rafmacalaba/datause-encoder-data huggingface dataset:
The classification of country groups is provided in Appendix Table 4.

- ** Gabon is also in oil exporters, we added it to the middle-income group, not to create another group for just one country.

Source: Based on partner data from UN COMTRADE Statistics.

Looking at net agricultural exporters and importers, rather than net raw food exporters changes the picture completely (Table 8). 32 out of 47 SSA countries are net agricultural exporters; and if the oil and conflict countries are excluded, than 20 out of 28 low-income SSA countries are net agricultural exporters.

In terms of trade balance, SSA countries have a massive export surplus in agricultural commodities which constitutes almost 9 percent of their imports. Unlike other low-income countries, raw food trade deficits have not decreased, but have increased over the last 25 years.

Unlike other low-income countries globally, in SSA, even the oil exporters and countries in conflict have agricultural surpluses. All groups of countries have agricultural trade surpluses. Other SSA low-income countries, that is excluding oil exporting and conflict countries, have a massive export surplus which is equal to almost 20 percent of their imports. Thus for most SSA countries, small changes in their agricultural production mix will generate enough food for their citizens and can turn most of them into net raw food exporters.

**Table 8: African Trade Balance for All Agriculture by Income Group Country**

||Net E<br>|xports/Im<br>|ports($mill<br>|ion)<br> <br>|Net Exp <br>|orts as % of<br>|All Goods<br>|Imports<br>|
|---|---|---|---|---|---|---|---|---|
|CountryGroup (No. of Country)|1980/81|1990/91|2000/01|2004/05|1980/81|1990/91|2000/01|2004/05|
|Sub-Saharan Africa, All (47)|8,485|8,367|9,612|11,314|16.8|18.2|12.8|9.0|
|SSA Middle-income (8)|2,082|2,141|2,860|3,353|14.2|14.2|10.0|6.7|
|SSA Low-income (39)|6,402|6,226|6,753|7,961|17.9|20.2|14.6|10.5|
|SSA Oil Exporters (6)|1,166|1,458|1,197|1,085|6.0|12.9|7.0|3.3|
|SSA Civil Conflict States (5)|511|313|130|34|10.5|5.6|2.2|0.5|
|Other SSA Low-income(28)|4,892|4,700|5,890|7,309|39.6|31.8|24.5|19.8|

Notes: All agriculture is defined as all raw food, cash crops and agricultural raw materials in SITC Revision 2 excluding processed food and seafood products (see detailed classifications in Appendix Table 1).

The classification of country groups is provided in Appendix Table 4.

** Gabon is also in oil exporters, we added it to the middle-income group, not to create another group for just one country.

Source: Based on partner data from UN COMTRADE Statistics.

In terms of vulnerability, using the 10 percent cut-off point, limiting the trade to raw foods, and excluding oil exporters and conflict countries, only 3 low-income countries can be 14
## **Figure 2. Monitoring progress toward the SDGs**

<!-- Start of picture text --> A. Extreme poverty B. Human capital index, by country group<br>Percent Millions Index, frontier=1<br>50 Share of population 2000 1.0<br>Number of people (RHS)<br>40 1600 0.8<br>0.6<br>30 1200<br>0.4<br>20 800<br>0.2<br>10 400<br>0.0<br>0 0 Low Lower Upper High<br>income middle middle income<br>1990 1993 1996 1999 2002 2005 2008 2010 2011 2012 2013 2015 income income<br><!-- End of picture text --> <!-- Start of picture text --> A. Extreme poverty<br><!-- End of picture text --> Source: PovcalNet <u>(http://iresearch.worldbank.org/PovcalNet/povOnDemand.aspx), World Bank Human Capital</u> Index.

A. Lines show incidence of poverty ($1.90 per day at 2011 purchasing power parity) at the global level. B. Bars show median of index of each country group. The human capital index calculates the contributions of health and education to worker productivity. The final index score ranges from zero to one and measures the productivity as a future worker of a child born today relative to the benchmark of full health and complete nutrition.

# **B. Partnerships with international agencies**

The WBG has partnered with other agencies on initiatives in several areas directly related to the SDGs. In 2018, the heads of the WBG and the United Nations jointly signed the _UN-WBG Strategic Partnership Framework_ to implement and finance the 2030 Agenda for Sustainable Development, among three other focus areas. The WBG has partnered with UN agencies on inequality and gender; with WHO and UNICEF on water, sanitation, and hygiene; and with the IMF to strengthen domestic tax systems. Since 2018, the WBG’s Vice President of Equitable Growth, Finance and Institutions serves as one of the co-chairs of the interagency _Informal Working Group on SDG Costing_ .<sup>16</sup>

# **C. Partnerships with client countries**

As a regular part of its operational work, the WBG partners with country authorities as they develop their plans to achieve the SDGs. WBG support in finance, data provision, and implementation are critical to achieving progress towards the SDGs. Examples of SDG-related WBG partnerships with client countries include:

- _Household surveys._ The WBG committed, in 2015, to helping its clients produce at least one household survey every three years—an important step for monitoring in data-poor countries.

- _Human Capital Project._ In the context of the _Human Capital Project_ , the WBG is working with nearly 61 countries to identify barriers to human development (in particular, education

> 16 Other co-chairs are high-level officials from the IMF, OECD, and UN Sustainable Development Solutions Network. The working group’s members include representatives of other international organizations, research institutions, and academia.

12
the differences between men and women are not statistically significant). In addition, rice price increases are associated with lower participation in household production for women age 25-64. The decline in participation in household production for adult women is large enough so that they experience a decline in participation in overall work.<sup>6</sup> Moreover, the 30 percent increase in the real price of rice, observed in Vietnam between 1993 and 1998, is associated with approximately 3 fewer hours worked per week for all age groups and genders. Though overall labor supply declines, we also observe changes in how time is spent in different types of work. People of all ages reduce time spent in household production, and adults increase time devoted to wage work. This pattern is consistent with increased specialization of households with economic development and could be due to the fact that higher household income associated with rice price increases enable households to purchase market goods and services previously produced within the household. Finally, with the exception of children, we find little evidence that hours worked of males and females respond differently to rice price changes.

This paper is organized as follows. In section 2 we overview how rice prices can affect labor supply. We describe the data and descriptive statistics in section 3. In section 4 we outline our empirical methodology and present the results in section 5. We summarize our main findings and discuss their implications in section 6.

# 2. **Theory Motivation**

This section overviews the channels through which changes in product prices following international product market integration can impact an individual’s labor supply. An individual’s labor supply depends on a person’s opportunity cost of time and household income.<sup>7</sup> Changes in rice prices can influence a person’s value of time in various types of work and affect

> 6 Although the gender difference is not statistically significant, the magnitude of the effect for adult men is basically zero.

> 7 See Rosenzweig (1988) and Schultz (1999) for a survey of labor markets in low-income countries.

5
**Table 6:  Reduced-Form OLS Regression of Earnings on Observed Circumstances.**

||**BRAZIL**|**COLOMBIA**|**ECUADOR**|**GUATEMALA**|**PANAMA**|**PERU**|
|---|---|---|---|---|---|---|
|Female|-0.589***<br>[0.009]|-0.487***<br>[0.028]|-0.799***<br>[0.030]|-1.028***<br>[0.060]|-0.399***<br>[0.042]|-0.638***<br>[0.032]|
|Member of an ethnic minority|-0.364***|-0.009|-0.111***|-0.261***|-0.758***|-0.175***|
||[0.009]|[0.043]|[0.038]|[0.059]|[0.101]|[0.036]|
|Father agricultural worker|-0.366***<br>[0.009]||-0.322***<br>[0.031]|-0.090<br>[0.056]|-0.320***<br>[0.046]||
|Father primary education|0.206***<br>[0.011]|0.204***<br>[0.032]|0.103***<br>[0.035]|0.174***<br>[0.069]|0.125*<br>[0.065]|0.240***<br>[0.042]|
|Father secondary education|0.559***<br>[0.019]|0.600***<br>[0.057]|0.420***<br>[0.058]|0.396***<br>[0.121]|0.369***<br>[0.079]|0.456***<br>[0.049]|
|Mother primary education|0.243***<br>[0.011]|0.220***<br>[0.033]|0.291***<br>[0.035]|0.349***<br>[0.097]|0.303***<br>[0.063]|0.165***<br>[0.041]|
|Mother secondary education|0.644***<br>[0.019]|0.608***<br>[0.061]|0.634***<br>[0.059]|0.689***<br>[0.125]|0.603***<br>[0.081]|0.486***<br>[0.051]|
|Birth region 2|-0.353***<br>[0.013]|0.197***<br>[0.030]|-0.183***<br>[0.030]|-0.195**<br>[0.077]|-0.008<br>[0.056]|0.076**<br>[0.033]|
|Birth region 3|-0.597***<br>[0.015]|0.427***<br>[0.047]|0.133***<br>[0.041]|-0.245***<br>[0.070]|-0.287***<br>[0.058]|0.365***<br>[0.042]|
|Constant|6.450***<br>[0.014]|12.262***<br>[0.032]|5.084***<br>[0.034]|7.005***<br>[0.075]|5.263***<br>[0.079]|6.709***<br>[0.038]|
|Observations|50560|16575|9259|4661|4127|9830|
|R-squared|0.35|0.15|0.20|0.22|0.22|0.19|

Standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%. Omitted categories are: male, ethnic majority, father and mother with no or unknown education, and birth region 1 (see Table 2 for the country-specific definitions).

39
announced that all Venezuelan forced migrants registered in RAMV could regularize their migratory status by applying for PEP. Second, the PEP program did not have any eligibility requirements and was not paired with policies other than registration in RAMV, which was open to all Venezuelan forced migrants in Colombia. Third, unlike other contexts in which language and cultural differences explain many obstacles faced by forced migrants in host countries, Venezuelans and Colombians speak the same language and share similar cultures and traits. Thus, PEP provides a clean context to study the effects of regularization programs unmediated by a culture clash.

To evaluate PEP’s impact, we surveyed 2,232 Venezuelan forced migrants who arrived in Colombia between January 2017 and December 2018, thus including migrants who arrived before and after RAMV. We designed the sample to represent cities with the largest share of Venezuelan forced migrants in Colombia: Barranquilla, Bogot ́a, Medell ́ın (three of the largest cities), and a fourth “region” of smaller cities. Since forced migrants are a hardto-reach population, we constructed the sampling frame for the survey using the RAMV census, referrals from other forced migrants, and databases of local migrant organizations.<sup>2</sup> The survey data enabled us to examine the impact of PEP on three groups of outcomes: socioeconomic and health well-being, access to rights and services, and labor market consequences. This paper concentrates on the first dimension—socioeconomic and health wellbeing—while the latter two allow us to discern possible mechanisms. Each dimension includes a series of individual outcomes and a summary index. The survey took place between October 2020 and February 2021. This analysis thus provides a picture of PEP’s short-term effects two years after its enactment.

Despite the advantages for causal identification produced by the circumstances of PEP’s rollout, registration in RAMV and PEP was voluntary, so self-selection could potentially confound the identification of effects. For this reason, our empirical analysis follows a fuzzy that forced who arrived before regression discontinuity methodology compares migrants

> 2Importantly, as shown in the analysis, migrants in these three data sources were otherwise similar across socioeconomic characteristics in Venezuela and in Colombia before the program was launched.

4
Commission after its Chairman, submitted its report in 2005 and pointed out that the status of Muslims in many areas was worse than that of non-Muslims and employment and earnings was one such area. The government responded by putting in place a number of special initiatives and vowed to track progress better. Third, with increasing numbers of Dalits gaining education, and increased opportunities in the new economy, the terms of the discourse about Dalits and employment have changed as well. Until recently, studies on caste and labor market had tended to focus more on access to reserved quotas and on the relationship between poverty and wage employment rather than opportunities to rise out of poverty and gain social status through self-employment.

In step with the developments cited above, the empirical work on Dalits and Muslims and their employment outcomes has also received renewed attention. In fact, such work in the last few years has served to move the issue of minority employment out of the anecdotal and the political and into the more solid realm of evidence based understanding. For instance, Dalit leaders have historically bemoaned the exclusion of Dalits from self-employment (see Thorat, 2007) but recently a new body of empirical evidence looks at differences between Dalits and non-Dalits (see Jodhka and Newman, 2007 and Thorat and Attewell, 2007 for discrimination in hiring practices; see Deshpande and Newman, 2007; Madeshwaran and Attewell, 2007 and Das and Dutta, 2008 for discrimination in earnings) and consciously refrains from conflating Dalits with Adivasis as earlier literature had tended to do (hence “SC/ST” was often said in the same breath).

The empirical literature on Muslim employment has not been as robust but the Sachar Commission report has certainly strengthened the foundations of the discourse. One of the major preoccupations of Muslim intellectuals had been the poor representation of Muslims in public service and their disproportionate _concentration_ in self-employment. Writing about the poor performance of Muslim candidates in the premier civil service examination, the Vice Chancellor of Aligarh Muslim University noted that this “deprives the community of a sense of participation in the governance and management of their country. They are in the process denied a role in the existing adventure of national reconstruction and development” (Hamid, 2001 quoted in Zaidi, 2001). Thus, the popular notion that Muslims have a cultural premium on self-employment and prefer it to salaried jobs is not borne out in the writings of Muslim scholars nor in the growing demand for reservation of jobs for Muslims (Zaidi, 2001; Sachar Commission Report, 2006).

That Muslims are concentrated in self-employment is known more through descriptive tabulations or some qualitative work than by multivariate analyses (such as Lanjouw and Shariff, 2000). Even empirical 2
<!-- Start of picture text --> (% workforce in farm ornonfarm)<br>400% A @ A A<br>Cl —<br>60%<br>40%<br>20%<br>0%<br>1903 1993-94 1999-00 200405 2008-10<br>eFam mNonfsm<br><!-- End of picture text --> <!-- Start of picture text --> (% annual growth infarm or nonfarm employment)<br>6.0% —£-<br>4.0%<br>2.0%<br>0.0% G3 to 93 SG to 99 59 to 04 Ls]<br>2.08<br>eFam eMonfam<br><!-- End of picture text -->
stabilizztion front, the problem of Hungary is how to converge to lower inflation levels (1015 percent per year? below 10 percent? ) without hampering other objectives of economic policy, like the resumption of growth, and the maintenance of external competitiveness.

This paper investigates the process of price formation in Hungary and evaluates the use of different nominal anchors for disinflation. It provides orders of magnitude of the trade-offs between inflation and the real exchange rate during the course of stabilization under alternative monetary and exchange rate policy rules. The paper is organized as follows: section 2 provides a background on recent economic developments in Hungary. Section 3 sets-up a simple model of inflation determination in which both aggregate demand and aggregate supply factors interact to determine the rate of inflation. In section 4, the model is estimated econoinetrically using 1990-1992 monthly data of consumer -ices (CPI), producer prices (PPI), wages, the exchange rate, international prices and money. This sample covers the most critical trandition period. In section _5,_ the econumetric estimates are used to simulate the following policy alternatives: (1) a policy of fixing the exchange rate; (2) a policy of reducing the rate of growth of the money supply; (3) a combined policy of frixing the exchange rate with reducing the rate of money growth; (4) a PPP (Purchasing Power Parity) based exchange rate policy. These policies are evaluated in terms of two criteria: (a) impact on inflation and (b) effects on the reai exchange rate. Finally, section 6 includes some concluding remarks.

2
|P152343|2017|2019|6/6/2019|4 Active|Mongolia|
|---|---|---|---|---|---|
|P152343|2017|2020|12/18/2019|5 Active|Mongolia|
|P152343|2017|2020|6/22/2020|6 Active|Mongolia|
|P152347|2017|2017|6/25/2017|1 Active|Bosnia and Herzegovin|
|P152347|2017|2018|2/13/2018|2 Active|Bosnia and Herzegovin|
|P152347|2017|2019|12/3/2018|3 Active|Bosnia and Herzegovin|
|P152347|2017|2019|6/28/2019|4 Active|Bosnia and Herzegovin|
|P152347|2017|2020|12/20/2019|5 Active|Bosnia and Herzegovin|
|P152347|2017|2020|1/4/2020|6 Active|Bosnia and Herzegovin|
|P152347|2017|2021|7/21/2020|7 Active|Bosnia and Herzegovin|
|P152358|2015|2016|1/23/2016|1 Closed|Somalia|
|P152358|2015|2017|8/31/2016|2 Closed|Somalia|
|P152358|2015|2017|3/2/2017|3 Closed|Somalia|
|P152358|2015|2018|11/3/2017|4 Closed|Somalia|
|P152358|2015|2018|4/29/2018|5 Closed|Somalia|
|P152358|2015|2019|11/8/2018|6 Closed|Somalia|
|P152358|2015|2019|5/13/2019|7 Closed|Somalia|
|P152358|2015|2020|12/20/2019|8 Closed|Somalia|
|P152358|2015|2020|6/19/2020|9 Closed|Somalia|
|P152359|2015|2015|10/17/2014|1 Active|Western Africa|
|P152359|2015|2015|4/23/2015|2 Active|Western Africa|
|P152359|2015|2016|8/6/2015<br>|3 Active|Western Africa|
|P152359|2015|2016|8/15/2015|4 Active|Western Africa|
|P152359|2015|2016|3/1/2016|5 Active|Western Africa|
|P152359|2015|2017|8/19/2016|6 Active|Western Africa|
|P152359|2015|2017|11/8/2016|7 Active|Western Africa|
|P152359|2015|2017|6/19/2017|8 Active|Western Africa|
|P152359|2015|2018|12/22/2017|9 Active|Western Africa|
|P152359|2015|2018|6/12/2018<br>|10 Active|Western Africa|
|P152359|2015|2019|11/9/2018|11 Active|Western Africa|
|P152359|2015|2019|6/3/2019|12 Active|Western Africa|
|P152359|2015|2020|12/11/2019|13 Active|Western Africa|
|P152359|2015|2020|6/30/2020|14 Active|Western Africa|
|P152377|2016|2018|12/7/2017|12 Active|Nepal|
|P152377|2016|2018|6/29/2018|13 Active|Nepal|
|P152379|2016|2016|6/30/2016|1 Closed|Somalia|
|P152379|2016|2017|1/18/2017|2 Closed|Somalia|
|P152379|2016|2017|6/30/2017|3 Closed|Somalia|
|P152394|2016|2017|9/9/2016|1 Active|Kenya|
|P152394|2016|2017|3/17/2017|2 Active|Kenya|
|P152394|2016|2018|9/11/2017|3 Active|Kenya|
|P152394|2016|2018|3/21/2018|4 Active|Kenya|
|P152394|2016|2019|9/27/2018|5 Active|Kenya|
|P152394|2016|2019|12/20/2018|6 Active|Kenya|
|P152394|2016|2019|6/23/2019|7 Active|Kenya|
|P152394|2016|2020|12/19/2019|8 Active|Kenya|
|P152394|2016|2020|6/12/2020|9 Active|Kenya|
The production of first generation biofuels grows as oil becomes more scarce along the baseline path and agricultural yields increase (panel b). However, along this optimal path, first generation biofuels still do not become a large source of energy consumption. In 2100 the production of first generation biofuels is 55 Mtoe, considerably higher than in 2004, but still small in relative terms (less than 1.5% of total liquid fuel consumption: see technical appendix, Figure D.1, panel b). Nonetheless, uncertainty in food crop yields has important implications for the first generation biofuels. The difference between the most optimistic and pessimistic states of the crop productivity index is about 35 million tons of oil equivalent, which is a large fraction (63 percent) of their expected consumption in 2100.

The production of raw timber expands with growing demand for timber products and further improvements in forest yields (panel c). By 2100, production of merchantable timber crops reaches to 6.2 billion tons, which is 46 percent larger than in 2004. The consumption of ecosystem services declines in the near decades as unmanaged natural forest lands are converted to crop lands (panel d). It then increases throughout the remaining part of the coming century as the demand for ecosystem services increases and more natural forest lands become institutionally protected. By 2100 consumption of ecosystem services is 18 percent larger than in 2004. Crop productivity uncertainty has very small impact on the consumption of merchantable timber and ecosystem services (panels c and d).

# **5 Conclusions**

The pattern of global land use has important implications for the world’s food and timber supplies, bioenergy, biodiversity and other eco-system services. However, the productivity of this resource is critically dependent on the world’s climate, as well as investments in, and dissemination of improved technology. This creates massive uncertainty about future land use requirements which compound the challenge faced by individual investors and governments seeking to make long term, sometimes irreversible investments in land conversion and land use. In this paper, we assess how the uncertainties associated with the underlying biophysical processes and technological change in agriculture affect the optimal profile of land use over the next century. To do so, we develop is a long-run dynamic stochastic partial equilibrium model, which brings together distinct strands of economic, agronomic, and biophysical literature and incorporates key drivers affecting global land-use, and solve it using recent state of art methods in parallel dynamic programming.

To quantify the uncertainty in climate impacts on agricultural yields, we use the results from the Decision Support System for Agrotechnology Transfer (DSSAT) crop simulation model for four major crops, run globally on a 0.5 degree grid and weighted by agricultural output under different GHG forcing scenarios using outputs from five different global climate models. Our estimates of uncertainty in agricultural technology are based on recent agro-economic 27
evidence from household surveys and census data. _Journal of Transport Geography_ , 17(3):187–197. 00020.

- Smith, J. J. and Gihring, T. A. (2006). Financing transit systems through value capture. _American Journal of Economics and Sociology_ , 65(3):751–786.

- Stefania Radopoulou, Sevara Melibaeva, Teng Huang, and Joseph Sussman (2011). Literature review of papers relevant to the topic of development impacts and economic evaluation methods of high-speed rail (HSR). Technical report, MIT.

- Thorsnes, P. (1997). Consistent estimates of the elasticity of substitution between land and non-land inputs in the production of housing. _Journal of Urban Economics_ , 42(1):98–108.

- UN-HABITAT (2011). _Global Report on Human Settlements 2011: Cities and Climate Change_ . Earthscan Ltd, 1 edition.

- Viguié, V., Hallegatte, S., and Rozenberg, J. (2014). Downscaling long term socio-economic scenarios at city scale: A case study on paris. _Technological Forecasting and Social Change_ . 00003.

- Viguié, V. (2012). _Urban dynamics modelling, application to economic assessment of climate change_ . PhD thesis, CIRED, Université Paris Est, Paris, France.

- Viguié, V. and Hallegatte, S. (2012). Trade-offs and synergies in urban climate policies. _Nature Climate Change_ , 2(5):334–337.

- Walters, L. (2012). Land value capture in policy and practice. Washington DC.

- Wright, L. and Fulton, L. (2005). Climate change mitigation and transport in developing nations. _Transport Reviews_ , 25(6):691–717.

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