rafmacalaba/datause-encoder-data huggingface dataset:
# **Housing policy should be based on a complete understanding of the land supply process and the current pattern of land consumption among income groups**

We have seen the complexity of the land supply process in Ahmedabad. Over the years, different cities have developed different ways of transforming rural land into urban floor space. No “best practice” in supplying urban land can be relevant to all the traditions, culture, and topography that make the world’s cities so different and attractive. It is possible, however, to recommend a method for analyzing quantitatively the land supply process and defining the limits of the potential land supply based on existing transport modes and their speed. This quantitative analysis could help establish priorities and eliminate the practices and regulations that have fewer benefits and higher costs.

## **_Get the numbers right on supply and demand_**

On the supply side, most cities are now using GIS technology to map existing land use, making it possible to extract fairly accurately the area of land consumed by different users. GIS and satellite imagery allow establishing a complete “land accounting” system that can detect the inefficient and costly leaks in the land development process. The unresponsiveness of land supply to demand caused by identifiable bottlenecks is the major obstacle to land development, reducing housing consumption for all and especially the poor.

Establishing the potential supply of land based on the commuting speed of income groups is also essential. Many housing policy failures have been caused by the notion that the cheapest land, whatever its accessibility, is the best location for the poor.

The patterns of land consumption for business, industries, and housing also have to be analyzed. Both technology and rising household incomes can dramatically change these patterns of consumption.

## **_Audit regulations and discard ones that impose high constraints but few benefits_**

Land use regulations are the result of years of government action reacting to the perceived problems of the moment. As the city economy changes and the understanding of what makes a city productive evolves, many regulations lose their rationale. But nothing is more resilient and long-lived than an obsolete regulation.

Many land regulations contradict each other. Some increase land consumption: minimum plot size, maximum floor-area ratio, minimum number of parking spaces per dwelling. Others reduce land supply: green belt, restriction of land conversion from agricultural to urban.

The combination of regulations that boost land consumption with those that reduce land supply greatly increases the price of land to the detriment of all households, more dramatically for the lowest income households.

23
|**Table 7(e): Hypotheses Tests for the Alpha Vector: Weak Exog**<br>**rm2**|**eneity a**<br>**Zero**|**nd Joint Te**<br>**Chi^2(1)**|**sts:**<br>**6.1845 [0.0129]***|
|---|---|---|---|
|**rgdp**|**Zero**|**Chi^2(1)**|**3.3496 [0.0672]**|
|**INFL**|**Zero**|**Chi^2(1)**|**12.546 [0.0004]****|
|**Interestrt**|**Zero**|**Chi^2(1)**|**6.1643 [0.0130]***|
|**DISC**|**Zero**|**Chi^2(1)**|**4.2857 [0.0384]***|
|**Joint Significance for alphas**|**Zero**|**Chi^2(4)**|**32.175 [0.0000]****|
|**Joint Significance for alphas and betas**|**Zero**|**Chi^2(9)**|**50.635 [0.0000]****|
|**Joint Significance for alpha of (rgdp), Interestrt and INFL**|**Zero**|**Chi^2(3)**|**17.956 [0.0004]****|
|**Joint Significance for alpha of (rgdp), Interestrt, INFL and betas of (INFL and**<br>**Constant)**|**Zero**|**Chi^2(5)**|**33.825 [0.0000]****|
|**Joint Significance for alpha of (rgdp) and betas of (INFL and Constant)**|**Zero**|**Chi^2(3)**|**9.562010 [0.0277]***|
|**Joint Significance for alpha of (DISC) and betas of (INFL and Constant)**|**Zero**|**Chi^2(3)**|**7.0710 [0.0697]**|
|**Joint Significance for alpha of (DISC), (rgdp) and betas of (INFL and Constant)**|**Zero**|**Chi^2(4)**|**13.440 [0.0093]****|
|**Joint Significance for alphas of (DISC and rm2) and betas of (INFL and**<br>**Constant)**|**Zero**|**Chi^2(4)**|**13.559 [0.0088]****|
|**Joint Significance for alphas of (DISC, rgdp and rm2) and betas of (INFL and**<br>**Constant)**|**Zero**|**Chi^2(5)**|**14.599 [0.0122]***|
|**alpha of (DISC) and betas of (INFL and Constant) are jointly zero and the betas**<br>**for Interest and Disc have opposite signs**||**Chi^2(3)**|**7.1491 [0.0673]**|

P-values are in brackets, *** Significance level at 1%; , ** Significance level at 5%; * Significance level at 10% 51
been recently upheld by the Supreme Court.<sup>20</sup> On the other hand, concerns have been raised that the wage rate on MGNREGS is being set too high, relative to actual casual labor market wages. The concern here is that the scheme will attract workers from market work and so bid up the market wage rate.<sup>21</sup> (Though to many supporters of the scheme this is counted as a benefit.) What does the evidence suggest? Table 6 gives the average wage rates from the administrative data. These are calculated as total MGNREGS spending on unskilled labor divided by total person days of employment provided.<sup>22</sup> The table also reports estimates for average wages in (private) casual labor from the NSS for the same year.<sup>23</sup> We see that it is not the case that the MGNREGS wage rate is everywhere well above the market wage rate. Indeed, for India as a whole the two wages are quite close. If rural India was one labor market one might conjecture that the scheme has indeed brought the two wage rates into parity. However, rural India is not one labor market, as mobility is clearly imperfect. Across states we see that for half of them the MGNREGS wage rate in 2009/10 is actually lower than the average wage rate for casual labor (Table 6).

Given the extent of rationing we have reported earlier in this paper, it does not seem plausible that the scheme would be having a large impact on wages for other casual work, let alone resulting in a higher casual wage than for MGNREGS in half the states. For example, with only 17% of those who wanted work on the scheme in Punjab getting that work, it is hard to believe that the fact that the casual (non-PW) wage rate is above the MGNREGS wage rate is due to competition with the scheme for workers.

That said, we do find that the relative wage—defined as the mean wage rate for casual (non-PW) labor divided by the MGNREGS wage—tends to be lower in states with higher levels of un-met demand, as measured by the difference between the demand rate and the participation

> 20 The Supreme Court has refused to stay a recent Karnataka High Court verdict that affirms that the Central Government is liable to pay wages in tandem with the state minimum wage rate. 21 Evidence for this effect is reported by Imbert and Papp (2011) who compare districts that started early on the scheme with those that started later. However, they do not examine the extent of rationing.

> 22 Note that the scheme stipulates both piece rates and daily rates. Under the piece rate, whether a given worker can earn the mandated wage rate depends on her work effort. If the scheme attracts workers with lower than average physical ability then the realized average wage rate by our calculations can fall short of the mandated wage.

> 23 Note that the reference periods for MGNREGS and casual market wages reported in the table are slightly different (see notes to Table 6).

14
Table 3 The Gains From Agricultural Tariff Liberalization Aggregated Data

|Country|Aggregate<br>Gains|St.Dev.<br>Gains|Consumption<br>Efects|Income<br>Efects|Pro-Poor<br>Bias|Gains<br>Poor|Gains<br>Rich|
|---|---|---|---|---|---|---|---|
|Developing world|2.39|0.85|4.33|-1.94|-0.33|2.16|2.48|
|Armenia|3.52|0.32|4.12|-0.60|0.07|3.70|3.62|
|Azerbaijan|1.32|0.56|2.96|-1.64|1.25|2.02|0.77|
|Bangladesh|1.17|0.27|3.55|-2.38|0.20|1.29|1.09|
|Benin|1.28|1.87|4.29|-3.01|-4.86|-1.13|3.72|
|Bhutan|6.47|1.57|10.45|-3.98|1.77|6.38|4.62|
|Bolivia|3.73|1.17|5.21|-1.48|0.03|2.84|2.82|
|Burkina Faso|1.70|0.57|4.43|-2.73|0.27|1.94|1.67|
|Burundi|-3.12|2.98|9.70|-12.82|-5.70|-5.20|0.50|
|Cambodia|1.53|0.82|4.33|-2.81|0.40|1.81|1.41|
|Cameroon|4.65|0.69|6.73|-2.08|-1.08|4.05|5.13|
|Central African Republic|1.34|0.99|9.28|-7.94|-1.31|0.75|2.05|
|Comoros|0.89|0.40|1.70|-0.81|-0.59|0.56|1.15|
|Cˆote d’Ivoire|1.72|0.99|3.70|-1.97|-2.31|0.16|2.46|
|Ecuador|4.35|0.81|5.84|-1.49|1.89|4.96|3.07|
|Egypt, Arab Rep.|2.11|0.69|4.09|-1.98|-1.61|1.22|2.83|
|Ethiopia|1.78|0.38|3.16|-1.38|0.10|2.02|1.92|
|Gambia, The|3.18|1.24|4.16|-0.98|-2.76|1.25|4.01|
|Georgia|1.86|0.23|2.39|-0.53|0.15|1.78|1.64|
|Ghana|-0.56|0.47|1.23|-1.79|-0.77|-0.95|-0.18|
|Guatemala|3.15|0.32|4.02|-0.87|0.15|3.04|2.89|
|Guinea|3.15|0.84|5.90|-2.75|-1.57|2.46|4.03|
|Guinea-Bissau|3.10|1.62|6.88|-3.78|-3.09|0.71|3.79|
|Indonesia|1.57|0.24|1.87|-0.30|0.48|1.77|1.28|
|Iraq|1.33|0.25|1.79|-0.46|0.57|1.48|0.91|
|Jordan|4.49|0.56|4.95|-0.46|1.38|5.07|3.69|
|Kenya|2.54|1.11|6.63|-4.09|-2.18|1.39|3.57|
|Kyrgyz Republic|1.97|0.32|2.77|-0.79|0.18|2.02|1.84|

Notes: Authors’ calculations based on HIT data. The table presents the gains associated with the elimination of country’s own agricultural import tariffs.

31
to be addressed; in particular, we discuss a variance correction that is required because we use imputed data. We present results in Section 5 and conclude in Section 6.

# **2. Data: macro, micro and small area welfare estimates**

The effect of income inequality on economic growth is the subject of a large literature. Aghion et al. (1999) and Thorbecke and Charumilind (2002) review this literature and show that theory does not provide firm predictions of the sign of the effect.<sup>2</sup> Empirical studies in the 1990s have been “.. impressively unambiguous ..” (Aghion et al., 1999, p1617) in concluding that the growth effect of inequality is negative, but more recently some authors have obtained contrasting results (e.g. Forbes, 2000; Banerjee and Duflo, 2003). The most common denominator in these studies is the nature of the data used: the empirical inequality-growth literature is largely based on cross-country data.

Cross-country inequality-growth studies, while providing the bulk of existing empirical evidence, have been criticized for a number of reasons. First and foremost, the quality and internal consistency of datasets, in particular inequality series, have been questioned. Deininger and Squire (1996) challenge the quality of inequality data in growth regressions and offer an improved ‘high quality’ dataset. However, the consistency of inequality data in this set has been challenged by Atkinson and

> 2 Positive inequality-growth effects can be attributed to a positive effect on savings, to the existence of investment indivisibilities or to positive incentive effects of inequality. A negative inequality-growth effect can be explained by political tension, instability and demands for redistribution due to inequality, by reduced investment opportunities for the poor, worsened borrowers’ incentives and by higher macro-economic volatility. A ‘unified’ model that aims to reconcile these conflicting effects is presented in Galor (2000): this paper predicts that the effect of inequality on growth is non -linear, with a positive effect at an ‘early stage of economic development’ and a negative effect at a ‘later stage’.

4
# TABLE III

BASELINE ACCOUNTING RESULTS AND COMPARISON TO QUALITY LITERATURE

|||Our|Estimates|||Recent|Literature||
|---|---|---|---|---|---|---|---|---|
||_w_= 0|_w_= .15|_w_= .2_0_|_w_= .25|B. Jones<br>(2014)|Hanushek and<br>Woessmann (2012)|Hendricks and<br>Shoellman (2017)|Caselli and<br>Ciccone (2018)|
|ln(h90)-ln(h10)<br>ln(y90)-ln(y10)|0.31|0.46|0.51|0.58|_Nearly All_|0.51|0.62|_Potentially None_|

_Notes:_ y: real output per worker (2000-2010); h: human capital based on school and learning estimates (2000-2010). We convert HLO units into standard deviations based on a cross-country standard deviation of 68 to correspond to the returns to schooling parameter value which is given per standard deviation. We assume rates of return based on the microeconomic literature. We take the value _r_ = .10 for the rate of return per school year, and _w =_ .20 per standard deviation increase in learning. The .20 value is based on U.S. data. However, we might expect that returns to skills will be higher in developing countries, where the supply of skills is lower, as is the case in the returns to schooling literature. Significant work has been done to identify this parameter value. For the purpose of this paper, our intention is not to provide a final result, but rather to motivate the use of the data for future use in the development accounting literature. To this end, we take parameter values as given and conduct sensitivity analyses with values _w_ = .15 and _w_ = .25. When _w = 0,_ our accounting de facto only includes schooling; for any value _w > 0_ , we include learning as well as schooling. We include 131 countries in this development accounting exercise.

_Source:_ Schooling data are from Barro-Lee (2013). GDP data are from PWT 9.0. Learning estimates are from our database. Literature estimates are derived from the referenced papers.

20
does the AIDS epidemic pose a formidable obstacle to Ethiopia’s attaining long-run growth? Second, is it socially profitable to commit substantial resources to combating the epidemic, even if its effects on the long-run rate of growth are modest? We seek to answer both these questions, and in so doing to shed some light on the damage inflicted by the pandemic on other parts of sub-Saharan Africa with similar characteristics.

To tackle this task, we employ an overlapping-generations (OLG) model of a dual economy, in which the two sectors produce the same aggregate good and young adults who grew up in villages can seek their fortunes in the towns. The model conforms to the Ethiopian setting and experience in two ways: first, the growth of income depends heavily on human capital formation,<sup>2</sup> and second, the availability of land influences not only intra-family allocations regarding education and child labor, but also population movements.<sup>3</sup> It draws on Bell, Devarajan and Gersbach (2006), whereby the extension to two sectors and three generations greatly enlarges the set of variables over whose future course agents must form their (rational) expectations. The resulting structure, when calibrated to Ethiopia, is then used to simulate the path of the economy under alternative demographic developments, namely, with and without the AIDS epidemic.

There is, of course, a substantial literature on the macro-economics of AIDS, but, to our knowledge, not a single formal application to Ethiopia. Among a number of notable studies, Cuddington (1993a) and Cuddington and Hancock (1994) use standard neoclassical (Solovian) models to simulate the impact of AIDS on macroeconomic aggregates. The epidemic affects both the size and quality (health and accumulated experience) of the labor force and the accumulation of physical capital (to the extent that higher medical expenditures are financed out of domestic savings). In closely related frameworks, Over (1992) allows for infection to be concentrated among educated

> 2 ‘[T]he fact that the majority of Ethiopians remains uneducated explains in part the poor development performance of the country over a prolonged period of time.’ (Degefe et al., 2002: 218).

> 3Abate (1995: 312) reports a statistically significant correlation between average land-holding and urban population. Golini et al. (2001) and Mberu (2006) provide thorough analyses of internal migration dynamics.

3
To deal with these issues, a panel data approach including changes over time in the variables in question would be preferable. Unfortunately, our regulations indicator is limited to information in 2003 only, which does allow us to explore changes in a dynamic setting.<sup>8</sup> Nevertheless, since changes in government regulations are relatively slow, it is reasonable to assume that our regulation index is roughly constant over a limited period of time, for instance, the last 10 years. Based on this assumption, we can analyze changes in the other key variables, that is, FDI flows, GDP growth rates and the control variables, over time in dynamic regressions. In effect, we follow the approach of Caselli, Esquivel and Lefort (1996) and Dollar and Kraay (2002) and transform (1) by taking into account that there are country effects η _i_ included in the error term that are likely to be correlated to the explanatory variables producing biased coefficients in a pure OLS estimation. Thus, the model can be rewritten as:

> where α is 1 + θ .

To avoid the country effect bias we estimate (2) in differences: In essence, we regress growth in the most recent period of 10 years, that is, 1994-2003, on growth in the previous period (1984-1993) and on changes from the previous to the current period in FDI and the other explanatory variables.<sup>9</sup> As the independent variables, we always use FDI inflows, measured as annual average of net FDI inflows as a share of GDP, and an indicator for the rule of law. The latter indicator measures various components of the institutional quality of a country that might not be captured by our regulation index, such as the incidence of crime or the effectiveness and predictability of the judiciary. Overall, countries with well-designed institutions are less likely to have bad regulations.

> 8 The World Bank’s Doing Business dataset will be updated and extended every year, which will allow panel data analysis in the future.

> 9 In the following, the period 1994-2003 will be referred as the current period, whereas 1984-1993 is the previous period.

10
diminish in the long run. In addition, we observe a decline in the domestic terms of trade (the ratio of export to import prices on the domestic market) in both the short run and, to a lesser degree, the long run as world import prices increase more than world export prices for Bangladesh. The increases in world prices and demand lead to higher factor returns, particularly for agricultural capital and unskilled workers. We also note that the consumer price index increases more in rural areas than in urban areas. The fact that consumer prices – and thus the poverty line – increases faster than wage rates is consistent with the decline in domestic terms of trade, the drop in welfare and the rise in poverty. In sum, the aggregate results suggest that the Doha scenario is accompanied by small negative impacts that are likely to be dissipated in the long run. Real GDP remains unchanged in the short run and increases slightly in the long run. The small positive impact in the long run is essentially due to capital accumulation. However, the current simulation affects households negatively. This apparently surprising result is due to the fact that the increased income is not sufficient to counterbalance the increase in prices under the Doha scenario. Thus, real consumption decreases and welfare change is negative.

_Sectoral Effects_ (Tables 4-7) _._ Simulations run with the GTAP world model show that the Doha scenario implies strong relative increases in the world prices and demand for agricultural goods, particularly for Commercial Crop and Livestock-Poultry sectors (Table 4). Among the manufacturing sectors, the textile and ready-made garment sectors also register a strong increase in world prices and demand. Faced with rising import prices, import volumes decline in all agricultural sectors except forestry, for which the increase in import prices is smallest. This leads to an increase in domestic demand for agricultural goods (except forestry) in both the short and long run. In the industrial sectors import volumes tend to expand as import prices stagnate, leading to a decrease in local demand for competing domestic output. In both the short and long runs, rising world export prices and demand lead to export growth in the commercial crop and 13
teachers tended to suffer from a lack of motivation and inability to identify with the goals of the system; and that there was little incentive to improve efficiency and contain costs (World Bank, 1986, and supporting documents).

# Links between training providers and employers

The dynamic efficiency of a training system depends on the availability of signals indicating desirable changes. At one time it was thought that the most appropriate methodology was the estimation of training needs derived from long-term projections of manpower requirements. However, even in local labor markets complete reversals in particular industries can occur suddenly and with little warning (Lynton et. al., 1979, referring to New York City) and long experience has shown that such projections are virtually worthless for planning purposes (Ahamad and Blaug, 1973; Hinchliffe and Youdi, 1985).

There is now a consensus that, for most types of manpower development, it is impossible to improve upon short-term planning techniques using heterogeneous forms of labor market information (see, for example, Richter, 1978, 1980, 1982, 1984, 1986; Dougherty, 1983; Hollister, 1984; Kelly, 1986a, 1986b). Although short-term planning avoids the major errors associated with its lonuer-term counterpart, it too involves errors in the volume, nature and timing of training and the objective of planners is to minimize these errors by both iesponding flexibly and quickly to appropriate signals, and by paying special attention to the anticipation of turning points.

An essential element of short-term planning is the establishment of close, permanently-institutionalized links between training providers and employers, or employers' associations (Squire, 1981; Dougherty, 1983; Herschbach, 1985; Herschbach, Reinhart, Darcy and Sanguinet6y, 1985; Kelly and Evans, 1985; Kelly, Evans and Garcia, 1985; World Bank, 1986; Noah and Middleton, 1988). The contact should at least provide valuable feedback information to the training provider and assist in the placement of its graduates. It may also generate resources.

# Adaptability of training providers

The dynamic efficiency of a training system depends not only on the generation of signals reflecting the demand for skilled labor but also on the. ability and willingness of training providers to respond to them aptly and quickly. This in turns depends upon the autonomy of the training provider discussed above, the adaptability of tne curriculum, training personnel and equipment.

# Modular Training Schemes

The curriculum rigidity problem may to some extent be overcome by the use of a modular training system. In the first place the modular approach makes it necessary only to replace outdated subcomponents of a course rather than to review the course as a whole.

- 23 -
Table F: The consistency between the household enterprise and the labor module in Malawi

||Inconsistent responses|Observations|
|---|---|---|
||(Share of household members that<br>worked on a plot, but did not report having<br>engaged in HH farming in the last 12<br>months)||
|Average|24.3%|5,010|
|Average (excluding owners and managers)|21.3%|4,497|
|Responsibility within the enterprise|||
|Manager|84.7%|59|
|Owner|82.5%|120|
|Worker|33.5%|334|
|Manager & owner|37.0%|871|
|Manager & worker|19.8%|116|
|Owner & worker|21.4%|112|
|Manager, owner & worker|17.3%|3,398|
|Hours worked (hours/year)<sup>1</sup>|||
|0‐200|34.0%|1,108|
|200‐630|22.7%|1,139|
|630‐1584|17.7%|1,123|
|>1584|10.9%|1,126|
|Gender|||
|Men|23.9%|2,367|
|Women|24.7%|2,643|
|Age|||
|5‐14|47.1%|280|
|15‐25|28.5%|942|
|25‐65|21.4%|3,580|
|>65|26.0%|208|

Source: Based on 2016/17 Malawi LSMS‐ISA data.

Note:<sup>1</sup> Calculated as the product of the number of hours/day, number of days in the last month, and number of months in the last year. Thresholds are set so that the four groups have a similar number of workers.

39