rafmacalaba/datause-encoder-data huggingface dataset:
> labor force, the number of affiliates rose from **30%** in **1981** to **_57%_** in **1985** and **79%** in **1990.** The number of active contributors rose from **0.9** million in **1982** to **1.3** million in **1985** and 2 million in

> **1990.** As a proportion of total affiliates, the number of active contributors fell from **63%** in **1982** to **_58%_** in **1985** and **_52%_** in **1990** but in relation to the labor force, their number increased from **_25%_** in **1981** to **33%** in **1985** and **41%** in **1990** (Table **5).**

The number of active contributors is based on data for December of each year and may understate the total number of contributors. Their number wouid be much greater if it was based on all affiliates who made at least one payment a year. On the other hand, the number of members who made contributions for the full year would be even smaller. Since pension benefits depend on total contributions made and the investment income earned, the low proportion of full-year contributors suggests that a large number of affiliates may fail to accumulate adequate balances and may thus receive a low pension. However, this problem is due to unstable employment patterns and interrupted contributions and not to the new system. In fact, a well functioning pay-as-you-go system would also pay lower pensions to workers with fewer contributions.

> The difference btween affiliates and active contributors is mostly made up **by** inactive

> accounts, mainly of unemployed workers, and **by** members who are in temporary employment schemes. But the large discrepancy implies that there may also be problems with evasion.

> Moreover, the interruptions in contributions that the data indicate suggest the likelihood of extensive future use of the government guarantee for minimum pensions, unless high rate of return make up for the difference.

Table **5**

|COVERAGE|
|---|

||Affiliates<br>(%laborforce)|Active Contrib<br>(%laborforce)|utors<br>**(%**aff.)|
|---|---|---|---|
|**1982**|**39.3**|24.8|**63.0**|
|**1983**|43.0|**28.0**|**65.1**|
|1984|49.6|**29.3**|**59.0**|
|**1985**|**56.8**|**32.9**|**57.9**|
|**1986**|**60.7**|**35.0**|**57.6**|
|**1987**|66.4|**38.5**|**58.0**|
|**1988**|**69.9**|**38.9**|**55.7**|
|**1989**|74.3|41.0|**55.3**|
|**1990**|**79.1**|41.5|**52.5**|

Source: Habitat **(1991)**

# **3.3 Contributions**

> Annual contributions as a proportion of **GDP** were relatively stable in the early 1980s at around **1.8%.** In **1988** they jumped to **3%** and reached 3.4% of **GDP** in **1990** (Table **6).** The continuing increase in the number of contributors and the rise of wages have been significant factors, especially in **1989** and **1990.** However, the big jump in **1988** is also partly due to a change in accounting practice. Since **1988** the premiums for disability and term life insurance have been

20
# 1. INTRODUCTION

In Ancient Egypt, dating back to the Old Kingdom (2686 BC – 2181 BC), the parcels along the Nile were allocated by the King to individuals, who were in turn recorded in the land administration system and were taxed for their land. However, the shape and the boundaries of the land on the banks of the Great River were often subject to change as a result of the annual flooding. This dynamic required the deployment of land surveyors, known as _rope stretchers,_ in order to re-measure the land, replace the inscriptions that marked the boundaries, and resolve potential disputes between the neighbors so that the taxes could be determined accurately. The measuring devices used by these surveyors included an A-frame shaped level with a plumb bob, and a rope knotted at intervals (Lyons, 1927; Berger, 1934).

Land area measurement endures to be a fundamental component agricultural statistics<sup>2</sup> , and of analyses undertaken by agricultural economists, including the large body of policyrelevant empirical work on testing the existence of an inverse scale–land productivity relationship (see Carletto et al., 2013 and the references cited therein). The traditional tools of land surveyors, such as compass and rope, dating back hundreds of years, have remained in the choice set of agricultural statisticians to measure land areas accurately. These conventional approaches are, however, neither time- nor cost-effective in the context of large data collection efforts, which often only record farmers’ own assessments of land areas. The availability of affordable, portable and relatively reliable Global Positioning System (GPS) devices over the last two decades has made GPS-based land area measurement a practical alternative that is increasingly being applied in surveys worldwide.<sup>3</sup> While the use of GPS technology in area measurement is the way of the future and there is increasing evidence of the benefits of utilizing GPS units as part of household survey operations in Africa (Dorward and Chirwa, 2010, Kelly and Donovan, 2008), in these relatively early stages of its application, it is important to assess the advantages it can provide over other methods as well as the potential drawbacks associated with its use, particularly in the context of low income countries where the numeracy of respondents is limited, “non-standard” measurement units of land are commonplace, cadastral records are nonexistent, and the financial and human resources of national statistical offices are severely limited.

> 2 For instance, in the World Bank World Development Indicators database, 15 out of 32 indicators classified under the agricultural and rural development category rely on land area information.

> 3 Schoning et al. (2005) document as part of the 2003 pilot of the Uganda Agricultural Census that the average time for measuring by rope and compass all parcels in a given agricultural holding was over 3 hours, which was more than three times as much as when GPS technology was employed to measure parcel areas.

2
**Policy Research Working Paper Series**

|Title|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS1425 On the Intersectoral Migration of<br>Agricultural Labor|Donald Larson<br>Yair Mundlak|February 1995|J.Jacobson<br>33710|
|WPS1426 Russian Unemployment Its<br>Magnitude, Characteristics, and<br>Regional Dimensions|Simon Commander<br>Ruslan Yemtsov|February 1995|V. Reid<br>35195|
|WPS1427 Corporate Govemance and Equity<br>Prices: Evidence from the Czech<br>and Slovak Republics|Stijn Claessens|February 1995|F. Hatab<br>35835|
|WPS1428 Short-Term Supply Response to a<br>Devaluation: A Model's Implications<br>for Primary Commodity-Exporling<br>Developing Countries|Bruno Boccara<br>Fabien Nsengiyumva|February 1995|M. Pfeiffenberger<br>34963|
|WPS1429 The World Trade Organization's<br>Agreement on Govemment<br>Procurement: Expanding Disciplines,<br>Declining Membership?|Bemard M. Hoekman<br>Petros C. Mavroidis|March 1995|F. Hatab<br>38535|
|WPS1430 Intergovemmental Fiscal Relations<br>and Poverty Alleviation in Viet Nam|Richard M. Bird<br>Jennie I. Utvack<br>M. Govinda Rao|March 1995|G. Coward<br>80494|
|WPS1431 The Industrial Pollution Projection<br>System|Hemamala Hettige<br>Paul Martin<br>Manjula Singh<br>David Wheeler|March 1995|A. Williams<br>37176|
|WPS1432 Using Financial Futures in Trading<br>and Risk Management|Ignacio Mas<br>Jes0s Saa-Requejo|March 1995|K Binidey<br>81143|
|WPS1433 Enterprise Restructuring in Eastem<br>Europe: How Much? How Fast?<br>Where? Preliminary Evidence from<br>Trade Data|Bemard Hoekman<br>Gerhard Pohl|March 1995|F. Hatab<br>35835|
|WPS1434 Govemment Credit Policy and<br>Industrial Perforrnance (Japanese<br>Machine Tool Producers, 1963-91)|Charles W. Calomiris<br>Charles P. Himmelberg|March 1995|P. Infante<br>37642|
|WPS1435 The Political Economy of Formal<br>Sector Pay and Employment in<br>Developing Countries|Arup Banerji<br>J. Edgardo Campos<br>Richard H. Sabot|March 1995|D. Evans<br>37496|
|WPS1436 An Empirical Model of Sunk Costs<br>and the Decision to Export|Mark J. Roberts<br>James R. Tybout|March 1995|J. Ngaine<br>37959|
competitive as platforms for assembly activity, the activities related to the processing of regionally‐ sourced natural resources may be a source of comparative advantage for African zones. The case of Nigeria highlights this – while the flagship development of an EPZ in Calabar never took off, a second, smaller initiative to establish a free zone in Port Harcourt to support Nigeria’s large oil and gas sector quickly attracted scores of international investors and now employs more than 20,000 workers (compared with little more than 1,000 in Calabar).

# <u>Exports</u>

The level of exports sustained is probably the most commonly referred to outcome measure for EPZ programs. Table 3 provides a detailed summary of EPZ export levels, growth, and the relative importance of EPZ exports in the national economy. Again, the small scale of the African programs under study stands out clearly, both on an absolute and a per capita basis (with the obvious exception of Lesotho). We see some evidence of success in Ghana’s program. Even in the Tema enclave, which hosts a small number of firms, exports reached US$280m in 2008; exports from the single‐factory units were almost four times this level. The success of exports under the Ghana program is partly attributable to cocoa processing activities, which account for a large share of activity under the free zone program and have grown robustly since the free zone program was launched in 1995<sup>15</sup> . But it does go beyond just cocoa, with firms in Tema involved in prefabricated housing (US$64m in exports) and plastic household products (US$11m in exports). Outside the enclave, the free zone includes several exporters including a tuna processor (US$100m in exports); a processor of fresh fruits and juices (US$33m in exports); and a number of timber companies (together accounting for up to US$200m in exports).

In contrast, Kenya’s EPZ program, often held up as an example of African success, looks rather anemic. Even including the single factory units, the program, which has been operating for nearly two decades, accounted for just over US$400m in exports in 2008 – just US$11 in exports per capita. The free zone programs in Nigeria and Senegal performed even worse, with Tanzania’s program also producing limited exports, but still in very early stages of development. As was the case with investment, what is notable in the African case is that while nominal exports from the African zones programs was extremely small (on average 10‐15 times smaller than the corresponding absolute and per capita exports in the non‐ African programs), their contribution to national exports was much more in line with international EPZ norms. In some countries – particularly Kenya, Nigeria, and Tanzania – the relative contribution of the EPZ program, however, remains limited.

> 15 According to trade statistics from UN COMTRADE, between 1996 (the first full year following the launch of the free zone program) and 2008, cocoa exports grew by an average of 10.7% annually, 57% of which is explained by quantity growth and the remainder by increasing prices of the commodity.

16
# **Figure 6: Support for assisting the poor as a top priority for Government and Government spending on social assistance**

<!-- Start of picture text --> UKR<br>BGR<br>LTU MKD<br>MTG SRB<br>ESTLVA<br>MDA<br>ROM<br>ALB<br>TUR<br>SWE BLR ARM<br>BIH<br>GBR AZE<br>FRAKAZ HUN<br>ITA KSV<br>CZE SVN<br>TJK<br>POL<br>KGZ<br>.1 .2 .3 .4 .5 .6<br>Assisting the poor should be the one of the two priorities of the Gvt<br>.3<br>.2<br>.1<br>Social assistance spending as % of Budget<br>0<br><!-- End of picture text --> Source: LITS II, WB ECA social protection database There is thus significant heterogeneity across countries in Europe in the levels and changes in inequality aversion, as well in support for various redistributive policies and the alignment between average beliefs and actual policies. In the sections that follow, we analyze inequality aversion and redistributive preferences at the individual level in the pooled sample, with country fixed effects.

# **4. Results:  Determinants of individual  attitudes toward income inequality and redistributive preferences**

# **Support for reducing inequality**

We first analyze the baseline multivariate profile of individual support for reducing inequality along key socio-demographic characteristics for the three groups of countries and the pooled sample (Table 4). In all models the estimates are derived from the within country variation in individual characteristics. The dependent variable in panel I (columns (1) to (4)) is a 5-step measure of the preference for a smaller gap between the rich and the poor, ranging from 1 – strongly disagree to 5 – strongly agree. Several results can be noted. First, preference for a smaller gap between the rich and the poor tends to get stronger with age. The preference for reducing inequality is also significantly stronger among women in non-EU transition economies.

15
<!-- Start of picture text --> Panel 3. Own-accourtanly Panelb. Formal firms with 6+ workers<br>y= 0.50 n= 0.60<br>2 e800 Cd]<br>ryEya2 . Ph%° ° es 8 . e «<br>23 “a0 * ao* ofLal e § 4 * 6 ? s *<br>8 ee<br>=ra a, . 4 ] .<br>Es ono a? @ bd . &* °<br>E . *. » oe °<br>322 F od “® af» J . : . s Seeet ° eofae4° . °<br>z3 oO oO .<br>500 5,000 50,000 500 5.000 50,000<br>Panel c. Formal finns with 20+ workers Panel d. Formal firms with 100+ workers<br>n= OBS n= O.6t<br>oas *<br>iaEy °4s<br>e . *<br>Zz5 2 é. . 4 os *<br>g=;a= .** °s ° . bd s on 2 * 5.* 6 osbedLJ .<br>E bd *.., ee | .<br>Psa @ 05 * oe ® © eee<br>z o * ae * Li * a Py . i] .<br>2 e?* « © #0 te” «<br>S00 5,000 50,000 S00 5,000 50,000<br>GDP per capite(USS 201,in loge} GOP per capite(USS 2045,in loge}<br><!-- End of picture text -->
|nsive Margin of Production<br>it Firm<br>2002-2010<br>(4)<br>0.00962***<br>(0.00218)<br>0.230<br>119,886<br>0.00332**<br>(0.00167)<br>0.229<br>119,886<br>0.0206***<br>(0.00485)<br>119,886<br>**n Working Age)**<br>0.467***<br>(0.0236)<br>393.01<br>119,886|Yes<br>Yes<br>Yes|
|---|---|
|Surveys: Exte<br>Ex<br>1995-2010<br>(3)<br>0.00396*<br>(0.00226)<br>0.275<br>67,326<br>0.00136<br>(0.00255)<br>0.275<br>67,326<br>0.0121*<br>(0.00694)<br>67,326<br>**IDP (% Pop i**<br>0.326***<br>(0.0176)<br>342.55<br>67,326|Yes<br>Yes<br>Yes|
|Labor Force<br>Firm<br>2002-2010<br>(2)<br>0.000425<br>(0.00236)<br>0.346<br>67,326<br>-0.00105<br>(0.00183)<br>0.346<br>67,326<br>0.00130<br>(0.00724)<br>67,326<br>**le: Share of**<br>0.326***<br>(0.0176)<br>342.55<br>67,326|Yes<br>Yes<br>Yes|
|vailable in the<br>Entry<br>1995-2010<br>(1)<br>-0.00413**<br>(0.00199)<br>0.611<br>119,886<br>-0.00327**<br>(0.00139)<br>0.611<br>119,886<br>-0.00885**<br>(0.00426)<br>119,886<br>**Dep. Variab**<br>0.467***<br>(0.0236)<br>393.01<br>119,886|Yes<br>Yes<br>Yes|
|**2)**<br>Estimates for the Municipalities A<br>**Dependent Variables (in logs)**<br>**Time Period**<br>**Panel A. Reduced Form**<br>Predicted IDP Infows<br>R-squared<br>Observations<br>**Panel B. OLS**<br>Share of IDP (%Pop in Working Age)<br>R-squared<br>Observations<br>**Panel C. 2SLS**<br>Share of IDP (%Pop in Working Age)<br>Observations<br>**Panel D. First Stage**<br>Predicted Infows<br>First Stage F-statistic<br>Observations|**Controls for all Panels**<br>Year FE<br>Firm FE<br>Additional Controls|

51
|Global Distance LearningCenter|Marc Jean Yves Lixi|
|---|---|
|Global Distance LearningCenter|Marc Jean Yves Lixi|
|Global Distance LearningCenter|Marc Jean Yves Lixi|
|Global Distance LearningCenter|Marc Jean Yves Lixi|
|Global Distance LearningCenter|Marc Jean Yves Lixi|
|Mozambique: EnergyReform and Access Program|Rob Mills|
|Mozambique: EnergyReform and Access Program|Rob Mills|
|Mozambique: EnergyReform and Access Program|Rob Mills|
|Mozambique: EnergyReform and Access Program|Rob Mills|
|Mozambique: EnergyReform and Access Program|Rob Mills|
|Mozambique: EnergyReform and Access Program|Rob Mills|
|Mozambique: EnergyReform and Access Program|Rob Mills|
|Mozambique: EnergyReform and Access Program|Rob Mills|
|Mozambique: EnergyReform and Access Program|Rob Mills|
|Mozambique: EnergyReform and Access Program|Rob Mills|
|Mozambique: EnergyReform and Access Program|Rob Mills|
|Mozambique: EnergyReform and Access Program|Rob Mills|
|Mozambique: EnergyReform and Access Program|Rob Mills|
|Mozambique: EnergyReform and Access Program|Rob Mills|
|Mozambique: EnergyReform and Access Program|Rob Mills|
|Road Rehabilitation and Maintenance Project|Justin Runji|
|Road Rehabilitation and Maintenance Project|Justin Runji|
|Road Rehabilitation and Maintenance Project|Justin Runji|
|Road Rehabilitation and Maintenance Project|Justin Runji|
|Road Rehabilitation and Maintenance Project|Justin Runji|
|Road Rehabilitation and Maintenance Project|Justin Runji|
|Road Rehabilitation and Maintenance Project|Justin Runji|
|Road Rehabilitation and Maintenance Project|Justin Runji|
|Road Rehabilitation and Maintenance Project|Justin Runji|
|Road Rehabilitation and Maintenance Project|Justin Runji|
|Road Rehabilitation and Maintenance Project|Justin Runji|
|Road Rehabilitation and Maintenance Project|Justin Runji|
|Road Rehabilitation and Maintenance Project|Justin Runji|
|Road Rehabilitation and Maintenance Project|Justin Runji|
|Road Rehabilitation and Maintenance Project|Justin Runji|
|Road Rehabilitation and Maintenance Project|Justin Runji|
|Road Rehabilitation and Maintenance Project|Justin Runji|
|Road Rehabilitation and Maintenance Project|Justin Runji|
|Road Rehabilitation and Maintenance Project|Justin Runji|
|Road Rehabilitation and Maintenance Project|Justin Runji|
|Road Rehabilitation and Maintenance Project|Justin Runji|
|PARLIAMENTARY ECONOMIC CAPACITY BUILDING|Mamadou Lamarane Deme|
|PovertyReduction StrategyCredit - 1st PRSC|NancyClaire Benjamin|
|PovertyReduction StrategyCredit - 1st PRSC|NancyClaire Benjamin|
|PovertyReduction StrategyCredit - 1st PRSC|NancyClaire Benjamin|
|Transmission Development Project|Prasad V. S. N. Tallapragada|
|Transmission Development Project|Prasad V. S. N. Tallapragada|
# **Online Appendix**

|**Table A1: D**|**escription of Variables**|
|---|---|
|**Variable name**|**Description of variable**|
|_Employment Growth_|Total number of permanent full-time workers<br>in the final year (last fiscal year) minus the<br>same in the initial year (beginning of the<br>growth period which is 2 fiscal years ago for<br>most countries and 3 fiscal years ago for other<br>countries) divided by the average (mean value)<br>number of permanent workers in the final and<br>initial year. The ratio is divided by the number<br>of years between the final and initial year and<br>multiplied by 100 to convert to percentage<br>terms.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|_Employment Growth_1|Log of the total number of permanent full-time<br>workers at the firm in the final year (last fiscal<br>year) minus the log of the total number of<br>permanent full-time workers at the firm in the<br>initial year (beginning of the growth period<br>which is 2 fiscal years ago for most countries<br>and 3 fiscal years ago for other countries). The<br>log difference is then divided by the number of<br>years between the initial and final year and the<br>multiplied by 100 to covert to percentage<br>terms.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|Overall Corruption (CI avg.)|The ES asked firms the amount of money firms<br>like itself pay as a proportion of their annual<br>sales to public officials to get “things done”.<br>We take the average of the reported bribe<br>amount (as percentage of firms’ annual sales)<br>where the average if taken over all other firms<br>(that is, excluding the firm in question) in the<br>same country-industry cell. Industry grouping<br>is defined at the 2-digit ISIC Rev. 3.1 level.<br>There are 7 industries in our sample.<br>Source: Enterprise Surveys.<br>wwwenterprisesurveysorg|
|Petty Corruption Incidence (CI avg.)|..<br>The ES asked firms if they were requested to<br>pay a bribe or expected to do so in soliciting<br>the following services: obtaining electricity<br>connection,<br>obtaining<br>water<br>connection,|

46
|P045264|2000|2005|5/16/2005|12 Closed|China|
|---|---|---|---|---|---|
|P045264|2000|2006|3/16/2006|13 Closed|China|
|P045303|1997|1997||0 Closed|Kazakhstan|
|P045303|1997|1998||2 Closed|Kazakhstan|
|P045303|1997|1999||3 Closed|Kazakhstan|
|P045303|1997|2000|6/29/2000|5 Closed|Kazakhstan|
|P045303|1997|2001|6/29/2001|7 Closed|Kazakhstan|
|P045303|1997|2002|6/29/2001|7 Closed|Kazakhstan|
|P045310|1997|1997||4 Closed|Bosnia and Herzegovin|
|P045310|1997|1998||6 Closed|Bosnia and Herzegovin|
|P045310|1997|1999||8 Closed|Bosnia and Herzegovin|
|P045310|1997|2000|6/20/2000|10 Closed|Bosnia and Herzegovin|
|P045311|1997|1997||1 Closed|Bosnia and Herzegovin|
|P045311|1997|1998||4 Closed|Bosnia and Herzegovin|
|P045311|1997|1999||6 Closed|Bosnia and Herzegovin|
|P045311|1997|2000|6/20/2000|9 Closed|Bosnia and Herzegovin|
|P045311|1997|2020|5/13/2020|10 Closed|Bosnia and Herzegovin|
|P045312|1998|1998||1 Closed|Albania|
|P045312|1998|1999||3 Closed|Albania|
|P045312|1998|2000|6/22/2000|5 Closed|Albania|
|P045312|1998|2001|6/26/2001|7 Closed|Albania|
|P045312|1998|2002|3/28/2002|9 Closed|Albania|
|P045312|1998|2003|3/13/2003|11 Closed|Albania|
|P045312|1998|2004|6/29/2004|15 Closed|Albania|
|P045312|1998|2005|12/26/2004|16 Closed|Albania|
|P045313|1998|1998||2 Closed|Bosnia and Herzegovin|
|P045313|1998|1999||6 Closed|Bosnia and Herzegovin|
|P045313|1998|2000|6/22/2000|8 Closed|Bosnia and Herzegovin|
|P045337|1998|1998||0 Closed|Indonesia|
|P045337|1998|1999||1 Closed|Indonesia|
|P045337|1998|2000|6/14/2000|4 Closed|Indonesia|
|P045337|1998|2001|5/31/2001|5 Closed|Indonesia|
|P045337|1998|2002|2/14/2002|6 Closed|Indonesia|
|P045337|1998|2003|8/19/2002|7 Closed|Indonesia|
|P045348|1999|1999||4 Closed|Cameroon|
|P045348|1999|2000|6/2/2000|8 Closed|Cameroon|
|P045348|1999|2001|4/24/2001|10 Closed|Cameroon|
|P045348|1999|2002|4/9/2002|13 Closed|Cameroon|
|P045348|1999|2003|4/9/2003|15 Closed|Cameroon|
|P045348|1999|2004|5/5/2004|17 Closed|Cameroon|
|P045348|1999|2019|5/7/2019|18 Closed|Cameroon|
|P045483|1998|1998||0 Closed|Bosnia and Herzegovin|
|P045483|1998|1999||3 Closed|Bosnia and Herzegovin|
|P045483|1998|2000|6/16/2000|5 Closed|Bosnia and Herzegovin|
|P045483|1998|2001|6/20/2001|7 Closed|Bosnia and Herzegovin|
|P045483|1998|2002|6/28/2002|9 Closed|Bosnia and Herzegovin|
|P045484|1998|1998||1 Closed|Bosnia and Herzegovin|