rafmacalaba/datause-encoder-data huggingface dataset:
To assess the macroeconomic significance of Ben Ali’s business interests and performance differences between connected and non-connected firms, the Tunisian firm census for the period 1996–2010 was merged with data on gross output and profits as declared to the tax authorities, which we obtained from the Ministry of Finance for the period 2000–2010. Information on tax regime and foreign ownership is available for the period 2006-2010. While these administrative data contain information on a limited set of firm characteristics only, lacking among other things information on capital and investment, their coverage is comprehensive: A unique aspect of the Tunisian firm census (RNE) is that it spans the universe of private sector firms. In 2010, the RNE contained information on 102,660 firms with employees and an additional 596,998 firms without paid employees (e.g. the registered selfemployed). This enables us to pinpoint which sectors connected entrepreneurs sorted into and how they performed relative to their competitors. In addition, the database enables us to follow the same firms over time, and track entry and exit. Moreover, the quality of the employment data is high.<sup>4</sup> By contrast, data on turnover and profits are missing for approximately one third of all firms. Officials confirmed that the data at our disposal covered all firms for which such information is available. The majority of firms for which information is lacking are operating in the so-called ‘régime totalement exportatrice’, commonly referred to as the offshore sector. Firms in this tax regime do not have to pay output tax, provided they export at least 70% of their output. As a consequence, tax inspectors have limited incentives to verify the accuracy of their declarations. We thus have to be cognizant of the limitations of administrative data.

# **2.3 Data on Entry Regulation**

To examine the relationship between regulation and the performance of politically connected firms, a data set documenting the evolution of Tunisian investment law was created, which we merge with the firm-level data. More specifically, we code entry regulations proclaimed by the Tunisian Investment Incentives Code (hereafter referred to as the investment code), which governs investment in all sectors

> 4 For example, a comparison of the employment numbers obtained from the RNE with those documented in the labor force survey suggest that underreporting of labor is quite low, typically on the order of 10-15% (Rijkers et al., 2014).

7
relative to the anticipated value of the research. But as illustrated in the following case study on research regarding domestic violence, this is not always the case. This case study comes from a survey conducted by one of the authors in 1992, which was one of the first attempts to collect quantitative data on domestic violence in the Indian sub-continent and, therefore, conducted without the advantage of the experience of other researchers (Rao 1997, 1998, Bloch and Rao 2002).

# _Case Study: Domestic Violence_

When a man hits his wife it is, usually, a private act about which the wife feels great shame and is thus reluctant to reveal, particularly to an outsider. Thus, asking such questions in a survey raises various ethical concerns. Mindful of the sensitivity of the issue - and on the recommendation of various women’s activists who had worked on the subject – in 1992 a team of social workers with specific experience in gender issues was hired to conduct field work. The team was strictly instructed to have all respondents sign an informed consent form and to ensure that interviews with women were conducted in private and behind closed doors at a time of day when men were away in the fields. They were also instructed to inform respondents that the survey was being conducted by the University of Michigan (where the principal investigator [PI] then worked).

The informed consent form had a general statement explaining that questions would be asked about the lives of women, that all responses would be kept confidential, and that respondents should feel free to stop the interview at any stage if they felt uncomfortable with the questions. The first ethical question raised by this is: Was this enough? Is documented “informed consent” – which followed the strict guidelines of the University of Michigan’s 7
multivariate regression is a joint estimator, it also estimates the between-equation covariances. Therefore, coefficients can be tested across equations to reveal whether costs are in line with productivity.

The analysis assumes that employees with different characteristics are perfect substitutes in the production process, but with potentially different productivities. The individual-level wage and production equations are estimated simultaneously, allowing for comparison of the average productivities and wages of employees distinguished by various characteristics. This empirical setting may reveal new information on selected issues related to the determination of wages in Ghana. For example, it may provide evidence about gender discrimination in wages and the causes of increasing remuneration over the life cycle.

When equality of the parameter estimates of a particular variable in the two equations cannot be rejected statistically, I interpret this result as evidence compatible with the existence of competitive spot labor markets. Rejection of equality of the parameter estimates points toward noncompetitive labor markets or discrimination. The analysis allows productivity to vary by type of employee and by type of enterprise by adding controls for enterprise characteristics, such as sector and region.<sup>4</sup> 4 There are numerous studies of the Ghanaian labor market. Canagarajah and Thomas (1997) analyze returns to education. Jones (1994) studies wages and productivity of workers in the manufacturing sector. Teal (1995) investigates whether the decline in real wages reflects the existence of a competitive labor market in Ghana. Velenchik (1995) focuses on apprenticeships in the manufacturing sector. Teal (1996) analyzes the possible existence of economic rents within the manufacturing sector.

10
whereby this happens. The median voter hypothesis may provide one such mechanism. However, decisively establishing that it is _the_ mechanism crucially depends on our ability to credibly decide who is the median, or rather the decisive, voter. I propose to term this broader hypothesis, the _redistribution hypothesis_ because we need to distinguish the hypothesis proper it from one specific mechanism (the median voter) whereby it may be implemented.

We test the two parts of the redistribution hypothesis using the most recent data from Luxembourg Income Study for 20 OECD countries covering the period 1967-2005 (total number of country/years is 110). We deal here with an (unbalanced) panel, which, especially in terms of econometric issues raised, differs from either static- or time-series analysis as conducted by Meltzer and Richard (1983).<sup>5</sup> Table 1 shows the results of a very simple testing of the first part of the redistribution hypothesis. Column (1) shows the simplest possible test where the only control is unobservable country fixed effect. Column (2) introduces two additional country controls. Obviously, further controls could be brought in but my contention is that _α1_ would remain positive and statistically significant.<sup>6</sup> Indeed, we see that its value is stable: a one point increase in marketP Gini is associated with between 0.438 and 0.473 Gini points _greater_ reduction brought about by the process of redistribution. In other words, a more market-unequal state of affairs is associated with greater reduction in inequality as government taxes and transfers cash incomes.

> 5 I am grateful for this point to an anonymous referee.

> 6 One obvious additional control is democracy but in this sample of established democracies its betweencountry variability is very small, and in most cases, for a single country it is also time-invariant and thus cannot be included in a country fixed effect regression.

8
To see if households are sorted across communities in terms of demographics also, we include community level average dependency ratio (de...ned as the ratio of the total number of children over household size) as an explanatory variable. Addition of this variable reduces the magnitudes of the spatial disparity indices modestly in both survey years. Panel G reports the results when human capital, employment and the dependency ratio are included simultaneously. The estimates of the spatial disparity indices are comparable to the estimates in Panel B. Once education di¤erences are controlled for, sorting of other household attributes (e.g. employment, or demographic factors) accounts for only an insigni...cant part of neighborhood similarities. The ...nal panel in Table 4 reports the results from regressions which controlled for geographic factors in addition to all indicators of household attributes considered so far. The results show that household attributes (mainly education) and geographic factors together can explain away 67 percent of spatial disparity in urban areas, 52 percent in the pooled sample and 34 percent in the rural sample in 2010.

# 4.4 Infrastructure, Sorting and Spatial Disparity

Table 5 reports the results when we introduced di¤erent sets of controls discussed above in di¤erent combinations. Panel B reports the estimates of spatial disparity indices when all infrastructure and services related variables and all household attributes are introduced simultaneously. Panel C reports the results when we add geographical controls in addition to the controls introduced in panel B regressions. An important concern in the estimation of spatial disparity is that the cost of living indices are constructed at a fairly aggregate geographical unit level (stratum levels in both surveys). Such aggregate costs of living may not be able to capture the ...ner variations in the cost of living across our communities. To see if this is so, we de...ne three price indices. An index of housing cost at district level is estimated using a hedonic regression that controlled for housing characteristics. We construct a rice price index at the community level which also nets out the quality di¤erences. To get an idea of the prices of non-traded goods, we regress the wages 22
# **c. Nigeria: Shrimp**

In many respects, the Nigerian shrimp industry is a **success story.** Once a declining industry (about ten years ago), it is now an industry with a promising outlook and boasting a significant growth potential. Nigeria’s shrimp industry has been able to turn the tables because it has met international standards of quality and health safety, cultivated and captured a segment of the international market, built port-side facilities for its own use, provided high value added activities, and achieved profitability in spite of a high interest rate environment. The industry is confident about its future market opportunities and seems ready to expand the trawler fleet.

**Figure 12: Shrimp Value Added and Shimpent Value Breakdown in Nigeria**

<!-- Start of picture text --> 16<br>14 2.33 14.92<br>12<br>0.45<br>1.76<br>10<br>1.13<br>8 0.9<br>4.71<br>6<br>4<br>2 1.43 0.18<br>1.86 0.17<br>0<br>Interm. InputsInb. LogisticsCapital ChargeRepairs & Maint. Fuel LaborInsuranceAdmin. OverheadsOutb. LogisticsTotal Shipment ValueProfits<br>Value (million naira)<br><!-- End of picture text --> <u>Source:</u> _Nigeria Value Chain Analysis_ , 2005<sup>28</sup> Looking at figure 12, we can make the following observations:

- Fuel is a key cost driver at 36% of value added because it is used for vessel operation in the trawling activity;

- Administrative overhead costs are significant relative to other value adding inputs but consistent with local practice. Relative to labor cost, the figure reveals vividly the importance devoted to management and the level of effort spent in overseeing operations;

- Capital charge is also a large cost driver, but one that is fully recovered;

- Although logistics account for only 5% of the value adding inputs, an efficient cooling chain is essential for perishable exports.

> 28 The Nigeria Value Chain project was jointly managed by Uma Subramanian (FIAS, World Bank Group) and Peter Mousley (Africa Region, The World Bank).

16
||||(0.558)|(0.554)|
|---|---|---|---|---|
|Firm spent on R&D Y:1 N:0|||0.007<br>(0.008)|0.006<br>(0.008)|
|Introduced new product Y:1|||0.004|0.004|
|N:0|||||
||||(0.006)|(0.006)|
|How Much Of An Obstacle:<br>Labor Regulations?||||0.446*<br>(0.264)|
|Ease of Starting a Business<br>(EFW, Fraser Institute))||||0.499<br>(0.477)|
|Industry fixed effects|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|
|Constant|-217.710***|-235.298***|-181.004***|-183.793***|
||(56.994)|(60.993)|(66.981)|(66.279)|
|Number of observations|80,272|69,136|53,440|53,230|
|R-squared|0.291|0.293|0.290|0.290|

OLS method used. Huber-White robust standard errors clustered on country-year pair (ES round) in brackets. *** (1%), ** (5%), * (10%).

54
**Women in Sri Lanka are mainly employed in the agriculture sector** . Around 28 percent (approximately 740,000) of women are employed in agriculture, compared with 25 percent in the manufacturing sector – approximately 650,000, social services and other sectors – 18 percent (approximately 470,000), and commerce sector – 12 percent (approximately 325,000) of the total jobs for women. These sectors are also the four largest employers of women (Figure 2 and Table 1). On the other hand, more than 50 percent of men are employed either in agriculture, manufacturing or trade-related activities.

**Table 1: Estimated number of workers, by sector and gender**

||**Total**||**Men**|||**Women**||
|---|---|---|---|---|---|---|---|
|**Sector**|**Estimated**<br>**workers**|**Obs.**|**Estimated**<br>**workers**|**% of total**|**Obs.**|**Estimated**<br>**workers**|**% of total**|
|Agriculture|1,913,073|5,066|1,175,861|61.5|2,986|737,212|38.5|
|Mining|51,798|186|48,475|93.5|15|3322.562|6.4|
|Manufacturing|1,404,570|2,879|754,379|53.7|2,547|650,191|46.3|
|Public utilities|39,633|133|32,441|81.9|31|7191.704|18.2|
|Construction|607,792|2,378|586,746|96.5|83|21,046|3.5|
|Commerce|1,063,299|2,869|738,379|69.4|1,247|324,920|30.6|
|Transports &<br>communications|737,296|2,532|648,494|88.0|350|88,802|12.0|
|Financial &<br>business services|356,071|906|237,835|66.8|449|118,236|33.2|
|Public<br>administration|575,378|1,463|360,764|62.7|872|214,613|37.3|
|Other services,<br>Unspecified|960,634|1,891|492,266|51.2|1,804|468,368|48.8|
|Total|7,709,543|20,303|5,075,641|65.8|10,384|2,633,903|34.2|

Source: Staff estimates based on Sri Lanka HIES 2016; confidence intervals of estimated workers can be requested from the authors.

9
43 Provisional statistics from the 1986 census show the country of birth of foreign-born persons resident in New Zealand (Table 19). Of the nearly half million foreign-born persons enumerated, nearly half came from England and Scotland and 10 percent came from Australia. The United Kingdom continues to dominate the flow of more recent arrivals, but Asians have become considerably more prominent in recent years.

Table 19. Foreign-Born Population Resident in New Zealand by Place of Birth

|Birthplace|Residents|
|---|---|
|Australia|46,761|
|England|196,527|
|Scotland|38,064|
|Netherlands|24,132|
|Samoa|33,843|
|Cook Islands|15,534|
|Other|126,063|

Source: Department of Statistics (1987:Table 3).

Emigration from New Zealand has been the real dynamic component of international migration in recent years since it has fluctuated more widely than immigration (see Table 18). The annual number of persons leaving New Zealand for more than one year peaked at 61,328 in the year ending March 31, 1979, but such departures have dropped steadily since that time as the economy has picked up steam. Permanent departures peaked at 25,536 in the year ending March 31, 1981, and they have also dropped since that time. Between 1979/1980 and 1983/1984, the sex ratio of persons departing was 107.2 among New Zealand residents departing long-term and 106.5 among residents departing permanently (Farmer, 1986:32).

The net flow of permanent and long-term migrants has fluctuated substantially over time with a net inflow in some years and a net outflow in other years. Figure 6 shows how erratic the net figure has been and also shows the strong negative relationship between net migration in Australia and New Zealand. In fact, trans-Tasman migration has been particularly variable in recent years, as can be seen from Figure 7.

Population Projections. Net migration has been quite volatile in the recent past, but its overall magnitude has been relatively small. The historical pattern suggests that projections for the rest of the century should be close to zero. The Department of Statistics in Christchurch is currently producing a new series of population projections based on the estimated 1988 population. Several migration assumptions will be incorporated into the projections but the three main variants will assume annual net migration levels of +5,000, zero, and -5,000. These assumptions are based on past levels of migration: an average of about +5,000 over the last 80 years, zero over the last 20 years, and -5,000 over the last 10 years.
The decrease in the growth rates of reserves reduce GDP and per capita GDP growth along the period 2023-2050. The growth rate of the reserves per capita falls by 2.5 ppts by the end of the simulation period, resulting in a normalized contribution of 0.04 ppts to the overall fall in GDP growth over 2023-50. This result suggests that GDP growth rates are less sensitive to reserves per capita.

**Table 2. Understanding the Drivers of Peru’s GDP Growth Slowdown**<sup>**12**</sup>

||Total Decreas<br>**He**<br>|e of GDP Growth(2023-2050)<br>**adline GDP Growth**<br>|
|---|---|---|
|**Baseline**|-0.78ppts|100.0%|
||**Factor Contribution**|**Normalized Share of Fall in Growth**|
|**Population Growth**|-0.27ppts|30.2%|
|**Working age-to-totalpopulation Growth**|-0.26ppts|29.4%|
|**HC Growth**|-0.15ppts|17.0%|
|**Non-Res. TFP Growth**|-0.12ppts|14.1%|
|**Copper TFP Growth**|-0.04ppts|4.8%|
|**Copper Reservesgrowth**|-0.04ppts|4.5%|

_Note:_ Investment is held constant throughout the simulation period, so we remove its contribution. The capital-to-output ratio is also approximately constant.

Decelerating economic growth is not a unique phenomenon to Peru. Kose and Ohnsorge (2024) finds that an internationally widespread decline in potential growth occurred in 2011-21 relative to 2000-10. This widespread decline reflected a multitude of factors. In terms of the production function framework, all the fundamental drivers of growth faded in 2011-21: TFP growth slowed, investment growth weakened, and labor force growth declined. The study also finds that recessions after strong shocks such as the Covid19 pandemic, were associated, on average, with a decline of about 1.4 percentage points in potential growth even after five years.

# **4. Reform scenarios for Peru to achieve High-Income status.**

In this section, we examine how different factors, namely investment, human capital, and TFP, affect economic growth in the non-natural resource sector. Our objective is to assess the feasibility of accelerating GDP growth to achieve HI status within the next two decades through domestic reforms. We analyze the individual impact of each growth factor and find that growing at around a 5 percent rate is not possible with a single shock to only one of them. Therefore, we simulate moderate and ambitious reform scenarios combining different shocks to all the aforementioned factors together. These scenarios allow us to examine the potential outcomes and determine the best approach for achieving the desired economic growth and HI designation. We first consider reform scenarios to TFP, as they are some of the most important (Section 4.1) and then reforms to boost human and physical capital (Section 4.2), with the resulting growth paths discussed in Section 4.3.

> 12 Because the model is nonlinear, the sum of the effects of changing growth factors at one-by-one is not equal to the total change in growth when the growth factors change together. This is especially true over the long term when capital in the nonresource and resource sectors change.

13
# **Table A10. Survey Methods’ Impacts on the Measurement of Labor Market Outcomes, Excluding Controls**

||(1)<br>Employed|(2)<br>Working|
|---|---|---|
|LOA|0.037|0.040*|
||(0.023)|(0.022)|
||[0.148]|[0.086]|
|ESR|-0.004|-0.014|
||(0.025)|(0.023)|
||[0.879]|[0.585]|
|Observations|2,480|2,480|
|Outcome(ControlGroup)Mean|0.571|0.670|

_Notes:_ This table shows the estimated effects of including the LOA module (LOA) or enforcing self-reporting (ESR) on reporting employment or work status. We present estimated coefficients from Equation (1). Each column is a separate dependent variable as defined in Section 3. The control mean refers to the mean of the control group for each outcome. All estimations include strata fixed effects only. Standard errors clustered at the household level are presented in parentheses, and the randomization inference _p_ -values are shown in brackets. *** _p_ <0.01, ** _p_ <0.05, * _p_ <0.1 47
# **1. Introduction**

The _Zona Metropolitana del Valle de Mexico_ (ZMVM) (Mexico City Metropolitan Area) is one of the world's largest urban areas and one of the most notorious for its poor air quality. In the 1990s, however, efforts to control air pollution seem to have diverted the trend. Table 1.1 shows a decline in overall air pollution during the last decade (GDF 2000). However, with the exception of lead, carbon monoxide and sulfur dioxide, pollution levels are still far above air quality standards.

**Table 1.1 Number of Days Per Year that Ozone and PM10 Concentrations in Mexico City Satisfied Air Quality Standards**

|_1995_|_1996_|_1997_|_1998_|_1999_|
|---|---|---|---|---|
|**Ozone**<br>41|39|43|45|65|
|**PM, 0**<br>**273**|**186**|**212**|176|345|
|_Source:_GDF (2000).|||||

The Third Air Quality Program 2001-2010 ("the Program") being developed by the _Comision Ambiental Metropolitana_ (CAM) includes further initiatives to improve air quality. These air quality efforts are expected to improve the health of the population and also reduce the number of environment-related alerts in the ZMVM. One element of the program, and the purpose of this study, is the economic evaluation of the benefits gained from improving air quality.

Air pollution has a range of negative effects on human health. It may also affect economic activity when excessive levels of pollution require _Contingencias Ambientales_ (restrictions on environmentally polluting activities). Health-related impacts include eye irritation, respiratory diseases, cardiovascular effects, and premature death. When a _Contingencia Ambientale_ is declared it limits activities of a range of manufacturing industries that generate emissions of air pollutants, and also restricts traffic.

In sum, current air pollution exceeds permitted standards, and prospects for the future, with some exceptions (such as lead), will not improve without more active air quality management and policies designed to improve air quality. Policy measures will be most useful if they can (1) be defined in terms of specific measures and the costs involved; (2) assess changes in air pollution using some form of air quality modelling; (3) assess and evaluate changes in air pollution impacts; and (4) rank the measures in terms of cost-effectiveness.

The rationale for making air quality policies is the welfare gain from improvements in air quality. This report attempts to assess in economic terms the reduced impacts on human health and economic activity associated with four prespecified air quality scenarios with a time horizon of 2010. It is limited to the impacts of ozone and PM1 o because these substances are the most important in terms of exceeding their standards and because relevant health information is not fully available for other pollutants (such as NO2).