rafmacalaba/datause-encoder-data huggingface dataset:
19 Table 5 shows the average EU tariff facing Africa typically ranges from zero to fivetenths of a percent with Uganda recording a high of 0.6 percent -- due mostly to a tariff of 18 percent on its exports of fresh grapes. The average preference margins African countries receive are in the two to four percentage point range, and reach a high of 4.9 points for Swaziland.'<sup>6</sup> Although the margins vary, all African countries faced average tariffs that are below those paid by other exporters. For some tariff line level products these preferential tariff margins were 20 percentage points or more below prevailing MFN tariffs. This situation is markedly different from that of the Asian NICs. For example, Taiwan (China) paid an average tariff of 7.5 percent on its exports -- a rate that was 4.0 percentage points **higher** than that facing other exporters of the same items. EU tariffs facing Korea average 7.8 percent - - 4.2 points higher than that facing other exporters (many of which receive preferences).

The situation in the US and Japan differs in several respects. First, the average pre-Uruguay Round United States and Japanese tariff facing Africa is often higher than in Europe (i.e., an average 9.8 percent duty is paid on Togo's exports to Japan; the tariffs on Mauritius' exports to the United States average 6.4 percent and 9 percent for the Central African Republic) and the margins of preference are often lower. The high Japanese average was largely due to a MFN tariff of 25 percent on Togo's exports of prepared and preserved tomatoes, while the results for Mauritius were due to US MFN tariffs of up to 35 percent on a number of textile and clothing products including men and boys shirts, babies blouses, and sweaters. In several cases where Canada and Israel had a high volume of exports to the US, FTAs resulted in zero tariff being applied to competitors' products with the result that African exporters paid '<sup>6</sup> Swaziland exports mandarin and other oranges to the European Union and pays a full MFN duty of 4 percent on these shipments -- as it does on exports of a several fresh agricultural products including asparagus, lemons and other citrus. Outside the agricultural sector, Swaziland faces a MFN tariff of 8.5 percent on coal exports to the EU of about $1.6 million.
# **12.  Economist Intelligence Unit’s Global Livability Rankings**

The Economist Intelligence Unit's livability ranking is an expansion on the methodology of previous ‘Hardship’ surveys. In addition to the factors that were previously attributed to specifically causing hardship a number of other factors have been included to give a more rounded impression of how livable a city is.

The survey takes over 40 factors into consideration which are weighted across five different categories: Stability; Healthcare; Culture & Environment; Education; and Infrastructure. Across the survey a mixture of quantitative and qualitative data are used, which are combined to give an overall Quality of Life Index rating. Each indicator is given a rating of between one and five, where one means there is no impact and five means the factor is extremely challenging. These are then weighted to produce an index, where 0% means a city is exceptional and 100% means it is intolerable.

||**© 2005 T**|**he Economist**|**Intellig **|**ence Unit Limited**|**- Overall rankings**|||
|---|---|---|---|---|---|---|---|
|**Country**|**City**|**Rating %**|**Rank**|**Country**|**City**|**Rating %**|**Rank**|
|Canada|Vancouver|1|1|UK|London|10|47|
|Australia|Melbourne|2|2|US|Los Angeles|10|47|
|Austria|Vienna|2|2|Ireland|Dublin|10|47|
|Switzerland|Geneva|2|2|Portugal|Lisbon|11|51|
|Australia|Perth|3|5|US|San Francisco|11|51|
|Australia|Adelaide|3|5|US|New York|11|51|
|Australia|Sydney|3|5|South Korea|Seoul|12|54|
|Switzerland|Zurich|3|5|Singapore|Singapore|12|54|
|Canada|Toronto|3|5|US|Lexington|13|56|
|Canada|Calgary|3|5|Hungary|Budapest|14|57|
|Australia|Brisbane|4|11|Italy|Milan|15|58|
|Denmark|Copenhagen|4|11|Italy|Rome|16|59|
|Finland|Helsinki|4|11|Czech Rep|Prague|16|59|
|Sweden|Stockholm|4|11|Greece|Athens|17|61|
|Germany|Frankfurt|4|11|Taiwan|Taipei|19|62|
|Canada|Montreal|5|16|Slovakia|Bratislava|19|62|
|Japan|Tokyo|5|16|Uruguay|Montevideo|20|64|
|Germany|Hamburg|5|16|Argentina|Buenos Aires|20|64|
|France|Paris|5|16|Chile|Santiago|20|64|
|Norway|Oslo|6|20|Poland|Warsaw|22|67|
|New Zealand|Auckland|6|20|Puerto Rico|San Juan|23|68|
|Germany|Berlin|6|20|Israel|Tel Aviv|23|68|
|Belgium|Brussels|6|20|Costa Rica|San Jose|24|70|
|Japan|Osaka Kobe|6|20|China|Shanghai|24|70|
|New Zealand|Wellington|6|20|China|Beijing|24|70|
|Germany|Düsseldorf|7|26|China|Tianjin|24|70|
|Netherlands|Amsterdam|7|26|UAE|Dubai|25|74|
|Iceland|Reykjavik|7|26|Russia|Moscow|25|74|
|Germany|Munich|7|26|UAE|Abu Dhabi|26|76|
|Luxembourg|Luxembourg|7|26|China|Shenzhen|26|76|
|US|Cleveland|7|26|Panama|Panama City|27|78|
|US|Pittsburgh|7|26|Russia|St Petersburg|27|78|
|US|Honolulu|8|33|Bahrain|Bahrain Manama|27|78|
|US|Boston|8|33|Malaysia|Kuala Lumpur|28|81|
|France|Lyon|8|33|Peru|Lima|28|81|
|US|Chicago|8|33|China|Guangzhou|29|83|

40
<!-- Start of picture text --> —— Fwd stepwise GBM<br>35 —— Group lasso<br>30 —— VariableRecursive importancefeature elimination(gain) (split)<br>— 25<br>*<br>2 20<br>oS2a2 15<br>10<br>5<br>0<br>0.26 0.28 0.30 0.32 0.34 0.36 0.38<br>Exclusion error rate<br><!-- End of picture text -->
each country’s research agenda, since these infections are not only important in and of themselves but also have significant relevance to the spread of HIV.

## **Bolivia**

In Bolivia, a total of 25 documents were included in the characterization and assessment phase, most of which were technical reports. A quarter of them had not been published at the time of assessment.

Almost all of the studies had a cross-sectional design with different variations, with KAP surveys being the most frequent. In both Bolivia and Peru, the methodological quality of the KAP surveys was inferior to the HIV serological surveys.

Only a third of the studies in Bolivia were done in most-at-risk populations. Among those groups, MSM and female sex workers were prioritized. The conclusion is that the overall prioritization of HIV-related studies in Bolivia does not match the type of concentrated epidemic that is present in Bolivia. This suggests a need to re-orient research objectives to the groups where the epidemic is most concentrated.

Approximately 70% of the studies had high methodological quality. However, this is not true for all population groups—the quality of the studies done in most-at-risk groups is significantly worse than those done in the general population. Of the three Andean countries, Bolivia is the one where this difference is most significant. The conclusion is that the studies done in key Bolivian populations are proportionally less and of lesser methodological quality. As discussed above, this suggests the need to re-prioritize the country’s research agenda towards those groups where the burden of the epidemic is more pronounced.

This analysis did not aim to evaluate aspects related to the standardization of the operational variables. All the same, it is important to point out that a wide variation in the operative definitions used was observed in the region. This variation limits the possibility of comparing the studies either across time or across geographical regions. Each of these countries needs to consider adopting a more consistent use of indicators and stronger prioritization of UNGASS indicators in their respective research agendas. It is important to establish guidelines and standards for the definition of operational variables in order: (i) to strengthen the evidence related to HIV and other STIs; (ii) to guide the research; and (iii) to guarantee the dissemination and use of data when planning the response to the spread of the HIV epidemic.

# **Conclusions about the use and usefulness of the tool**

- The absence of simple tools for assessing the quality of existing HIV data has motivated the development of this checklist tool.

- The experience of implementing this tool in Latin America proved to be easy and feasible.  The tool permitted a group of users to assess more than 300 research

38
**Table B4** Treatment-Control Balance at Baseline: Intimate Partner Violence, Balanced Panel, Ever Had Sex at Baseline

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|**Outcome**|**ELA Only**<br>**Control Mean**|**Boys-ELA**|**Supply-ELA**|**No Goal**<br>**Control Mean**|**Goal -**<br>**No Goal**|
|Psychological abuse often|0.060|0.032<br>(0.033)|-0.009<br>(0.024)|0.059|0.039<br>(0.029)|
|Physical abuse often|0.010|0.054**<br>(0.026)|0.010<br>(0.014)|0.026|0.023<br>(0.020)|
|Force sex often|0.020|0.032<br>(0.020)|0.011<br>(0.020)|0.037|-0.005<br>(0.018)|
|Psychological abuse in last year|0.169|0.095*<br>(0.052)|0.017<br>(0.054)|0.196|0.040<br>(0.046)|
|Physical abuse in last year|0.109|0.089*<br>(0.049)|0.041<br>(0.049)|0.148|0.012<br>(0.041)|
|Force sex in last year|0.085|0.037<br>(0.041)|0.017<br>(0.041)|0.102|0.000<br>(0.029)|
|Observations|201||637|459|637|

_Notes._ Column 1 shows control means for females in ELA only communitues and column 4 shows control means for females not assigned to the _Goal_ treatment. Columns 2–3 and column 5 shows the difference between means in the corresponding community- or individual-level treatment arms and the corresponding control group means, controlling for randomization strata. Estimates in columns 2–3 come from a single regression, and estimates in column 5 come from another. See Table 1 for indicator definitions. Standard errors, clustered at the club level, are presented in parentheses below the coefficient estimates in columns 2–3 and column 5. .01 ***, .05 **, .10 *. _Source._ Female baseline data, balanced panel, but restricted to females who had ever had sex at baseline.

72
# **1. Introduction**

Informality is a prevalent issue across developing countries, especially in Sub-Saharan Africa (SSA), where roughly 9 out of 10 individuals are informal as of 2020 (ILO 2020). The current global crisis driven by the COVID-19 pandemic has put extra pressure on an already fragile labor market lacking the adaptive capacity to absorb exogeneous shocks. This leaves the livelihoods of those at the low end of the income distribution highly exposed. Lockdown measures may have also destroyed formal jobs in Africa as well as significantly impacting about 325 million informal workers (ILO 2020).

The study of informality in Sub-Saharan Africa has focused on a few countries such as South Africa, Kenya, Ghana and Tanzania, or aggregated cross-country analysis, often due to data limitations such as lack of labor force data (Bhorat and Tarp 2016; Golub 2014; Mbaye and Benjamin 2014; Benjamin and Mbaye 2012; Grimm et al. 2012; Falco et al 2011; Haan 2006). Expanding the share of the workforce in formal employment is a critical policy objective for developing countries since these types of jobs are typically associated with higher productivity (La Porta and Shleifer 2016; Busso et al. 2012; Benjamin et al. 2012; Hsieh and Klenow 2009, Steel and Snodgrass 2008; Gelb et al. 2009; La Porta and Shleifer 2008) and better quality of jobs (World Bank 2020c; Brummund et al. 2016). As such, policies promoting formal employment can contribute to improve productivity and livelihoods across the region.

This paper studies the current state of informality in Senegal, defined as those that do not contribute to social security or do not have a formal accounting system in their nonagricultural enterprise, and estimates the relative importance of self-selection versus entry barriers. Senegal is an interesting case study since 97 percent of firms and 93.7 percent workers are classified as informal, in line with 92.4 percent across West Africa (ILO, 2018), and has among the most rigid labor market regulations in the world (Golub et al. 2015, World Bank 2020a). Furthermore, Senegal’s informal sector generated 42 percent of GDP between 2010 and 2014 (World Bank 2020a). Thus, the question of the relative importance of self-selection versus barriers to formality is key to tackle in this context, since in the short term, relaxing some of these regulations together with complementary policies of improving the business climate and critical infrastructure could increase the share of the workforce in formal jobs.
|Latn Amerlea and the Carbban<br> <br>|
|---|
|Reglon (and NorthernAmerica) .....<br>45|
|LatinAmericaand the Caribbean ....<br>46|
|Country Departnment I .......<br>.....<br>48|
|CountryDepartment II ......<br>.....<br>50|
|CountzyDepartment II<br>......<br>.... 52|
|Nonborrowers ..................<br>54|

|Reglon (and NorthernAmerica) .....<br>45|Grenada .....................<br>92|
|---|---|
||Guadeloupe .....................<br>94|
|LatinAmericaand the Caribbean ....<br>46|Guatemala .....................<br>96|
|Country Departnment I .......<br>.....<br>48|Guyana .....................<br>98|
|CountryDepartment II ......<br>.....<br>50|Haiti .....................<br>100|
|CountzyDepartment II<br>......<br>.... 52|Honduras .....................<br>102|
|Nonborrowers ..................<br>54|Jamaica .....................<br>104|
||Martinique ....................<br>106|
|Countries, Economies,|<br>Mdex .<br>M<br>eo108|
|and Terrltorles ...........<br>..<br>59|Montserrat ....................<br>110|
||Netherlands Antilles ........<br>.....<br>112|
|Antigua and Barbuda .............<br>60|Nicaragua .....................<br>114|
|Argentina ......<br>.......<br>62|Other Latin America ........<br>....<br>116|
|Bahamas, The .........<br>....<br>64|Other Norther America ......<br>....<br>118|
|Barbados ......<br>.......<br>66|Panama .....................<br>120|
|BeUze .............<br>68|Paraguay .....................<br>122|
|Bolivia .............<br>70|Peru .....................<br>124|
|Brazil.............<br>72|PuertoRico ...................<br>126|
|Canada .....<br>........<br>74|St. Kittsand Nevis .........<br>.....<br>128|
|Chile .............<br>76|St. Lucia .....................<br>130|
|Colombia......<br>.......<br>78|St. Vincent and the Grenadines ....<br>132|
|Costa Rica ......<br>.......<br>80|Suriname .....................<br>134|
|Cuba .............<br>82|Trinidadand Tobago .......<br>.....<br>136|
|Dominica ......<br>.......<br>84|United States of America ......<br>... 138|
|Dominican Rep .............<br>86|Uruguay .....................<br>140|
|Ecuador .............<br>88<br>|Venezuela ....................<br>142|
|El Salvador....................<br>90|Virgin Islands (U.S.) .144|

ii
# Can There Be Growth with Equity? An Initial Assessment of Land Reform in South Africa

**Abstract:** We use evidence from a survey of about<sup>1,200 South African land reform beneficiaries to assess</sup> performance of the initial phase of South Africa's<sup>land reform program. We find that even though the</sup> program has not lived up to the quantitative goals<sup>set, it was targeted to the poor and has led to the</sup> establishment to a significant number of projects that generate<sup>sustainable revenues. The lessons from these</sup> projects is that greater beneficiary participation<sup>and involvement of NGOs and the private sector are likely to</sup> be key to establishing a program that can make a substantive<sup>contribution to solving the problems confronting</sup> rural South Africa.

## **1 Introduction**

Whether redistribution and the associated focus on<sup>distributional equity may be consistent with, or even</sup> conducive to, a growth-oriented strategy has been<sup>one of the key concerns of development economics.</sup> Traditionally, there was broad consensus that growth<sup>would be helped by and/or associated with</sup> increasing inequality; a notion often associated with<sup>the famous hypothesis by Kuznets (1955). This was</sup> contested by scholars emphasizing the importance<sup>of "growth with equity" and redistribution as a basis</sup> for subsequent growth (Adelman 1995). More recently,<sup>theoretical arguments that link aggregate growth</sup> to inequality have led to renewed interest in this<sup>debate, generally supporting the notion of a systematic</sup> link between inequality in the distribution of assets<sup>and economic growth. There is now broad stream of</sup> literature now suggests that redistributive efforts<sup>by government could, through increased investment by</sup> the poor, actually lead to higher growth (Aghion<sup>et al. 1999, Bardhan et al. 1999 and references cited</sup> therein), contrary to some earlier models which<sup>maintained that redistribution would reduce growth</sup> (Persson and Tabellini 1994).

Empirical support for these hypotheses, in the form<sup>of demonstrating the importance of initial conditions</sup> on subsequent economic performance, is available<sup>both at the household and the cross-country level. For</sup> individuals, it has been shown that initial wealth and<sup>associated borrowing constraints affect households'</sup> ability to make productive investments, acquire human<sup>capital, and start up enterprises (Blanchflower and</sup> Oswald 1998), resulting in possibly different trajectories<sup>of development (Piketty 1999), and affecting</sup> 2
Table 6 Olley and Pakes Regressions with Contemporaneous Spillover Variables and Tariff controls: non-SOEs vs. SOEs (with the sample of all firms, foreign-invested, and domestic firms with zero foreign share)

||All f<br>|irms<br>|Foreign-inv<br>|ested firms<br>|Domestic firms (<br>|zero foreign share)<br>|
|---|---|---|---|---|---|---|
||Non-SOEs|SOEs|Non-SOEs|SOEs|Non-SOEs|SOEs|
|Foreign<br>share<br>(by<br>|-0.0037|0.0348|0.0033|-0.0734|||
|HK-Taiwan-Macau)|(0.003)|(0.0545)|(0.0048)|(0.0663)|||
|Foreign<br>share<br>(by<br>other<br>|0.0052|0.098**|0.013***|0.082|||
|countries)|(0.0031)|(0.0393)|(0.0049)|(0.0692)|||
|State share|-0.0016|-0.0256***|0.0037|0.0172|-0.0017|-0.0259***|
||(0.0030)|(0.0041)|(0.0078)|(0.0305)|(0.0033)|(0.0041)|
|Horizontal|0.164*|0.109|0.117|-0.506|0.194**|0.111|
||(0.088)|(0.100)|(0.099)|(0.444)|(0.089)|(0.100)|
|Backward|0.785***|1.027***|0.850***|2.893***|0.765***|1.005***|
||(0.253)|(0.349)|(0.275)|(0.844)|(0.262)|(0.350)|
|Forward|0.162*|0.166|0.245***|0.483**|0.0867|0.169|
||(0.0851)|(0.128)|(0.0875)|(0.219)|(0.0882)|(0.132)|
|lnTariff|-0.0349**|-0.0526**|-0.0237|-0.0676**|-0.0375**|-0.0519**|
||(0.0154)|(0.0198)|(0.0183)|(0.0312)|(0.0171)|(0.0198)|
|lnTariff_backward|-0.0285|-0.0163|-0.0169|0.00579|-0.0354*|-0.0159|
||(0.0185)|(0.0337)|(0.0176)|(0.0705)|(0.0207)|(0.0338)|
|lnTariff_forward|-0.0086|0.0065|-0.0205*|0.0705***|-0.0046|0.006|
||(0.0082)|(0.0077)|(0.0115)|(0.0245)|(0.0074)|(0.0077)|
|Constant|1.721***|2.545***|1.913***|1.413***|1.663***|2.550***|
||(0.0576)|(0.0690)|(0.0723)|(0.118)|(0.0598)|(0.0701)|
|Observations|1,418,632|126,994|345,631|2,398|1,073,001|124,596|
|R-squared|0.186|0.078|0.204|0.222|0.173|0.077|
|Horizontal*ownership|-0.1|84**|-0.|030|-0.2|01**|
||(0.0|91)|(0.1|93)|(0.|095)|
|_F_-stat (Horizontal * ownership<br>= 0)|4.|06|0.|02|4|.4|

43
# References

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- [2] Barnett, W.A. and M. Pasupathy. “Regularity of the Generalized Quadratic Production Model: A Counterexample.” Econometric Reviews 22 (2003), 135-154.

- [3] Barnett, W.A. and A. Serletis. “Consumer Preferences and Demand Systems.” Journal of Econometrics 147 (2008), 210-224.

- [4] Barnett, W.A. and P. Yue. “Semi-nonparametric Estimation of the Asymptotically Ideal Model: The AIM Demand System.” In Advances in Econometrics, Vol VII. Eds.: G. Rhodes and T.B. Fomby. Greenwich, CT: JAI Press (1988).

- [5] Barnett, W.A., Geweke, J., and Wolfe, M. “Semi-Nonparametric Bayesian Estimation of the Asymptotically Ideal Production Model.” Journal of Econometrics 49 (1991), 5-50.

- [6] Berndt, E.R. and D. Wood. “Technology, Prices, and the Derived Demand for Energy.” Review of Economics and Statistics 57 (1975), 259-268.

- [7] Blackorby, C. and R.R. Russell. “Will the Real Elasticity of Substitution Please Stand Up?” American Economic Review 79 (1989), 882-888.

- [8] Burnside, C. “Production Function Regressions, Returns to Scale, and Externalities.” Journal of Monetary Economics 37 (1996), 177—201.

- [9] Christensen, L., D.W. Jorgenson, and L.J. Lau. “Transendendal Logarithmic Utility Functions.” American Economic Review 65 (1975), 367-364.

- [10] Considine, T.J. “Separability, Functional Form, and Regulatory Policy in Models of Interfuel Substitution.” Energy Economics 11 (1989), 82-94.

- [11] Diewert, W.E. “An Application of the Shephard Duality Theorem: A Generalized Leontief Production Function.” Journal of Political Economy 79 (1971), 481-507.

- [12] Diewert, W.E. “Applications of Duality Theory.” In M.D. Intriligator and D.A. Kendrick, eds., Frontiers of Quantitative Economics, Vol. II, Amsterdam: NorthHolland (1974), pp. 106-171.

- [13] Diewert, W.E. and Fox, K.J. “On the Estimation of Returns to Scale, Technical Progress and Monopolistic Markups.” Journal of Econometrics 145 (2008), 174—193.

19
_Figure 1. Population distribution across the city (two settings)_ <!-- Start of picture text --> Low income-high income configuration High income-low income configuration<br><!-- End of picture text --> Landowners supply the housing and choose how much capital K(r) to invest at each distance from the CBD to produce a housing surface H(r) , assuming that they own a quantity of land L(r) and that the housing production function is: Landowners maximize their profit function to identify the optimal amount of capital to invest at each location: with i the interest rate and ρ the depreciation rate of capital. Due to the decreasing returns to capital, landowners will invest in tall buildings only when they anticipate rents that offset the extra building cost. At the edge of the city, the anticipated profit does not exceed the agricultural rent Ra , and landowners choose to rent their land to farmers instead of building dwellings. For simplicity, we assume that agricultural rent is null.

We calibrate our idealized city on the Paris agglomeration for illustrative purposes (table 1). To calibrate the parameters governing urban densities, rents, and population, we follow Viguié, Hallegatte and Rozenberg (2014). We consider two household groups with the same number of households, where the low-income group is the bottom 50 percent of the income distribution and the high-income group is the top 50 percent. Building on survey data from the National Institute of Statistics and Economic Studies’ (INSEE) localized disposable income system, FiLoSoFi, there are 4.9 million households in the Paris agglomeration, with a median annual income of €37,232 for low-income households and €80,244 for high-income households (INSEE ‐ DGFiP ‐ Cnaf ‐ Cnav ‐ CCMSA 2015). Transportation costs include fuel and vehicle maintenance t ∗ r , proportional to distance from the city center and identical for both household groups, and an opportunity cost of time (considering an average speed of 25 kilometers per hour) that is proportional to hourly salary and therefore higher for high-income households.

5