11|17|110|
|Lithuania|11|62|5|85|
|**Moldova**|11|41|31|106|
|Poland|11|58|23|23|
|Romania|9|46|36|415|
|**EU economies**|||||
|_Low unemployment_|||||
|Denmark|3|3|0|47|
|Ireland|3|16|10|0|
|United Kingdom|5|4|1|0|
|_High unemployment_|||||
|France|10|53|3|29|
|Germany|9|45|6|93|
|Spain|11|100|16|18|

Source: The World Bank Doing Business database (http://rru.worldbank.org/DoingBusiness) The costs of opening a business in Moldova rise substantially if one accounts for bribes which often need to be paid to obtain necessary permits and licenses. One firm in five reports that it paid bribes in order to register, and the average payment was USD 120, which is a non-negligible amount in the Moldavian context. It is impossible to know how many potential entrepreneurs get discouraged by corruption and thus abandoned the idea of starting a business, or moved to the informal sector.

All in all, there is room for lowering the costs of starting a business in Moldova. First, registration procedures can be streamlined, and the associated payments and capital requirements can be lowered. For example, those with entrepreneurial talent who are poor may not be able to afford registration costs, which are non-negligible. This may 35
and uncertainty about quality may explain why some small firms are not using this existing market for business services.

# **3. Measuring the Quality of Business Service Providers**

In order to obtain an independent and objective measure of business service provider quality, we conducted a mystery shopper exercise between June and July 2018. We recruited eight real business owners, who had been in business between 3 and 10 years, and were running firms with between 4 and 18 workers. These business owners were trained on a standardized approach to contact a GEM business service provider and arrange a meeting with them to discuss the challenges of their business in a given area (accounting, marketing or human resource needs). They then had a checklist to go through at the pre-contact stage (examining the website of the provider), initial call phase (ease of booking appointments, quality of phone interaction) and then at the meeting stage (quality of interaction, service options, capabilities, quality of needs assessment done).

A total of 53 business service providers in Lagos and Abuja were visited by these mystery shoppers. They were scored by the mystery shopper for each of these areas on the checklist, and then at the end the mystery shopper was asked whether or not they would recommend another SME use this provider, and to give an overall score out of 5. These providers were positively selected, since they had already gone through an earlier screening process to be deemed eligible to offer services under the GEM project. Even after this, the mystery shopper said they would never or rarely recommend 25 percent of the providers, and 23 percent had an overall rating of 3 out of 5 or lower. The median score was 4 out of 5. We use these ratings in our information experiment.

# **4. Information and Quality Ratings Experiment**

# **4.1 The Sample**

We aimed to conduct our information and quality rating experiment on similar types of firms to those that had been part of the earlier GEM project. To do this, we hired Kantar/TNS RMS Nigeria, the same survey firm that had worked on the GEM project to conduct a baseline survey of firms in Lagos and Abuja operating in the same five sectors as had been the focus of GEM (light 8
13

# **The Private Agricultural Technology Transfer Service (ATP2)**

In order to continue the diversification of its services, in mid-December 1994 INTA invited a number of private firms to jointly assess the possibility of providing private technical assistance to small and medium producers. The use of private firms to provide agricultural technology transfer services was enhanced by the need to: (a) use market incentives to provide better services; (b) minimize the risk of a larger public bureaucracy; and (c) reduce the costs to the public sector through a cost-sharing scheme by which the clients would participate in the financing of the service. During the first years of this program, most of the costs are expected to be covered by the government through a loan from the World Bank.

After several months of demand evaluation, the first contracts --between producers, the government, and private firms--were signed in August 1995. According to these tripartite contracts, the producers were expected to pay a sum covering about 20% of the cost in the first year, and their contribution is expected to increase to nearly 80% in about five years. At the time of writing, no producer was paying more than _50%_ of the service provision costs. An estimate of the average cost of public extension provision in Nicaragua (based on INTA budget for 1995), suggests a direct cost per producer of $115/year. This estimate appears to be quite high compared with actual data for ATP I and ATP2, as can be seen from the analysis in this paper. Producers in five regions were organized in groups and by end-July 1996 some 5,700 producers were served by 46 technicians and 7 supervisors contracted by 5 firms (each firm is responsible to provide extension services in a given region).

In contrast to the ATP1 concept, the service in ATP2 covers a wide range of farm production and marketing aspects. The size of the groups varies between 10 and 20 people. Each technician serves between 8 and 10 groups and attends between 100 and 150 producers. Figure 4 provides the participation trends in ATP2 during the first two years of the program. Contrary to the trends in ATP 1, it can be seen in the case of ATP2 that each private company reached its ceiling quite fast (I to 2 seasons, and in one case in the first season). This result is directly related to the government assessment of each company's capacity, and its decision to allow the company to provide extension services to a given amount of producers. Given this regulatory rule, firms
Policy Research Working Paper 8215

# **Abstract**

The objective of this paper is to analyze the employment profile trends in Argentina and Uruguay according to the task content performed by the workers in their jobs. The paper aims to produce an approximation of the impact of technological change on the labor market. The paper uses the definition of an indicator that captures the relative importance of four types of tasks, cognitive/manual and routine/nonroutine, based on the information provided by Occupational Information Network and household surveys. The analysis finds that in the last two decades the relative importance of cognitive tasks in the workplace has increased in line with the reduction of manual tasks, particularly among the youngest cohorts.

This paper is a product of the Social Protection and Labor Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at iapella@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Figure A2.1: Global poverty for unadjusted and top income adjusted HHS**

<!-- Start of picture text --> 1980 1990 2000 2010 2020<br>requestyear<br>HHS unadjusted HHS top income adjusted<br>.5<br>.4<br>.3<br>.2<br>Share in Poverty (IPL $1.90/day 2011 PPP)<br>.1<br><!-- End of picture text -->
# **Employment growth across age classes**

**Older firms grow slower than younger firms by relative measures. Yet, the bulk of employment generation in absolute figures occurs in companies that are older than 26 years.** Firms in the age class of over 25 years grow significantly slower than younger firms. However, they also create significantly more employment than firms in other size classes. Firms older than 26 years represent 21% of the sample; however, they create approximately half of all jobs, since they are significantly larger than companies of other age classes. Young firms that are not older than five years account for 17% of all observations, and generated 16% of the net employment (see table 3).

**Table 3: Firm growth across age classes**

|**Age class**|**Employment**<br>**(median)**|**Employment**<br>**(mean)**|**Share of**<br>**investing**<br>**firms**|**Age**<br>**(median)**|**Annual**<br>**relative**<br>**emp.**<br>**growth**<br>**(median)**|**Annual**<br>**relative**<br>**emp.**<br>**growth**<br>**(mean)**|**Birch**<br>**index**<br>**(mean)**|
|---|---|---|---|---|---|---|---|
|5 years or<br>younger|12|47|41%|4|2%|18%|78|
|6-15years|16|73|43%|10|0%|9%|22|
|16-25years|22|145|45%|20|0%|8%|49|
|>26years|40|273|46%|35|0%|7%|103|

_Source: Interim Enterprise Survey, 2009/10; Own calculations_

**Evidence across size and age classes confirms that large firms drive employment growth.** The median growth rate of large firms declines across age classes slightly declines, but remains significantly higher than the growth rate of other size classes across the board. Only companies which are younger than 6 years and medium sized outperform large companies of the same age class. These findings are independent to the choice of the median or mean (see figure 4).

13
**Figure 2.5 Breaking down PPPs in MENA**

<!-- Start of picture text -->
1. Total Investment by Country<br>(Millions of U.S. dollar)<br>3000<br>2500<br>2000<br>1500<br>1000<br>500<br>0<br>2. Total Investment by Sector 2005 - 2016<br>(Billions of U.S. dollar)<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Telecom Electricity Seaports Treatment Natural Gas Airports Water Utility Railroads<br>plant<br>Sources: World Bank, Private Participation in Infrastructure Database; and World Bank staff calculations.<br>Note: Total value of investment and number of projects are in accordance to year 2016, or latest year available before that,<br>unless otherw ise noted.<br><!-- End of picture text -->

# **The political economy of implementation and management in MENA**

MENA states have had a poor track record in both the delivery and the management of public utilities. The are many reasons.

A primary factor is the so-called curse of financing. The excess profits from oil have turned governments into principals and citizens into agents. It is the reverse in economies in which taxpayer monies are the main sources of government revenue. As a result, there is little pressure on governments to do better. Moreover, the excessive reliance on the traditional source of financing (entirely from public finance) also played a role in inducing inefficiency. Public financing of infrastructure without internal checks and balances allows for overpricing and capture. It also prevents efficient pricing of risks and risk sharing with other partners such as the private sector and MDBs.

Another factor in the poor track record of MENA countries is the concentrated nature of bureaucracies that make decisions from distant central states but have limited understanding of and pay little attention to local needs. The result often is poor implementation and delivery of services to citizens. That overconcentration of the state and the lack of information accessible to citizens make it unlikely that states will achieve the dynamic adjustments needed to address the region’s socio-economic 7
**Table 4. Employment**

|||**Midli**|**ne Survey**|||**Endl**|**ine Surve**|**y**|
|---|---|---|---|---|---|---|---|---|
|||Control|Treatment -|Control||Control|Treatm|ent - Control|
|||Group||||Group|||
||Obs|Mean|Coeff.|_p-value_|Obs|Mean|Coeff.|_p-value_|
|**Beneficiary woman**|||||||||
|Not employed & not<br>searching|951|0.551|-0.352 ***|_0.000_|875|0.629|0.050|*<br>_0.073_|
|Not employed & searching|951|0.234|-0.193 ***|_0.000_|875|0.127|-0.012|_0.498_|
|Employed|952|0.215|0.545 ***|_0.000_|875|0.244|-0.038|_0.145_|
|Day worker|952|0.014|0.007|_0.277_|875|0.016|0.001|_0.861_|
|Salaried|952|0.021|-0.010|_0.185_|875|0.016|0.000|_0.966_|
|Self-employed|952|0.166|-0.111 ***|_0.000_|875|0.213|-0.038|_0.130_|
|Public works|952|0.017|0.689 ***|_0.000_|875|0.000|0.000|_0.000_|
|Hours worked|952|7.9|14.4 ***|_0.000_|875|10.3|-2.3|*<br>_0.056_|
|Will look for a job or start a self-<br>employment activity in next 6 months|n.a|n.a|n.a|_n.a_|875|0.289|0.008|_0.770_|
|**Share of adult members**(excludes woma|n beneficia|ry)|||||||
|Not employed & not<br>searching|913|0.436|-0.003|_0.869_|893|0.516|-0.021|_0.358_|
|Not employed & searching|913|0.037|-0.013 *|_0.054_|893|0.014|0.007|_0.216_|
|Employed|913|0.540|0.006|_0.761_|893|0.516|0.025|_0.234_|
|Day worker|913|0.252|0.012|_0.580_|893|0.266|0.002|_0.925_|
|Salaried|913|0.249|-0.023|_0.270_|893|0.226|0.021|_0.311_|
|Self-employed|913|0.049|-0.020 **|_0.037_|893|0.031|0.001|_0.895_|
|Public works|913|0.003|0.038 ***|_0.000_|893|0.001|-0.001|_0.637_|
|Hours worked|913|26.8|0.6|_0.728_|893|28.9|0.3|_0.854_|
|**Husband**|||||||||
|Not employed & not<br>searching|744|0.071|0.002|_0.916_|724|0.079|0.012|_0.497_|
|Not employed & searching|744|0.089|-0.028 *|_0.080_|724|0.050|-0.020|*<br>_0.088_|
|Employed|744|0.839|0.026|_0.242_|724|0.870|0.009|_0.688_|
|Day worker|744|0.459|-0.024|_0.443_|724|0.482|-0.035|_0.271_|
|Salaried|744|0.339|0.001|_0.968_|724|0.355|0.036|_0.267_|
|Self-employed|744|0.045|0|_0.997_|724|0.048|0.002|_0.919_|
|Public works|744|0.003|0.064 ***|_0.000_|724|0.002|0.001|_0.700_|
|Hours worked|743|39.9|1.5|_0.385_|724|44.5|2.0|_0.290_|

Notes _:_ Coefficients are from an OLS regression of the left-hand side variable on a treatment dummy, controlling for strata dummies, survey-time dummies, and baseline traits (see Table 3 note). ***, **, * indicate significance at 1, 5 and 10 percent.

43
# **APPENDIX C: VARIANCE OF MEAN INCOME GROWTH**

<!-- Start of picture text --> The standard deviation of mean income growth is presented in Figure C 1, Appendix C. This country-specific variability in ROCs is<br>generally higher than that faced by NROC economies (Figure C 1, Panel B, Appendix C). In terms of their median, LOGCs present<br>larger standard deviations of mean income growth than HOGCs and MOGCs, with MOGCs representing the economies with the smallest<br>median value (Figure C 1, Panel C, Appendix C). Furthermore, via a standard variance decomposition of the OLS estimates of Equation<br>(1), we identified three main features of the dispersion of the growth in the mean income (Figure C 2 in Appendix C). First, idiosyncratic<br>factors drive the variability in income growth faced by all countries and country typologies. 37 Second, global factors affecting income<br>growth appear to be less relevant in ROCs and LOGCs. Third, the covariance component between the growth of global income per<br>capita and global commodity prices is negligible, c ] ≈0, over the period 1980–2014 and across countries.<br>FIGURE C 1. VttARIABILITY OF tt MEAN INCOME GROWTH<br>vv[gg , pp<br>Panel A. All countries Panel B. Resource typology Panel C. Output growth typology<br>16 16 16<br>12 12 12<br>8 8<br>8<br>4 4<br>4<br>0 0<br>0 NROC ROC HOGC LOGC MOGC<br>Annual growth (percentage) Annual growth (percentage)<br>Annual growth (percentage)<br><!-- End of picture text --> _Source:_ Penn World Table 9.0, World Development Indicators, United Nations, and the author’s estimates. _Note:_ Variability measured by the standard deviation of mean income growth. Country regressions over the period 1980–2014. Similar results are obtained using the following periods instead: 1990–2014, 2000–2014, and 2005–2014. The total number of countries used in this estimation is 150. The number of NROC and ROC economies is 104 and 46, respectively. The number of countries in the classification LOGCs, MOGCs, and HOGCs are 8, 117, and 25, respectively.

37 Despite the inclusion of two global factors affecting mean income growth, the model in Equation (1) does not disaggregate the idiosyncratic components affecting the country performance of growth of mean income. Besides, note that all the idiosyncratic random forces are captured via the error term in Equation (1). This error term, however, could include omitted global components as well.

Page **42** of **45**
precision, or might miss these people altogether. On the other hand, the tax data intentionally oversample the rich. Third, aspects of survey design such as top-coding or the elimination of “outliers”, manipulate top incomes. On the other hand, tax data are not without problems, e.g. due to tax evasion and income minimisation which may be particularly strong in developing countries.

There is some evidence to support the argument that top incomes are missing in household surveys. Alvaredo (2010) finds that a household survey in Argentina records no observations with incomes exceeding $1 million whereas the Argentinian tax data contain close to 700 observations in that range. In a comparison of household surveys from 16 Latin American countries, Székely and Hilgert (1999) find that the 10 richest households in the survey receive incomes similar to a managerial wage. It would appear plausible that the top capital owners in these countries receive substantially greater incomes than a manager. Some studies compare top shares estimated from household surveys and tax data, and in some cases obtain very similar results, although this typically depends on the availability of exceptional surveys which have sufficient sample sizes and are not subject to top coding (Burkhauser et al (2012) using internal United States CPS data, Leigh and van der Eng (2009) for Indonesia, and Morival (2011) for South Africa).

Given that tax data appear to be more accurate at measuring top incomes and household surveys offer more precise information about the rest of the distribution, a natural next step would be to combine the two sources of information to obtain a complete distribution of income. However, the still sparse availability of tax data across countries, limits the usefulness of such an exercise for the purpose of analysing the global distribution. In addition, the population and the welfare measure are fundamentally different between the two data sources which makes such an exercise difficult.<sup>20</sup>

> 20 In the tax data, the unit of analysis is a tax unit, which depending on the jurisdiction could be a married couple or an individual. A household would typically be bigger than a tax unit. The tax data literature uses taxable (and usually before-tax) income, whereas the household survey collects disposable income. Taxable income excludes some real income, such as tax-exempt interest on government bonds, and deductions and exemptions, although most empirical work using tax data adds these back. On the other hand, household surveys typically measure capital incomes and gains poorly compared with tax data, which cover at least taxable capital incomes and gains. Because the tax data typically do not contain sufficient information in order to construct units and incomes which are similar to those in a standard household survey, the only possibility is to construct tax units and taxable income in the household survey, as in Alvaredo (2010). In the final step of such an exercise, one needs to assume which parts of the true distribution

13
# **8.3. Cost-Effectiveness Estimates**

We estimate the cost of a DALY saved through Plan Nacer’s financing of maternal health services was $814 with a 95 percent confidence region of $442 to $5,086 (table 9). As a way to assess the effectiveness of this program, we follow the World Health Organization recommendation based on the Commission of Macroeconomics and health: investing in health for economic development (2001, Geneva). These results are hugely cost-effective in the Argentine context when compared to GDP per capita over this period. The cost per DALY saved is only 18 percent of average income.

**Table 9** : **Plan Nacer Maternal Health Services Cost-Effectiveness 2005–08**

||**Lower**<br>**bound**|**Point**<br>**estimate**|**Upper**<br>**bound**|
|---|---|---|---|
|Neonatal deaths averted|117|733|1,349|
|Low-birth weight babies averted|171|1,071|1,970|
|Plan Nacer DALYs saved|4,064|25,401|46,738|
|Costper DALY averted|$5,086|$814|$442|
|Annual real GDPper capita(2005–08)|$6,075|$6,075|$6,075|

# **9. SUMMARY AND CONCLUSIONS**

In this study, we analyzed the impact of Plan Nacer on birth outcomes using data from the universe of birth records in seven northern Argentine provinces for the period 2004-08. We exploited the geographic phasing in of Plan Nacer at the provincial level to identify plausible causal impacts. We investigated three related questions: (1) What is the impact of incorporating a clinic into Plan Nacer on the outcomes of the population regardless of beneficiary status? (2) What is the impact of Plan Nacer on the health outcomes of program beneficiaries who receive care from a clinic that is enrolled in Plan Nacer? and (3) What is the spillover impact of the program on the health outcomes of nonbeneficiaries who receive care from a clinic that is enrolled in Plan Nacer?

Overall, we conclude that the Plan Nacer incentive-based model has had large positive effects on birth outcomes and is a promising model for emulation both within the Argentine health sector and internationally. We find that the program increases the use and quality of prenatal care services as measured by the number of prenatal care visits and the probability of receiving a tetanus vaccine (as a 31