rafmacalaba/datause-encoder-data huggingface dataset:
# LIST **OF FIGURES**

|1.1:<br>1.2:|Real GNP Per Capita . . . . <br>Change in Real GNP Per Capit|. . . .<br>a . . .|. . . .<br> . . . .|. . . <br> . . .|. . . . <br> . . . .|. . . .<br> . . . .|1<br>1|
|---|---|---|---|---|---|---|---|
|1.3:|Housing Investment and Capit|al Forma|tion . .|. . .|. . . .|. . . .|2|
|2.1:|How Housing Markets Work . .|. . . .|. . . .|. . .|. . . .|. . . .|18|
|2.2:|Nominal Interest Rates . . .|. . . .|. . . .|. . .|. . . .|. . . .|20|
|2.3:|Real Interest Rates . . . .|. . . .|. . . .|. . .|. . . .|. . . .|20|
|2.4:|Measures of Financial Depth|.....|. .. .|. . .|.. . .|. .<br>.|21|
|2.5:|Selected Prices, Logarithmic|Scale .|. . . .|. . .|. . . .|. . . .|29|
|3.1:|Map of Kumasi<br>. . . . . . .|. . . .|. . . .|. . .|. . . .|. . . .|42|
|3.2:|Distribution of Households by|Tenure,|Sector,|House|Type and|Number||
||of Rooms . . . . . . .|. . . .|. . . .|. . .|. . . .|. . . .|44|
|3.3.|Tenure by Sector . . . . . .|. . . .|. . . .|. . .|. . . .|. . . .|45|
|3.4:|Households Witho-.;_<br>Access to|Service|s . . .|. . .|. . . .|. . . .|46|
|3.5:|Monthly Rents<br>. . . . . . .|. . . .|. . . .|. . .|. . . .|. . . .|47|
|3.6:|Consumption by Tenure<br>. . .|. . . .|. . . .|. . .|. . . .|. . . .|49|
|3.7:|Household Size, Selected Yea|rs . . .|. . . .|. . .|. . . .|. . . .|50|
|3.8:|Size Distribution by Sector|. . . .|. . . .|. . .|. . . .|. . . .|50|
|3.9:|Household Size by Tenure . .|. . . .|. . . .|. . .|. . . .|. . . .|52|
|3.10:|Rooms Occupied<br>. . . . . .|. . . .|. . . .|. . .|. . . .|. . . .|53|
|4.1:|Rent Control as Effectiv_ Pr|ice Cont|rol . .|. . .|. . . .|. . . .|57|
|4.2:|Rent Control with Elastic Su|pply . .|. . . .|. . .|. . . .|. . . .|58|
|4.3:|Rent Control as Expenditure|Control|. . . .|. . .|. . . .|. . . .|59|
|4.4:|Rent Control and Consumer's|Surplus|. . . .|. . .|. . . .|. . . .|62|
|5.1:|Effect of Selectivity Bias .|. . . .|. . . .|. . .|. . . .|. . . .|71|
|5.2:|Advance by Length of Tenure|. . . .|. . . .|. . .|. . . .|. . . .|73|
|5.3:|Rents by Sector . . . . . .|. . . .|. . . .|. . .|. . . .|. . . .|75|
|5.4:|Rent to Income Ratios, Cross|Country|Model .|. . .|. . . .|. . . .|78|
|5.5:|Rent-to-Income Comparisons,|Various|Models .|. . .|. . . .|. . . .|81|
|5.6:|Rents and Predictions from t|he Cross|Country|Model|. . . .|. . . .|86|
|5.7:|Cost Benefit Measures by Sec|tor . .|. . . .|. . .|. . . .|. . . .|89|
|5.8:|Changes in Consumers Surplus|for Typ|ical Ten|ants .|. . . .|. . . .|89|
|5.9:|Cost Benefit by Consumption|. . . .|. . . .|. . .|. . . .|. . . .|91|
|5.10:|Cost Benefit by Length of Te|nure . .|. . . .|. . .|. . . .|. . . .|92|
|5.11:|Income Distribution by Tenur|e .....|<br>. . .|. . .|. . . .|..<br>.|93|

xiii
<mark>Rowlands, J., (1995). Empowerment examined.</mark> _<mark>Development in practice</mark>_ <mark>,</mark> _<mark>5</mark>_ <mark>(2), 101-107.</mark> <mark>Samuelson, P. A. (1956). Social indifference curves.</mark> _<mark>The Quarterly Journal of Economics</mark>_ <mark>,</mark> _<mark>70</mark>_ <mark>(1),</mark> <mark>1-22.</mark> <mark>Seymour, G., & Peterman, A. (2018). Context and measurement: an analysis of the relationship between intrahousehold decision making and autonomy.</mark> _<mark>World Development</mark>_ <mark>,</mark> _<mark>111</mark>_ <mark>, 97-112.</mark> <mark>Steinem, G. (2012).</mark> _<mark>Outrageous Acts and Everyday Rebellions</mark>_ <mark>. Open Road Media.</mark> <mark>Story, W. T., & Burgard, S. A. (2012). Couples’ reports of householddecision-making and the utilization of maternal health services in Bangladesh.</mark> _<mark>Social science & medicine</mark>_ <mark>,</mark> _<mark>75</mark>_ <mark>(12), 24032411.</mark> <mark>Ting, H. L., Ao, C. K., & Lin, M. J. (2014). Television on women’s empowerment in India.</mark> _<mark>The Journal of Development Studies</mark>_ <mark>,</mark> _<mark>50</mark>_ <mark>(11), 1523-1537.</mark> <mark>Thomas, K. W., & Tymon, W. G. (1994). Does empowerment always work: Understanding the role of intrinsic motivation and personal interpretation.</mark> _<mark>Journal of Management systems</mark>_ <mark>,</mark> _<mark>6</mark>_ <mark>(2), 1- 13.</mark> 35
# **Food Insecurity and Rising Food Prices: What Do We Learn from Experiential Measures?**

Dean Jolliffe, Ilana Seff and Alejandro de la Fuente* _Keywords_ : Food security, food prices, anxiety, stability, LSMS, Malawi. _JEL Codes:_ Q18, I30, I32, E31

* Jolliffe (djolliffe@worldbank.org) is a Lead Economist in the World Bank’s Development Data Group, Seff is a doctoral candidate at Columbia University in the Population and Family Health Department, de la Fuente is a Senior Economist in the World Bank’s Poverty and Equity Global Practice. Jolliffe is also a Research Fellow with the Institute for the Study of Labor (IZA) in Bonn and a Fellow of the Global Labor Organization (GLO).

_Acknowledgements:_ This paper builds on analysis done for the Malawi Poverty Assessment report (World Bank, 2016). The authors wish to thank Jose Cuesta, Sailesh Tiwari, and Pablo Fajnzylber for comments and guidance. The analysis in this paper is based on the publicly available data from two rounds of Malawi’s Integrated Household Survey. These have received technical and financial assistance from the Living Standards Measurement StudyIntegrated Surveys on Agriculture (LSMS-ISA) project. See <u>http://go.worldbank.org/ZIWEL8UHQ0 for details on LSMS-ISA and links for</u> downloading the files. The findings, interpretations, and conclusions of this paper are those of the authors and should not be attributed to the World Bank Group or its member countries.
Table 7. The Incidence of OECD Nontariff Measures on Major Sub-Saharan African C **untries'** Non-Fuel **Exports**

||Value of|African Exports (|$million)||NTB T|rade Covera|ge Ratios(%|)<br>l|
|---|---|---|---|---|---|---|---|---|
|Exporting Country|OECD|EEC|USA|Japan|OECD|EEC|USA|Japan|
|ALL SUB-SAHARAN AFRICA|16,836.1|12,682.0|1,618.2|1,369.8|18.1|22.2|10.1|23.4|
|Angola|251.0|219.9|26.4|0.8|3.3|3.7|0.0|0.0|
|Botswana|na|na|na|nWI|na|na|na|na|
|Cameroon|908.6|894.4|24.7|18.0|19.6|20.8|0.0|0.0|
|Central African Republic|99.3|94.3|2.6|0.5|5.0|5.1|0.0|0.0|
|<br>Chad|<br>57.2|<br>51.1|<br>0.1|<br>4.1|<br>9.4|<br>10.3|<br>0.0|0.0|
|Congo|237.5|208.7|5.7|17.5|42.6|46.5|0.0|0.0|
|<br>Cote d'Ivoire|2,140.6|1,844.9|200.4|20.0|20.6|21.6|4.4|17.5|
|Ethiopia|312.2|167.4|68.5|57.7|15.9|26.5|0.0|0.0|
|<br>Gabon|714.8|638.5|20.6|30.2|54.9|60.8|0.9|8.9|
|<br>Ghana|<br>806.4|<br>570.7|<br>117.2|<br>77.5|<br>18.8|<br>10.7|<br>0.0|88.9|
|Guinea|443.2|286.0|132.4|1.1|1.1|1.3|0.0|95.2|
|Kenya|728.8|588.1|65.6|16.2|21.7|23.2|0.2|26.7|
|<br>Liberia|1,491.9|791.6|105.9|32.5|8.3|15.5|0.1|0.0|
|Madagascar|282.4|191.9|37.4|42.8|18.3|12.0|7.9|58.8|
|<br>Malawi|223.6|130.3|30.7|37.2|14.8|19.4|13.6|0.0|
|Mali|84.6|67.4|7.2|0.8|10.4|12.8|0.6|0.3|
|Mauritania|402.9|207.0|10.2|185.6|47.9|4.5|0.0|98.8|
|<br>Mauritius|<br>898.7|<br>702.6|<br>157.4|<br>2.7|<br>64.6|<br>61.9|<br>80.3|58.5|
|Niger|300.0|282.9|4.7|-|4.5|.I|6.4||
|<br>Nigeria|693.6|583.4|88.6|5.1|22.8|25.1|4.1|88.9|
|<br>Senegal|471.1|445.7|3.3|18.6|30.3|29.3|0.6|57.9|
|Sierra Leone|200.7|137.4|52.6|-|0.8|0.3|0.0||
|Sudan|322.9|232.9|19.3|59.4|18.9|24.6|0.0|2.3|
|Swaziland|na|na|na|na|na|na|na|na|
|Tanzania|276.8|201.5|33.6|25.0|11.0|11.5|0.0|18.7|
|Togo|191.1|142.7|1.8|1.4|7.0|7.3|3.8|0.1|
|<br>Uganda|295.1|235.1|39.8|10.0|7.6|7.1|0.0|0.0|
|<br>Zaire|1351.5|1077.1|182.8|72.0|2.5|3.1|0.0|0.1|
|<br>Zambia|,<br>774.7|,<br>336.1|<br>23.9|<br>391.2|<br>1.3|<br>1.9|<br>0.0|0.1|
|Zimbabwe|981.6|656.2|118.3|155.5|24.6|33.2|8.5|4.6|
|**MEMO ITEM**|||||||||
|All Developing Countries|247,734.9|145,254.1|63,406.3|26,702 0|21.8|43.6|21.7|22.3|

Source: World Bank-UNCTAD SMART Data Base. The Sub-Saharan Africa totals include several counhies not listed in the table.
The solution for _z_ is an obvious candidate for a poverty line; people with income above _z_ tend to feel that their income is adequate, while those below _z_ tend to feel that it is not. We can call _z_ the <u>social subjective poverty line</u> (SSPL).

The specification of the _x_ vector raises a difficult but poorly understood issue. Should this include all the observable covariates of subjective welfare, or only those things that one would deem relevant to poverty lines on _a priori_ grounds? Consider, for example, the common finding in the literature for developed economies that unemployment reduces subjective welfare at a given level of income.<sup>37</sup> If one included this variable in the vector _x_ then one would probably conclude that the unemployed should have a higher poverty line than the employed, _ceteris paribus_ . Yet most other approaches to setting a poverty line would probably argue that, if anything, the unemployed need less income to attain the same level of welfare. In the end, one cannot escape the need for normative judgments about what should be considered in a measure of poverty, though once the set of relevant variables is decided, subjective welfare data can guide how those variables are weighted.

A further issue is that the income concept respondents to the MIQ have in mind need not correspond to that used by the analyst in estimating the SSPL. This will bias the SSPL, though there are correctives (Kapteyn et al., 1988; Tummers, 1994). In applying the MIQ in many developing countries, one will also find that “income” is not a well-defined concept, particularly (but not only) in rural areas. It is not at all clear whether one could get sensible answers to the MIQ. This suggests that the second approach to deriving a SSPL might be more relevant. Pradhan and Ravallion (2000) propose a method for estimating the SSPL based on qualitative data on consumption adequacy, as given by responses to appropriate survey questions. Instead of asking respondents what the precise minimum consumption is that they need, one simply asks whether their current consumptions are adequate. This provides a multidimensional extension to the one-dimensional MIQ. The SSPL is the level of total spending above which respondents say (on average) that their expenditures are adequate for their needs. For empirical implementation, the probability that a sampled household will respond that its actual consumption of each type of commodity is adequate can be modeled as a probit regression. Under certain technical

> 37 Examples include Clark and Oswald (1994), Theodossiou (1998), Winkelmann and Winkelmann (1998) and Ravallion and Lokshin (2001).  Note that this is not what the simplest economic models of work-leisure choice would suggest. There is clearly an independent disutility of unemployment that counteracts the gain in leisure, probably associated with the quantity constraints on choice that involuntary unemployment entails.

22
**42 |** P a g e

- Panagariya, Arvind and Dani Rodrik. 1993. “Political Economy Arguments for a Uniform Tariff.” _International Economic Review_ (34) 3:685-703.

- Pursell, Garry. 2011a. “Trade Policies in South Asia,” in Raghbendra Jha, ed., _Handbook of South Asian Economics_ , Routledge, London (forthcoming in early 2011).

- Pursell, Garry. 2011b. “Para-Tariffs and Sri Lanka’s New Protectionism,” _Economic & Political Weekly EPW_ , June 18, 2011, vol xlvi no 25, p. 31.

- Pursell, Garry and F.M. Ziaul Ahsan. 2011. “Sri Lanka’s Trade Policies: Back to Protectionism.” ASARC Working Paper 2011/03, Australia South Asia Research Centre, The Australian National University.

- Rodrik, Dani. 2007. _One Economics Many Recipes: Globalization, Institutions, and Economic Growth_ , Princeton University Press, Princeton and Oxford

- Sala-I-Martin, X., J. Blanke, M. Drzeniek Hanouz, Th. Geiger, I. Mia, and F. Paula. 2009. Chapter 1.1. in _Global Competitiveness Report, 2008-09_ , The World Economic Forum, Geneva.

- Singh, Anoop. 2001. “Asia is moving into the leadership role in the world economy,” _Finance & Development_ No. 4, June.

- [WEF 2011] _The Global Competitiveness Report, 2010-11_ . The World Economic Forum, Geneva 2010

- [WEF 2009]: _The Global Competitiveness Report 2008-09_ .  The World Economic Forum, Geneva 2009

- Winters, L Alan. 1997. ‘The Economics of “Catching up” Revisited’, _The Vienna Institute for Comparative Economic Studies_ , Reprint Series, No. 168, June.

- World Bank 2004: Vol II, Chapter 4. Trade Policies in South Asia: An Overview, Report No. 29949, in three volumes, Washington DC, September.

- World Bank 1993: _The East Asian Miracle, Economic Growth and Public Policies_ , World Bank, Washington DC 1993.

- [WTO 2011]: _Trade Policy Review: Sri Lanka_ , WT/TPR/S/237 29, Report by the Secretariat, World Trade Organization, Geneva, 29 September.

- [WTO 2004] _Trade Policy Review Sri Lanka_ WT/TPR/S/128, World Trade Organization, Geneva, 4 February.
||**T**<br>|**able A.2: Sum**|**mary statistics**<br>|||
|---|---|---|---|---|---|
|Variables|Obs|Mean|Std. Dev.|Min|Max|
|Headcount poverty|630|0.249|0.252|0.000|0.895|
|Poverty gap index|630|0.059|0.063|0.000|0.345|
|Poverty severity index|630|0.024|0.030|0.000|0.188|
|Log of per capita expenditure|630|8.724|0.563|7.411|10.148|
|Gini index|630|0.331|0.040|0.257|0.475|
|Log of state spending|630|15.085|1.761|0.000|18.180|
|Log of investment spending|630|13.927|1.130|8.397|17.275|
|Log of population density|630|-1.261|0.999|-3.413|1.482|
|Share of urban population|630|25.337|16.499|5.023|87.511|
|Share of population with high-school diploma|630|0.072|0.036|0.006|0.237|
|Share of ethnic minority population|630|20.851|27.157|0.000|97.066|
|Share of wage income|630|0.369|0.107|0.145|0.694|
|Share of non-farm income|630|0.207|0.061|0.045|0.559|

37
secondly the growth of foreign knowledge produces two contrasting effects on the accumulation of domestic knowledge, a positive one insofar as it favors the expansion of the knowledge intensive sectors through knowledge diffusion, a negative one insofar as it constraints such expansion through technological competition, i.e. we come once again across the fact that domestic and foreign technology are both complements and substitutes.

From (16.2) we can derive the conditions for _pT_ > 0, and PT = A Assuming that the denominator of (16.2) is positive27 a positive rate of growth of domestic knowledge requires.

P<sup>3</sup> AF+(P9 _/la6_ **_+ 13)AT>0 6 (01 4_** _+_ **_38 )XT_** The terms on the l.h.s. are the domestic and foreign factors affecting domestic knowledge accumulation (the foreign factors represent the complementarity of foreign and domestic knowledge), the terms on the r.h.s. represent the depressing effect of foreign knowledge accumulation (the substitution of foreign for domestic knowledge). Domestic and foreign knowledge will grow at the same rate (which, from (13)-(15) is a condition for global balanced growth) if {1 -[1PIO(P<sup>J</sup> 4 6<sup>-113)</sup> **_9]Ia6_** _+_ P6I14 | .I 3 }XT =|XF (18) The domestic effort on knowledge accumulation and the _net_ foreign 28 knowledge accumulation must grow at the same rate . In other words, if we assume (some form of) international knowledge diffusion, the condition for a uniform rate of growth of knowledge in all countries is not sufficient for assuring balanced growth.

We now turn to the aggregate trade share growth rates. The steady state growth rate of the aggregate export share implies _(PA +_ **_XA)_** _=(PB +_ kB) = _(PC + AC)_ = _(PD +_ **_XD)_** (19) If PA = 0 from (12) balanced growth implies that all sectoral shares grow at **_AA =_** _AB_ = AC = **_XD_** and hence _Px_ = 0. If we allow for unbalanced growth the rate of growth of the export share can be non zero and it is as follows _PX=(PA +XA)(X/XA)+(PB +AB)(X/XB)_ _+(PC +XC)(XIXC)+(PD +XD)(X/XD)_ **_-AW_** (20)

> 27 In principal nothing can be said about the values of the parameters included in the denominator. Note however that if a 6 is very small with respect to 10 the denominator could be negative and, other things equal, the rate of growth of domestic knowledge could become negative. This would be the consequence of an excessively slow domestic knowledge accumulation and an excessive dependence on imports of high tech goods.

> _28 Net_ foreign knowledge can be defined as the total stock of foreign knowledge less the spillover effects through imports and direct diffusion.

16
Tanzania was one such country, instituting a crisis-induced export ban on maize (FAO, 2009) that has only recently been lifted. The World Bank (2009) makes the case that, in addition to limiting crossborder trade<sup>5</sup> , it also has other impacts – in particular reducing producer prices and reducing potential output. This stands in sharp contrast to the underlying, sizable production potential in Tanzania, and the potential to supply neighboring countries that may experience structural food deficits (World Bank, 2009). Therefore, it can be argued that the export ban has not only lowered exports, but has also led to slower agricultural growth, and lost opportunities for farmers and consumers.

Due to the potentially important role that trade policy can play in mitigating the effects of climate on production, the interaction of international trade with agricultural production shocks arising from climate change has been explored in the literature (e.g. Tobey et al., 1992; Reilly et al., 1994; Tsigas et al., 1997; Randhir and Hertel, 2000). However, the aforementioned studies focus only on decadal scale, mean climate change and do not consider the impact of changing climate volatility and the incidence and intensity of extreme events. Also, the scope of many of these studies is constrained by data limitations that prohibited analysis of individual African countries. Reimer and Li’s (2009) more recent examination of cereal and oilseeds trade finds that world trade volumes will need to increase if yield variability increases. However, this analysis is unable to examine the complex interaction of factor incomes and prices in determining how household level welfare – especially of poor households – may vary, as has been shown to be the case in the presence of international price shocks following trade liberalization (e.g. Hertel et al., 2004; Hertel and Winters, 2006; Hertel et al., 2009) and in recent analyses of climate change and poverty (Ahmed et al., 2009, 2011).

Tanzania is a country where grain production variability may increase due to changing climate volatility (Ahmed et al., 2011). In this paper, we examine the sensitivity of prices and factor incomes in Tanzania due to climate volatility. Of particular interest are the potential inter-annual trading opportunities created by heterogeneous climate shocks, as well as the potential for trade to modulate the effects of climate-induced shocks on Tanzanian poverty.

In order to examine these potential trade effects, we first estimate the historical covariance of maize productivity shocks in Tanzania and her key trading partners. We then use an augmented version of the GTAP global trade model and associated database to quantitatively examine the interaction between trade policy and the climate-induced maize production volatility, with emphasis on the

> 5 Data from RATIN (2009) indicate that there is substantial informal cross-border trade.

3
# **Abstract**

Since the revision of the Japanese immigration law in 1990, there has been a dramatic influx of Latin Americans, mostly Brazilians, of Japanese origin ( _Nikkeijin_ ) working in Japan. This is mainly because the revision has basically allowed _Nikkeijin_ to enter Japan legally even as unskilled workers, while the Japanese law, in principle, prohibits foreigners from taking unskilled jobs in the country. In response, the number of these Latin American migrants has increased from practically zero to more than 250,000. The migration of _Nikkeijin_ is likely to have a significant impact on both the Brazilian and the Japanese economies, given the substantial amount of remittances they send to Brazil. The impact is likely to be felt especially in the _Nikkeijin_ community in Brazil.

In spite of their importance, the detailed characteristics of _Nikkei_ migrants and the prospect for future migration and remittances are under-researched. The major purpose of this paper is therefore to provide a more comprehensive account of the migration of _Nikkeijin_ workers to Japan. The paper contains a brief review of the history of Japanese emigration to Latin America (mostly Brazil), a study of the characteristics of _Nikkeijin_ workers in Japan and their current living conditions, and a discussion on trends and issues regarding immigration in Japan and migration policy. The final part of the paper briefly notes the limitation of existing studies and describes the _Brazil Nikkei Household Survey_ , which is being conducted by the World Bank Research Group at the time of writing this paper. The availability of the survey data will certainly contribute to a better understanding of the Japan-Brazil migration and remittance corridor.

2
|House is made of<br>sticks and mud|2013|0.061<br>(0.679)|-0.056<br>(0.799)|-0.080<br>(0.870)|**-0.439**<sup>******</sup><br>**(0.867)**|
|---|---|---|---|---|---|
|Households<br>size<br>(adult equivalent)|2013||**-0.037**<sup>*******</sup><br>**(0.041)**<br>|**-0.039**<sup>*******</sup><br>**(0.045)**<br>|-0.003<br>(0.056)<br>|
|Respondent<br>is<br>Oromo<br>ethnic<br>group (0/1)|2013||**-0.33**<sup>*******</sup><br>**(0.588)**|**-0.374**<sup>*******</sup><br>**(0.605)**|**-0.19**<sup>*******</sup><br>**(0.663)**|
|Respondent<br>is<br>Amhara<br>ethnic<br>group (0/1)|2013||**-0.305**<sup>*******</sup><br>**(0.491)**|**-0.314**<sup>*******</sup><br>**(0.614)**|**-0.106**<sup>*******</sup><br>**(0.832)**|
|Respondent used a<br>bank in the past<br>year(0/1)|2013||-0.057<br>(0.306)|-0.073<br>(0.327)|**-0.046**<sup>******</sup><br>**(0.701)**|
|Respondent<br>is<br>member of_equb_<br>savingsgroup (0/1)|2013||0.000009<br>(0.262)|0.035<br>(0.331)|0.041<br>(0.325)|
|Age of respondent<br>(years)|2013||-0.001<br>(0.009)|-0.001<br>(0.009)|0.0002<br>(0.020)|
|Respondent is at<br>least literate(0/1)|2013||-0.0.38<br>(0.200)|-0.058<br>(0.198)|-0.007<br>(0.363)|
|Respondent is a<br>man(0/1)|2013||0.042<br>(0.445)|0.090<br>(0.461)|0.003<br>(0.497)|
|Ethiopian<br>Orthodox(0/1)|2013||-0.129<br>(0.310)|**-0.221**<sup>******</sup><br>**(0.421)**|-0.006<br>(0.453)|
|Muslim (0/1)|2013||0.263<br>(0.612)|0.237<br>(0.584)|**0.406**<sup>*******</sup><br>**(0.631)**|
|Uses dung for fuel<br>(0/1)<sup>13</sup>|2016||**0.179**<sup>*****</sup><br>**(0.370)**|**0.152**<sup>*****</sup><br>**(0.355)**|0.032<br>(0.361)|
|Uses<br>agricultural<br>waste for fuel(0/1)|2016||0.014<br>(0.294)|0.046<br>(0.316)|**0.066*****<br>**(0.312)**|
|Two-way<br>walking<br>distance<br>to<br>all-<br>weather<br>road<br>(mins)|2013|||**0.0009**<sup>*****</sup><br>**(0.002)**|**0.0004**<sup>*******</sup><br>**(0.003)**|
|Average times per<br>day those over 10<br>years eat<sup>14</sup>|2016|||-0.124<br>(0.357)|-0.020<br>(0.371)|
|Average times per<br>day cooking occurs|2016|||0.018<br>(0.138)|-0.008<br>(0.195)|
|Average times per<br>week injera baking<br>occurs|2016|||**-0.011**<sup>*****</sup><br>**(0.024)**|**-0.004**<sup>*****</sup><br>**(0.038)**|

> 13 Fuelwood is not included, as virtually 100% of households use fuelwood.

> 14 The frequencies at which younger people eat are available, but not reported, because 25% of the observations have no data.

16
Sources of Income Inequality in Rural Pakistan: A Decomposition Analysis

# by Richard H. Adams. Jr. International Food Policy Research Institute

# and

Harold Alderman World Bank (AGRAP)

# Table of Contents

|1.|The Decomposition of Income Inequality|3|
|---|---|---|
|2.|Data Set|7|
|3.|Income Inequality in Rural Pakistan, 1986-89|9|
|4.|Sources of Agricultural Income Inequality in Rural Pakistan|13|
|5 .|Conclusion|17|
|Notes||2 0|
|Refere|nces|2 3|
|Tables||2 6|