rafmacalaba/datause-encoder-data huggingface dataset:
17

# **5.3 Simulation with Separate Urban/Rural Growth Rates**

Appendix Table A4 and Figure 6 show results from a simulation applying separate urban and rural growth rates, and the national population growth rate of 2.9%. The separate growth rates were calculated from the data in the _Economic Survey 2002_ by grouping changes in output by sector; the rural growth rate was calculated as the growth rate of the agricultural sector (monetary and nonmonetary), and the total growth of other sectors was taken as the urban growth rate.

The simulated 2001 headcount rates are lower across the board than in the national growth scenario, and in all cases the simulated values fall within the confidence intervals of the estimates from the 2000/01 HBS. While the overall pattern is broadly the same as in the first scenario, this simulation shows a much steeper rise and fall for poverty in Dar es Salaam, and a flatter poverty trajectory in rural areas and the nation as a whole.

**Figure 6**

<!-- Start of picture text --> Simulated changes in poverty<br>using separate urban and rural growth rates<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br>Year<br>Mainland Tanzania Dar es Salaam<br>Other urban areas Rural areas<br>.45<br>.4<br>.35<br>.3<br>.25<br>Fraction below poverty line<br>.2<br><!-- End of picture text -->

# **5.4 Simulation with Alternative Urban/Rural Population Growth Rates**

The sectoral (urban/rural) GDP per capita growth rates employed in the previous section are the same as those displayed in Table 2. These were calculated from sectoral GDP figures, assuming a uniform national population growth rate of 2.9%. This population growth rate corresponds to the change in national population between the 1988 and 2002 censuses. Alternatively, we can use sectoral population figures to calculate the per capita growth rates. Annual population growth averaged 4.6% in urban areas and 2.5% in rural areas.

Appendix Table A5 and Figure 7 below display results from a simulation employing these alternative growth rate figures. This simulation fairs much worse than others in terms of matching the true 2002 poverty rates. It shows the rural headcount rate declining to 0.318, far below the true
performance: data from close to 1,500 industrial parks in China suggests that those that had their land allocated based on economic potential rather than political connections generated more manufacturing jobs and attracted higher levels of foreign direct investment than those that were not (Kahn _et al._ 2021).

# **5. Conclusion and policy implications**

New digital technology provides an enormous opportunity for African countries to improve quality of land service delivery and in doing so help address informality and improve access to finance in urban areas, expand functioning of rural factor markets, strengthen resilience to climate risks, and empower women and other groups of land users whose rights have traditionally often been ignored in practice. To capitalize on these opportunities, legal, regulatory, and institutional change is needed in three dimensions. First, to eliminate supply-side constraints such as survey fees and procedures that impeded first-time registration of titles in urban areas and precluded registration of other use rights or rights to public land even in settings where such rights enjoy legal recognition. Second, there is need improve the quality and publicity of registry information and eliminate barriers to registering and enforcing contracts involving registered rights. Last but by no means least, it is important to ensure private benefits from more secure land rights are balanced by the public interest by (i) taking measures (including regulation and publication of prices) to ensure competitive land markets; (ii) planning land use in a way that is transparent and that provides a basis for public infrastructure provision to guide and leverage private investment; and (iii) taxing land and increases in its value in a manner that supports delivery of local public services and, by allowing local governments to benefit from actions that increase land values, contributes to effective decentralization.

While this provides an opportunity to go beyond a narrow project-centered focus on individual titling through policy-based lending and subsequent support to results-based approaches, it also requires adjusting general principles to local contexts including the political economy surrounding land institutions. Supporting legal and institutional change will require evidence-based analytical work and policy advice at country level to help prioritize and sequence actions. Objective data on the performance of land institutionsideally sourced directly from digital registries via API-and quality of the regulatory framework in an way that is comparable globally and that can be linked to benchmarks in a way that overcomes the shortcomings of perception-based indicators is an opportunity for institutions such as the World Bank have a strong comparative advantage could exercise leadership to focus the debate and support reforms that could improve African countries’ ability to address the challenges facing them in the years and decades to come.

31
**Figure 12.  Saving and Real Exchange Rate Gaps in LAC1 Countries 2011-2012**

<!-- Start of picture text --> 0.5<br>PAN*<br>ARG*<br>VEN<br>PER<br>MEX CHL<br>0<br>ECU<br>CRI<br>COL<br>BRA<br>-0.5 URY<br>-1<br>-0.4 -0.2 0 0.2 0 .4<br>Real Exchange Rate<br>National Saving<br><!-- End of picture text --> Sources: Authors' calculations based on UN, WDI and Institutional Investor data. Notes: This figure shows the national saving and real exchange gaps for LAC1 countries for the 2011-2012 period, except for the data identified with a star: Argentina is 2005-2007 and Panama is 2008-2010. See Section 3 for the details of how the benchmarks are calculated. The dashed line is the linear fit for the per-period version of the whole sample for 1990-2012.

40
# **Table 13: Impact of Subsidy Reform on the Government Budget (Million LYD)**

||**Q1 **|**Q2 **|**Q3**|**Q4 **|**Q5**|**Scenario 1**<br>**Total **|**Scenario 2**<br>**Total **|**Scenario 2**<br>**(% govt. exp.)**|
|---|---|---|---|---|---|---|---|---|
|Flour|13.7|11.4|9.9|8.2|5.2|48.5|89.3|0.1|
|Flour-bread|56.4|52.2|45.4|37.8|30.0|221.7|351.2|0.5|
|Semolina|2.2|1.9|1.5|1.0|0.6|7.2|13.2|0.0|
|Rice|22.8|20.2|17.9|14.3|9.8|84.9|157.4|0.2|
|Sugar|13.2|11.5|9.7|7.9|5.1|47.4|104.6|0.2|
|Tea|3.3|2.9|2.5|1.9|1.2|11.8|29.1|0.0|
|Macaroni|15.7|13.1|11.0|8.7|5.8|54.2|111.7|0.2|
|Vegetable Oil|32.7|27.4|23.5|18.6|12.5|114.6|248.3|0.4|
|Paste tomatoes|10.7|9.0|7.7|6.0|4.0|37.5|91.4|0.1|
|Milk for children|1.0|1.4|1.8|2.1|1.9|8.1|24.2|0.0|
|Milk (concentrated)|6.7|6.0|5.0|3.7|2.4|23.9|62.6|0.1|
|Total|178.4|157.1|135.7|110.0|78.6|659.8|1283.0|2.0|

Source: Libyan Household Consumption Survey 2007/8; Libyan authorities; and Bank staff calculations.

Should a gradual approach to reform be considered, measuring the government budgetary impact may help with the decision regarding the sequencing and size of subsidy reforms. Figure 3 traces, for each product, the impact of a proportional reduction in subsidy (shown in percent on the _x-axis_ ) on government expenditure in absolute values (measured in LYD on the y-axis). The impact would differ across products because of different quantities consumed, different initial levels of subsidies and different price changes associated with a specific subsidy reduction. The fastest decline in government spending would result from reforming first the subsidy on flour used in bread production, and then that on vegetable oil. We note that the curves are not linear, implying decreasing marginal returns in terms of lower government spending should prices increase. This is mainly explained by the importance of the decrease in consumed quantities in response to price increases.

16
Figure 3 Nominal Labor Earnings in Senegal (Base 1980=100.0) <!-- Start of picture text --> 280 - Labor earnings in<br>the informal sector<br>230-<br>Labor costs in<br>the formal sector<br>180 -<br>/ )---} ~ ~ I, ~~~~/<br>130 - , ,' <Minimum<br>wage<br>1980 1985 1990 1995<br><!-- End of picture text --> 28
minants of trade and FDI. Thus, mitigating omitted variable bias and endogeneity concerns with the key variables of interest to us. In addition to PTAs and DTAs, we control for other policy variables such as WTO membership, economic sanctions, and bilateral investment treaties.

To perform the empirical analysis we build a balanced panel data set for 89 countries covering more than 96 percent of world GDP and more than 94 percent of FDI throughout the sample period, 1990-2011. Our data set covers foreign direct investment, trade agreements, trade ows, gross domestic product (GDP), employment, physical capital, bilateral investment treaties, sanctions, and WTO membership. An important feature of the data set is that we capitalize on the richness of the Database on the Content of Regional Trade Agreements (DCRTA), cf. Hofmann et al. (2019) and Mattoo et al., eds (2020). Specically, the DCRTA enables us to distinguish between several indicator and continuous PTA variables, including a standard dummy variable for PTAs, an indicator variable for DTAs, an indicator for DTAs that include investment provisions, and two continuous variables for the overall depth of DTAs and for the depth of the DTAs with investment provisions.

Three main ndings stand out from our estimates of the eects of DTAs on trade. First, we nd that the average impact of PTAs in our sample is not statistically signicant. However, second, we obtain positive and statistically signicant estimates of the eects of deep trade agreements. Specically, our estimates suggest that the DTAs in our sample have led to a 16.1% (std.err. 3.184) increase in bilateral trade among member countries. Finally, our estimates reveal that deeper trade agreements (as measured by the number of provisions) lead to larger increases in the trade ows among DTA members. Depending on the number of provisions that they include, the DTAs in our sample have led to trade increases between 0.576% (std.err. 0.289) and 23.012% (std.err. 12.744). Overall, our estimates of the DTA effects in trade are consistent with ndings from recent studies that have utilized the database on the Content of Regional Trade Agreements and reinforce the view that `depth' matters 4
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Fafchamps, M., and Lund, S., 2003. Risk-sharing networks in rural Philippines. _Journal of Development Economics,_ 71(2), p.261-87.

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Foster, J., and Sen, A., 1997. On economic inequality after a quarter century. In Sen (1997).

- Foster, J., and Shorrocks, T., 1991. Subgroup consistent poverty indices **.** _Econometrica_ , 59(3), p.687-709.

Frank, R.H., 1997. The frame of reference as a public good. _Economic Journal,_ 107(445), p.1832-47.

22
**Table B.9:** Water and Sanitation

||Median|Mean|SD|Min|Max|N|BL data|
|---|---|---|---|---|---|---|---|
|(1) Water and sanitation HH index|0.043|0.050|0.987|_−_5.667|6.236|5053|Yes|
|(2) Water and sanitation village index|_−_0.012|0.098|1.012|_−_1.212|3.791|5053|Yes|
|(3) Satisfaction with village sanitation index|0.290|0.050|0.973|_−_3.415|1.885|5051|Yes|

The household index is comprised of water sources for drinking, for other uses, the existence and type of toilet facilities and the actions households take to make water safe to drink. The village index contains the existence and types of water sources for drinking and general use as well as the existence of public toilets and waste facilities. The satisfaction index consists of questions about sanitation and health services offered as well as village cleanliness. †: dummy variable; §: likert scale (1-4); _¶_ : likert scale (1-5); _♦_ : measured across all health episode types (ante and post natal care, child birth, under-2 vaccinations, and illness and injury); w , 1 , 6 , and 12 stand for measures referring to the last week, one month, six months and twelve months respectively.

**Table B.10:** Economic Outcomes

||Median|Mean|SD|Min|Max|N|BL data|
|---|---|---|---|---|---|---|---|
|(1) Physical asset index|_−_0.245|0.014|1.052|_−_0.701|14.438|5052|Yes|
|(2) Agricultural asset index|_−_0.284|0.075|1.803|_−_0.560|58.153|5051|No|
|(3) Dwelling materials index|_−_0.163|0.038|1.018|_−_7.644|6.723|5052|Yes|
|(4) Total consumption expenditure<br>1|_−_0.225|0.016|1.031|_−_1.311|14.540|5053|Yes|

†: dummy variable; §: likert scale (1-4); _¶_ : likert scale (1-5); _♦_ : measured across all health episode types (ante and post natal care, child birth, under-2 vaccinations, and illness and injury); w , 1 , 6 , and 12 stand for measures referring to the last week, one month, six months and twelve months respectively.

A13
<!-- Start of picture text --> (Box 8)<br>14 0.18<br>0.16<br>12<br>0.14<br>10<br>0.12<br>8 0.1<br>6 0.08<br>0.06<br>4<br>0.04<br>2<br>0.02<br>0 0<br>brachiaria + 0.5 kg conc + 1 kg conc + 2 kg conc + 4 kg conc + 6 kg conc<br>milk methane / kg milk<br>milk (kg)<br> kg methane / kg milk<br><!-- End of picture text --> _Figure B8-1. The effect of concentrate supplementation on milk production and the methane-inefficiency of milk production (methane / kg milk) in a 500 kg cow consuming a basal diet of Brachiaria spp._ <!-- Start of picture text --> 14 120<br>12<br>100<br>10<br>80<br>8<br>60<br>6<br>40<br>4<br>20<br>2<br>0 0<br>brachiaria + 0.5 kg conc + 1 kg conc + 2 kg conc + 4 kg conc + 6 kg conc<br>number of cows milk (kg)<br>milk ( '000 kg)<br>number of cows ('000)<br><!-- End of picture text --> _Figure B8-2. Effects of diet quality on milk production and herd size to reach a methane emissions quota of 1 tonne._ and livestock numbers. For different locations, the impacts of a wider array of diet combinations using local feed resources such as crop by-products, legumes, agroforestry practices, and improved grasses and forages could be estimated. These would yield different amounts of milk and animal numbers at similar emission targets, thus identifying different ways of sustainably using local natural resources while contributing to climate change mitigation. This framework can be expanded to include the economic efficiency and profitability of each of the diets, and therefore the economics of reaching methane emission targets. This could be extremely important for commercial livestock production and situations where resources are scarce and their use needs to be carefully planned.

Adapted from Herrero et al. (2009b) 67
# III. MECHANISMS

We now use additional information gathered from both program operational surveys and administrative sources in order to shed some light on the interpretation behind the patterns uncovered in section 3. The finding of spillovers on school enrollment operating over short distances supports a simple model of peer effects on program take-up decisions of eligible households.<sup>22</sup> As we do not have measures of the occurrences of interactions of beneficiaries from different neighboring villages, we cannot report direct evidence of this. Hence, we conduct several indirect checks for the presence of such interactions. On the other hand, some spatial variations in the local implementation of the program could also a priori explain the observed relationship between the local density of the treatment and program impacts. We thus also test for the presence of such spatial variations in the implementation of the intervention under study.

# _Knowledge Spillovers Among Program Participants_

In spite of the emphasis placed on informing the potential participants about the objectives, design, and requirements of the intervention, concerns have been expressed by those involved in the initial phases of the implementation regarding the effectiveness of the diffusion of information about the program among targeted households (Adato et al. <u>2000).</u> To further corroborate this anecdotal evidence, we use information from an operational follow-up survey conducted among eligible households in the evaluation treatment-group villages in May 1999 (i.e., 14 months after the inception of the program). Program beneficiaries were asked to identify three sets of benefits distributed by _Progresa_ : (i) scholarships and school supplies; (ii) food stipends and nutritional supplements; and (iii) preventive healthcare and health check-ups. Most of the respondents who were to receive the transfers were mothers. While 98 percent of the respondents were able to spontaneously

> 22. Non-market interactions may affect take-up decisions through two channels: information and social norms. While conceptually different, these two forms of social behaviors can hardly be distinguished empirically. We thus broadly refer to the influence of others on individual responses as peer effects.

25
Table 1: Property Owner Baseline Summary Statistics

||Units|N|Mean|Median|SD|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|**Owner demographics and income**||||||
|Age|Years|2255|51.25|50.00|13.55|
|Male|%|2287|59.73|-|49.06|
|Secondary education or higher [1]|%|2266|54.50|-|49.81|
|Monthly income [2]|US$|1633|550.70|250.00|825.27|
|**Property wealth**||||||
|Property owner is sole owner of compound|%|2287|94.23|-|23.33|
|Property is fully paid of [3]|%|2275|98.51|-|12.14|
|Estimated property sale value|US$|1759|20000.00|17500.00|16400.50|
|**Rental activity**||||||
|Owner occupied without tenants|%|2287|13.77|-|34.47|
|Rental property with resident property owner|%|2287|33.58|-|47.24|
|Rental property with non-resident property owner|%|2287|48.67|-|49.99|
|# of rental dwellings in compound [4]|No.|1881|7.68|7.00|5.25|
|**Rental income**||||||
|Monthly rental income [5]|US$|1722|114.54|90.00|108.88|
|Monthly proft (revenue – expenditure)|US$|1648|83.31|60.00|106.75|
|Monthly electricity bill|US$|1543|12.26|5.00|19.65|
|Property owner pays electricity bill|%|1943|44.47|-|49.71|
|Monthly water bill [6]|US$|3318|4.20|2.54|9.18|
|Property owner pays water bill|%|1525|91.93|-|27.24|
|**Water repayment practices**||||||
|Length of time being a customer|Years|3324|1.31|1.63|0.64|
|Current balance|US$|3324|23.66|9.82|48.08|
|Outstanding water connection loan|US$|3327|38.30|46.58|25.47|
|Number of unique payments made|No.|3324|3.96|2.00|5.08|
|Time since most recent payment|Years|3324|0.50|0.36|0.50|
|Percent that have ever made a payment|%|3324|60.74|-|48.84|
|**Toilet facilities and sanitation services**||||||
|Number of toilets in compound|No.|2281|1.53|1.00|1.18|
|Current toilet type [7]||||||
|Pour-fush|%|2287|36.99|-|48.29|
|Pit latrine|%|2287|59.34|-|49.13|
|Monthly expenditure on sanitation [8]|US$|1133|8.19|5.83|15.27|

_Notes_ . 2016 property owner baseline survey data used except for "Water payment practices" which uses March 2016 utility billing data. All monetary amounts are in USD using an exchange rate of 100:1, unless otherwise stated. [1] Includes some secondary education or higher. [2] Estimated as rental income divided by the percentage rent of total income. [3] Includes property that is received for free or inherited with a mortgage paid off. [4] Assumes 92% reduction factor from dwellings to households, and assumes households equals 1 if property owner resides in the compound and does not rent out dwellings. [5] Includes actual rental income received. When including all rental that was not received for the month prior to the survey, the average monthly rental income would be US$ 134.42. [6] Using billing and payment data as of March 2016. [7] Options do not sum to 100% as some compounds have multiple types. [8] Conditional on having a pit latrine.

30
<!-- Start of picture text --> 250<br>8© Kigali<br>r= 200 Bujumbura ® @<br>oO<br>= ¢ 150 a<br>a & ® Nairobi<br>% F 100<br>o5<br>5 50<br>a<br>£<br>a 0<br>_ 0 50 100 150 200 250<br>Reliability (%)<br><!-- End of picture text -->
# _Pv(ps:+l@em ;rt@nt,_ t) 2dt

where the effective discount rate is now r,-n1<sup>, the difference</sup> between the real interest rate and the rate of growth of real GDP.

Given constant values of the ratio of the primary surplus and seignorage to GDP, the preceding result implies that the present value of debt service payments will be infinite whenever the rate of growth of real GDP exceeds the real rate of interest. This is so because receipts from the issue of new debt would always be more than sufficient to service existing debt at the market rate of interest without increasing the debt/GDP ratio. In this case, solvency is guaranteed for any initial finite stock of debt, regardless of the value taken by the sum of the primary surplus and seignorage revenue. In other words, when the rate of growth of real GDP exceeds the real rate of interest, the solvency requirement does not impose a constraint on the future values of the sum of the public sector's primary surplus and seignorage revenue, essentially because the existing debt can be serviced by the sale of new debt, rather than out of the public sector's own resources. On the other hand, when the rate of interest exceeds the rate of growth of real GDP, the proceeds from the sale of new debt at a constant debt/GDP ratio are not sufficient to service the old debt, and the public sector must service the debt using its own resources -- i.e., by generating sufficiently large primary surpluses and seignorage revenue. The solvency condition described previously determines just how large the magnitudes of the future resources raised by these means must be, and thus acts as a constraint on the Fresent value of future primary surpluses and seignorage revenue.

## b. Why a Debt Crisis ?

The immediate trigger for the debt crisis was a reversal in the relationship between the "safe" real interest rate in international capital markets and the rate of growth of real GDP in the HICs<sup>8</sup> . During most of the decade of the seventies, the real long-term rate of interest in the industrial countries fell well short of the rate of real GDP growth registered by the HICs as a 7 See Cohen (1985) for a detailed discussion of the roles of the real interest rate and the growth rate in imposing a solvency constraint on sovereign borrowers.

## 8 See Sachs (1985).

6