rafmacalaba/datause-encoder-data huggingface dataset:
## **<mark>Box 10</mark>**

## **<mark>The Case of NWSC</mark>**

<mark>In an effort to address managerial inefficiencies in the Uganda state owned service provider, NWSC, the government appointed a new Board of Directors. The new Board included representatives from local governments, business community, professional bodies, environment, ministry of finance, ministry of water, ministry of health, and small- scale industries. The composition and structure of the Board enabled it to exercise its governance functions properly, and it was able to shield the corporation from political interference and patronage. The new Board, in turn, appointed a new managing director, who was given the mandate to re-think strategies for performance improvement. The appointment led to an emphasis on commercial viability, utilizing “customer care” as an organizing theme. The new Board and management came up with a series of programs to ensure enhanced performance, including:</mark>

- <mark>100-Days Program (Feb-May, 1999) was a high-impact program that focused on reversing operational and financial inefficiencies through aggressive revenue collection strategies and cost-cutting measures. A number of cost-cutting measures implemented during this program include rationalization of the medical scheme and reduction of travel costs.</mark>

- <mark>Service and Revenue Enhancement Program (August, 1999-August, 2000) aimed at restoring customer confidence in the ability of NWSC to deliver services. Under this program, NWSC established customer service centers and front desks, conducted customer surveys to capture customer wants, and instituted amnesty for illegal water use.</mark>

- <mark>Area and Service Performance Contracts (2000-2003) focused on making service providers reach commercial sustainability: managers had the authority to make important decisions and were accountable for outcomes.</mark>

_<mark>Source:</mark>_ <mark>Mugisha and Berg</mark> <u><mark>(2006)</mark></u>

# **5.4 Theoretical Predictions**

The Hermalin-Weisbach and the Adam-Ferreira models derive a number of predictions about the dynamics of the CEO-Board’s relationship:

1. CEO turnover is more sensitive to performance when the Board is more independent.

2. The probability of independent directors being added to the Board rises following poor firm performance.

3. Board independence declines over the course of a CEO’s tenure.

4. Accounting measures of performance are better predictors of management turnover than stock price performance.

5. A CEO’s salary should be insensitive to past performance at relatively low levels of past performance, but sensitive at relatively high levels of past performance.

6. Boards are more effective ways of supplying information to management in a dual model rather than in a sole Board 7. The presence of the audit committee composed by independent directors can help solving moral hazard issues on the provision of insider information by management.

17
WPS 3464

# **Microeconomic Evidence of Creative Destruction in Industrial and Developing Countries**

**By Eric Bartelsman, John Haltiwanger and Stefano Scarpetta**<sup>**1**</sup>

## _Abstract_

_In this paper we provide an analysis of the process of creative destruction across 24 countries and 2-digit industries over the past decade. We rely on a newly assembled dataset that draws from different micro data sources (business registers, census, or representative enterprise surveys). The novelty of our approach is in the harmonization of firm-level data across countries, which enables international comparisons and the identification of country-specific factors as opposed to sector and time effects. All countries display a massive reallocation of resources, with the entry and exit of many firms in all markets, the failure of many newcomers and the expansion of successful ones. This process of creative destruction affects productivity directly by reallocating resources toward more productive uses, but also indirectly through the effects of increased market contestability. There are also large differences across groups of countries. While entry and exit rates are fairly similar across industrial countries, post-entry performance differs markedly between Europe and the United States, a potential indication of the importance of barriers to firm growth as opposed to barriers to entry. Transition economies show an even more impressive process of creative destruction and, those that have progressed the most toward a market economy show better outcomes from this process. Finally, Mexico shows large firm dynamics with many new firms entering the battle but also many failing rapidly, while Argentina resembles Continental Europe with smaller flows and less impressive post-entry growth of successful firms._ _JEL classification: L11, G33, D92,_ _Keywords: entry, exit, survival, firm size, productivity, micro data_

> 1. Respectively: Free University Amsterdam and Tinbergen Institute; University of Maryland,  U.S. Census Bureau, and NBER; The World Bank.   We are grateful to the World Bank for financial support of this project and to Karin Bouwmeester, Helena Schweiger and Victor Sulla for excellent research assistance.  The views expressed in this paper are those of the authors and should not be held to represent those of the World Bank or its countries.

1
Table 8: The impact of joint annuities and unisex tables'

|(r=5%duringaccumul|ations,4%duringannuit|ystage,g=2%)|||
|---|---|---|---|---|
|**Education**<br>**11**|**2**<br>|**3**<br>**14**|**15**||
|<br> <br>|<br> <br>**Chile**<br>**Males,retirin**<br> <br>|<br> <br>at65<br>|||
|Individual-gen.spec.|$271<br>$393|$586|$816|$1880|
|Individual-unisex|252<br>365|544|758|1746|
|Joint-genderspec. <sup>2</sup>|236<br>342|510|710|1,63f|
|Joint-unisex|236<br>342|509|7091|1,634|
||Females,retirin<br> <br>|gat60<br>||l<br>|
|Individual-gen.spec.<sup>2</sup>|$76<br>$106|$186|$308|$565|
|Individual-unisex|80<br>111|196|324|595|
|Widow'sbenefit<br>|142<br>205<br> <br>|306<br>|426<br>|982<br>|
|Widow+own|218<br>311|492|734|1547|
|_-%_ofhusband+wife|70%<br>70%|71%|72%|70%|
||**Argentin**<br>**Males,retirin**<br> <br>|**a**<br>at65<br>|||
|Individual-gen.spec.|617<br>940|1407|1458|2824|
|Individual-unisex<br>|562<br>856|1282|1328|2572|
|Joint--genderspec.<sup>2</sup>|507<br>772|1156|1198|2319|
|Joint-unisex|518<br>789|1181|12251|2371|
||**Females,retiri**|**gat60**|||
|Individual-gen.spec.<sup>2</sup>|107<br>154|304|424|830|
|Individual-unisex|115<br>165|326|454|888|
|Widow'sbenefit|355<br>540|809|838|1623|
|Widow+own|462<br>694|1113|1262|2453|
|_-%_ofhusband+wife|75%<br>75%|76%|78%|78%|
||**Mexico**||||
||**Males,retirins**<br> <br>|at65<br>|||
|Individual-gen.spec.|$335<br>$375|$440|$528|$838|
|Individual-unisex|316<br>354|416|498|791|
|Joint-genderspec.<sup>2</sup>|294<br>329|387|463|736|
|Joint-unisex|296<br>331|389|466|740|
|<br>|<br> <br>Females,retirin|<br>gat65|||
|Individual-gen.spec. <sup>2</sup>|_105_<br>117|148|216|398|
|Individual-unisex|111<br>124|156|228|419|
|Widow'sbenefit|177<br>198|232|278|441|
|Widow+own|282<br>315|380|494|839|
|-%ofhusband+wife|71%<br>71%|71%|73%|74%|

Notes:

1. The MPG and flat are not included in this table. SQ is included since it is part of annuity. Life expectancy is based on national averages; full mortality tables are not used. Joint annuity assumes 60% to survivor. Given deterministic assumptions, woman would never purchase joint annuity.For education categories see Table 1A.

2. Corresponds to own-pensions in Tables 5 and 6 for Chile and Argentina, own+SQ for Mexico.

56
**9 Tables**

Table 1: Summary statistics

|Variable|Observations|Municipalities|Mean|Std. Dev.|
|---|---|---|---|---|
|_Panel A: All_|||||
|Minimum deviation|4311|976|71.07|83.09|
|Min deviation _<_ 0|4311|976|0.11|.32|
|% ha receiving payment|4311|976|0.04|0.16|
|∆log ha sowed|4311|976|-.01|.47|
|Log ha sowed|4311|976|7.50|1.36|
|Log yield|4311|976|0.99|0.55|
|Log income p.c.|5879|192|8.35|0.79|
|Log expenditure p.c.|5879|192|8.36|0.76|
|Remittances|5879|192|764.36|3050.78|
|Remittances _>_ 0|5879|192|0.12|0.33|
|_Panel B: Payment = 0_|||||
|Minimum deviation|3900|943|80.24|81.28|
|Min deviation _<_ 0|3900|943|0.04|0.20|
|% ha receiving payment|3900|943|0|0|
|_Panel C: Payment = 1_|||||
|Minimum deviation|411|267|-15.85|37.23|
|Min deviation _<_ 0|411|267|.77|0.42|
|% ha receiving payment|411|267|0.45|0.32|

Note. Rainfall and agricultural variables in panel A come from the agricultural panel, which has municipal-level observations and consists of policy data merged with agricultural production data, as do the statistics in panels B and C. The economic variables in panel A come from the economic panel, which consists of household level observations from ENIGH merged with policy data.

25
questionnaire (Deaton and Kozel, 2005). We ask therefore, whether there is any call for similar disquiet with the surveys examined in this paper.

We tackle this question with an imputation-based method recently explored in Dang, Lanjouw and Serajuddin (2014b) that builds on a number of earlier studies (Elbers, Lanjouw, and Lanjouw, 2003; Tarozzi, 2007).<sup>5</sup> Our findings suggest that the 2009/10 and 2011/12 survey rounds do not appear to suffer from serious comparability issues. The observation of a sharply accelerated poverty decline after the 2009/10 round, from 29.9 to 20 percent in 2011/12, seems robust. We also appear to be on solid footing with respect to the data underpinnings for converting these three NSS rounds into synthetic panels.

We start, in Section II, with a brief discussion of poverty trends during the late 2000s and explore further the question of whether the 2009/10 and 2011/12 NSS rounds are comparable. Section III describes our efforts to assess the comparability of the 2009/10 and 2011/12 surveys.

Besides offering validation evidence for the recent poverty decline, these two sections also describe the preparatory data work required to construct synthetic panels with which to study poverty dynamics. Section IV then implements our approach to convert the three most recent NSS rounds between 2004/05 and 2011/12 into synthetic panels. We also describe and implement in this section an approach to construct “vulnerability lines” that permits a richer mobility analysis by allowing us to identify a particular portion of the population that is non-poor but that faces a heightened risk of falling into poverty. We implement a procedure proposed in Dang and Lanjouw (2014) for specifying vulnerability lines anchored explicitly to the observed incidence of non-poor

> 5 Elbers et al. (2003) provide a method that imputes household consumption from a survey into a population census. Adapting this approach for survey-to-survey imputation, Christiaensen et al. (2012) impute poverty estimates using data from several countries including China, Kenya, Russia, and Vietnam; other studies analyze data from Morocco (Douidich et al., 2014) and Uganda (Mathiassen, 2013). See also Tarozzi and Deaton (2009) and Rao (2003) for other studies on survey-to-census imputation.

5
What is notable about the FSU countries is that, when we rank countries both according to their HCI and their GDP PPP per worker, all FSU countries with the exception of Azerbaijan have a much better HCI ranking than a GDP PPP per worker ranking. This is shown in table 5, which gives rankings out of 141 countries. The nine FSU countries shown in the table have an HCI ranking which is 19 places higher on average than their GDP PPP per worker ranking. Of the four remittance receiving FSU countries in CASC, the Kyrgyz Republic has an HCI ranking which is 35 places better than its GDP PPP per worker ranking, that of Georgia is 26 places better, Tajikistan 16 places better and Armenia eight places better. There could be various reasons for these differentials. In some of the FSU countries, the capital stock per worker is lower than is typical for a county at similar levels of income: Armenia, Georgia and the Kyrgyz Republic each have low capital-output ratios compared to other lower-middle-income countries, although somewhat surprisingly, Tajikistan has a very high capital-output ratio. A second reason could be poor total factor productivity because of structural constraints in the economy. Whatever the reason, the fact that GDP per worker is lower in these countries than would be typical for a country with a similar endowment of human capital suggests that workers, which can take their human capital with them, could migrate to another country, with a higher capital stock per worker or with higher total factor productivity, and produce more output and thereby earn higher wages than they would in their home country.

To explore this issue further, we estimate a production function using cross-country data in which GDP PPP per worker is regressed on the HCI and the capital stock per worker for each country, with all data in logs. There are 141 countries for which there are both data on the HCI and the capital stock in the Penn World Tables. The GDP PPP per worker and the capital stock (also at PPP prices) per worker are both averages over 2015-17. The regression result is the following: lnGDP PPP per worker = 0.065 + 0.4*lnHCI + 0.74 * lncapital stock per worker The T statistic for the coefficient on the HCI is 2.26 and that on the capital stock per worker is 19.0. The regression has an adjusted R squared of 0.91.

We can use these coefficients to estimate what the output per worker would be if a worker from, for example, Tajikistan, with the average HCI for that country were to migrate to Russia and work with the average capital per worker of Russia. These estimates, for the four remittance receiving FSU countries of CASC, are shown in table 6.

7
|**South Atlantic**|**80.0**|**80.0**|**83.1**|**83.1**|
|---|---|---|---|---|
|**Local Asia**|**67.9**|**63.4 **|**81.1 **|**95.3**|
|**Europe - Middle**<br>**East - Africa – Asia**|**73.6**|**54.2 **|**79.5**|**96.9**|
|**North & Mid Pacific**|**78.6**|**78.6**|**84.0**|**84.0**|
|**South Pacific**|**78.7 **|**60.4 **|**84.8**|**94.4**|

<u>Source:</u> August 2010 documentation of the ICAO Carbon Emissions Calculator, valid for Calendar Year 2008.

*Calculated passenger share of overall aircraft fuel consumption (the rest being the freight share).

Assuming an industry-wide average load factor of 71% for 2008, we can consider impacts of alternative assumptions about the combinations of load factors by class, as is done for the calculations in Table 4.5. We there for each travel class indicate the “average footprint” factors for each of three average load factor assumptions: 0.3 (“very low”), 0.6 (“normal”), and 0.9 (“very high”). We consider the same average class-specific load factors for wide-body and single-aisle aircraft.

The figures in Table 4.5 are notable in particular for first-class travel where the footprint (not corrected for passenger weight) could reach a multiple of 8.96 relative to the average (for widebody aircraft), far higher than for the other classes; although this number is likely an overestimate, since the corresponding average load factor of 0.3 for first class is likely an underestimate. Note however that previous estimates, and our knowledge of the aviation industry, imply that load factors are lower in premium classes (and lower in first than in business class).

14
the variations available with oDesk itself, accrue more to the overseas ethnic Indian, while the workers in India have compensation and performance more in line with market conditions.

Table 9 closes this analysis by considering broader variations across ethnic Indian and non-ethnic Indian company contacts. We analyze the aggregate consequences of company contacts outsourcing a lot of their work to India. Indexing company contacts by _f_ , we first calculate the aggregate share of their contracts that are placed into India. We then relate this to the overall wage and performance outcomes of the company contacts with the specification _Outcome f_ =η _tjc_ + δ⋅ _ShareContractsInIndia f_ + β⋅ _CompanyContactEthnicIndian f_ + χ⋅ _ShareContractsInIndia f_ ⋅ _CompanyContactEthnicIndian f_ + ε _f_ .

Our outcome variables are the cumulative wages paid and performance consequences of these contracts for company contacts, as indicated in the column headers of Table 9. We interact the India share metric with whether the company contact is ethnically Indian. Regressions are unweighted, include fixed effects for (year that company contact started on oDesk) x (modal job type of company contact) x (country of company contact), and report robust standard errors.

The results suggest that greater outsourcing to India is broadly associated with paying lower wages and being less satisfied with performance. This is measured in comparison to other company contacts that started on oDesk in the same year and from the same country with similar types of jobs. While this pattern is itself interesting, our main focus for this study is on the interaction term that delivers null results across the board. This pattern says that all of the consequences (good and bad) from outsourcing to India come through greater engagement with the country, not from being an ethnic Indian. This stark set of results is consistent with a tastebased preferences account, and it is inconsistent with most other accounts of why ethnic Indians are placing work into India (e.g., productivity advantages). From these and prior results, we conclude that taste-based preferences among oDesk actors in the originating countries is the key driver of the ethnic bias observed in outsourced contract placement to India.

# **7. Link to Broader Diaspora Connections**

To this point, our analysis has only exploited data derived from oDesk itself, and yet at multiple points we have framed our analysis in terms of the general diaspora literature. There remains a 20
amount of mitigation up to 2025, from gradually increasing the carbon tax over this period (in the $10 case, this annual tax will be zero in 2020, $2 in 2021, $4 in 2022, etc.).

**Table 2: Emissions and mitigation due to alternative carbon tax rates imposed gradually from 2021 to 2025, in five selected countries, under the Enerbase simulation alternatives**

**(Million tons CO2 e)**

|Country|Tax rate<br>imposed by<br>2025|BAU<br>emissions in<br>2025|Emissions in<br>2025 with<br>carbon tax|Mitigation<br>in 2025|Cumulative<br>mitigation by<br>2025|
|---|---|---|---|---|---|
|Mexico|$10|678|662|16|35|
||$20|678|654|24|53|
|Chile|$10|117.1|114.2|2.9|6.4|
||$20|117.1|112.9|4.2|8.4|
|Ukraine|$10|289|276|13|29|
||$20|289|271|18|40|
|Romania|$10|103.4|100.3|3.1|6.8|
||$20|103.4|99.1|4.3|9.5|
|Bulgaria|$10|53.8|51.6|2.2|4.8|
||$20|53.8|50.4|3.4|7.5|

We next calculate the amounts of carbon tax implementation support implied by our two derived cost prices, the ICP and the OCP, using these figures as our alternative cases. A formula similar to (6) (based on the compensation parameter _α_ times the applied carbon tax times the induced mitigation) can then be used to calculate the tax implementation support in any given year, including the last year, 2025. Given and our baseline price point values, _α1_ = 1⁄2 (for the ICP) and _α2_ = 1.5 (for the OCP), we arrive in Table 3 at a set of figures for tax implementation support in 2025.

# **Table 3: Tax implementation support payments in 2025 under the ICP and OCP, by host country and carbon tax alternative, in the numerical alternatives from Table 2**

# **(US$ Million)**

|Country|IC|P|O|CP|
|---|---|---|---|---|
||Tax=$10|Tax=$20|Tax=$10|Tax=$20|
|Mexico|80|240|240|720|
|Chile|15|42|44|126|
|Ukraine|65|180|195|540|
|Romania|16|43|46|129|
|Bulgaria|11|34|33|102|

18
**Figure 6. Progressivity of fiscal interventions (Kakwani coefficient)**

<!-- Start of picture text --> 0.29<br>0.32<br>0.41<br>0.73<br>0.74<br>0.75<br>0.79<br>0.84<br>0.85<br><!-- End of picture text --> Source: authors’ estimations based on surveys PNAD-C, POF, and PNS, and administrative data from the Ministry of Finance, Ministry of Health, and Government Open Data Portal. Notes: DT = direct transfers; IK = in-kind benefits. The vertical line indicates the Gini coefficient considering market income plus pensions.

# 3.5 Incidence and concentration of taxes and transfers

## _Taxes_

The tax burden for the first MIPP income quintile is much higher than that for the rest of the population (Figure 7). Practically all taxes paid by the first decile are in the form of indirect taxes. Indirect taxes as a share of MIPP for the first quintile households are high (51.3%). F<sup>34</sup> This is likely a consequence of the existence of a high share of households with low MIPP who rely completely on direct transfers to finance their consumption. 34F<sup>35</sup> At the same time, the tax incidence for quintiles 2 to 4 is similar, though the tax burden increases slightly because the direct taxes burden increases more than the decline in the incidence of indirect taxes. Tax incidence rises in the fifth decile, mainly due to a more significant effect of direct taxes, even though the top 20% of the population pays less indirect taxes as a share of their market income plus pensions. The importance of property taxes (IPTU/ITR) as a share of the market income plus pensions is very low (0.2%).

34 According to CEQ Standard Indicators there are a few country cases that, as Brazil, have a high tax incidence for the bottom quintile: Argentina 2017 (46.3%), Spain 2017 (34.4%), South Africa 2010 (119.0%) and South Africa 2015 (100.7%). The factor that influences these high values the most is the low share of market income plus pensions in the first quintile. The average share of the first quintile of countries available in the CEQ Data Center is 4%. For Brazil 2019, only 1.4% of income is captured by the first quintile, while South Africa's values do not exceed 0.5%, and Argentina and Spain do not exceed the first quartile value. In Brazil, it is feasible for households in the first quintile to pay such a high proportion of their pre-fiscal income because the amount received from direct transfers (Figure 9) raises their consumption capacity and, therefore, the amount of taxes paid.

35 The average disposable income for households in the first decile of the MIPP distribution is almost 12 times higher than the average of their market income plus pensions. For the second decile, the average disposable income is 67% higher than the MIPP.

24