rafmacalaba/datause-encoder-data huggingface dataset:
# 1. INTRODUCTION AND SUMMARY<sup>2</sup>

One enduring feature of the development process, highlighted by Lipton (1977), is that rural populations are lagging behind their urban compatriots – this observation also applies to Millennium Development Goal (MDG) indicators (Figure 1.1). This paper is focused on policies aimed at closing this gap for one of the MDGs, the under-five mortality rate (U5MR or MDG 4), trying to shed light on what may be required to raise the rural population up to the urban level for this indicator or, more modestly, in terms of the government health services that reach them. The size of the effort that is required to achieve such objectives depends on a number of structural features of the modeled economy, including the rate of urbanization, rural-urban cost differentials for health services, and the magnitudes of initial rural-urban gaps in terms of the indicator itself, the U5MR, and its determinants, including access to health services and other aspects of rural living conditions.

Figure 1.1. Rural and urban MDG data for low-income countries in 2009 Analytically, this paper relies on MAMS (Maquette for MDG Simulations), a computable general equilibrium (CGE) model, applied to the database of an archetypical low-income country (LIC). The rationale for choosing a general equilibrium approach is that, in the presence of large ruralurban gaps and large rural population shares in most LICs, it would be misleading to limit an analysis of policies aimed at achieving such objectives to a partial-equilibrium framework: given macroeconomic and market constraints, required changes in government activities (spending level, spending efficiency, or revenue level, from domestic or foreign sources) would have effects that make themselves felt throughout the economy, often pointing to trade-offs and

> 2 The paper was prepared as background to Global Monitoring Report 2013. The author is indebted to the Knowledge for Change Program (KCP) Trust Fund for funding of the MAMS-based research program and is grateful for research support from Eugenia Suarez Moran and feedback on this analysis from her as well as from Vandana Chandra, Delfin Go, Hans Timmer, and Jos Verbeek.

2
Return on assets

*Big0.5

-0.056 1.933 <u>(0.216) (2.133)</u>

|Panel E: Big1 included|||||||||
|---|---|---|---|---|---|---|---|---|
|Big1|0.007|0.015|0.015|0.008|-0.059*|-0.155***|-0.141***|-0.077**|
||(0.005)|(0.010)|(0.010)|(0.006)|(0.032)|(0.053)|(0.052)|(0.038)|
|Equity *Big1||-0.186||||2.221**|||
|||(0.134)||||(1.013)|||
|Liquidity *Big1|||-0.176||||2.071**||
||||(0.136)||||(0.970)||
|Return on assets *Big1||||-0.186||||4.114|
|||||(0.265)||||(3.115)|

35
Table 8: How much are these factors affecting growth? Fraction of owners answering ”somewhat” or more.

||ICT<sup>_∗_</sup>|nonICT|Manufacturing|Retail|Other<br>industries|
|---|---|---|---|---|---|
|obstacles<br>to<br>mobility<br>of<br>people<br>within the West Bank|52%|40%|32%*|39%|48%|
|_Ho_: [_ICT _<sup>_∗_</sup>=_j_]||(0.20)|(0.10)|(0.21)|(0.72)|
|obstacles to mobility of people in|51%|42%|38%|44%|36%*|
|and out of the West||||||
|_Ho_: [_ICT _<sup>_∗_</sup>=_j_]||(0.29)|(0.35)|(0.47)|(0.10)|
|obstacles to mobility of goods within|38%|34%|29%|36%|30%|
|the West Bank||||||
|_Ho_: [_ICT _<sup>_∗_</sup>=_j_]||(0.67)|(0.45)|(0.85)|(0.32)|
|obstacles to mobility of goods in and<br>out of the West Bank|47%|31%<sup>_∗∗_</sup>|37%|36%|27%**|
|_Ho_: [_ICT _<sup>_∗_</sup>=_j_]||(0.02)|(0.50)|(0.28)|(0.03)|
|proximity to settlements|24%|26%|47%|25%|22%|
|_Ho_: [_ICT _<sup>_∗_</sup>=_j_]||(0.77)|(0.11)|(0.92)|(0.82)|
|proximity<br>to<br>areas<br>of<br>frequent<br>clashes|37%|35%|25%|37%|30%|
|_Ho_:[_ICT _<sup>_∗_</sup>= _j_]||(0.80)|(0.27)|(0.99)|(0.50)|

_Notes:_ ICT<sup>_<u>∗</u>_</sup> excludes retailers and repairers. Results are unchanged when including them in the sample. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. P-values for Wald tests reported in parentheses. _Sources:_ Authors’ survey.

effect of obstacles, firms in both industry groupings declare that demand from clients is reduced, they experience delays from suppliers, higher transportation costs. These findings are consistent with our own experience, during the COVID-19 pandemic, when reduced mobility decreased the demand for goods and services, and disrupted supply chains both locally and globally. The answers highlight the relevance of the interactions with clients and suppliers, and the tight economic linkages existing between firms.

# **6.1 Computing Counterfactuals**

To solve the model, we follow the approach first proposed by Dekle et al. (2008), who compute the counterfactuals in changes. For each variable _x_ , they define the relative change of _x_ as 24
reconciling community forestry program within an overall system of community development. In the present scenario, community forestry opens new avenues for sustainable utilization of resources, exploitation of non-timber forest products and conservation of biodiversity and poverty alleviation in forest-based rural economies in the mid-hills of Nepal.

# **_REDD in Nepal_**

Nepal became interested in REDD and submitted the Readiness Program Idea Note (RPIN) to Forest Carbon Partnership Facility (FCPF) of the World Bank for getting assistance in REDD related preparedness development (MFSC, 2008). Nepal’s RPIN was accepted in 2008 and got financial assistance from FCPF to develop Readiness Preparation Proposal (RPP) (MFSC, 2010). At the center level, REDD structures (REDD Apex body, REDD working group and REDD cell) have been formed to support the readiness process which is implementing World Bank (FCPF) supported activities to implement the Readiness Preparation Proposal (RPP) since 2010. Similarly there are several non-state organizations working on diverse aspects of REDD readiness including general awareness, methodologies for biomass assessment, institutional arrangements for equitable benefit sharing, defending rights of local and indigenous communities, social and environmental safeguards and so on.

While the additional benefits from REDD is expected to get peoples' support in conservation, there might be additional costs which can potentially produce trade-offs with the expected benefits. Some of these costs include sacrifices to reduce the amount of fuelwood consumption, reduce the amount and frequency of grazing, additional forest protection activities. In this paper we analyze these costs and the preferences for REDD+ contracts using a choice experiment survey.

13
for those concerned with job creation in rural Tanzania – and consitent with the small enterprise literature in developing countries.

The remainder of the paper is structured as follows: in section 2 we provide a profile of the rural economy. In section 3, based on the 2005 RICS, we present basic characteristics of rural nonfarm enterprises in Tanzania. In section 4 we assess their dynamics in terms of entry, exit and employment growth. In Section 5 we look at correlations of the rural investment climate on enterprise employment growth. Section 6 concludes.

# **2. Tanzania’s rural economy**

Tanzania is among the world’s poorest countries, with a per capita income of about US$ 350 when measured at the official exchange rate. During most of its post-independence history, Tanzania pursued socialist policies that led to extended periods when economic performance was below the country’s potential. In the mid-1980s, Tanzania embarked on economic reforms that were not sustained, and after an initial period of economic growth in the late 1980s, the early 1990s were again characterized by macroeconomic disequilibria and poor economic growth.

In the mid-1990s, Tanzania resumed its reform course with a commitment to macroeconomic stability. Macroeconomic stabilization was accompanied by wide-ranging reforms, including privatization of state owned enterprises, liberalization of the agriculture sector, efforts to improve the business environment, and strengthening the management of public expenditures. Economic performance in Tanzania has improved consistently over the past decade. Inflation fell from 27% in 1995 to 6% in 2006. The exchange rate is more stable, with positive effects on agricultural trade, in particular export crops. Annual average GDP growth increased from about 3.5% in the mid-1990s to about 5.9% in 2006. A key feature of the Tanzanian economy is the continued large share of informal sector activities: estimates suggest that informal activities may count for up for 60% of Tanzania’s GDP (for a summary see World Bank, 2007a).

5
- 44 -

Table : UNRESTRICTED DYNAMIC EQUATIONS (Dependent variable is the difference of the logarithm of real private investment)

|VARIABLE|(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|C|-1.32<br>073|-2.10<br>226**|-0.12<br>067|-0.13<br>075|
||(.)<br>|(.)<br>|(.)<br>|(.)<br>|
|**DGDP**|1.02<br>|1.06<br>|1.41<br>|0.83<br>|
|DDP1|(1.12)<br>071|(2.15)**|(1.63)<br>|(1.43)|
|G(-)|-.||0.47||
|DICOSTS<br>DICOSTS(-1)|(0.65)<br>-0.29<br>(0.32)<br>1.44<br>169|1.00<br>192*|(0.53)<br>-0.68<br>(0.74)<br>0.43<br>053||
|DMARKUP|(.)<br>-0.06<br>|(.)|(.)<br>0.44<br>||
|DKARKUP(-1)|(0.08)<br>0.96<br>|0.88<br>*|(0.68)<br>0.87<br>|0.93<br>*|
|DGVIXNF|(1.38)<br>0.36|(1.95)<br>0.34|(1.36)|(1.86)|
|<br>DGVIINF(-1)<br>DPVCRDY<br>(<br>DPRVCRDY(-1)|<br>(1.57)<br>0.41<br>(1.52)<br>1.12<br>3.64)**<br>0.66|(2.16)**<br>0.33<br>(1.84)*<br>1.05<br>(4.30)**<br>0.74|<br>1.07<br>(3.08)**<br> <br>0.87|1.10<br>(3.67)**<br>0.81|
|<br>|(1.87)*|(3.50)**|(2.16)**|(2.97)**|
|**DGO VEX**|||||
|DGOVEX(-l)<br>**ONIGVEX**|||||
|DNIGVEX(-1)<br>**DGOVI**|||||
|OGOVI(-1<br>)|||||
|DPRIVI(-1)|-0.07<br>||-0.10<br>||
|ECM(-1)|(0.41)<br>-0.16<br>(0.80)|-0.25<br>(2.35)**|(0.44)<br>-0.08<br> <br>(1.38)|<br>-0.08<br> <br>(1.77)*|
|R2|0.85|<br>0.83|<br>0.54|<br>O.S0|
|R2(ADJ)|0.60|<br>0.71|<br>0.21|<br>0.34|
|SE|0.15|<br>0.13|<br>0.19|<br>0.18|
|DW|1.64|<br>1.66|<br>1.59|<br>1.87|
|F|3.33|<br>6.75|<br>1.62|<br>3.51|
|DF|**-4.1**|-4.21|<br>_4.73|<br>-5.29|
|ADF(2)<br>CHow|-2.16|<br>-2.31<br>1.13|<br>-3.55<br>|<br>-4.11<br>2.07|

NOTE: **THE** ECM TERM IS DEFINED AS THE DIFFERENCE INI TIHE LEVELS OF TIIE LEFT **IIAND** SIDE AND RIGHIT HAND SIDE VARIABLES.
paper spans the period 1998 to 2018<sup>7</sup> while data on 3G/4G coverage covers the period 2006 to 2018.

The mobile coverage maps provide granular data on spatial coverage of the mobile networks at a 1km _×_ 1km grid-cell. However, it does not indicate the share of people in the respective grid-cells with access to the network. I, therefore, combine this data with 1km _×_ 1km grid-cell population density<sup>8</sup> data to calculate the share of population across subnational districts living in places covered by the mobile networks. Specifically, for a given subnational district _J_ with underlying grid-cells, I calculate network coverage as: where 1 {Covered by network} turns on if a grid-cell _j_ is covered by mobile network and 0 otherwise. Mobile coverage, therefore, implies the share of the population in a given district living in areas covered by mobile networks. Three main coverage rates are computed and used in this paper: mobile coverage (all),<sup>9</sup> 2G coverage, and 3G coverage.<sup>10</sup> Figure 2 shows the global distribution of 2G and 3G mobile network coverage during 1999– 2018 and 2007–2018 respectively. Figure 3 also shows the temporal growth in penetration of the respective mobile technologies. The figure shows a significant increase in coverage rates for 2G over the past two decades with the coverage rate for the average second administrative district increasing from 25% in 1999 to 91% in 2018, while 3G coverage also increased from 13% in 2007 to 74% in 2018.

# **2.2 Nightlights**

Since the pioneering works of Henderson et al. (2011) and Henderson et al. (2012), satellite data on nighttime lights have gained popularity in the economics literature as a proxy for measuring economic activities, particularly, in districts where high frequency and disaggregated data on economic outcomes are scant. These nightlight data come from NASA’s Defense Meteorological Satellite Program/Operational Linescan System (OLS) covering the period 1992-2013 and the Visible Infrared Radiometer Suite (VIIRS)<sup>11</sup> from 2012 to present. However, due to incon-

> 7Data for 2005 and 2010 were not available due to a change in the company in charge of the data collection. Following Manacorda and Tesei (2020), I use linear interpolation across years to obtain estimates for the missing years.

> 9reflecting the maximum coverage for all available networks, i.e., 2G, 3G, 4G

> 10reflecting the maximum coverage for 3G or 4G networks

> 11https://ngdc.noaa.gov/eog/download.html

6
data in 2010.<sup>19</sup> Table 5: Aggregate Employment Shares and Investment Shares

||Agriculture|Manufacturing|Services|Electricity Share|
|---|---|---|---|---|
|Data|21.45|23.74|54.81|71.88|
|Fully Optimal|35.34|21.07|43.59|61.11|
|Efciency + Coordination|34.84|21.32|43.84|71.88|
|Efciency-Roads|32.84|21.46|45.70|71.88|
|Efciency-Electricity|30.94|22.38|46.68|71.88|

||Correlations with the Data in|Shares of Investment|
|---|---|---|
||Roads|Electricity|
|Fully Optimal|0.71|0.31|
|Efciency + Coordination|0.79|0.23|
|Efciency-Roads|1.00|0.22|
|Efciency-Electricity|0.80|1.00|

# **5.1 Data versus Predictions**

We start by comparing the model’s optimal sectoral employment and investment allocations to the actual data. The top panel in Table 5 shows the data in the first row and the solutions of the Fully Optimal and the Efficiency + Coordination versions of the government’s problem in the second and third rows, respectively. They indicate that optimal government investment policies result in a substantial reallocation across sectors. We can see that the agricultural employment share increases relative to the data while one of the non-primary activities falls. Furthermore, the share of investment in electricity declines in the fully optimal model version, which suggests underinvestment in roads in the data.

The employment share rises in agriculture because there is a larger positive impact of the infrastructure investment improvement on the productivity of the non-primary sectors, and then, either consumers purchase more from those sectors or labor must be pushed towards the primary activity. However, a unitary elasticity of substitution between consumption products implies that the sectoral consumption shares are constant, and hence, what dominates at the national level is the reallocation of labor towards agriculture. The effect at the local level is different. As will become clear in Section 5.4, additional investment in infrastructure in a given microregion does not increase the labor share of agriculture in that location.

> 19This is necessary to reduce the numerical complexity of finding the optimal allocation. Alternative upper bounds provide similar results.

26
# A Ricardian Analysis of the Impact of Climate Change on Latin

# American Farms

## **1. Introduction**

A growing number of scientific studies indicate that the world is warming and will continue to warm as the concentration of greenhouse gases rises in the future (Houghton et al. 2001). However, there remains considerable debate about how harmful climate change will actually be (McCarthy et al. 2001). This paper examines the climate sensitivity of agricultural production in Latin America (which is defined as the Western Hemisphere south of the United States). Agriculture in Latin America is already highly vulnerable because of high current temperatures and poor farmers. Further, agriculture is responsible for 8.6% of the GDP in Latin America (World Development Indicators 2004) and uses approximately one-third of the land area of the continent (World Resources 2005). There have been several country level agronomic studies of selected crops in Latin America, but there have been very few agro-economic studies. One notable exception is a Ricardian study of farmland values in Brazil (Mendelsohn et al. 2001) which indicated that farm land values are sensitive to climate.

This paper examines over 2200 farmers in seven countries across South America: Argentina, Brazil, Chile, Colombia, Ecuador, Uruguay, and Venezuela. Surveys were collected in collaboration with teams from each country. Additional data on soils, climates, and future climate scenarios were collected from various sources. The Ricardian approach was then applied to measure the sensitivity of land value and net revenue per hectare to climate and other factors (Mendelsohn, Nordhaus, and Shaw 1994).

2
11 result, most of the information in this paper and in particular the regression analysis is based on the 1982 benchmark survey. During non-survey years, the Department collects less extensive information from smaller samples of affiliates. The results of these surveys are eiailable in revised form up to 1988.

Table 1 first reports how the size of U.S. foreign direct investment, as measured by assets, has changed during the 1980s. It shows that U.S. investment in the developed countries more than doubled in assets from 1982 to 1989, which amounts to an increase in real terms of around 58 per cent. U.S. direct investments in the developing countries instead declined by around 6 per cent in real terms.' This evidence shows that, at least for U.S. foreign direct investment, there has been a trend towards relatively less investment in the developing countries.

Table 2 provides information on trends regarding the tax and employee compensation benefits host countries receive from U.S. foreign investment. The T/A and T/I variable are host country income plus payroll taxes over assets and over before-foreign-tax income (and also before interest is deducted). Interestingly, for developed and developing countries alike, tax revenues as a percentage of assets, as well as before-tax income, have declined. In absolute terms, tax revenues have increased from $17.3 billion **_ir._** 1982 to $20.2 billion in 1989 for the developed countries, while they have declined from $12.7 billion to $8.4 billion for the developing countries. The general decline in tax rates, and in absolute tax revenues for dhe developing countries, suggests that tax competition to attract foreign direct investment has become fiercer in the 1980s. The L/A variable measures affiliate liabilities divided by assets. The table shows that this ratio has increased somewhat from 0.63 to 0.65 for the developed countries, while it has declined from 0.58 to 0.49 for the developing countries. In this respect investments in developed and developing countries are thus becoming more dissimilar. P EXP/A stands for plant and equipment expenditures divided by assets. Here the ratio has declined for the developed countries, from 0.30 in 1982 to 0.23 in 1989, while it has increased from 0.19 to 0.23 for the developing countries. These trends suggest a general shift of plant and equipment from developed countries to developing countries. The W/A variable
**Table** 7: **Indicators of Cashew Nut Industry Performance 1984/8S** - **1990/91**

|Indicator|1984/85|1985/86|1986/87|1987/88|1988/89|1989/90|1990/91|
|---|---|---|---|---|---|---|---|
|Marketed<br>Production<br>(Tons)|32,073|18,956<br>_|16,544|24,374|19,275|17,059|29,325<br>l|
|Producer Price<br>(rsh./kg)<br>Nominal'<br>Constant<br>(84/85)|9.66<br>9.66|11.58<br>8.98<br>_1__|17.93<br>10.49<br>_|29.50<br>13.23|39.35<br>13.76|82.60<br>23.14<br>_|108.15<br>25.27|
|Producer Price<br>as % of Unit<br>Export Value<sup>2</sup>|71%|37%|25%|32%|38%|57%|_56%_<br>l|
|Cooperative<br>Union Losses<br>(Tsh.<br>Million)|N.A.|94.63|84.14|95.94|131.34<br>_|135.64|332.24|
|TCMB<br>Profits/(Losses)<br>(Tsh. Million)|N.A.|N.A.|488|386|72|(493)|N.A.|
|Raw Nut<br>Exports (Tons)5|13,853|13,379|13,871<br>_|14,004|7,485|7,429<br>_|19,000<br>__l|
|Kernel Exports<br>(rons)'|518|0|0|1014|1711|1412|956|
|Processing<br>Value Added<br>(%)6|12%<br>_|N.A.<br>_|N.A.<br>_|-4%<br>_|-2%|12%<br>_|36%<br>_|
|Factory<br>Capacity<br>Utilization**(%)'**|2.1%|0|0|4.1%|8.4%|12.9%|4.3%<br>ll|
|1 Weighted Aver|age, 95% st|andard grad|e and 5% u|ndergrade.||||

2 Takes into account exports of raw nuts, kernels, and CNSL.

3 Aggregate for Unions for Lindi, Coast, Mtwara, and DSM Regions.

4 Aggregate for (3) above plus Tanga Cooperative Union.

5 For calender year (e.g. 1985, 1986, etc.) 6 Unit value of kernels and CNSL over and above raw nut export value.

7 Utilization rate for total national capacity.

Sources: Marketing Development Bureau, Antnual Report of Cashewnuts, various years.; Unpublished Cooperative Audit report.

20
13 Table 2-9. Increases in outward YDI Stock fro the Ubited Statea and Japan by Sector sad Raglen, 1977-85 (U$8 billions, increase from 1977 to 1985)

||Latin<br>Amorica|Offshore<br>Centers'|Asia|Africa|Middle<br>East|All<br>developing<br>countries|
|---|---|---|---|---|---|---|
|UNITED S_iATES|||||||
|Primary'|2.3|0.6|4.3|2.7|7.1|17.1|
|Manufacturing|5.3|0.4|2.0|0.1|0.3|8.2|
|Servicesa|1.7|12.7|3.1|0.1|1.0|17.8|
|Banking|0.4|3.8|0.9|0.2|0.2|5.4|
|Financial insurance|||||||
|real estate|0.8|8.5|0.6|-0.1|-0.1|9.8|
|Whole&ale trading|0.3|0.5|1.3|0.0|0.5|2.6|
|All industries|9.3|13.7|9.4|2.9|8.4|43.1|
|JAPAN|||||||
|Primary'|1.0|n.a.|4.8|0.2|0.2|5.2|
|Manufacturing|2.5|n.a.|5.5|0.1|0.8|8.5|
|<br>Servicesd|8.3|n.a.|5.5|2.1|-0.2|14.3|
|Financial and|||||||
|insurance|1.5|n.a.|0.9|0.0|0.1|2.1|
|Real estate|0.0|n.a.|0.4|0.0|-0.4|-0.1|
|Trading|0.8|n.a.|1.4|0.0|0.0|1.7|
|All industries|11.9|n.a.|16.0|2.5|1.5|28.9|

n.a. Not available.

a. Bahamas, Bermuda, Panama, Trinidad and Tobago, and other Caribbean islands. b. Asia and Pacific for United States, Asia plus Oceans for Japan.

c. Includes petroleum, mining, forestry, agriculture and fishing. For U.S. figures for Latin America and offshore centers, one-half of the "other industries" plus "other services" sum has been allocated to the primary sector.

d. Services totals do not equal sums of the subtotals for the more refined industry grouping because some subtotals have been omitted. The services totals were computed by subtracting the primary and manufactuing sectors from the overall totals for the United States in order to correct for its contamination by some primary-sector projects in the source tables. Source: United Nations Centre on Transnational Corporations (1988), annex tables C-1 and C-2, p. 589. Some lines in the source tables have been aggregated and others deleted to simplify the present presentation.