rafmacalaba/datause-encoder-data huggingface dataset:
the uniform growth scenario. The difference can be partly explained by the component coming from the correlation between income growth rates and the share differentials (7 percent in logdifference terms) and partly by the component resulting from the relationship between income growth and income elasticities (4.4 percent in log-difference terms).

The food-share-weighted decomposition of per capita demand growth and population growth in this section differs from that shown in Table 1. The latter decomposition was populationshare-weighted for comparability with the existing literature, while the population growth rates in Table 3 are food-demand-share weighted to attribute correctly the impact of population growth on food demand. Dividing the 23 percent population impact in log-difference terms in Table 3 by the 41 years of the projection period reveals a lower estimate (0.56 percent per year) of the impact of population growth than in Table 1 (0.68 percent per year).

Table 3. Decomposition of Food Demand Growth 2009-2050: <u>SSP2 vs. Uniform Growth Scenarios</u>

||SSP2 Scenario<br>(%)|Uniform Scenario<br>(%)|
|---|---|---|
|**Food-weighted Per Capita Demand Growth**|**48**|**36**|
|Of which�[∑(��− ��). ��<br>]|7.0||
|Ofwhich∑��( ��−�)(��−�)|4.4||
|**Food-weighted population growth**|**23**|**23**|
|**Total Global Food Demand Growth**|**70**|**58**|

_Source_ : Authors’ simulation results based on a sample of 134 countries. _Note_ : Percentage changes are based on log-differences.

# Modeling Food Supply

<mark>While our primary focus in this paper is on economic convergence and food demand, an obvious question is how the increase in demand for food associated with convergence might be met, and what the implications for food prices might be. In this section, we develop a parsimonious representation of supply based on per capita GDP, land availability and labor supply. This model captures three important stylized facts—that agricultural output rises with a country’s land endowment; that higher economy-wide productivity increases output; and that agricultural output increases by less than total output as the economy grows (Martin and Warr 1993).</mark> <mark>How rising food demand is met will be heavily influenced by the availability of agricultural land and other natural resources. Between 1992 and 2012, the world’s arable land (arable land and</mark> 17
firm performance requires specific measures of indicators. The investment climate survey collected data on a wide variety of quantitative as well as qualitative, or perceptionbased, indicators of the investment climate. While perception based indicators are useful, they must be augmented by quantitative indicators to give an accurate picture of the investment climate.

The reason is that managers have different frames of reference. An issue perceived as a major problem in one country may actually impose a smaller cost on businesses in that country than it does in a country where the problem is rated as merely moderate. So whenever possible it is important to gather quantitative measures of a problem (for example, how much companies pay in bribes on average) rather than just perceptions (how big a problem corruption is).

There are a vast number of possible indicators and the challenge is to narrow this large selection down to a manageable number that accurately describe the investment climate. In doing so, researchers face two major problems: endogeneity and multicollinearity.

This analysis treats the investment climate indicators as exogenous determinants of firms’ performance. However, high-performing firms may be proactive in reducing their own investment climate constraints, producing a simultaneity bias in the estimation exercise. For example, a well-managed, productive firm may be able to work with authorities to limit inspections or secure a more reliable power supply. To address this endogeneity issue, the empirical analysis uses average investment climate indicators taken across 9
Table 3: Acute care for diarrhea and incidence of diarrhea (last 7 days).

||Acu|te care receiv|ed|In|cidence (no. of d|ays out o|f 7)|
|---|---|---|---|---|---|---|---|
||CSCOM/<br>CSREF|Any<br>formal care|ORT<br>given|Any<br>diarrhea|Three or more<br> loose stools/day|Blood in<br> the stool|Mother<br> concerned|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|CHW visits|0_._041|_−_0_._014|0_._127|_−_0_._047|_−_0_._046|_−_0_._026|0_._114|
||(0_._089)|(0_._139)|(0_._108)|(0_._057)|(0_._049)|(0_._027)|(0_._112)|
|Subsidy|_−_0_._060|_−_0_._088|0_._174<sup>_∗∗_</sup>|_−_0_._061|_−_0_._051|_−_0_._027|_−_0_._037|
||(0_._076)|(0_._119)|(0_._079)|(0_._057)|(0_._050)|(0_._023)|(0_._101)|
|Full program|0_._170|0_._088|0_._266<sup>_∗∗_</sup>|_−_0_._149<sup>_∗∗_</sup>|<sup>_∗_</sup><br>_−_0_._131<sup>_∗∗∗_</sup>|_−_0_._022|_−_0_._118|
||(0_._105)|(0_._184)|(0_._121)|(0_._051)|(0_._043)|(0_._026)|(0_._100)|
|Control group mean|0_._083|0_._222|0_._000|0_._210|0_._161|0_._045|0_._492|
|N|102|102|102<br>1|649|1649<br>|1649|1649|
|p-value: CHW = full|0_._28|0_._59|0_._39|0_._02|0_._01|0_._87|0_._03|
|p-value: subsidy = full|0_._04|0_._31|0_._45|0_._04|0_._02|0_._79|0_._40|
|p-value: C+S = full|0_._21|0_._38|0_._85|0_._56|0_._57|0_._33|0_._18|

_Notes:_ All regressions include stratum fixed effects, control for covariates, and cluster standard errors at the compound level. The outcome in (1) is an indicator whether the caretaker reported that an NGO worker who checked on the child’s health visited in the last two weeks. (2)-(4) report measures of acute care received and include only children with any diarrhea symptoms in the last 7 days. (5)-(8) report diarrhea symptom incidence and caretaker’s concern for the child’s health (in number of days with occurrence, out of 7).

Significance levels: * 0.1, ** 0.05, *** 0.01.

23
- Barrett, C. B., T. Reardon & P. Webb, 2001, Nonfarm Income Diversification and Household Livelihood Strategies in Rural Africa: Concepts, Dynamics, and Policy Implications, _Food Policy_ 26(4), 315331.

Bhalla, A. S., 1990, Rural-Urban Disparities in India and China, _World Development_ 18(8), 1097-1110.

- Bright, H., J. Davis, M. Janowski, A. Low & D. Pearce, 2000, Rural Non-Farm Livelihoods in Central and Eastern Europe and Central Asia and the Reform Process: A Literature Review, World Bank Natural Resources Institute Report No. 2633.

- Burgess, R., 1998, Diversification and Welfare in Rural China, mimeo, Department of Economics and STICERD, London School of Economics.

- Byrd, W. & Q. Lin, 1994, _China’s Rural Industry: Structure, Development, and Reform_ (Oxford, Oxford University Press).

- Chapman, R., & R. Tripp, 2004, Background Paper on Rural Livelihood Diversity and Agriculture, mimeo, AgREN electronic conference on the Implications of Rural Livelihood Diversity for Pro-poor Agricultural Initiatives.

- Chinn, D. L., 1979, Rural Poverty and the Structure of Farm Household Income in Developing Countries: Evidence from Taiwan, _Economic Development and Cultural Change_ 27(2), 283-301.

- Davin, D., 1999, _Internal Migration in Contemporary China_ (New York, St. Martin’s Press, Inc.).

de Beer, P. & J-L. Rocca, 1997, _La Chine à la fin de l’ère DENG Xiaoping_ (Paris, Le Monde-Editions).

- de Janvry, A. & E. Sadoulet, 2001, Income Strategies Among Rural Households in Mexico: The Role of Off-farm Activities, _World Development_ 29(3), 467-480.

- Elbers, C. & P. Lanjouw, 2001, Intersectoral Transfer, Growth, and Inequality in Rural Ecuador, _World Development_ 29(3), 481-496.

- Escobal, J., 2001, The Determinants of Nonfarm Income Diversification in Rural Peru, _World Development_ 29(3), 497-508.

FAO, 1998, _The state of food and agriculture 1998_ (Rome, FAO).

- Goldstein, S. & A. Goldstein, 1991, Rural industrialization and migration in the People’s Republic of China, _Social Science History_ 15(3), 289-314.

Greene, W. H., 1997, _Econometric Analysis_ (New Jersey, Prentice-Hall International).

Heckman, J., 1979, Sample selection bias as a specification error, _Econometrica_ 47(1), 153-161.

- Hussain, A., P. Lanjouw & N. Stern, 1994, Income Inequalities in China: Evidence from Household Survey Data, _World Development_ 22(12), 1947-1957.

- Islam, N., 1997,  The non-farm sector and rural development – review of issues and evidence, _Food, Agriculture and the Environment Discussion Paper_ 22, IFPRI, Washington D.C.

- Khan, A. R. & C. Riskin, 2001, _Inequality and Poverty in China in the Age of Globalization_ (New York, Oxford University Press).

- Kimhi, A., 1994, Quasi Maximum Likelihood Estimation of Multivariate Probit Models: Farm Couples’ Labor Participation, _American Journal of Agricultural Economics_ 76(4), 828-835.

- Knight, J. & L. Song, 1993, The spatial contribution to income inequality in rural China, _Cambridge Journal of Economics_ 17, 195-213.

- Lachaud, J-P., 1999, Envois de fonds, inégalité et pauvreté au Burkina Faso, _Revue Tiers Monde_ 40(160), 793-827.

22
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of trade costs and rents, though increasingly with a better basis in firm surveys and econometrics.

# **4 Implementation Issues**

## **4.1 Quantifying policies**

The need to consider trade, FDI, and cross-border movement of people (suppliers, consumers) has meant that efforts in the research community to characterize and quantify barriers to market access in services (a basic requirement if we want to gauge liberalization in a CGE model) have been problematic. The initial approach (see Hoekman, 1996) was to establish inventory measures of policies, distinguishing between different modes of supply. This involves simply counting the instances in which certain types of measures and policies are observed for – various services sectors and modes of supply cross-boder trade, FDI, etc.<sup>14</sup> As an alternative approach, there has also been a branch of research based on priceand cost comparisons by industries across countries (see Kalirajan et al., 2000; Nguyen-Hong, 2000; OECD, 2009). More recently, there is an increasing emphasis on integrating econometric estimates of trade costs from gravity models with firmlevel, survey-based information on access to and quality of services inputs (such as energy, finance, etc.) that can be used to determine what share of observed price margins can be attributed to the effects of policy, which in turn can be used when defining services policy reform experiments in CGE models. Such estimates are admittedly crude, but may be the best immediate way forward for the purposes of simulating the effects of liberalization.

A range of gravity and price- and cost-based estimates of trade costs in services, much of which is surveyed in Francois and Hoekman (2010), concludes that the extent of barriers to trade in services may be very substantial, and potentially larger than the barriers presented by conventional trade measures such as tariffs and subsidies (see, e.g., Deardorff and Stern, 2008; Dee, Hanslow and Phamduc, 2003; Dee, 2005; Department of Foreign Affairs and Trade, 1999; Francois, 2000; Francois and Hoekman, 1999; Fontagne et al., 2010). Most often, these findings have been incorporated in CGE analyses in the form of tax or tariff equivalents.

Information on the extent to which policies affect prices and costs is important for accurate modeling of policy reforms, including whether policies create ”rents” as opposed to being resource-using (generating ”waste”), and the identity (ownership) of the entities and groups to whom any rents accrue. This is a well-

> 14See Hoekman and Primo-Braga (1997) for a survey of the early literature on barriers to trade in services.

15
men and close to 3% for women), many fewer men than women are currently widowers (1.3% of ever married-men versus 13.6% of ever-married women).

The paper uses recent nationally representative data from a new household survey and data from DHSs to document Senegalese women’s marital trajectories. We describe a simple framework to help clarify the potential trade-offs faced by women considering remarriage. This provides a schematic description of the channels through which marital dissolution and remarriage may affect women’s well-being. We then turn to the data to examine selection into marital statuses, and how different marital trajectories correlate with current consumption levels and other individual dimensions of welfare.

The paper finds that marital breakdowns and their aftermath are far from neutral in terms of women’s well-being. Poorer women are found to be more vulnerable to both dissolutions and remarriage, and hence to bear more of the costs while being nevertheless afforded a safety net in the form of a male protector following marriage dissolution.

Naturally, the form a woman’s marital trajectory takes is the result of myriad influences including her family and individual characteristics, social norms and chance, as well as the legal and economic setting. Selection and endogeneity are rife in women’s life courses. While the evidence is compatible with our expectations, we acknowledge that the paper does not establish beyond doubt a one-directional causality between marital status (and its trajectories) and welfare.

Using an individualized measure of consumption and the marital status-specific parameters estimated from a consumption model, we simulate counterfactual consumption levels for women with different marital trajectories but otherwise identical characteristics. A subsequent decomposition analysis, which allows us to isolate the consumption impacts of remarriage, points to the persistent vulnerability of widows. In addition, the findings suggest that the safety net provided by remarriage is least effective in ensuring consumption levels for the most vulnerable widows who tend to remarry in leviratic marriages. We suspect such widows were left no choice but to remarry given their need for economic support, and that, as suggested by the data, many were constrained to remain within their late husband’s lineage due to the presence of young (male) children. For their part, divorcees suffer from smaller consumption losses relative to women in their first union, and do not fare differently by whether they remarry or not. Furthermore, individual characteristics that affect the quality of their marriages seem to matter in the same way for their second marriage.

The paper begins in Section 2 with some background on the legal and institutional context for marriage in Senegal. Section 3 follows with a brief description of our data. Section 4
workers and complying with applicable laws); mixed firms (with salaried and non-salaried workers partly complying with these laws); informal and legal firms (associating only with nonsalaried workers); and informal and illegal firms (hiring salaried workers, but violating applicable laws). In parallel, formal workers are salaried employees covered by labor and social insurance regulations; all other workers, whether salaried or nonsalaried, are informal.<sup>4</sup> While the formal-informal nomenclature is used throughout the paper, we are concerned here with how firms behave, not with how firms are labeled. Specifically, the focus is on whether firms demand workers with more or less educational attainment, and whether they offer jobs with coverage of labor and social insurance regulations.

It is useful to make a few observations on the distribution of firms and employment in 2008. First, 90 percent of all firms are small (up to five workers), and 90.3 percent are informal (legal and illegal). Second, while informal firms have, on average, 2.8 workers, they account for more than half of all employment captured in the census (57.3 percent). Third, larger firms have proportionately more salaried workers than smaller ones: firms with only salaried workers, formal and informal, are only 25.6 percent of all firms, but employ 38.7 percent of all workers. Finally, within firms that only have salaried contracts, formal ones are larger than informal ones: on average, 27.8 vs. 4.0 workers<sup>5</sup> . Table 1 summarizes these observations.

> 4 The existence of mixed and illegal informal firms indicates that some firms are exploiting two different margins of informality: an intensive margin (hiring some workers illegally) and an extensive margin (not registering the firm at all and thus hiring all workers illegally). This is consistent with the findings of Ulyssea (2018) for Brazil, who also finds evidence of firms’ informal behavior along these margins. Additionally, however, our paper documents one aspect of firm informality not associated with these two margins, where firms employ nonsalaried workers, and are informal but legal.

> 5 Using the Economic Census for 1993, 2003, 2008 and 2013, Levy (2018) shows that the size distribution of firms in Mexico has been practically constant over this 15-year period. Unfortunately, the 1993 Economic Census is not available, which would help to assess the impact that the North American Free Trade Agreement (NAFTA), which started in 1994, had on the size distribution. Other analysis have shown that trade liberalization has changed this distribution. For instance, Nataraj (2011) shows that India’s trade liberalization in the 1990s increased average firm size. Since our data begin in 1998, it already incorporates the effects of Mexico’s main liberalization episode.

8
comparable qualities of the goods identified in the ICP price surveys. Regional-specific product lists were derived, which aimed to balance the twin objectives of being internationally comparable goods within the region and being representative of the consumption bundles found in each country. Creating the product listings took about two years, as it involved extensive collaboration amongst the countries and the relevant regional ICP office. Ring comparisons were used to linking the regional PPP estimates through a common set of global prices; these comparisons were done for 18 countries in all—a marked improvement over past ICP rounds.<sup>9</sup> As in the past, the Bank uses a multilateral extension of the bilateral Fisher price index known as the EKS method.<sup>10</sup> The changes in the methods of product listing and pricing are of particular relevance to global poverty measurement. The 2005 ICP applied more rigorous standards of specifying internationally comparable commodities for linking across countries (World Bank, 2008b). In comparison to 2005, it is likely that the 1993 ICP would have used lower qualities of goods in poor countries than would have been found in (say) the US market.11 The goods priced by the 1993 ICP tended to be more typical of the items available in local markets. The 1993 ICP round also over-valued the services derived from government in developing countries. RCS show that a sizable underestimation of the 1993 PPP is implied by the new PPP data and the data on rates of inflation. Furthermore, the extent of this underestimation tends to be greater for poorer countries.

Given the changes in data and methodology, PPPs for different benchmark years cannot be easily compared, and cannot be expected to be consistent with national data sources (Dalgaard and Sørensen, 2002; World Bank, 2008b). We follow common practice in letting the national data override the ICP data for inter-temporal comparisons; this is the most reasonable position to take given the changes in methodology between different ICP rounds (World Bank, 2008b). Thus the PPP conversion is only done once for a given country, and all estimates are revised back in time consistently with the data for that country. So the PPPs serve the role of locating the

> 9 There were other differences, less relevant to global poverty measurement. New methods were used for measuring government compensation and housing. Adjustments were also made for the lower average productivity of public sector workers in developing countries (lowering the imputed value of the services derived from public administration, education and health). 10

> On the advantages of this method over the alternative (Geary-Khamis) method see Ackland et al. (2006). In the 2005 ICP the Africa region chose a different aggregation method (African Development Bank, 2007); World Bank (2008b) describes this as a minor difference to the EKS method.

> 11 There were a number of problems in the implementation of the 1993 ICP round, as discussed in Ahmed (2003).

6
# Table 9

# Cotton Productivity

Non-Linearity of Unobserved Productivity

||Mod|el 1|Mo|del 2|
|---|---|---|---|---|
||Tobit<br>(1)|Robinson<br>(2)|Tobit<br>(3)|Robinson<br>(4)|
|Age of household head|0_._009|0_._009|0_._009|0_._009|
||0_._007|0_._008|0_._007|0_._008|
|Age squared|_−_0_._0001|_−_0_._0001|_−_0_._0001|_−_0_._0001|
||0_._00007_∗_|0_._00007_∗_|0_._00007_∗_|0_._00007_∗_|
|Male household head|0_._091|0_._091|0_._091|0_._091|
||0_._047_∗_|0_._052_∗_|0_._047_∗_|0_._052_∗_|
|Family size|_−_0_._001|_−_0_._002|0|0|
||0_._036|0_._036|0_._036|0_._036|
|Share of males|_−_0_._048|_−_0_._048|_−_0_._048|_−_0_._048|
||0_._105|0_._101|0_._105|0_._101|
|Farm type|0_._052|0_._052|0_._05|0_._05|
||0_._044|0_._042|0_._044|0_._042|
|Relative plot size|_−_0_._543|_−_0_._544|_−_0_._546|_−_0_._547|
||0_._048_∗∗∗_|0_._046_∗∗∗_|0_._048_∗∗∗_|0_._046_∗∗∗_|
|Livestock|_−_0_._067|_−_0_._067|_−_0_._065|_−_0_._065|
||0_._040_∗_|0_._040_∗_|0_._04|0_._04|
|Harrows|_−_0_._069|_−_0_._069|_−_0_._083|_−_0_._083|
||0_._053|0_._048|0_._053|0_._048_∗_|
|Ploughs|0_._024|0_._024|0_._021|0_._021|
||0_._027|0_._021|0_._027|0_._021|
|Basal fertilizer|_−_0_._001|_−_0_._001|_−_0_._001|_−_0_._001|
||0_._000_∗∗∗_|0_._000_∗∗∗_|0_._000_∗∗∗_|0_._000_∗∗∗_|
|Top-dressing fertilizer|_−_0_._001|_−_0_._001|_−_0_._001|_−_0_._001|
||0_._000_∗∗_|0_._000_∗∗∗_|0_._000_∗∗_|0_._000_∗∗∗_|
|Cotton prices|0_._08|0_._081|0_._082|0_._082|
||0_._062|0_._059|0_._062|0_._059|
|_F_ <sup>1</sup>|_−_0_._503|_−_0_._504|_−_0_._505|_−_0_._506|
||0_._058_∗∗∗_|0_._056_∗∗∗_|0_._058_∗∗∗_|0_._056_∗∗∗_|
|_F_ <sup>2</sup>|0_._193|0_._192|0_._19|0_._19|
||0_._039_∗∗∗_|0_._040_∗∗∗_|0_._039_∗∗∗_|0_._040_∗∗∗_|
|_φ_|0_._009||0_._01||
||0_._002_∗∗∗_||0_._002_∗∗∗_||
|Constant|_−_0_._951|0_._0001|_−_0_._948|_−_0_._0001|
||0_._309_∗∗∗_|0_._016|0_._309_∗∗∗_|0_._016|
|Observations|3418|3418|3418|3418|
|_R_<sup>2</sup>|0.15|0.13|0.15|0.14|

Robust standard errors in parentheses: * significant at 10%; ** significant at 5%; *** significant at 1%. For a description of Models 1 and 2, see note in Table 8. Columns (1) and (3) use a Tobit procedure in the selection model and OLS in the productivity model. Columns (2) and (4) use a Tobit procedure in the selection model and a partially linear, Robinson procedure in the productivity model.

33