rafmacalaba/datause-encoder-data huggingface dataset:
_COTE D’IVOIRE: Competitiveness, Cocoa, and Real Exchange Rates_

**Figure 9: World & Cote d’Ivoire Cocoa Production, World Prices,  Cocoa Land Use in Cote’ d’Ivoire, and Share of World Market**

<!-- Start of picture text --> Physical Cocoa Bean Production International Cocoa Prices 1960-2003: Estimated Spot Price,<br> In the W orld a nd Cote d'Iv oire & Price Adusted by US Consumer Price Index (Base 1995)<br>3.5 Based on near term futures prices converted to spot equivalents,<br>Millions of Metric Tons<br>Average, London & New York price quotations, at London exchange closing<br>1961-2003 450<br>3<br>COTE D'IVOIRE 400<br>WORLD<br>2.5 350<br>300<br>2<br>250<br>1.5<br>200<br>1 150<br>100<br>0.5<br>50<br>0 0<br>Annual Output Average Annual Prices, 1960 to 2003<br>US cents/lb. Real price<br>Cote d'Ivoire: Use of Land for Cocoa Bean Production Cote d'Ivoire Share of W orld Cocoa Bean Output<br>(Share of Physical Production in Metric Tons. Source: FAO)<br>60% 45%<br>Cocoa as % of Arable and Permanent Crop Land<br>40%<br>50% Cocoa Land as % Permanent Crop Land<br>35%<br>40% 30%<br>25%<br>30%<br>20%<br>20% 15%<br>10%<br>10%<br>5%<br>0% 0%<br>Annual Market Share, 1961-2003<br>Annual 1961-2001<br>1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003<br>Millions<br>Metric Tons<br>US cents per pound<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002<br>Percent Land Used for Cocoa Cote d'Ivoire's Market Share<br>1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003<br><!-- End of picture text --> 10
terms. The MIC will eventually win the race, but it will be a very long race. One should note that the official World Bank threshold for “high-income country” is substantially lower than US or OECD averages. If one were to compare a prototypical MIC to that threshold, then the relevant _R_ ratio would be much lower: around 3. And the ratio would be 12 or less, even for the poorest LMICs. On the other hand, recent MIC graduates tend to grow faster than high income countries with a longer tenure at that status, so the relevant might be higher for constructing a table for catch-up to borderline HICs.

**Table 1:  Number of Years for Convergence to Rich Countries GDP per capita, as a function of MIC Growth rate and Initial per capita GDP Ratio (HIC threshold/country’s level)**

|( =|1.8%)<br>|||||||||
|---|---|---|---|---|---|---|---|---|---|
|_R_|2%|3%|4%|5%|6%|7%|8%|9%|10%|
|25|1640|275|151|104|80|65|54|47|42|
|20|1526|256|140|97|74|60|51|44|39|
|15|1380|231|127|87|67|54|46|40|35|
|10|1173|196|108|74|57|46|39|34|30|
|5|820|137|75|52|40|32|27|24|21|
|4|706|118|65|45|34|28|23|20|18|
|3|560|94|51|35|27|22|19|16|14|
|2|353|59|32|22|17|14|12|10|9|
|1.5|207|35|19|13|10|8|7|6|5|

Source: Authors’ calculations based on Equation (1).

Notes: Assuming 1.8 percent growth in real GDP per capita in the rich country reference. _R_ is the ratio of rich country GDP per capita to MIC GDP per capita. The is the rate of growth of GDP per capita in the MIC. Dividing line is to show minimum growth rates to converge to future rich country reference level in under 50 years.

One can look at country examples to get a better feeling for what would be involved in catching up over the next 50 years.<sup>10</sup> The table below presents calculations of the required growth rate for catch-up for a collection of current middle-income countries, along with the actual growth rate in the last column.<sup>11</sup> Both the United States and an aggregate for high-income OECD countries are used as a reference point. Note that the GDP per capita of these high income references are assumed to grow at 1.8 percent, resulting in GDP per capita of about $103,000 and $82,000 for the US and OECD respectively 50 years from now (measured in constant 2005 PPP dollars). Comparing the middle columns with the last column, we see that most countries have been substantially off-track in recent decades in terms of catching up to either a US standard or a high-income OECD standard.

> 10 The Commission on Growth and Development (2008) did a similar exercise (page 113 in the statistical annex), but with different assumptions on growth trajectories.

> 11 Actual is based on 1980 and 2011 levels in constant PPP adjusted dollars.  The PPP adjusted series only goes back to 1980 in the World Development Indicators database.

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References: Barnes, J. I. and J.L.V. de Jager. 1996. Economic and financial incentives for wildlife use on private land in Namibia and the implications for policy. South African Journal of Wildlife Research 26 (2): 37-46.

Barnes, J.I., J. MacGregor and L.C. Weaver. 2002. Economic efficiency and incentives for change within Namibia’s community wildlife use initiatives. World Development 30(4): 667-681.

Child, Brian et al, 2001, Mid-Term Review of LIFE-II and Assessment of the Namibia National CBNRM Programme – Final Report, USAID/Namibia, October Angus, Deaton (1997), The analysis of household surveys: A micro-econometrics approach to development policy, The World Bank, The Johns Hopkins University Press.

Heckman, James (1997), “Instrumental Variables. A study of implicit behavior assumptions used in making program evaluation”, Journal of Human Resources 32 (3): 441-61 Heckman, J, R. Lalonde, and J. Smith (1999), “The economics and econometrics of active labor market programs”, in O. Ashenfelter and D. Card, eds, Handbook of labour economics, Vol. III. New York: Elsevier Science Publishing Co.

Humphrey, Aino and Ed Humphrey, 2003, A Profile of Four Communal Area Conservancies in Namibia, DFID, February Janan, Jyotsna and M. Ravallion, 2002, “Does piped water reduce diarrhea for children in rural India? “ Journal of Econometrics.

Jones, B. and M. Murphree, 2001. The Evolution of Policy and Community Conservation in Namibia and Zimbabwe. In, D. Hulme and M. Murphree edited, African Wldlife and Livelihoods: The Promise and Performance of Community Conservation, Oxford: James Currey Ltd.

Jones, B., 2001. “The Evolution of Community-based Approach to Wildlife Management at Kunene, Namibia” in, D. Hulme and M. Murphree edited, African Wldlife and Livelihoods: The Promise and Performance of Community Conservation, Oxford: James Currey Ltd.

Jones, B., 1999a. “Policy Lessons from the Evolution of a Community-Based Approach to Wildlife Management, Kunene Region, Namibia” in Journal of International Development 11: 295-304 18
morbidity, implying that it will be important to complement the evidence on use of facilities with data on actual incidence of sickness in the population to obtain a comprehensive assessment of the impact of the policy change.

Thus, while the use of health services conditional on an individual being sick is a good measure of how _access_ to public services has improved, it fails to measure the _impact_ of such reforms, which is the variable of ultimate interest for policy makers. Focusing attention on the latter is important and may yield different results to the extent that the policy change is likely to have affected not only demand for curative care, but also for preventive measures. More importantly, unobserved changes in the quality of service provision will have a more immediate impact on observed health outcomes than on households’ demand for such services. For these reasons, exploring the impact of the policy measure on morbidity, as measured by the probability of households falling sick or the number of days they lost due to sickness, will be necessary.

# **3.2 Estimation strategy**

_Facility level data:_ Before testing the hypotheses at the household level in more detail, we use evidence from a time series of administrative data for different types of treatment (indexed by _k_ ) to asses the impact of the policy change on changes in supply of services to the public. In the simplest form, the impact of the policy change on the amount of treatments dispensed can be obtained by a simple regression such as where _Vklt_ denotes the logarithm of the number of services _V_ of type _k_ provided in health unit _l_ during period _t_ , _T_ is a time dummy that equals 1 if the observation comes from after the abolition of user fees and 0 otherwise, **_R_** is a vector of regional dummies and **_C_** is a vector of controls as explained below. The underlying principle is that the coefficient α _1k_ -or different elements of the coefficient vector α _2k_ - correspond to the mean percentage increase in service delivery after abolition of user fees and after controlling for other factors.<sup>7</sup> The latter include the fact that data are not available at the facility but at the district level and that there are gaps in the level of reporting by different facilities. We therefore report in table 5 the coefficients α _1k_ or the four coefficients α _2k_ for different types of services ( _k_ ) together with their level of significance and the number of observations used in each of the regressions.

While equation (1) will provide a first indication of the impact of the policy reform on delivery and supply of health services, it is associated with a number of shortcomings, in particular (i) it will be impossible to distinguish supply and demand; (ii) we do not know anything about the extent to which

> 6 To keep matters simple, we assume that the price charged by private providers does not change as a result of the policy reform, an assumption that is justified if private providers do not act strategically but rather aim to cover their operating costs.

9
resource-cost-based measure of food demand is much more responsive to income growth than alternative measures based on final calories consumed.

Using the GDP and population projections from the SSP2 data set, we find that CE food demand would roughly double, increasing by 102 percent between 2009 and 2050. The decomposition of demand growth into per capita demand growth and population growth reveals that per capita growth plays an increasingly important role in raising food demand. This trend contrasts with the historical pattern in which population growth dominated consumption per capita growth in influencing food demand growth. It makes the relative importance of phenomena such as income convergence or divergence much greater than would have been the case in earlier decades.

The implications of income convergence for food demand seems timely, given the apparent reversal of fortunes in moving from the Great Divergence (Pritchett 1997) to the Great Convergence which started around 1990 (Baldwin 2016). We find that the coefficient for unconditional income convergence in our sample countries was not significant in the 1980s or 1990s, but became significant in the first decade of the 2000s, when developing countries grew, on average, much more rapidly than the developed countries. We also find that the rate of income convergence using the middle-ground GDP projections from the SSP database (SSP2) between 2009 and 2050 is about twice as rapid as the last decade, although still about half the rate estimated by Dowrick and Nguyen (1989) for the OECD countries in their post-war golden age of 19501985.

A series of simulation results reveal that the impact of convergence on food demand increase can be substantial. <mark>The rise in demand of our base scenario (102 percent), which embodies the assumption of income convergence from SSP2, is about one-third greater than that under the counterfactual non-convergent growth scenario (78 percent).</mark> The regional decomposition shows that developing countries as a group dominate the increase in food demand and that their income convergence does matter. We find that convergence by middle-income countries, especially such populous countries as India, China, Indonesia and Nigeria, is particularly important for global food demand. This is partly due to the inverted-U shaped pattern of income elasticities for aggregate food demand, with middle-income countries experiencing the largest income elasticities due to their dietary upgrading towards more resource demanding products.

24
frequency cycles, common to all variables in the system, have periods of 1.55 and 3.04 years respectively.

**Venezuela: Combined Model for First Differences of Log Real Money Balances, Log Real GDP and Inflation** **_Key Summary Statistics (T=35)_**

|**Stalistic**|**ALog**<br>**RealMl**|**ALog**<br>**Real**<br>**GDP**|**Alnflation**|
|---|---|---|---|
|Normality(Bowman-Shenton):n<br>**21.81**||0.54|4.41|
|Skewness:**s****___**_X,_|1.65|0.44|2.29|
|Kurtosis:_k- X,_|0.15|0.10|2.12|
|Heteroskedasticity:_H(1_1)_- F,_|0.42|0.15|0.15|
|Autocorrelation(upto12order)|11.85|18.02|9.82|
|(Box-Ljung):Q(12,6)_- X6_||||
|Autocorrelation(firstorder)|2.11|1.84|2.00|
|GoodnessofFit (w.r.t.seasonal mean)|0.72|0.53|0.52|
|GoodnessofFit (ordinaryR<sup>2</sup>)|0.85|0.73|0.29|
|Akaike Information Criterion|-4.22|-5.70|-5.22|
|BayesInformationCrilerion|-2.87|-4.36|-3.87|

Heteroskedasticity, normality and autocorrelation statistics are satisfactory for the first differences of log real Ml and the inflation rate. Nevertheless, there appears to be (higher-order) residual aulocorrelation in the case of the change in log real GDP. Goodness of fit measures indicate a generally good fit, including with respect to the seasonal mean.

Results for a second imodel, where first differenced of log real Ml, log real GDP and the log of the inflation rate are taken as endogenous variables, are presented below. In this case, very strong convergence is achieved in 43 iterations. The value of the loglikelihood is 260.81. Common cycles were estimated with periods 1.70 and 2.86 years. In the case of the equation for the first difference of log real Ml, results were generally Money Demand in Venezuela, Mario A. Cuevas 20
Table 9 synthesizes the key components of what has been effective in several countries.

**Table 9: Financial Inclusion Approach and Examples**

|**APPROACH**|**1. Data**<br>**underpins**<br>**Reforms and**<br>**Innovation**|**2. Public and**<br>**private sectors**<br>**agree financial**<br>**inclusiongoals**|**3. Public sector removes**<br>**regulatory barriers and**<br>**monitors progress on goals**|**4. Private sector**<br>**develops new financial**<br>**services and delivery**<br>**mechanisms**|
|---|---|---|---|---|
|South Africa:|FinScope Data<br>Survey|Financial Sector<br>Charter|Financial Advisory and<br>Intermediary Services Act,<br>Dedicated Banks Bill|Basic bank accounts and<br>mobile phone banking|
|UK:|Family<br>Resources<br>Survey|Financial<br>Inclusion Task<br>Force|Progress monitored by<br>Financial Inclusion<br>Indicators|Basic Bank Accounts,<br>Financial services<br>through Post Office|
|Canada:||Canadian Banking<br>Code|Financial Consumer Agency<br>of Canada|Low cost bank accounts<br>Basic bank accounts,|
|India:||Banking Codes<br>and Standards<br>Board of India|Reserve Bank of India‟s<br>guidance on Financial<br>Inclusion|Innovative use of<br>technology to reach<br>customers|

Source: Pearce & Bajaj, 2006.

_Data availability_

**Improved data on financial inclusion and unbanked markets/customers is needed to underpin a sustainable expansion in access to finance** , as emphasized by the G20 Principle for Financial Inclusion, _Knowledge._ Household and enterprise-level survey questions can provide governments and regulators with the data they need for designing reforms and then monitoring the effectiveness of those reform measures. Financial service providers need market data on financial service demand and needs for new market segments that they have not had previous involvement in, in order to design appropriate financial products and delivery mechanisms.  In addition to financial regulator monitoring of progress on financial inclusion, government support may be needed in generating market data for financial service providers, at least initially to address this market failure.

_Use Government Payments as a Stimulus for Financial Inclusion_

**An area that the Government has direct control over is how it pays benefits (social transfers and pension payments) and salaries.** By shifting government payments to individuals (G2P, or „Government to Person‟) from payments in cash disbursed from post offices or bank tellers, to bank accounts and smart cards (or even mobile phones), significant advances in financial inclusion can be made. Governments make regular payments to at least 170 million poor people worldwide.  Yet in most countries, fewer than one-quarter of G2P payments to the

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# **6. Discussion: Does the Recent Growth in Africa Resemble Asian Experiences?**

Many wonder whether Africa’s recent growth is a prelude of economic takeoff and seek similarities with the Asian growth experience. We present the initial conditions and the trends in various growth factors for several selected countries at various points of time (see Table 2). We first consider the four East Asian “Tiger” countries that experienced a huge leap from the mid-1960s up until the early 1990s. The next group of countries includes large emerging economies including Brazil, China, India, and Malaysia. Finally, we add four African countries that represent different types and income groups: Ethiopia (low income), Ghana (lower middle income), Nigeria (oil producing), and South Africa (upper middle income).<sup>43</sup> As indicated by many studies, the four Tiger countries have followed a relatively homogeneous pattern despite their differences in initial conditions, including the level of GDP per capita. First, they experienced rapid changes in demographic structure, benefiting from drastic declines in fertility and increases in life expectancy, and thus large increases in working age share (by 12-19 percentage points). Second, during the takeoff period, there were large increases in the labor force participation rates (except for Taiwan) and the years of schooling ranged from a 2.3 year increase in Singapore to 4.8 years in Korea. Finally, apart from Hong Kong, all three countries experienced a substantial increase in investment.

The large emerging countries – Brazil, China, India, and Malaysia – are quite heterogeneous in both the initial level and size of changes. Focusing on the relatively high growth period of 1990-2010, their demographic change and increase in labor force participation were not as drastic as the Tiger countries. Regarding investment, both China and India experienced enormous increases. One notable feature is that by 1990 the education level of the large emerging countries reached that of the Tiger countries in 1966 (average 5.2 years). This suggests that economic takeoff may

> 43 We follow classification of the World Bank where low income is defined as GNI per capita of $1,005 or less, lower middle income between $1,006 and $3,975, and upper middle income between $3,976 and $12,275 as of 2010.

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## subcategories alongside each other.

The stock of trees on farms identified in our study likely represents a lower bound. First, home gardens are plausibly underreported as plots (and thus also trees in home gardens) and trees with no immediate productive function may have been left out of household questionnaires. Second, respondents may not recall all trees on their lands or may be hesitant to report them where, for example, colonial legacies of state control of tree resources persist (Ribot, 1999). Lastly, the study was unable to classify a few species for which only the local name was available (Table A.1). Yet, such omissions would especially affect the number of trees reported, and not so much their incidence or the share of land allocated to trees (for each plot it is recorded whether trees are present or not). Consequently, this paper focuses on analyzing the prevalence of trees on farms and the share of land allocated to trees as opposed to the number of trees per se.

# 2.2 Contribution to Household Income and Welfare

To examine the contribution of trees on farms to farmers’ livelihoods, three indicators are examined: 1) how tree products are used (as a source of cash or mainly for own use or consumption); 2) their share in household crop and income portfolios (as an indication of their direct economic importance) and 3) the consumption levels among farmers with and without trees on farms. As a broader reflection of overall wealth, the last of these indicators also captures some of the more indirect contributions of trees on farms such as soil conservation, nitrogen fixing, water regulation, and carbon sequestration (Booth and Wickens, 1988; Nair, 2007; Place and Garrity, 2015) or their use as fodder for livestock or provider of organic fertilizer. None of these indirect aspects is typically addressed in the LSMS-ISA surveys (or household surveys more generally).

Information on the harvested amounts of the tree products and their different uses (sale, autoconsumption, or other uses<sup>3</sup> ) was directly obtained in all surveys, except in Tanzania. Second, to assess their contribution to household income, we estimated the share of gross income derived from trees on farms as part of gross agricultural and gross overall income.<sup>4</sup> Gross household income was calculated

> 3 This concerns their use as inputs into another production process (e.g. fodder for livestock, fruits for jam, timber for own house construction or fencing).

> 4 A more refined measure would be to calculate the share of net income from trees on farms over the total net income per household. However, complete expense data were not collected in all study countries and when collected, they were not

5
particularly important in ethnically divided societies which are at higher risk of a return to civil war.<sup>39</sup> We now turn to an analysis of peace failure using the time-series cross-section version of our dataset.

_Analysis of the Hazard of Peace Failure Using Time Varying Covariates (TVCs)_ We can think of our dataset as a multiple-event, multiple-failure dataset with both ordered and unordered events (peace failures). The dependent variable is again months until peace failure. Since we often have multiple peace failures in a country and our cross-sectional unit is the peace process ( _cnumb_ ), we add multiple records for each crosssection, making the use of clustering on country necessary, since multiple records of country-level variables will be the same across conflicts. Our unit of analysis is the peace process-year, not the country-year as is typical with binary time-series cross-sectional (BTSCS) datasets in international relations and comparative politics. We discuss the method in more detail in the supplement and present key results below.

Even though the model is now slightly different, we nevertheless include measures for all three concept variables – hostility, international and local capacities. We estimate a Cox PH model controlling for _lagged log of per capita GDP, lagged oil export-dependence,_ the _log of deaths and displacements, ethnic war, signed treaty, level of ethnic fractionalization, population size_ and our UN intervention variables.

The presence of any UN mission ( _anyun_ ) is our focus. This is a binary variable coded 0 if there was no UN mission and 1 if there was a UN mission, starting at the year of initial deployment and coded as q until the peace either fails or analysis time runs out.

39 In the supplement, we present several more robustness tests. We utilize different coding of peace duration, based on different assumptions about which cases of war resumption represent war “recurrence” and which are “new” wars. It is not always easy to make those distinctions. We also drop cases that some scholars might not characterize as civil wars, but rather as different forms of violence. The results are generally robust to those changes, though there are some differences when we use the alternative peace duration variable (see supplement). The logistic regression estimates are robust to all those coding changes.

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