rafmacalaba/datause-encoder-data huggingface dataset:
HOUSING MARKETS AND RENT CONTROL IN GHANA 33

# D. Current Status of Controls

# Coverage

Initially rent control only applied to housing occupied by low- income households. Anyone who could afford to pay £100 per annum rent during World War II was regarded as too well off to need protection (Gold Coast, 1951a and Legislative Council Debates No.2 of 1949, p.68). The 1952 Ordinance removed this upper limit but excluded any government housing or housing used by government officers as a consequence of their employment. These have been excluded throughout the legislation since that date. The 1963 Act also had no upper limit to controlled premises.

Where premises have been liable to control but fall outside the limits of the collective treatment applied to rooms with shared amenities, the recoverable rent has been assessed on an individual basis. United Nations (1979) points out that, where this happens, the extent of coverage is generally lower than that obtained by across-the- board measures but the rents assessed generally come closer to the true value of each property. The Rent (Amendment) Decree, 1973 stipulated rents for single rooms but excluded from control any single room occupied by a tenant whose income exceeds Cl,OOO per annum. At that time, minimum wage was Cl.OO per day but mean wage rates for Africans in reporting industrial establishments was C950 per annum (Ghana Economic Survey, 1972-4, p.125). Thus, anyone on more than mean industrial wage but living in one room was liable to be outside the protection of rent control.

The 1979 and 1982 legislation removed any limit to controls but the 1986 Rent Control Law again removed premises at the top end of the market, this time with rents above Cl,OOO per month. This is again quite low -- being only 3.3 times the rent for a sandcrete room but only 7% of households paid Cl,OOO or more per month in 1986. At this level, the tenant pays whatever rent is estimated to be recoverable buit the landlord should pay half as tax to the state. There has never been any differentiation between sitting tenants and new tenants in the application of rent controls; neither has there ever been any exemption of new propertieE. Although landlords have been able to remove tenants to restore, renovate or improve properties, there has never been any incentive given, such as removal from rent control, unless the renovations removed the premises from the range in which controls operated at the time.

# Advances and side payments

In countries where rent control is imposed, the demanding of side payments by landlords is very common. It provides a simple way for landlords to make up the difference in income between market rent and controlled rent, through once and for all payments at the beginning of a tenancy or through periodic extra payments in cash or in kind. As the Mate Kole Report (Gold Coast, 1951a) pointed out, some landlords, notably in Kumasi, began taking advantage of wartime conditions to demand up to one year's rent in advance from would-be tenants. ThiL
**Table 2. Correlations Between Measures of Skills**

||REA|EXT|CONS|OPE|EMO|AGR|GRI|DMG|
|---|---|---|---|---|---|---|---|---|
|Reading proficiency (REA)|1||||||||
|Extraversion (EXT)|0.08***|1|||||||
|Conscientiousness (CONS)|0.06***|0.07***|1||||||
|Openness to experience (OPE)|0.21***|0.19***|0.16***|1|||||
|Emotional stability (EMO)|0.11***|0.11***|0.05***|0.08***|1||||
|Agreeableness (AGR)|-0.02|0.12***|0.14***|0.19***|0.04**|1|||
|Grit (GRI)|0.02|0.08***|0.22***|0.21***|0.00|0.20***|1||
|Decision making (DMG)|0.20***|0.09***|0.17***|0.28***|-0.07***|0.16***|0.21***|1|
|Hostile attribution bias(HAB)|-0.18***|-0.03|-0.04**|0.00|-0.16***|0.01|-0.02|-0.06***|

_Source_ : Authors’ calculations based on Colombia STEP Household Survey (2012).

* _p_ < 0.1  ** _p_ < 0.05  *** _p_ < 0.01

# _Estimated latent skills_

We estimate latent skills using structural estimation methods to mitigate skills measurement error and acknowledge that skills are latent rather than observable, as developed in Keane and Wolpin (1997), Hansen, and Heckman (2003), Heckman, Stixrud, and Urzúa (2006), and Sarzosa and Urzúa (2016). In this method, individual scores in cognitive tests or in socioemotional survey questions are treated as results of the individual’s latent skills, rather than its direct measures. Latent skills are estimated from a measurement system that gives a single score for each dimension of skills, one for cognitive and one for socioemotional ones. The identification of the measurement system requires at least three scores for each dimension explored (Kotlarski 1967; Carneiro ̧ Hansen, and Heckman 2003).

The three scores used for the measurement system identifying the factor of latent cognitive skills are the following: (1) a plausible value of reading proficiency, the raw measure of cognitive skills from the test, randomly chosen among the 10 available; (2) a measure of language abilities that is an average of additional dimensions of reading proficiency measured by the test but that are not used for the main score of reading proficiency. These additional dimensions are: average print vocabulary, sentence processing, and passage comprehension (ETS 2014, Pierre et al. 2014); (3) a measure of how much adults use reading at work and outside and the length of these reading, which is based on survey questions that are not linked to the reading-proficiency test.

The three scores used for the measurement system identifying the factor of latent socioemotional skills are based on the aggregation of the eight raw measures of socioemotional skills. We group the measures based on the correlations among them and the similar dimensions they capture to ensure the smoothness needed in the measurement system given that all these measures come from categorical answers. The 8
Public spending on primary and secondary education is in general progressive, with the poorer sections of the population benefitting proportionately more. The degree of progressivity is greatest in Costa Rica and Panama, while it is smallest in Honduras. Nevertheless, what is important is not just the quantity but also the quality of the spending that goes to the poor. As was clear from the analysis of transition, many poor students simply fail to complete primary education and enter and complete secondary education.

Another way to measure the redistributive effect of the public expenditure on education is the Reynolds and Smolensky (RS) index, which measures the redistributive effect as the reduction in the Gini coefficient due to public expenditure.<sup>27</sup> This is done by assuming that each individual using the education service receives the same amount of benefit which is equal to the mean public expenditure on that service. For example, if the person attends a public primary school then the per student public expenditure on primary education is assigned to him as extra income. This index is presented in Table 8.

These estimations show that while public spending on primary and secondary education has some redistributive capacity in all the countries, expenditure on tertiary education has virtually no effect. For countries with relatively high Gini coefficients - Panama, Guatemala and Honduras - public spending on primary and secondary education is largely equitable: RS indexes are above 2 points implying a large subsidy to families in lower quintiles. In tertiary education, RS indexes are much closer to zero so public spending does not alter income distribution.<sup>28</sup>

|**Table 8: R**|**edistributiv**<br>|**e capacity**<br>**C**<br>|**of Public Ex**<br>**irca 2008**<br>|**penditure in**<br>|**Education (**<br>|**PEE)**<br>|
|---|---|---|---|---|---|---|
||**Costa Rica**|**Panama**|**Guatemala**|**El Salvador**|**Honduras**|**Nicaragua**|
|||**P**<br>|**rimary and sec**<br>|**ondary educati**<br>|**on**<br>||
|**Ginibefore PEE**|0.510|0.539|0.538|0.475|0.540|0.509|
|**Giniafter PEE**|0.476|0.515|0.515|0.450|0.515|0.497|
|**Difference -RS**<br>**id**|0.011|0.024|0.023|0.026|0.026|0.011|
|**nex**|||**Tertiary **<br>|**education**<br>|||
|**Giniprevious PEE**|0.510|0.539|0.538|0.475|0.540|0.509|
|**Gini after PEE**|0.514|0.538|0.540|0.476|0.544|0.512|
|**Difference –RS**<br>**index **|-0.003|0.000|-0.002|-0.001|-0.003|-0.003|
|Source: ICEFI, 2010|||||||

> 27 Specifically . Where is the Reynolds Smolesky index, the Gini index based on current income reported ( _y_ ) and the Gini index of the current income plus the average expenditure in the public service ( _y+b_ ).

> 28 The above analysis refers only to the generation currently studying and considers only short term benefits (the actual public expenditure).  If those attending and/or completing the tertiary level of education generate further benefits to those in lower quintiles, such as employment generation, larger tax payments or improvements in the education system, long term income inequality could be reduced.

34
2. The C6te d'Ivoire Living Standards Survey (CILSS) The CMte d'Ivoire Living Standards Survey (CILSS) was conducted from 1985 to 1988 by the Direction de la Statistique in Cdte d'Ivoire, with financial and technical support from the World Bank during the first two years. The sample size each year was 1600 households and the sample design was a rotating panel, i.e. 50% of the households were revisited the following year and the other half was replaced by new households. The survey thus produced a sequence of four cross-sectional data sets, each of which is representative of the country as a whole, as well as three overlapping panels of approximately 800 households each (1985-86, 1986-87, 1987-88). The survey collected detailed information on expenditures, income, employment, assets, basic needs and other socio-economic characteristics of households and individuals. Over the four years, coverage and methodology of the data collection were held constant so results are comparable over time. A more detailed discussion of the methodology and content of the CILSS can be found in Ainsworth and Munoz (1986), Grootaert (1986), and Daho (1992).

T1he CILSS data have been the subject of much analysis of welfare and poverty, but most studies have only used the 1985-86 data (for a review, see Glewwe, 1990, and Grootaert, 1992). The reflections in this paper are based on research which used both the four years of cross-sectional data and the three overlapping panels.l/ 1/A general review of living standards surveys can be found in Glewwe (1990); Grosh (1991) reviews the Jamaica Living Standards Survey.

2
Distributional Impact Analysis of Climate Variability in Rural Indonesia

**Our preferred specification includes two lags of onset.** During the years and regions covered in the survey, rainfall exhibits negative serial correlation. As a result, late onset in a particular year is associated with a reduced probability that the monsoon will be delayed the following year.<sup>12</sup> If late onset is detrimental to household consumption and this effect persists for two years, then the estimated effect of late rainfall in equation (1) will underestimate the true negative effect of delayed onset. This is because the effect of late onset, estimated in equation (1), will also capture the diminished probability of late onset the previous year. Less intuitively, if late onset two years ago is associated with an increase in per capita consumption, perhaps because households experiencing late onset adapt in ways that improve their income-generating capacity, then the estimated effect from equation (1) will be overestimated. Regardless, to capture the potential effects of prior years’ onset, our preferred specification includes an additional lag of monsoon onset:

In addition, we also estimated the effect of another specification that included the second lag without the first lag. The results change little, and therefore for the sake of brevity we only present results from equation (2).<sup>13</sup>

**The model is re-estimated for poor and rich farm and non-farm households.** Households are classified as rich or poor on the basis of their average real per capita consumption, over the course of the panel. Poor households are those whose average consumption falls in the bottom quintile, while rich households are those whose average consumption falls in the top quintile. Households are classified as farm households if at least one member reported working on a farm business on household-owned land in 1993. Across all three years of the survey, 58 percent of rural households are farm households. Both owning a farm and being rich or poor do not vary for households over the course of the panel, and are therefore collinear with  _i_ in equations (1)-(2). As a result, these variables are uncorrelated with the error term, and restricting the sample does not introduce bias into the estimates.

# **1. Results from IFLS**

_All rural households_

**To facilitate interpretation, we focus on the implied effects of monsoons that arrive one standard deviation early or late, relative to the average date.** We report the estimated effect of early onset, defined as the effect on per capita consumption when onset is set to negative one:

This is equal to the average effect on consumption of experiencing monsoon onset one standard deviation early, relative to the average date of onset. The estimate effect of late onset is calculated analogously, as:

> 12 The correlation between rainfall and lagged rainfall in the data is -0.33.

> 13 We also estimated versions of the model with three lags, with qualitatively similar results.

9/35
**<u>Table 9: Type of remarriage for widows by other union characteristics</u>**

||Has a civil<br>contract|Lives with in-laws|Lives with<br>children from<br>previous union|
|---|---|---|---|
|Dissolution before age 40|0.077|0.019|-0.114|
||(0.066)|(0.044)|(0.083)|
|Last dissolution: rural resident|-0.179<sup>***</sup><br>(0.056)|0.051<br>(0.044)|-0.034<br>(0.073)|
|No children born in last union|0.005<br>(0.093)|0.027<br>(0.065)|.<br>.|
|Had a son at widowhood|-0.080+|0.003|0.347***|
||(0.051)|(0.035)|(0.051)|
|Number of marital dissolutions|-0.029|0.074<sup>***</sup>|-0.042|
||(0.065)|(0.028)|(0.064)|
|Ever been to a French school|0.077<br>(0.060)|0.047<br>(0.051)|0.113<br>(0.105)|
|Mean of dep. var|0.11|0.063|0.65|
|N|142|142|134|
|Pseudo R2|0.221|0.124|0.204|

Note: Logit model, marginal effects shown. Sample of ever widowed women.<sup>***</sup> _p_ < 0.01,<sup>**</sup> _p_ < 0.05,<sup>*</sup> _p_ < 0.1,<sup>+</sup> _p_ < 0.12.

Source: Authors’ estimations using PSF1.

40
Figure <u>7.5: Access to Paved or Dirt Roads by Income Quintile in Haiti, 2001 (percent)</u> <!-- Start of picture text --> 90<br>Metropolitan<br>80<br>Urban<br>70<br>Rural<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1(poorest) 2 3 4 5(richest)<br>Source: Own calculations based on HLCS 2001.<br><!-- End of picture text --> Haiti has never had an environment conducive to sustainable growth (World Bank 1998). Instead, an economic elite has supported a "predatory state" that makes only negligible investments in human resources and basic infrastructure. This pattern needs to be changed and more attention needs to be given to the extreme poor.

Findings in this section show that access to assets such as education and infrastructural services is highly unequal and strongly correlated with poverty. Educational attainment has increased over the last century and more so in urban than rural areas. Large differences exist in school attendance across regions and children and youth in the poorest regions fall behind their peers in richer regions. Moreover, children of indigent households attain less education than children from nonpoor households.

Access to safe water supplied by a public or private company is a major problem in Haiti, and access to electricity is the most unequal among locations. Most of the urban population has access to electricity, compared to only 10 percent of the rural population. Moreover, only about 8 percent of Haitians have access to a paved road and 3 percent have a telephone. Finally, the extremely poor have much less access to services than their nonpoor peers.

# **8. Rural and Urban Poverty Correlates—Are They Different?**

The previous sections examined the disparities in key assets between the poor and nonpoor. This section takes the analysis a step further and analyzes the relative importance of some of these and other correlates of rural and urban poverty in a multivariate setting, and investigates the marginal impact of each individual attribute on the likelihood of a household falling below the indigence poverty line, taking into account other characteristics. The section analyzes the impact of experience, labor market 44
- 21 -

levels of education - the vast majority have an elementary education or less, and in some countries most have no education at all.

Yet there are also some differences across these five countries. Most strikingly, the heads of poor households in Jamaica and Mauritania are quite likely to be without jobs, either unemployed or out of the labor force. This is not the case in the other 3 countries. Second, in Jamaica and Peru about 10 to 20 percent of the poor live in households where the head works for a private employer, but in the three African countries this percentage is substantially smaller. Third, in Peru most poor households are headed by someone who has had at least an elementary ievel of education, but in the African countries heads of poor households usually have no education at all.

# B. Comparability of Different Poverty Definitions: Results from Cate d'Ivoire

As poverty is a multi-dimensional concept, there are many ways of defining poverty. Some definitions focus on income, others on expenditures, and for both cases some look at household figures and others look at per capita figures. Others focus on food intake using data such as food expenditures, the fraction of total expenditures going towards food, and nutritional definitions. Some look to the living conditions of households (basic needs), using concepts like "adequate housing" and "adequate health care." One could even define poverty in terms of productive assets, such as agricultural land or education of household members.

Do these definitions usually classify the same people as poor, or do they each point to a separate group as "the poor." A recent paper by Glewwe and van der Caag (1988) using the LSMS data from C6te d'Ivoire addresses these
87 ESTIMATES OF COSTS AND BFENEFITS PmQm-PcQc is the estimated difference between expenditures with and without controls. We can compare these results with those from Olsen's method, presented in the next section. How efficient is the transfer of purchasing power to tenants, i.e. are the benefits received by tenants in line with the costs?

# Costs and Benefits Constructed from the Hedonic and Demand Eguations

Table 5.8 and corresponding Figure 5.7 present estimates of the various welfare measures and their components, using % variant of Olsen's model which was described in Chapter 4. Each welfare measure can be calculated separately for each sample observation (the approach used to derive the sample statistics); or calculated for a representative consumer, constructed using, say. medians of the components. The two approaches do not yield exactly the same results. Again, note that rows of the table do not add up, because! the sum or difference of medians (or of other statistics) is not generally the median of the sum or difference.

The representative consumer rows are calculated for a representative renter using median values of the components PcQc, PmQc and PmQm. Computing costs and be-iefits separately for each observation allows us to study their distriLution. But since the true demand relation is unknown, and every household is off their estimated demand curve, there is no information in the sample to sort out how much of the difference between costs and benefits is due to the stochastic nature of the demand relation, and how much is due to rent control (Olsen and Agrawal, 1982; halpezzi, 1986; Gyourko and Linneman, 1986). However, unlike Malpezzi (1986) and the other studies cited, we see from Table 5.8 that both approaches yield similar results in the Kumasi case.
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