rafmacalaba/datause-encoder-data huggingface dataset:
- 3 -

Supply and demand constraints are complex in developing counties. Particularly in SubSaharan Africa, urban–rural inequality is significant in terms of health care access. In Liberia, for instance, the average distance to a functioning health facility is approximately 7 kilometers. Approximately 40 percent of rural residents must spend more than 2 hours to walk to the nearest health facility (Kruk et al. 2010). In Zambia, approximately 75 percent of urban residents, but only 30 percent of rural residents, live within 2 kilometers of the nearest health facility (Zambia Central Statistical Office 2012).

The current paper examines the effect of transport connectivity on health care access in Liberia. Before the Ebola crisis, Liberia was experiencing robust economic growth, at more than 7 percent on average, mainly driven by strong natural resource exports such as iron ore and rubber. The majority of people engage in subsistence farming, with limited market access, and live below the poverty line. The national poverty rate was estimated at 63.8 percent in 2007. The Ebola crisis in 2014 and 2015 revealed vulnerability of the country’s transport and health care systems to unexpected external shocks. The transport network does not seem to have been adequate to manage a rapid pandemic of disease in rural and remote areas. Economic activity is highly concentrated in Monrovia, but more than 2.2 million people, or approximately half of the total population, live in rural areas.

To examine the effect of transport connectivity on health care access, this paper uses newly collected spatial data at the facility level with the potential endogeneity issue associated with infrastructure placement taken into account. Although attribution is weak, the paper shows that road infrastructure is important to meet health care demand in rural Liberia. The following sections are organized as follows: Section II provides an overview of the road network and health facilities in Liberia. Section III describes data and methodologies. Section IV presents main estimation results and discusses policy implications. Section V concludes.
China. In addition Government has guaranteed more than $300 million in external loans to cotton traders. Net external financing is projected to rise to around 14% of GDP in 2007 and 2008 before falling to 11% of GDP in 2009. This will lift the NPV of Tajikistan’s PPG external debt over at least two of the five thresholds at which debt is deemed sustainable under the IMF/IDA Low-Income Country Debt Sustainability Assessment Framework.

Our projections for external financing are derived using the following methodology. For the 2007-2011 period we adopt the projections made in the most recent DSA, which was completed in January 2007 (IMF & World Bank, 2007). External loan disbursements and amortization in this period mainly reflect the implementation of loan agreements already signed. We make our own projections for 2012-16. After 2011 there is more scope for loan disbursements to vary, as these will involve loan contracts not yet signed. As the purpose of this chapter is to assess the scope for fiscal space which is consistent with sustainable borrowing, our projections are based on the assumption that the government will aim to bring back, over the long term, its external debt indicators to levels which do not exceed the thresholds at which external debt is deemed unsustainable, by restricting new external borrowing. Hence, given the projected loan repayments and the grant element of the loans, we construct a profile of loan disbursements which ensures that all sustainability indicators are met by 2016.

The projected net external financing over the medium term will lift the NPV of debt to both GDP and exports above the sustainability thresholds, although the indicators will start to decline after 2009. The NPV of debt to exports is the most problematic, as this exceeds the threshold by the largest margin. Under our projections, this indicator falls back to the threshold of 150% by 2016. The NPV of debt to GDP returns to the threshold earlier, by 2014.

**Chart 2**

24
# VI. RENT CONTROL, PROFITABILITY, AND SUPPLY

The previous chapter described the costs and benefits to individual landlords and tenants. What are the market wide effects, particularly on supply? This chapter begins by describing the supply side of the market in more detail. To anticipate the key results, despite very strict controls some additional housing is being supplied. To explain the apparent anomaly between rents which seem to offer no real return and a rental stock which, while poorly maintained still houses two thirds of the city, we will develop a simple present value model of hous!.ng investment. While simple, the model captures the essentials of the landlord's investment decision, as well as effects on tenants and government revenue. The model will also be used to examine alternatives for relaxation or decontrol.

# A. Housing SuDppV in Kumasi

As discussed in detail in Chapter 3, the great majority of Kumasi's households live in rental housing, and most of those in rooms in compound houses. How has the supply of this housing in general and the rental stock in particular changed over time? Here we discuss new construction (and its counterpart of removals from the stock), conversion to other uses, and the supply from the existing stock.

# New Construction

From the above, it can be seen that foregone starts on new building are probably the most likely response to rent control. The evidence for Kumasi shows substantial support for this. According to Census data and Boapeah's survey, the number of houses grew from 11,600 in 1960 to 21,000 in 1981 in response to, but not keeping up with, population growth (Boapeah and Tipple, 1983). By 1980, impressionistic evidence showed that the rate of growth of houses in the city had already slackened as the economic problems of the late seventies reduced real incomes, and as rents fell behind prices and costs. Apart from a modest spurt of starts during the Limann government (September, 1979 to December, 1981), most of which were at the very top end of the market, there had been a noticeable decline in house starts since the mid 1970's.

The new survey of the number of houses in Kumasi in 1988 supports impressions formed in 1986 that building had continued only slowly since 1981. Less than 800 houses appear to have been added between 1982 and 1985 and only 900 between 1986 and 1988, with the index for houses falling further behind that of population.11 However, the increase in rooms per house between the 1980 and 1986 samples (supported by no increase in occupancy rates) gives some grounds to believe that extensions to existing houses have allowed the growth in rooms to keep pace with population growth since 1980.

- .1/ The slow rate of new house building in Kumasi over at least the past decade is all the more notable because there is a tradition of gaining status through building houses in Kumasi; so much so that it has been a priority over building in the home village in some periods. (See quotation from Fortes in McCaskie, 1986. See also Schildkrout, 1978).
<u>Panel C. Sample used in Column 11 of Table 1 Panel B (91 observations)</u>

|**Full sample**|Unemployment|Internet<br>payments|Internet<br>users|Informality|GDP<br>deviation|Severance<br>pay|Bank Credit|
|---|---|---|---|---|---|---|---|
|Unemployment|1.000|||||||
|Internet<br>payments|0.020|1.000||||||
|Internet users|0.128|0.947|1.000|||||
|Informality|-0.312|-0.867|-0.925|1.000||||
|GDP deviation|-0.293|0.334|0.305|-0.221|1.000|||
|Severancepay|-0.069|-0.364|-0.341|0.290|-0.159|1.000||
|Bank Credit|-0.014|0.634|0.628|-0.579|0.212|-0.269|1.000|

<u>Panel D. Sample used in Column 11 of Table 1 Panel C (59 observations)</u>

|**Small sample**|Unemployment|Internet<br>payments|Internet<br>users|Informality|GDP<br>deviation|Severance<br>pay|Bank Credit|
|---|---|---|---|---|---|---|---|
|Unemployment|1.000|||||||
|Internet<br>payments|0.279|1.000||||||
|Internet users|0.466|0.765|1.000|||||
|Informality|-0.615|-0.711|-0.824|1.000||||
|GDP deviation|-0.293|-0.096|-0.108|0.177|1.000|||
|Severancepay|-0.154|0.088|0.075|-0.017|0.054|1.000||
|Bank Credit|0.018|0.134|0.118|-0.136|-0.146|0.017|1.000|

34
# **Table 2 Revenue from Land Leasing: Hong Kong**

|**Land Leasing As %**<br>**Of:**|**1996**|**1997**|**1998**|**1999**|**2000**|**Average**|
|---|---|---|---|---|---|---|
|**Expenditure**<br>**On Public Works**|101%|229%|82%|133%|105%|130%|
|**Total**<br>**Expenditure**|16%|34%|11%|18%|14%|18%|

Source: Hong (2003) With recovery in the real estate market, Hong Kong has resumed land leasing, but changed its role from supplier of pre-announced amounts of land in locations designated for development by planners, to a more passive role intended to be consistent with market forces. It adopted an Application List system, under which the government provides land only in response to developer requests at acceptable prices. It was hoped that this change in procedure would also moderate the large swings in revenues, but to date revenue stabilization has been less than hoped for. Hong Kong in its Medium Range Forecast prepared in 2003 foresaw relatively stable revenues from leasing land assets, at 12-13% of annual total government revenue. In the first year of the five-year planning period, 2004-05, actual revenue from land leasing substantially exceeded the target at HK$34 billion, but in the next fiscal year only HK$4 billion was realized.

The government has now convened a public consultative process preparatory to introducing a Goods and Services Tax, motivated in part by the desire to reduce exposure of the general budget to the high volatility of revenue resulting from land leasing. This reform would allow land leasing to continue as a major contributor to the public works budget, while partially insulating the operating budget from revenue fluctuations.<sup>7</sup>

> 7 An IMF Mission in October 2005 emphasized the importance of broadening Hong Kong’s tax base. “A substantial part of non-tax revenues accrues from land sales and investment income that are very volatile.”  (IMF 2005)

11
Andr ́ee Inspecting the new Q-Q plots in figure 9 highlights that Model 5c is still prone to distributional mis-specification. This also suggests that the outliers whose values are now replaced with values closer to the normal range of the data are not necessarily outliers in an additive sense, but simply reflect the exponential nature of the data. From that regard, Model 5d is preferred, as it applies a suitable exponential transformation that clearly neutralizes any outlier or non-Gaussian behavior. In both models, the relationship with PM2 _._ 5 remains significant and positive hence the conclusion is that the main findings of the analysis are not sensitive to outliers.

# 6.2.3 Re-estimation using March 30 confirmed cases per 100,000 inhabitants

Model 5e, in table 6 below, re-estimates the step-wise AIC regression and then uses the covariates in the same specification as Model 5 using confirmed cases from March 30. This is to evaluate whether the relationship with PM22 _._ 5 is robust to using data from a different date. The correlation between confirmed cases per 100,000 inhabitants on March 22 and March 30 is approximately _._ 90. It is clear from the estimation results that using the newer data does not alter the main conclusions. In particular, similar covariates are preferred by the AIC and the parameter estimate for PM2 _._ 5 increased in value and remains significant at the highest level.

Table 6: Dependent Variable: Confirmed COVID-19 cases per 100,000 inhabitants using March 30 cases.

|**Variable**|**Model 5e**|
|---|---|
|(Intercept)|-374.01***<br>119.87|
|Population density|-2.28|
||(3.54)|
|Share from 25 to 44|3.12<br>(1.99)|
|Share above 65|4.79***<br>(1.89)|
|Share of unmarried|5.03***<br>(1.45)|
|Share of single households|-2.72***<br>(0.83)|
|Share of non-western immigrants|-1.70**<br>(0.70)|
|Share with long-term illness|1.14<br>(1.04)|
|Mean PM2_._5|6.39***<br>2.42|
|_λ_|-0.11<br>0.16|
|_ρ_|0.71<br>0.08|
|R2|0.56|
|AICc|3736.24|

Standard Errors in parenthesis, significance levels as:<sup>***</sup> _p <_ 0 _._ 01,<sup>**</sup> _p <_ 0 _._ 05,<sup>*</sup> _p <_ 0 _._ 1 20
infrastructure, literacy, distance to nodal cities, access to finance, population, and then several measures of distortions, but again, disaggregated by age.<sup>12</sup> Panel B shows that for local infrastructure, as with the overall results, only the young cohort of plants shows significant results, and these are mixed. Overall district output, and firm size measured by employment are higher for young plants with better local infrastructure, but size measured by output, TFP and labor productivity are actually higher in places with worse, a result shared with older firms. The possibility that poor local infrastructure and other distortions may serve to protect local markets, while access to cheaper inputs may lead to higher markups and hence higher measured productivity is discussed below.

The most striking result from the disaggregation by district-level education (panel C) is that in high literacy areas, mature firms now show significant responses for output and plant size, and older firms also have a significant response in output comparable to that of young firms as well as rising TFP. That is, the null or declining impact of proximity to the highway network on older firms is being driven by those plants in low literacy areas.

This proxy might literally be measuring the availability of skilled workers. Alternatively, if literacy is proxying for capabilities/managerial practices, plants closer to the technological frontier are more likely to innovate out of increased competition while laggards will be less able to compete and will decline a la Aghion et al. Somewhat puzzling is that young plants appear to do slightly better across all indicators for _lower_ skill districts although as the true plant-level panel estimates below will show, this does not imply better performance by lower capability firms – the reverse is true. It may be that entering firms have better managerial capital but seek low skilled labor. This is the only cut of the data that reveals statistically significant responses for mature and older firms (although with lower point estimates) and hence we focus on the young plants going forward.

Several variables suggest the positive effect of enabling environment on the response of young firms to connectivity. Those plants in higher TFP districts (panel G), do generally better with the exception of increases in labor productivity which may be capturing some productivity mean reversion. Response is higher for districts close to nodal cities across all performance measures except employment, perhaps reflecting relatively lower transport and information costs, or access to other inputs (Panel D). Access to bank finance increases performance response across the board (Panel E). Plants in higher population density

> 12 We prefer this simpler approach of interacting separately for each district trait because the data variation becomes very thin when partitioning the sample by additional traits beyond proximity to the GQ network.

11
wage offered to nationals in their occupation. McKenzie et al. (2014) show that this effectively results in binding minimum wages for immigrants, which provides one reason for the large wage gains possible with migration, but means that the supply of potential migrants exceeds demand at the legally required wage. Some migrant-origin countries also impose these restrictions. For example, McKenzie et al. (2014) show that when the Philippines increased the minimum wage at which Filipinas could take jobs as domestic workers abroad, this increased the wages of those who did migrate, but resulted in a large fall in the number who were able to migrate.

While these policy barriers matter, some research suggests that they may not be the main reason more people do not migrate in many cases. For example, Jasso and Rosenzweig (2008) compare immigration into Australia and the United States, and argue that the differences in their migration policies matter far less than the differences in the returns to skills in the two countries, and than their geography – which determinants the size and level of development of close neighbors. Moreover, we still see many people not move even in cases where there are no formal policy barriers. For example, Akee (2010) finds that less than 1 percent of the population moves from the Federated States of Micronesia to the United States within three years, despite per capita income levels being one-tenth of those in the United States and a compact of association allowing free movement. Likewise, the expansion of the European Union to include poorer Eastern European countries was accompanied by fewer than 5 percent of the population of the EU accession countries residing abroad five years after EU enlargement (Kahanec, 2012).

Although we might think explicit policy barriers to directly limit internal migration are much rarer, apart from a few exceptions such as China’s hukou household registration system, in fact many governments do try to discourage rural to urban migration. Bundervoet (2018) reports on a 2013 United Nations survey, in which 148 out of 185 countries had government policies aimed at reducing migration from rural to urban areas, and only five had policies designed to speed up this form of internal migration. These policies tend not to be legal restrictions against internal migration, but rather policies that make it more costly or difficult for migrants to access various public services when they arrive. Bundervoet gives the example of Ethiopia, where internal migrants can struggle to access urban ID cards, access housing, or access subsidized food.

_The costs of moving are so large that they exceed the benefits._ Apart from explicit policy barriers, another potential reason for not migrating is that the other components of _C_ are large, so that even 12
<!-- Start of picture text --> 18<br>140<br>16<br>120<br>14<br>12 100<br>10 80<br>8<br>60<br>6<br>40<br>4<br>20<br>2<br>0<br>0 0 0.1 0.2 0.3 0.4<br>0 0.05 0.1 0.15<br>Strength of On Board Security<br>Strength of On Board Security<br>(b) 2009<br>(a) 2006<br>140<br>100<br>120 90<br>100 80<br>80 70<br>60 60<br>40 50<br>40<br>20<br>30<br>0<br>0 0.1 0.2 0.3 0.4 0.5 0.65 0.7 0.75 0.8 0.85 0.9<br>Strength of On Board Security Strength of On Board Security<br>(c) 2010 (d) 2011<br>125<br>100<br>25<br>150<br>7550<br>225<br>200<br>175<br>125<br>125<br>125<br>200<br>250<br>150<br>300<br>225<br>350<br>150<br>100<br>200<br>250<br>7550<br>125<br>100<br>175<br>175<br>25<br>75<br>150<br>50<br>250<br>125<br>150<br>225<br>150<br>100<br>200<br>200<br>225<br>75<br>175<br>50<br>225<br>175<br>150<br>25<br>200 100<br>30<br>20<br>7550<br>75<br>125<br>175<br>175<br>25<br>5025<br>10<br>175<br>20<br>100<br>125<br>150<br>10<br>5<br>5<br>30<br>Number of Navy Patrols Number of Navy Patrols<br>Number of Navy Patrols Number of Navy Patrols<br><!-- End of picture text --> Figure 13: Navy patrols vs. private maritime security: iso-crime curves for all vessels Note: The figures plot the combination of navy patrols and intensity of private maritime security in year _t_ that achieves a certain number of attacks on all vessels in year _t_ + 1. The black line in each graph indicates the optimal combination of navy patrols and private maritime security contractors that minimizes the total costs for each level of attacks. All other model parameters are set to their benchmark value.

41
-7-

as "ultra-poor"; those whose physical<sup>personal maintenance is unstable (Lipton</sup> 1988). The ultra-poor cut off paint<sup>of Rs4O corresponds closely to the</sup> poverty line used by Bardhan (1970)<sup>and Dandekar and Rath (1971), which is</sup> Rsl5 at 1960-6i prices (this line<sup>at 1973-74 rural prices is equivalent to the</sup> per capita expenditure of Rs42.5).

To compare poverty across different<sup>tir.e</sup> periods, one needs to adjust the distributions given in current prices for price<sup>changes over time. Minhas</sup> (1970) and Dandekar and Rath (1971)<sup>used the national income deflator, which</sup> has been criticized by Bardhan (1974).<sup>This deflator does not reflect the</sup> prices which people actually pay for<sup>their consumption goods. We have</sup> followed Bardhan in using the Consumer<sup>Price Indices for Agricultural</sup> Labourers (CPIAL) prepared by the<sup>Labor Bureau. This index, constructed on</sup> the basis of the monthly retail prices<sup>of 75 consumer items collected from</sup> selected rural centers, uses the consumption<sup>pattern of rural agricultural</sup> labour households observed in 1956-57.<sup>A major objection that has been raised</sup> against this index is that the agricultural labor households constitute only about 30 percent of the total rural<sup>population and the remaining 70 percent of</sup> the rural households which constitute a<sup>large number of poor small farmers may</sup> hav;. a quite different consumption pattern<sup>(Minhas, et al 1987). This</sup> objection is, of course, relevant<sup>but Bardhan (1974) has observed that the</sup> consumption patterns of agricultural<sup>labor households are not very different</sup> from those of the other household<sup>groups in the rural areas.</sup> Recently, Minhas, et al (1987) have<sup>constructed a new price index</sup> which depicts the movement of consumer<sup>prices for the entire rural</sup> population. An attractive feature of<sup>this index is that it is based on the</sup>
to have high minimum ratios (Iraq, Jordan, Lebanon, UAE), and some of these have high maximum ratios as well (Lebanon, UAE). There is scope for calibrating coverage ratios in some of these cases.

**Most importantly, some schemes in MENA do not link coverage ratios to the borrowers’ risk profile** . Morocco, Tunisia, Egypt and Saudi Arabia offer higher coverage ratios for riskier types of borrowers. However, in Syria, Jordan, Iraq, UAE and the Palestine, the coverage ratio is flat and not linked to the risk exposure. These schemes could consider introducing variable coverage ratios, in line with international practice.

**Table 6 : Coverage Ratios of MENA PCGs**

||**C**|**overage rat**|**io**|**Link to Risk Exposure**|
|---|---|---|---|---|
||**Min**|**Median**|**Max**||
|Egypt|50%|60%|70%|Medium firms 50% ( >10 employees); Small firms 75% (< 10<br>employees),|
|Iraq|75%|75%|75%|No scalability|
|Jordan|70%|70%|70%|No scalability|
|Lebanon|75%|82.5|90%|Small-sized loans (< US$ 200,000): 75%, Medium-sized loans<br>(< US$ 400,000): 85%;, Innovative loans: 90%|
|Morocco|50%|65%|80%|Working capital 50% , Fixed assets 60%, Start-ups 80% (70%<br>for loans > US$125,000)|
|Palestine|60%|60%|60%|No scalability|
|Saudi Arabia|50%|62.5%|75%|General: 50%; Start-ups 75%,|
|Syria|50%|50%|50%|No scalability|
|Tunisia|60%|67.5%|75%|General: 60%; Prioritized firms 75% (Development zones, start-<br>ups)|
|UAE|90%|90%|90%|No scalability|

**Table 7 : Average Coverage ratios in MENA and Benchmark countries**

||**Average Min**|**Average Median**|**Average Max**|
|---|---|---|---|
|**Benchmark countries**|54%|69%|84%|
|**MENA**|63%|68%|74%|

_4.4 Fees_ _General guiding principles and international experience_

**Fees should be related to the risk exposure and contribute to the financial sustainability of the guarantee scheme.** Linking the price of the guarantee to the risk exposure is a basic insurance principle that should generally be adopted by guarantee schemes.  Moreover, fees are not only a critical source of revenue (and therefore financial sustainability) for guarantee

14