rafmacalaba/datause-encoder-data huggingface dataset:
approach simply hopes that managers will be ethically and professionally motivated for performance.

**Table 2 Comparison of Two Alternate Results Based Accountability Approaches**

|**Theoretical Models**|**Make the Managers Manage**|**Let the Managers Manage**|
|---|---|---|
|**Strategies**|Market-like arrangements|Managerial norms and competence|
|**Mechanism**|Contracts|Empowerment|
|**Commonality**|Give public managers the flexibility they|need to improve performance|
|**Differences**|-- Using specific, tightly written<br>performance contracts that leave little<br>room for trust<br>-- motivate improvements with extrinsic<br>rewards|-- Implicitly trusting public managers to<br>exercise their judgment intelligently<br>-- motivate primarily by the intrinsic<br>rewards of public service.|
|**Examples**|New Zealand|Australia, Sweden, USA|

Source: Shah and Shen, 2007 It is important to stress that managerial accountability must be based on outputs rather than outcomes as the latter are beyond mangers’ direct control, difficult to define and quantify, and impossible to use as a costing basis. Major justifications for including output based accountability are: (1) It is difficult or implausible to link outcomes directly with managerial actions and decisions as outcomes are remote in time and space from what the program does and how it interacts with other factors. The extent of a manager’s direct control over outputs is usually much more substantial than outcomes; (2) Outcomes are immensely difficult to identify, and certainly difficult to quantify. The timescale for measuring outcomes normally spans some time after the program intervention, and is generally not in sync with the same budgeting cycle; and (3) Calculating the cost of the effort to achieve outcomes can be more difficult than costing outputs (Kristensen, et al. 2002, 16). Outcomes are typically achieved not just as the result of a single intervention by one program in isolation, but by the interaction of a number of different 14
# EMPLOYMENT AND OUTPUT IN FOOTWEAR FOOTWEAR RELATIVE TO ALL MANUFACTURING

<!-- Start of picture text -->
1.8 -<br>1.7  -<br>1.6-<br>1.5-<br>1.4-<br>1.3-<br>1.2  -<br>x<br>w~~~~~<br>z<br>L- ~0.9<br>0<br>0  0.8<br>0.7<br>0.6<br>Ii..  ~ ~ ~  ~  ~  ~  ~  ~  ~  YA<br>0.5<br>LL~0.<br>1974  1975  1976  1977  197Li  1979  1980  1981  1982  1983<br>YEAR<br>0  EMPLOYMENT  4-  OUTPUT<br><!-- End of picture text -->
# **Table 6: Simple average applied MFN tariff, percent**

||**1997**|**2001**|**2007**|**Change 97-07 %**|
|---|---|---|---|---|
|Angola||8.81|7.2|-1.48<sup>a</sup>|
|Madagascar|6.94|4.61|12.4|5.11|
|Malawi|25.3|13.1|13.3|-9.58|
|Mauritius|28.7|18.4|3.15|-19.85|
|Mozambique|15.7|13.8|10.3|-4.67|
|Seychelles||28.3|7.12|-16.51<sup>a</sup>|
|SACU|11.3|8|7.74|-3.20|
|Tanzania|24.3|16.3|12.6|-9.41|
|Zambia|14.1|12.6|13.7|-0.35|
|Zimbabwe|23.8|19.6|14.1|-7.84|
|Pooled simple average|18.8|14.4|10.2|-7.24|
|Pooled import-weighted|8.42|6.95|6.45|-2.00|
|_Tariffs by End-Use_|||||
|Consumption goods|31.3|26.3|19.7|-8.9|
|Intermediate inputs|15.2|11.0|8.7|-5.7|
|Capital goods|12.4|8.1|6.2|-5.5|
|Source: Team calculations<br>1997 are obtained from Ed|using TRAINS<br>wards (2005)|data at HS|6-digit level;|SACU tariffs from|

Notes: a. Change is based on the 2001-2007 period. *1995 tariff used in Madagascar for 1997 period *2002 tariff used for 2001 period for Mauritius, Zambia and Angola *2006 tariff used for 2007 for Angola and Malawi. 2008 tariff used for Zambia The end-use classification is based on the BEC classification obtained UN Statistics. Passenger vehicles are excluded as they are both a capital and consumption good. The percentage change in the tariff inclusive border price is calculated as ( _t_ 1  _t_ 0)/(1 + _t_ 0), where _t_ 1 and _t_ 0 refer to tariff rates in the final and initial periods, respectively. These rates do not include ad valorem equivalents and are therefore not directly comparable to those obtained from the WTO World Tariff Profiles 2009 and used in the prior table.

In addition to regional or unilateral measures to reduce tariffs, many SADC countries are members of the WTO and benefit from preferential access to developed markets through the Generalized System of Preferences, Everything But Arms and the Africa Growth and Opportunity Act (AGOA). For example, preferential access for apparel is important for Lesotho, Botswana, Namibia, Madagascar and Malawi, who have all seen a rise in exports as a result of AGOA (de Melo and Portugal-Perez, 2009).

In sum, the SADC FTA has largely eliminated within SADC tariffs, but many countries remain members of multiple overlapping and sometimes inconsistent agreements on the continent. MFN tariffs fell from the mid-1990s, although the degree of liberalization varies across members and effective protection remains high on consumer goods. The structure of tariffs is also complex in some SADC countries and will inhibit product market integration, despite the formation of a free trade area.

16
to its growth. Malaysia; Taiwan, China; and South Korea have more recently recognized the potential role of migrant workers in their long run economic planning, and have been reforming their migrant labor policies as a result. For example, Malaysia has been reforming its migrant labor policies since the 1980s, signing a series of bilateral labor supply agreements to regulate labor inflows and introducing industry and skill specific levies on employers hiring migrant workers (Kanapathy, 2004).

# **3. Analytical Framework**

Our analytical framework involves the application of a dynamic global general equilibrium model ( _GMig2Dyn_ ) to simulate a projected growth path of the world based on current best-estimates of population, real GDP and labor growth from international institutions over the period 2007-2050.<sup>4</sup> As part of this projected growth path, we simulate more liberal international migration policies that allow the labor-force shrinking Asian countries to import labor from those Asian countries where the labor-force is expanding.

## **3.1 Simulation Modeling**

The dynamic migration model ( _GMig2Dyn_ ) is based on the Dynamic GTAP ( _GDyn_ ) model developed by Ianchovichina and McDougall (2001) and the bilateral migration model ( _GMig2_ ), developed by Walmsley et al. (2009). Both the _GDyn_ and _GMig2_ models are based on the _GTAP_ standard general equilibrium model. The standard _GTAP_ model is a comparative-static general equilibrium model of the world economy (Hertel 1997).

In the standard _GTAP_ model, capital can move between industries within a region, but not across regions. The _GDyn_ model extends the standard model by incorporating international capital mobility and capital accumulation. Furthermore, _GDyn_ takes account of foreign income flows and wealth, by keeping track of both the ownership and location of capital assets. In the _GDyn_ model, international capital mobility is modeled using a disequilibrium approach. _GDyn_ assumes an adaptive expectations mechanism that permits errors in expectations. These errors

> 4 We show results from 2007, but the GTAP 7 Data Base has a base year of 2004 so we had to update it first to 2007.

9
**Table 2: Share of Non-Residential Landowners Across Countries**

|||**Et**<br>|**hiopia**<br>|**Tanz**<br>|**ania**<br>|**Ma**<br>|**lawi**<br>|
|---|---|---|---|---|---|---|---|
|||Urban|Rural|Urban|Rural|Urban|Rural|
|Share of in|dividual landowners|||||||
|Men|Reported owner|0.08*|0.33***|0.19|0.37|0.10***|0.30**|
||Exclusive owner|0.02|0.06|0.07|0.20|0.04**|0.15***|
||Joint owner|0.06|0.29***|0.13|0.20|0.06|0.17|
|Women|Reported owner|0.06*|0.30***|0.19|0.33|0.14***|0.36**|
||Exclusive owner|0.01|0.05|0.08|0.16|0.07**|0.21***|
||Joint owner|0.05|0.25***|0.11|0.19|0.07|0.17|
|Share of ho|usehold with any land|0.09|0.44|0.23|0.55|0.19|0.50|
|# of obs (la|ndowner + non-|||||||
|landowners|)|8073|7315|1199|1785|1307|3428|
|# ofobs (ho|useholds)|3655|3115|502|683|649|1799|

**Notes:** Landowners are individuals who self-report as the owner of the land. Significant difference across men and women (conducted within urban and rural areas) are indicated by asterisks: ***p<0.01, **p<0.05, *p<0.1. Some individuals have both exclusive and joint ownership of land which resulted in a higher share of combined exclusive and joint owners relative to reported owners.

Within the sample of non-residential landowners, Figures 1 and 2 illustrate that despite selfreporting as an owner, respondents often do not have full rights and decision-making power over their land parcels. Figure 1 reports the distribution of rights and decision-making variables among landowners across the three countries. Figure 2 shows the number of rights held per parcel by landowners (an individual can own multiple parcels). Consistent with the findings of Slavchevska et. al. (2021), rights tend to not overlap with each other. In Tanzania, for example, the majority of landowners (53 percent) do not have the right to bequeath, while in Malawi, 52 percent of landowners do not have the right to use their land as collateral. Across countries, men are more likely to have these rights than women.

Across countries, less than half of landowners reported having all rights, with the exception of Ethiopia, with men more likely to than women to hold all rights. Figure 2 focuses specifically on rights to bequeath, sell, rent, improve, and use as collateral and reported the number of rights a landowner holds. In Ethiopia, most landowners have all four rights (right to sell is not allowed by the state), covering nearly three-quarters of landowners. In Tanzania and Malawi, on the other hand, more than half of landowners do not have all rights to land.

Additional differences arise with documented ownership (Figure 1), which is about 80 percent among landowners in Ethiopia, but significantly more limited in Malawi and Tanzania. Starting in 1996, Ethiopia pursued a land certification program which is one of the most ambitious among Sub-Saharan Countries. Holden et al. (2011) discuss how the land certification program helped enhance rental land market participation in Ethiopia. Women landowners were especially affected by the certification program. Since men tend to be the ones who cultivate land, women have less 9
# **Labor Market Transitions and Social Security in Colombia**

Jose Cuesta and Camilo Bohórquez<sup>1</sup> World Bank

**JEL:** J61, O17, J21

**Key Words:** Transitions, Labor Market, Informality, Asymmetries, Colombia, Crisis.

1 Jose Cuesta, Poverty Reduction and Equity Unit, World Bank. Camilo Bohorquez, Latin American and Caribbean Social Protection Sector.
informality (Falco and Haywood 2016; Falco et al. 2011), self-employment or informal jobs are not necessarily ‘last resort’ options in low-income countries. Indeed, profound consumption gains among self-employment households have contributed to urban poverty reduction during the last decade in Ethiopia (World Bank 2019). Nevertheless, there is no doubt that increasing wage/formal jobs is a key to promoting economic development in the long run.

Second, the link between town population size and wage/formal employment is found to be particularly strong among disadvantaged workers. The estimated probability change in wage employment to town population size is 6.4 percentage points among women (as opposed to 2.5 percentage points among men) and 7.7 percentage points among the youth (as opposed to 2.7 percentage points among the non-youth workers). Moreover, a similarly high e↵ect is estimated for unskilled workers (7.4 percentage points). The previous studies of the U.S. and European contexts demonstrate strong agglomeration e↵ects among high-skilled workers. Our analysis of urban Ethiopia instead suggests that unskilled workers may find more opportunities to find better employment arrangements in larger towns, conditional on their observed characteristics.

Third, we also find that other important labor outcomes are associated with town population size in Ethiopia. For example, women are more likely to be active labor forces in larger towns. In addition, larger towns tend to reduce underemployment, the status in which workers are willing to work additional hours. In this respect, wage employment has an advantage, also reflected in the low transition of wage (or formal) workers into poverty over time between 2012 and 2016 (World Bank 2019). These may not necessarily indicate a positive situation if they are simply ‘working poor’, as they are required to work more just to make ends meet in larger towns, where costs of living are also higher. Nevertheless, poverty incidence is lower in larger towns in Ethiopia.

All these findings suggest that it is important to remove barriers to migrations to cities, such as low human capital, low connectivity to cities, lack of property rights for farmland, and lack of information about economic opportunities in cities, to name a few.

Finally, our analysis shows that nominal wages are higher in larger towns after observed worker characteristics are controlled for. In our preferred specification, the elasticity of nominal wages with respect to the town population is estimated to be 5.7 percent. This elasticity, however, becomes smaller if Addis Ababa is excluded (4.0 percent and not statistically significant at the 10 percent level). Our elasticity estimate is similar to the previously reported estimates in other developing 23
**Model 2 Results: Is the discrepancy or divergence between ERR estimated at appraisal and completion worsening?**

Table 6 summarizes the average differences in estimated ERRs between appraisal and completion. For the Bank as a whole, the average mean difference is ‐12.1 percent and the average median difference is ‐ 15.4 percent. Across regions of the Bank, the average ERRs by region fall within 2 to 5 percentage points of one another at appraisal, and up to 9 percentage points at project completion, with ECA exhibiting the highest estimated ERRs, followed by SAR. Table 6 also shows by how much the ERR at appraisal exceeds the ERR at completion for each region. For SAR, there is a ‐8.7 percent difference between the two estimates (for all other regions this difference ranges from ‐18 to +3.6 percent). Similarly, the median difference of estimated ERRs between appraisal and completion for SAR is ‐16 percent (and for all other regions it ranges from ‐22 to ‐7 percent).

**Table 6: Average ERRs and their differences by Region, 1975‐2015**

||Average<br>ERR at<br>appraisal|Average<br>ERR at<br>completion|Mean<br>difference<br>between<br>the two<br>ERRs, %|Median<br>difference<br>between<br>the two<br>ERRs, %|Standard<br>Deviation<br>of the<br>differences|total # of<br>projects<br>with any<br>ERRs|
|---|---|---|---|---|---|---|
|AFR|25.3|19.4|‐18.0|‐21.8|75.7|997|
|EAP|24.9|21.3|‐10.1|‐10.7|52.7|726|
|ECA|28.5|28.3|3.6|‐7.2|78.6|392|
|LCR|25.4|20.9|‐15.8|‐22.0|56.8|709|
|MNA|22.8|19.5|‐15.8|‐12.5|62.2|345|
|SAR|25.7|21.6|‐8.7|‐16.0|63.8|526|
|World<br>Bank|25.4|21.4|‐12.1|‐15.4|65.5|3,695|

Source: IEG data set and staff calculation

*The difference between the two ERRs is calculated for every project with two ERRs as 100*(ERR at completion‐ ERR at appraisal)/ERR at appraisal. The mean difference was then taken on the percent of differences.

The differences in estimates are also summarized graphically in Figure 2. Ideally, the distribution of the difference between ERR at appraisal and completion would be centered on zero with a narrow spread. This, however, varies across regions. The distribution is slightly squatter and wider for SAR and ECA compared to other regions while EAP and MNA seem to have the narrowest distribution.

Table 7 presents the data by GP. The mean difference and median differences are lowest for the Education, Environment, and Transport/ICT. Social and Urban Development also have relatively low average deviations between appraisal and completion. The high‐CBA sectors of water and agriculture exhibit higher deviations between ERRs estimated at appraisal and completion compared to those in the soft or low‐CBA sectors. The results for the low‐CBA sectors may be a reflection of the small sample size of this group in the full data set (based on Table 1, only 3 to 7 percent of low‐CBA projects report ERRs in the PAD and ICR). It could also reflect a pattern noted in IEG 2010 that the estimation or reporting of an 19
them as shocks, they might have long-lasting consequences. Given that these two phenomena can precede regime transitions, I test for the stability of my proxy of democracy by including proxies that account for these events in my main regressions. However, given their transitory nature it would not be wise to measure wars and economic crisis as stock variables. Instead, I compute several proxies as level variables at different periods around a person’s year of birth. For both wars and economic crisis I compute the percentage of years of war or crisis that an individual born in year _t_ experienced between _t −_ 30 and _t −_ 1; _t −_ 20 and _t −_ 1; _t −_ 10 and _t −_ 1; _t_ and _t_ + 10; _t_ and _t_ + 20 ; _t −_ 30 and _t_ + 20 ; _t −_ 20 and _t_ + 20, and _t −_ 10 and _t_ + 10. These comprise a comprehensive set of proxies. Table A3, in the appendix, presents the periods of wars and economic crisis that took place in the countries of interest.

Given that for every individual born in any given year ( _t_ ) belonging to any given country ( _p_ ), and for a given time interval, the percentage of years of war or crisis would be the same, I compute the simple average of the proxy for the three birth years that compose the cohort to obtain the cohort average. That is where _Ht,c,p_ denotes the percentage of years of war or economic crisis for the birth year _t_ belonging to cohort _c_ and country _p_ , and for a given time interval.

I run 648 different regressions, which come from including each proxy in my main regressions and first set of robustness tests with replacement. These regressions can be also found in the online appendix. Overall, my main results hold for the relationship between inequality in labor income and educational attainment and democracy; that is, _β <_ 0 and statistically significant. Furthermore, I also find _β >_ 0 in most specifications when analyzing the relationship between democracy and the level of educational attainment.

# **6 Conclusions**

Despite many research documents assessing the link between democracy and inequality, empirical research is still not conclusive on whether and how democracy and inequality relate. This relationship is a complex and multidimensional process that involves many socioeconomic phenomena. Empirical research usually resorts to cross-country panel data sets, but these impose several measurement error and endogeneity problems that are hard to solve. This paper tries to overcome many of these difficulties using pseudo-panel data built from several household surveys of nine Latin American countries, exploiting panel data techniques to control for endogeneity due to unobserved factors, and proposing a renewed approach which seeks to determine if long-run patterns in the 19
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52
that incomes continue to rise after the mine shrinks or disappears. This may be because some of the night light that we observe near mines is due to gold mining itself, thereby throwing doubt on the relevance of night light as a measure of incomes in our case. Similarly, we do not find an effect on nightlights at 20-30km from mines, as we did with the census data. This might partly be due to bottom-coding of the nightlights measures, which applies to most rural areas.

# **6 Conclusion**

Central place theory indeed predicts that, when conditions are suitable for the creation of new urban centers, concentration of population driven by external shocks can determine where the new urban centers locate. We are interested in testing the idea that a permanent urban settlement can arise as a result of a shock that temporarily generates a concentration of economic activity in an arbitrary location. The external shock we examine here is gold mining, which tends to locate where minerals are found and is largely unaffected by other geographical features that make a site suitable for urban location. This is particularly true in Ghana where the geography is fairly undifferentiated with few mountains and navigable rivers.

We investigate whether gold mines in Ghana are associated with tell-tale signs of protourbanization. Although gold has been extracted from Ghanaian soil for centuries, the last two decades have seen a large increase in production after the sector was liberalized and foreign investors entered gold mining in Ghana. The Ghanaian economy has been growing steadily, and the population is expanding and urbanizing rapidly. These are ideal conditions to study town formation.

Using cross-sectional data we find that locations with gold mines have a larger proportion of people employed in industry and services, and a larger proportion of people employed in the wage sector or looking for work. The presence of a gold mine is also associated with more night light, which suggests higher incomes and a more urban setting with electrification. We also find evidence suggesting that the agglomeration of non-farm employment around gold mines is accompanied by a decrease of non-farm employment at distances of 20-30 km from gold mines. These findings are consistent with agglomeration effects that induce non-farm activities to coalesce in one particular location.

25