rafmacalaba/datause-encoder-data huggingface dataset:
AL, although the unconditional relationship in Figure 1 partly reflects demographic composition.

The finding that TEE is falling in household size is notable given the observation by (Deaton and Paxson 1998) that for several developing countries, measured food expenditure per capita declines in household size. A similar pattern holds for measured caloric intake. (Deaton and Paxson 1998) argue that this is a “puzzle” in the sense that larger households with a given demographic composition should benefit from economies of scale that increase their per capita consumption of relatively private/rival goods like food. They – suggest that “caloric overheads” energy expenditures that only need to be incurred by one household member on behalf of the entire household – could be one possible explanation. The TUS data is rich enough to capture differences in time spent on domestic chores, travel, etc. that would be hard to detect in most household surveys.

To examine this further and to also control for demographic composition, Table 2 estimates a log-log regression specification similar to the one used by (Deaton and Paxson 1998). The dependent variable is log household average TEE, REE or AL. Detailed demographic controls for age/sex ratios, log household size, log per capita expenditures, and mean years of adult education are included. When including all household sizes (1-14), there is close to a zero partial elasticity of TEE to household size, resulting from a perfect offset of REE and AL. Larger households economize on energy, but their members are also physically “larger” on average. When restricting to household sizes between 2 and 8, which make up 92% of the sample, there is a significant negative partial elasticity of about 0.017, due to a negative effect on AL that is much larger than the positive effect on REE. Both expenditure and education reduce _TEE_ holding constant demographics and appear to have independent effects.

Columns 7 and 8 of Table 2 estimate the same specification but replace TEE with caloric intake estimated for the 1993-94 NSS 50th round Schedule 1 (consumption survey) in the same states (see Appendix C). The coefficient is similar in magnitude (-0.029 using households sized 2 to 8) suggesting that scale economies in energy expenditures can potentially explain much of the apparent decline in caloric intake with household size.<sup>5</sup> Note that the

> 5The decline is “apparent” because (Gibson 2002) and (Gibson and Kim 2007) show that much of the puzzling decline first documented in (Deaton and Paxson 1998) is reduced when using survey instruments less subject to recall errors, implying that a substantial part of the measured decline in most household surveys is simply measurement error that

9
Table 9. Variability and growth rates for export volumes, values, and unit values, and domestic production of commodity exports for Zimbabwe.,

|||196|141973|||197|4-1987||
|---|---|---|---|---|---|---|---|---|
||Volume|Value|Unit<br>Value|Production|I<br>Volume|<br> <br>Value|IUnit<br>Value|Production|
|Coffee|76<br>(25.4)|83<br>(28.3)|31<br>(6.2)|79<br>(31.9)|22<br>(12.4)|30<br>(7.5)|31<br>(4.2)|17<br>(11.1)|
|Cotton|48<br>(26.2)|50<br>(26.1)|19<br>(1.6)|43<br>(22.9)|12<br>(5.6)|18<br>_(5.0)_|_15_<br>(-4.4)|24<br>(4.7)|
|Tea|49<br>'A1.3)|45<br>(5.4)|12<br>(4.6)|12<br>(12.7)|_1S_<br>(8.1)|35<br>(3.3)|25<br>_(-5.0)_|11<br>(7.9)|
|Tobacco|46<br>_(-5.5)_|_45_<br>(-8.4)|_15_<br>(-2.5)|27<br>(-4.5)|18<br>(3.3)|19<br>(2.6)|13<br>(-O. 1)|16<br>(2.7)|
|Sugar|50<br>(7.7)|47<br>(7.0)|20<br>(-3.0)|22<br>(7.6)|14<br>(4.4)|45<br>(-4.3)|50<br>(-8.3)|10<br>(4.2)|
|Maize|54<br>_(5.8)_|60<br>(6.1)|43<br>(-2.8)|34<br>_(6.5)_|58<br>_(-5.1)_|_51_<br>(-8.7)|95<br>(2.8)|37<br>(-0.3)|
|Beef<br>.__________|10<br> <br>(9.7)<br>|14<br>(9.6)<br>|10<br>(6.9)|2<br>(1.2)|_50_<br>(-12.5)|40<br>(-11.1)|26<br>(2.8)|13<br>(4.5)|
|Total Exports|<br> <br>=1|25<br> <br>(0.3)||-||10<br>(-1.3)|||

8CVs for instability and average annual percentage growth (in parentheses).

30
27 (Gainesville, FL: lJniversity Press of Florida, 1997), p. 89.

° The 1991 F'ersian Gulf crisis did much to reduce Egypt's exlernal debt. In May 1991 a Paris Club agreement agreed to write off about US $28 billion of Egypt's civilian and military debt. This debt relief was linked to the successful Egyptian completion of a IMF package of economic reforms.

1' Interview with Dr. Hlamed Latif el-Sayeh, New York Times, February 26,1977, p. 7.

12 There are three basic types of bread produced in Egypt: a coarse brown loaf called baladi (82 percent extraction rate); a whiter loaf called shami (76 percent extractiDn rate); and a longer loaf resembling French bread, called fino (72 percent extraction). At present, the only type of bread which is subsidized in Egypt is the coarse, baladi bread.

13 In 1980 the full list of subsidized food items included: breal (baladi, shami and fino), flour, sugar, rice, tea, edible oil., beans, lentils, macaroni, coffee, sesame, shortening, imported cheese, frozen meat, fish, eggs and chicken.

14 Ali and Adarns, (note (3) above), Table 2.

15 1 feddan eqiuals 1.038 acres.

16 Harold Alcderman and Joachim von Braun, The Effects of the Egyptian Food Ration and Subsidy System on Income Distribution and Consumption, Research Report 4f; (Washington, DC: International Food Policy Research Institiute, 1984), Table 14.

17 It has been estimated that 80 percent of the wheat flour consumed in Egypt comes from subsidized baladi wheat flour . See Adel Mostafa, "Leakage in the Food Subsidy System" (draft report prepared for Food Security Research Project in Egypt, Cairo, January 1997, p. 28).

18 Ali and Adtams, (note (3) above).

19The 1990/91 HI1ES Survey included 14,232 households; 8,352 urban and 5,880 rural households. 20 For more details on the 1997 IFPRI Egypt Integrated Household Survey, see Guarav Datt, Dean Joliffe and Manohar Sharma, "A Profile of Poverty in Egypt: 1997" (final draft report, International Food Policy Research Institute, Washington, DC, March 31, 1998).

21 In this paper, expenditures are used as a proxy for income. For this reason, the terms "expenditures"
# **_Why do absolute lines vary systematically with the mean?_**

It is obvious why we see the relativist gradient in poverty lines amongst developed countries, given that they use lines that have this feature built in. Possibly more surprising is that absolute lines also vary this way amongst developing countries. The range is from $0.63 to $9.06 per day and the positive gradient is quite pronounced amongst middle-income developing countries, even though they are all using absolute poverty lines.<sup>15</sup> Why might we see this marked relativist gradient in absolute poverty lines amongst all but the poorest fifth of countries? In the benchmark definition (section 2), average poverty lines consistent with a common utility function defined on “own consumption” will only vary with relative prices and household characteristics. While these factors may play some role, they do not seem likely to be the main reason for the relativist gradient in Figure 2.<sup>16</sup> A more obvious route to explaining these differences is to recognize that all national poverty lines must be considered socially relevant in the specific country. If a proposed poverty line is widely seen as too frugal by the standards of society then it will surely be rejected. Nor will a line that is too generous be easily accepted. This point has long been recognized. For example, Scitovsky (1978, p.116) noted that, amongst developed countries in the 1960s, richer countries tended to have higher poverty lines and he explained this as follows: “...in the advanced countries, the poverty norm has long ago ceased to reflect a physiological minimum necessary for survival and has become instead a ‘minimum social standard of decency,’ the life-style that a particular society considers the minimum qualification for membership.” The new insight here is that Scitovsky’s observation does not only apply to the “advanced countries” today, but in fact applies to all except the poorest countries. As is clear from the preceding discussion of the main methods used to set absolute lines, there are many free parameters that can be brought into the analysis to influence the line obtained. The stipulated food-energy requirements are similar across countries, but the food bundles that yield a given nutritional intake can vary enormously (such as in the share of calories from coarse starchy staples rather than more processed foodgrains, and the share from meat and fish). The nonfood components also vary, either explicitly or implicitly (through shifts in the food demand

> 15 The gradient of national lines is still evident if one focuses solely on CBN poverty lines for developing countries (as can be verified from the data in Ravallion et al., 2009).

> 16 Note that differences across countries in the relative prices of traded versus non-traded goods are already embodied in the PPP exchange rates used to convert poverty lines to a common currency in Figure 1.

12
_tributed according to P_ 0 _, with P_ 0 _a particular element of P ._ Therefore, for a given GMM estimator that satisfies Equation 36, there is an equivalent optimal ’well-behaved’ MLE estimator associated. Building on Back and Brown (1992), the equivalent MLE must satisfy the following conditions

- (i) The probability limit of Θ _M_ say _L_ ( _P_ ) exists and satisfies

where _x_ is the vector of exogenous variables in the system of equations 32, and the function _e_ () is the linear representation of _et_ as a function of the dependent and exogenous variables.

- (ii) There exits a constant _κ_ ( _P_ ) such that

where _E_ the expectation term with respect to _P_ , and _{_ Θ<sup> ̃</sup> _M }_ a sequence of MLE estimators.

- (iii) Defining the estimated covariance Σ _M_ , the limit Σ = lim _M −→∞_ Σ _M_ exists. Moreover,

Notice that for short samples (e.g. very small _N_ and _T_ ) the three conditions aforementioned are hardly simultaneously satisfied. In this case, neither the QMLE nor GMM estimators lead to consistent outcomes.

20
Table 3 Fraction of Land Allocated to Cotton 1997 - 2002

|Province|1997|1998|1999|2000|2001|2002|
|---|---|---|---|---|---|---|
|Total|9.2|9.3|8.1|4.3|7.6|9.9|
|Central|12.1|10.7|6.7|3.5|6.3|8.5|
|Eastern|12.4|13.0|12.3|7.2|11.9|14.6|
|Southern|4.1|4.2|3.7|1.3|3.2|5.1|
|Lusaka|2.4|1.4|1.7|0.1|1.9|3.3|

Note: Own calculations based on the Post Harvest Surveys 1997-2002.

||Yields|Ta<br> per He<br>1997|ble 4<br>ctare in<br> - 2002|Cotto|n||
|---|---|---|---|---|---|---|
|Province|1997|1998|1999|2000|2001|2002|
|Total|6.18|6.53|6.38|6.21|6.44|6.39|
|Central|6.33|6.67|6.72|7.04|6.98|6.73|
|Eastern|6.14|6.45|6.28|6.07|6.32|6.32|
|Southern|6.09|6.65|6.40|5.56|6.57|6.23|
|Lusaka|6.00|6.40|6.43|6.40|5.51|6.57|

Note: Own calculations based on the Post Harvest Surveys 1997-2002.

Table 5

|Percen|tage of|Househ<br>1997|olds th<br> - 2002|at Grow|Maiz|e|
|---|---|---|---|---|---|---|
|Province|1997|1998|1999|2000|2001|2002|
|Central|90.2|92.2|93.5|94.3|94.0|93.2|
|Eastern|99.9|99.5|99.2|99.5|99.7|99.6|
|Southern|93.5|92.0|94.4|96.3|97.3|97.6|
|Lusaka|100.0|99.5|98.9|100.0|98.9|99.4|
|Copperbelt|96.7|94.3|90.9|93.5|93.5|93.6|
|Luapula|28.5|24.3|31.6|35.1|31.8|41.1|
|Northern|45.1|35.9|48.8|46.9|46.9|59.3|
|NWestern|75.7|65.9|71.9|66.7|80.7|77.7|
|Western|89.9|82.2|88.5|82.6|90.1|87.2|
|Total|76.2|72.1|76.1|76.2|77.7|80.7|

Note: Own calculations based on the Post Harvest Surveys 1997-2002.

29
Furthermore, our survey allows us to calculate the implicit cost of trade credit offered (AR_cost) using information on the discount offered for cash payment and the number of days before the supplier imposes penalties. Table 12 provides summary statistics on this rate and on the interest rate charged by banks ( _Bank_cost)_ . The evidence is intriguing for several reasons. We find that 10% of firms in our sample receive an implicit rate lower than 4% and 25% of firms receive an implicit rate lower than 10%; the median implicit rate in our sample is 27%. Next, we examine the spread between the cost of bank financing and trade credit, accounting for annual fees that banks charge borrowers ( _Diff_AR_Bank_fee_ ). We find that for over 20% of firms in our sample trade credit is indeed cheaper than bank loan. In addition, for over 40% of firms in our sample, the difference between the two rates is below 10 percentage points. Although we are unable to precisely measure the cost of trade credit, this evidence suggests that large firms might use trade credit as a relatively cheap source of working capital financing.

A second important issue is that our analysis does not address the _causality_ issue between payables and receivable, i.e. which of the two decisions – the offer of trade credit to customers or the demand of trade credit from suppliers – do firms make first? We take a first step in addressing this issue by estimating a simultaneous-equation-model where we let accounts receivable ( _AR_d_ and _AR_per_ ) depend on accounts payable ( _AP_d_ and _AP_Per_ ) and vice versa (after controlling for the usual firm characteristics, sector and city heterogeneity).<sup>18</sup> The results show that accounts payable depend significantly and positively on accounts receivables, while accounts receivable are significantly related only to the degree of competition in the product market – but not on accounts payable (not shown). This would suggest that firms _first_ decide to offer customers delayed

> 18 We use as identification condition the market structure of the input and output markets.

30
where _pijT_ denotes the net price obtained for a quintal of paddy by household _i_ living in village _j_ in period _T_ . Model (5) is estimated by OLS with robust standard errors and clustering on villages. The results are set out in Table 4. Note that, apart from the trip variables, the regressors comprising _Xij_ and _Yij_ are identical.

Table 4: Net price obtained for paddy, _kharif_ 2009

|Variable|Coef.|Std. Err.|_t_|_P > |t|_|[ 95% Conf|. Interv.]|
|---|---|---|---|---|---|---|
|1_/QijT_|_−_50.83|15.26|_−_3.33|0.002|_−_82.04|_−_19.63|
|ownhold (ha.)|0.28|1.00|0.28|0.783|_−_1.76|2.32|
|sc|_−_2.47|5.22|_−_0.47|0.640|_−_13.16|8.21|
|obc|12.54|6.87|1.82|0.078|_−_1.52|26.59|
|totarea (ha.)|0.07|0.04|1.93|0.063|_−_0.00|0.15|
|popdens|1.99|3.27|0.61|0.547|_−_4.70|8.69|
|block2|21.14|12.75|1.66|0.108|_−_4.94|47.21|
|block3|3.68|16.28|0.230|0.823|_−_29.62|36.98|
|block4|42.01|13.82|3.04|0.005|13.75|70.27|
|block5|20.44|12.32|1.66|0.108|_−_4.77|45.64|
|Vd0 (km.)|_−_5.20|1.58|_−_3.30|0.003|_−_8.42|_−_1.96|
|Vd1 (km.)|2.75|1.22|2.26|0.032|0.26|5.24|
|Vd2 (km.)|3.19|1.00|3.19|0.003|1.14|5.24|
|Vd2c3*Vd2|7.85|4.10|1.91|0.066|_−_0.54|16.24|
|Vd3 (km.)|_−_0.79|0.55|_−_1.44|0.162|_−_1.91|0.33|
|Vd3c3*Vd3|_−_10.12|1.47|_−_6.87|0.000|_−_13.14|_−_7.11|
|const.|821.28|15.66|52.44|0.000|789.25|852.31|

_n_ = 136; 30 groups. _R_<sup>2</sup> = 0 _._ 730. Root MSE = 23.19. Vd0: trip segment on a track or _kutcha_ road; Vd1, Vd2 and Vd3 analogously defined for segments of village all-weather road, district road and state/national highways, respectively; Vd2c3 is a dummy for a Vd2 segment in deplorable condition; Vd3c3 analogously defined.

Recall that the market for paddy is strongly influenced by public policy, notably in the form of the support price. This is duly reflected in the constant term: Rs. 821 per quintal, with a tight 95 percent confidence interval. With the price so tethered, we turn to the specific influences thereon. The net price rose with the quantity marketed, and the coefficient is statistically highly significant: all else being equal, a farmer who sold 10 quintals obtained Rs. 45.75 (= 0 _._ 9 _×_ 50 _._ 83) more per quintal than one who sold but a single quintal. Households with bigger holdings did receive slightly higher prices, but the effect is not remotely statistically significant, controlling for the quantity marketed. The null that caste had no influence cannot quite be rejected at the 5 percent level. Farmers who lived in large and populous villages also enjoyed higher prices: every additional 100 hectares yielded an extra Rs. 7.5 per quintal, though the effect 13
# **Measuring Women’s Agency**

Aletheia Donald, Gayatri Koolwal, Jeannie Annan, Kathryn Falb, and Markus Goldstein* Keywords: gender, households, survey methodology, measurement error JEL codes : C8, D13, J16, I32

*Donald: World Bank, adonald@worldbank.org; Koolwal: Consultant, World Bank, koolwalg@gmail.com; Annan: International Rescue Committee, <u>jeannie.annan@rescue.org; Falb: International Rescue Committee, kathryn.falb@rescue.org; Goldstein: World</u> Bank, <u>mgoldstein@worldbank.org. The authors would like to thank Cheryl Doss, Anna Kasimatis, Patti Kristjanson and Greg</u> Seymour, as well as participants of the 2016 Annual Bank Conference on Development Economics, for valuable comments on an earlier draft. We are grateful to UN Women and the World Bank Group’s Umbrella Facility for Gender Equality for financial support. This paper is part of a larger project on methodological experimentation to improve the measurement of time use, women’s agency, and ownership and control of assets: three key constructs in women’s empowerment, known both for their centrality in the current policy debate on gender equality and for the challenges posed by their measurement. This research aims to achieve three goals: (1) shed light on the relative quality of the existing methods of measuring these constructs; (2) design and test new ideas to measure these constructs; and (3) generate evidence on which measurement method is most appropriate given the policy and research question at hand.
Table 2: Renegotiations by type of initiator and year

||**1989 **|**1990 **|**1991 **|**1992 **|**1993 **|**1994 **|**1995 **|**1996 **|**1997 **|**1998 **|**1999 **|**2000 **|**Total**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Argentina**||||||||||||||
|outstanding concessions|1|15|4|3|10|14|23|22|19|11|10|10||
|Number of renegotiations|0|12|2|1|0|0|1|3|11|3|0|0|33|
|Firm-led reneg.|0|12|2|1|0|0|1|3|10|3|0|0|32|
|Govt-led reneg.|0|0|0|0|0|0|0|0|0|0|0|0|0|
|Joint-led reneg.|0|0|0|0|0|0|0|0|1|0|0|0|1|
|**Brazil**||||||||||||||
|outstanding concessions||||||1|6|20|36|59|54|30||
|Number of renegotiations||||||0|0|0|1|5|24|6|36|
|Firm-led reneg.||||||0|0|0|0|0|0|0|0|
|Govt-led reneg.||||||0|0|0|1|5|24|6|36|
|Joint-led reneg.||||||0|0|0|0|0|0|0|0|
|<br>**Chile**||||||||||||||
|outstanding concessions||||1|1|3|6|9|16|19|20|24||
|Number of renegotiations||||0|0|0|0|0|0|1|0|0|1|
|Firm-led reneg.||||0|0|0|0|0|0|1|0|0|1|
|Govt-led reneg.||||0|0|0|0|0|0|0|0|0|0|
|Joint-led reneg.||||0|0|0|0|0|0|0|0|0|0|
|**Colombia**||||||||||||||
|outstanding concessions||||1|6|18|22|29|37|42|45|43||
|Number of renegotiations||||0|0|0|1|1|0|0|3|14|19|
|Firm-led reneg.||||0|0|0|0|0|0|0|1|14|15|
|Govt-led reneg.||||0|0|0|0|0|0|0|1|0|1|
|Joint-led reneg.||||0|0|0|1|1|0|0|1|0|3|
|**Mexico**||||||||||||||
|outstanding concessions|9|23|34|45|61|67|66|52|48|56|58|58||
|Number of renegotiations|0|1|1|8|12|14|21|11|3|2|0|0|73|
|Firm-led reneg.|0|0|0|1|0|1|2|0|1|0|0|0|5|
|<br>Govt-led reneg.|0|0|0|0|10|13|19|11|2|2|0|0|57|
|Joint-led reneg.|0|1|1|7|2|0|0|0|0|0|0|0|11|
|**All countries**||||||||||||||
|outstanding concessions|10|38|38|50|78|103|123|132|156|187|187|165|0|
|<br>Number of renegotiations|0|13|3|9|12|14|23|15|15|11|27|20|162|
|Firm-led reneg.|0|12|2|2|0|1|3|3|11|4|1|14|53|
|<br>Gtld|0|0|0|0|10|13|19|11|3|7|25|6|94|
|ov-e reneg.||||||||||||||
|Joint-led reneg.|0|1|1|7|2|0|1|1|1|0|1|0|15|

23
<!-- Start of picture text --> 35 —— Fwd stepwise GBM<br>—— Group lasso<br>30 —— Variable importance (gain)<br>—— Recursive feature elimination (split)<br>— 25<br>*<br>alc<br>2Pauwvo 20<br>D><br>O15<br>10<br>5<br>0.260 0.265 0.270 0.275 0.280 0.285 0.290<br>Exclusion error rate<br><!-- End of picture text -->
The 2007 flood began in late December 2006 after the dam in the Cahora Bassa lake (situated in the Tete Province) overflowed following heavy rains in Northeastern South Africa. The flooding conditions worsened after heavy rains hit the area in February 2007 and the Zambezi river flooded parts of Zambezia, Tete, Sofala and Manica provinces. The devastating floods resulted in massive destruction of crops and public infrastructure, weakened food security and disrupted the provision of critical services. To measure the location and intensity of the floods, we first calculate daily rainfall averages for each grid within a district using TMPA data. Next, area-weighted averages are aggregated at the district level. Given that the floods unfolded over 2.5 months, daily areaweighted averages are aggregated over this period to obtain cumulative rainfall values for each district. Finally, flood intensity is expressed in standardized z-scores of rainfall totals in the months of the disaster in 2007 using the historical distribution from the reference period 2000-2012 for each district. The first graph in the top (left to right) of Figure 2 shows the distribution of accumulated rainfall for the 2007 flood during the reference period.

The second event examined is Jokwe, a tropical cyclone that struck parts of Central Mozambique in March 2018, particularly Nampula province. Jokwe reached gusty winds of up to 270 km/h (165 mph) that jointly with heavy rains caused serious agricultural damage and destroyed schools, health centers, roads and housing. Storm intensity is defined in a similar fashion as flood intensity. Daily grid-level wind speed data are aggregated over the days of Cyclone Jokwe using area weights and expressed as Z-scores.<sup>4</sup> The second graph in the top (left to right) of Figure 2 shows the distribution of accumulated windspeed across the country during the reference period.

Finally, we investigate the impacts of a severe drought experienced in 2005. Several parts of Central and Southern Mozambique recorded large rainfall deficits. The most severe dry spells happened in January and February, overlapping with critical stages of planting and crop development in affected areas. Large crop losses, particularly of maize, led to weak food security conditions. We measure the intensity and location parameters of this drought using the one-month

> 4 <mark>Gust wind speed is the more accurate information that can be used to measure the intensity and timing of storms and hurricanes. Gusts winds are measured as the average wind speed over a 2-3 seconds period. Using gust windspeed for this study requires data on the exact time (day/hour/minute/second) of the maximum windspeed to accurately track the path of the storm across districts. Data with this fine temporal resolution are not available for Mozambique. A second option, followed in this study, relies on the daily average wind speed, provided by the African Flood and Drought Monitor. The indicator measures the accumulated daily intensity over the days of the duration of the storm as reported by the EM-DAT database.</mark>

10
# **Consensus, Institutions, and Supply Response:**

**The Political Economy of Agricultural Reforms in Sub-Saharan Africa**

Ataman Aksoy and Anil Onal* World Bank August 2011 JEL classification: D78, O13, N57, P48, Q18 Keywords: Agriculture, commodity markets, policy reform, supply response, Sub-Saharan Africa, political economy

*Corresponding author: Ataman.Aksoy@gmail.com.  The authors would like to thank John Baffes, Brian Levy, Donald Mitchell, Bernard Hoekman, Dani Rodrik, Caglar Özden, Jose Sokol, Harry de Gorter, and Fahrettin Yagci for helpful comments. This research was funded by DFID Global Trade and Financial Architecture Project.  The findings, interpretations, and conclusions expressed herein are those of the authors and do not necessarily reflect the views of the World Bank and its affiliated organizations.