rafmacalaba/datause-encoder-data huggingface dataset:
66 administrative structure, and to assure that audits verify fair distribution of funds. These modifications would likely trim the number of recipients, help ensure that those who remain are the most needy, and curb the fiscal costs while probably providing some scope for increasing the benefit per recipient.

# **_VI.  Strategies for the longer term_**

# _A.  Baseline prospects for growth and principal challenges_

If stability can be maintained through progress in the areas of immediate challenge just outlined, the chances seem good that Argentina can enjoy substantial growth for this year and the next two or so years merely from returning back toward full capacity at the peak level of GDP in the second quarter of 1998. Even after some rebound, the fourth quarter real GDP in 2002 was still about 20 percent below that highwater mark. As time passes, however, increasingly the question will be how Argentina can achieve sustained growth that goes beyond recovery from depression. The challenge of doing so will be especially demanding considering that in the past, Argentina’s periods of growth have tended to coincide with those of large capital inflows (figure 9), but in the future the aftermath of the debt workout will likely mean the need to turn to domestic saving rather than foreign capital as the source of investment for growth. There are many structural aspects of the economy (and society) that will affect growth potential. This section focuses on some of the most important, but others (such as education for the formation of human capital and institutions to spur personal saving) will also be important.

Figure 9 Economic growth and net private capital flows (% and $billions) <!-- Start of picture text --> 20<br>15<br>10<br>5<br>0<br>-5<br>-10<br>-15<br> Growth<br>-20 PKFL<br>1992 1994 1996 1998 2000 2002<br><!-- End of picture text --> Before turning to the major individual issue areas, it is useful to consider the baseline economic outlook over the medium term. The initial recovery phase should generate relatively high annual growth rates, merely because last year’s GDP fell so low. In real terms, in 1998 GDP was 288 billion pesos; by last year it had fallen to 235 billion
the panel was that the enterprise had no paid workers. Therefore, in the baseline survey, no firms hire paid workers. By the October 2007 survey, just under nine percent of the firms had hired one or more paid employees. Is this transition from non-employer to employer associated with the same factors which differentiate own account workers from SME owners in tables 1 through 8? If the same variables are association with growth in the panel, then the case for a causal relationship is somewhat stronger.<sup>11</sup> Table 9 shows the results of logistic regressions on the determinants of employment growth in the microenterprise panel. The dependent variable takes a value of one for the 8.8 percent of the sample adding at least one paid labor between April 2005 and October 2007, and zero otherwise. Given the relatively small number of enterprises that add employees, we limit the number of variables included on the right hand side in any specification. We find neither parental background nor childhood conditions to be significant in any specification. The specification in the first column measures ability as the first principal component of years of schooling and the scores on the Raven, digitspan and cognitive reflection tests. The four entrepreneurial attitudes variables are measured as in Table 6.

The results in column 1 are consistent with those in Table 7 with respect to ability and motivation. Higher ability owners are more likely to have added employees, as are owners who are motivated by personal achievement. Moreover, owners with lower power motivation—that is, those more willing to give up some control over their situations—are more likely to grow. In the second regression, we add the owner’s age at the time of the baseline survey, and a variable indicating the owner is female. We find that older owners are less likely to grow. On the surface this appears to conflict with the results from Table 7. However, Table 7 measures a stock, or cumulative hazard, while Table 9 measures a flow or hazard, so the results with respect to age are actually not inconsistent. But, the ability measure loses significance once we control for age. Years of schooling and age are negatively correlating, reflecting the trend of increasing education across time. The achievement and power motivation variables remain significant with little change in

> 11 Some of the questions were asked in later rounds, so one could still argue that they post-date the growth experience. For example, entrepreneurial attitudes were included in the survey administered in October 2006. Not enough firms grow after this point for analysis of growth following the responses to be possible.
10 associations, controlling for years of schooling and other socioeconomic characteristics. We take the mean estimate across all studies: moving from moderate stunting (defined as the height for age z- scores being below 2 standard deviations from the reference population) to non-stunting increases the height on average by 5.98 centimeters.

When looking at the height premium in the labor market, several studies have documented how height gets rewarded in the labor market, over and above schooling and cognition. The results are mainly from middle-income countries, and available only for men, to avoid having to model participation or selection into the labor market by females. On average, an additional centimeter in height translates into 1.7% higher wages in the labor market, after controlling for years of schooling, and sometimes cognition too. The second panel B looks at the association of having been stunted in childhood and completed years of schooling: on average, being stunted in early childhood translates into 1.59 fewer years of schooling completed, which is reduced by about half when controlling for socioeconomic status and maternal education. Finally, the left-hand columns in panel C of Table A1 summarize the estimates of the association between moderate stunting in childhood and cognitive deficits on the left-hand panel: the magnitude of the association is quantitatively important, with an average cognitive deficit of 0.625 standard deviation associated with moderate stunting. The rightside of panel C presents estimates of the conditional returns to cognition in the labor market, controlling for years of schooling and attained height, derived from longitudinal studies in middleincome countries, and available only for men, to avoid having to model participation or selection into the labor market by females.

We want ∆lny to be the percentage difference between actual per capita income today and what it would have been if none of today’s workers had been stunted in childhood. We therefore set ∆S� equal to the average rate of childhood stunting among today’s workers, i.e. those working in
business and 0 otherwise.<sup>3</sup> The mean value of _Computers_ is .19 and standard deviation is .39. Our main explanatory variable is an index of labor regulation defined at the state level.<sup>4</sup> In one survey question, stores were asked if labor regulations were an obstacle for their business. Responses were recorded on a 0-4 scale defined as no obstacle (0), minor obstacle (1), moderate obstacle (2), major obstacle (3), and very severe obstacle (4). Our main index of labor regulation, _Perception_ index, equals the proportion of stores in a state who report labor regulations as a problem (minor obstacle or higher).<sup>5</sup> Being a group (state) average, the _Perception_ index suffers less from measurement errors and endogeneity problems associated with firm perceptions (Kreuger and Angrist, 2001). As a robustness check, we also use an index of labor regulation for the manufacturing sectors in India ( _Law Book_ index) due to Besley and Burgess (2004). For the _Law Book_ index, we use the latest year (2000) values for which the index is available. The motivation is that pro-labor governments are likely to implement labor-friendly laws in both manufacturing and services. Correlation between _Perception_ and _Law Book_ index is .564.

One concern could be that labor regulation is likely to be correlated with other regulations such as tax rates, land laws, corruption, etc. However, this is not a problem for us because we expect these (other) regulations to be positively correlated with labor regulation but have a negative effect on computer usage. Thus, failure to control for tax rates, etc., will only weaken the positive association we intend to show between computer usage and labor regulations (downward bias in the estimated coefficient of labor regulation). To confirm this, we use store response (on the same 0-4 scale as above) to

> 3 Data on the number of workers using computers or hours of computer usage is not available.

> 4 In India, labor regulations are made and implemented by the state governments.

> 5 Our results are virtually unchanged if we use state level averages of the actual scores here. The _Perception_ index varies between .09 (Gujarat) and .528 (West Bengal) with a mean of .249.

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**Figure 11:  Long-Run Fees Over Contributions Equivalent to a 1% Asset Management Fee**

<!-- Start of picture text --> 0<br>-5<br>-10<br>-15<br>-20<br>-25<br>-30<br>-35<br>-40<br>1.2 2.4 3.6 4.8 6.0 7.2 8.4 9.6 10.8 12.0 13.2 14.4 15.6 16.8 18.0 19.2<br>nr<br><!-- End of picture text --> Note: The Y axis measures the long run fee over contribution required to compensate (hence, the negative sign) a 1% asset management fee for increasing asset net rate of return measured along the X axis. Other assumptions used: wage growth = 5%, _T_ = 40, _T_<sup>*</sup> = 40.

49. Finally, the long-run impact of fees charged during the accumulation period can also be assessed through the computation of charge ratios.<sup>18</sup> A charge ratio is defined as one minus the accumulation ratio. The accumulation ratio is the ratio of the final cash balance produced when fees are charged over the cash balance where fees are not charged. Table 20 reports the charge ratios for the average MPF in Hungary defined as the MPF that charges an operating fee of 6.49 percent of contributions and an asset management fee of 0.97 percent of assets. The ratio is calculated for different wage growth rates, rates of return, interruption in service ( _T_ – _T_ *) assumptions, and for a 40 year career individual ( _T_ = 40).

**Table 20: Charge ratios for the average MPF in Hungary,** **<u>(%)</u>**

||||**Rate of return (% p.a**<br> <br>|**.)**<br>|
|---|---|---|---|---|
||||**2**<br>**5**|**8**|
|**T* = 30**<br>|**wage**<br>**growth**|**2**<br>**5**|26.9<br>25.3<br>28.4<br>26.9|23.8<br>25.3|
|**(**|**% p.a.)**|**8**|29.8<br>28.4|26.9|
||**wage**|**2**|25.2<br>23.0|20.9|
|**T* = 35**<br>|**growth**|**5**|27.3<br>25.2|23.0|
|**(**|**% p.a.)**|**8**|29.2<br>27.3|25.2|
||**wage**|**2**|23.5<br>20.5|17.7|
|**T* = 40**<br>|**growth**|**5**|26.3<br>23.5|20.5|
|**( **|**%p.a.)**|**8**|28.6<br>26.3|23.5|

Note: Contribution fee = 6.49%, asset management fee 0.97%, _T_ = 40 50. Note that the charge ratio increases with non-contributory periods (due to unemployment, for instance), as even if the contribution fee is not charged, the asset 18 See Diamond (2000), Haukur, Trygvi, and Orszag (1999), Murthi, Orszag, and Orszag (2001 and 2001b), Rusconi (2004), and Whitehouse (2000).
Table 4: Two least squares models with demand measures for news consumption used as an instrumental variable

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Severity of<br>censorship|Severity of<br>censorship|Severity of<br>censorship|Severity of<br>censorship|Severity of<br>censorship|
|Media reach<br>|-0.412**<br>||-0.593**<br>|||
|(complete)|(0.148)||(0.200)|||
|Media reach||-3.627**|||-4.102*|
|(dichotomous)||(1.277)|||(1.805)|
|Media reach||||-0.529*||
|(partial)||||(0.229)||
|GDP pc (ln)|1.397|1.804~|1.820|-0.250|0.398|
||(0.981)|(0.993)|(1.527)|(1.334)|(1.444)|
|Population (ln)|-2.472|-5.159|-0.298|-3.832|-5.455|
||(3.771)|(3.729)|(4.708)|(6.060)|(6.008)|
|Political|-0.00723|-0.00952|0.00474|0.00647|0.00178|
|competition|(0.00913)|(0.00939)|(0.0131)|(0.0164)|(0.0193)|
|Government|1.284|0.937|0.0757|-1.109|-1.170|
|fractionalization|(1.338)|(1.209)|(1.233)|(2.077)|(2.222)|
|Incumbent|-0.00339|-0.00781|0.0263|-0.00941|-0.0109|
|tenure|(0.0323)|(0.0298)|(0.0286)|(0.0421)|(0.0443)|
|Corruption|-6.787|-5.586|-17.76**|-15.74*|-19.01*|
||(5.067)|(5.505)|(5.603)|(7.858)|(9.297)|
|Legal|0.0444|0.0209|-0.00212|0.114|0.0844|
|constraints|(0.0703)|(0.0705)|(0.115)|(0.144)|(0.149)|
|Political|0.153*|0.140~|0.160~|0.0486|0.0874|
|constraints|(0.0701)|(0.0747)|(0.0851)|(0.102)|(0.106)|
|Economic|-0.0619|-0.0690|-0.142|0.0692|0.00794|
|constraints|(0.0807)|(0.0796)|(0.150)|(0.188)|(0.223)|
|Country FE|√|√|√|√|√|
|Year FE|√|√|√|√|√|
|_cons|45.86|97.88|11.79|91.79|124.4|
||(71.97)|(70.74)|(93.91)|(118.4)|(117.3)|
|_N_|1258|1258|700|488|488|
|_Countries_|49|49|34|34|34|
|<br>_Years_|<br>15|<br>15|<br>15|<br>15|<br>15|
|_Censor type_|All|All|All|Government|Government|
|_Model_|2SLS|2SLS|2SLS|2SLS|2SLS|
|_Instrument_|All surveys|All surveys|Representative|Representative|Representative|
|_Shea partial R sq_|<br>0.64|<br>0.06|<br>0.60|<br>0.55|<br>0.05|
|<br>_F-statistic_|407.85|47.80|183.29|80.53|22.11|
|_p_|0.0000|0.0000|0.0000|0.0000|0.0000|

Standard errors in parentheses ~ p<0.10, * p<0.05, ** p<0.01, *** p<0.001 37
empirical analysis for Chile, and discusses why electricity production serves as an instrument. We then outline our methodology and empirical structure. Section 6 presents our results. A brief conclusion follows.

# **2 The cross-country impact of pollution on labor productivity**

There is strong anecdotal evidence that pollution (as measured by PM2.5) is strongly correlated with a reduction in labor productivity. Figure 1 shows that the correlation is negative using two standard labor productivity measures (output per worker and output per hour worked). This negative relationship is fairly consistent regardless of income level.

**Figure 1:** Pollution and labor productivity

<!-- Start of picture text --> East Asia & Pacific Europe & Central Asia Latin America & Caribbean<br>Middle East & North Africa North America South Asia<br>Sub-Saharan Africa<br>4<br>3<br>2<br>1<br>0<br>2 3 4<br>log(pollution)<br>log(Output per hours worked)<br><!-- End of picture text --> Source: ILO, WDI and authors’ calculations Note: The Figure uses World Bank region definitions. Fitted values generated using nonparametric smoothing. Shaded are are confidence regions.

These correlations can be extracted when accounting for country idiosyncrasies and time fixed effects using a linear regression of the log of pollution ( _PM_ 2 _._ 5) on the log of labor productivity as defined by the _<u>Yit</u>_ ratio of total value added with respect to the total number of workers in the economy � _Nit_ �.

or in terms of output per hour worked: The results indicate that pollution negatively correlates with labor productivity. Specifically, a 1 percent increase in PM2.5 correlates with a decline of 0.20 and 0.08 percent in labor productivity, depending on whether labor productivity is measured as value added per number of workers or hours worked, respectively (see Figure 2). Furthermore, we note that the effects are more pronounced for low-income-countries (LI) 3
# **2 The Zambia Road Transport Market**

# **2.1 Freight Volumes**

Zambia’s main trading partner is South Africa<sup>6</sup> , accounting for 1.8 million tonnes per year (44% of the estimated total freight traffic in 2005). The DRC and Zimbabwe account for about 9% and 8% respectively of total trade. Zambia’s international trade outside the SADC region accounts for about 36% of total trade.

The main products transported by road within Zambia are:

- Mining products (both inputs and outputs: ores, concentrates, metals, sulphur, sulphuric acid, coal),

- Agricultural products (sugar, tobacco, cotton),

- Fuels (diesel and petrol),

- Food (bulk grain).

According to data from the Zambian Revenue Authority, the total volume of Zambian regional and international trade is 4.1 million tonnes in 2005, made up of 2.3 million of imports and 1.6 million of exports (see Figure 1).

Road transport is the dominant mode in Zambia and is expected to remain so. Current modal share is about 71% of Zambia’s trade (in volume) is carried by road, 24% by rail<sup>7</sup> and about 5% accounts for oil imports by pipeline from Dar es Salaam (TAZAMA pipeline).

High value mining and agricultural goods (cobalt and fresh / frozen products) are generally transported by air freight except for copper cathodes (about USD 250,000 per load), which are exported by road to Gauteng, where they are containerized in order to be exported through Durban.

> 6 On Zambia’s routes from South Africa, only about 10% of general cargo is containerized for costs savings reasons. Trucks have increased payloads and volumetric capacity when not containerized.

7 There are two rail operators relevant for Zambia’s trade. Railway Systems of Zambia (RSZ) is the rail operator of Zambian Railways following a 20-year concession agreement signed in February 2003. The total traffic carried by Zambia railway has fallen from more than 6 million of tonnes per year in 1975, to 4.5 million in 1988 and to less than 1.5 million in 1998. For such low volumes, the minimum required railway tariffs to achieve financially viable operations will be, in most cases, higher than the equivalent road tariffs with longer transit times. This is partly due to deregulation of the road transport sector, but also due to shrinkage of the economy, particularly mining and agricultural sectors. _Tazara_ is jointly owned by the governments of Zambia and Tanzania, and initially financed by China in 1975. The capacity of rail infrastructure is in the region of 5 million of tonnes per year, but operations and equipment capacity to 1.5 million. Current traffic amount 0.5 million, of which about 0.2 million is traffic to and from Zambia and the DRC.

4
determines how much de jure exemption of food can redistributive. Because food Engel curves are downward set reduced rates or typically sloping, many governments fully exempt food products for redistributive purposes.<sup>26</sup> Previous research has studied the magnitude and approximate log-level linearity of the food Engel curves.<sup>27</sup> We combine our core data with microdata from the Global Consumption Database (discussed in section 2.3) to estimate food Engel curves in a uniquely large sample of 89 low and middle income countries.

In the top panels of Figure 4, we present the aggregate food budget share (Panel A) and food Engel slope (Panel B) against countries’ GDP per capita. The aggregate budget share spent on food decreases with development, but the percentage-point drop over development is less pronounced for food than for informal consumption. Food Engel curves are typically downward sloping and there is no relationship between the magnitude of the slope and development. In Panels C and D of4 we show the _formal_ food aggregate budget share and Engel curve slopes, respectively. The aggregate formal food budget share is small on average (11%) but increases with development. The slope of the formal food Engel curve is small in magnitude but positive on average in low-income countries; it becomes negative in upper-middle income countries.

Panels A and B show that the levels and of food curves are Finally, slopes Engel similar between our core and extended for countries at the same very sample sample, level of development. This could suggest that while we can only characterize informal consumption patterns in our core sample, our results may be relevant for developing countries more broadly.

# **4 Progressivity of Consumption Taxes**

The consumption patterns described in Section 3 determine the progressivity of consumption taxes both in the average developing country and across development, which we turn to in this section. We say a tax policy is progressive if the effective tax rate (ratio of taxes paid to household income) increases with household income.

> 26Some countries apply reduced rates or exempt all food goods, while other countries target ‘basic’ food items. We follow the former approach; targeting narrow items may improve redistribution, but also increases the scope for cross-goods misreporting and distortions.

> 27Recent studies include 10 countries in Alm ̊as (2012), 22 in Anker et al. (2011) and 38 in Pritchett and Spivack (2013).

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event. Solving for 0, we obtains e- E _(11_ (1-)) _(MC_ (1-0) + fMp(o-oT)/((l) + p(q-T)) further manipulations yield: a -, [ (f3m(l-r)) + (ftc - _Om)I((l-u)_ + p(q-qT))J with the two terms being, first the expected utility from the individualistic regime from now on, and second, the single period gains accruing each period from now on from the cooperative behavior, properly discounted.

Then the credit group problem is: max _0_ - max [(ftml (1-0r)) + (fic - em) I ()(l-o) + _p(a-gT))_ -t-, - (U4/aai) 1a* i d°(OPI84i) ( (C _cO/m(-Gr+Pf) _)._ and, (Ofi/ai)la* i U(5PIjai)((Oc where the first constraint guarantees that it will not be possible to increase the agent's expected utility by increasing the share of capital for nonproductive purposes beyond a*. The second constraint states that the agent cannot increase his expected utility by decreasing his effort below a*. The solution of this problem will determine a*, O*, Q*, and T* which provides for the maximum expected utility and makes the credit group sustainable.

It could well be that the optimal solution is to set T equal to infinity. Then if the output level falls below a certain specified trigger level Q*, the credit group is dissolved forever. However, that policy might not always be optimal, since it might induce agents to make excessive efforts, when on average they all can be made better off by selecting a
paid is in part determined by the purity the buyer expects at the time of purchase based on information such as the size of the purchase, location, and other observable characteristics. Given the lack of official regulation in illegal drug markets, sellers can deceive costumers about the purity of the product being transacted. At the same time, buyers can later argue that the product was of lower quality than that agreed at the time of the transaction. These disputes, not surprisingly, end up many times generating a substantial amount of violence.<sup>22</sup>

# **A BRIEF LOOK AT CONSUMPTION TRENDS**

Although this paper concentrates on cocaine production and trafficking, in order to say anything meaningful about the price of cocaine in world markets, we must briefly review demand.

The United States, where cocaine is the second-most consumed illegal drug after marihuana is the main consumer country in the world. In most European countries, cocaine is the third illegal drug consumed, after marihuana and heroin.<sup>23</sup> While in the US, the annual prevalence rate of abuse in the 15 to 64-year-old population is about 2.8%, in European countries, on average, this rate is 2.7% in Spain, 2.4% in the UK<sup>24</sup> , 1.1% in the Netherlands, Italy and Ireland, and less than 0,3% in countries such as France, Sweden and Poland.

Cocaine consumption in the US decreased rapidly between 1985 and 1993; since then it has remained relatively stable. While in 1985 the annual prevalence rate among the 12-year-olds in the general population and

> 22 This is not the only link in the trafficking chain where violence arises as a method to resolve disputes. In fact, the recourse to violence is one prominent characteristic of organizations involved in illegal drug trafficking.

> 23 According to UNODC (2005a) there are approximately 13.4 million cocaine users in the world. Twothirds are in the Americas (about 6.5 million in the US and 1.9 in South America). 24 For the population between 16 and 59 years of age.

17