rafmacalaba/datause-encoder-data huggingface dataset:
||**Appen**|**dix Table 4. Tr**<br>**_Matched to ch_**|**ansparency In**<br>**_eapest product_**|**dex with Altern**<br>|**ative Matchin**<br>**_M_**|**g**<br>**_atched to most_**|**_expensive prod_**|**_uct_**|
|---|---|---|---|---|---|---|---|---|
||**Sav**|**ings**|**Cre**|**dit**|**Sav**|**ings**|**Cr**|**edit**|
|||TI|T|I||TI||TI|
||All|costs|All|costs|All|costs|All|costs|
||All|Voluntary|All|Voluntary|All|Voluntary|All|Voluntary|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|HL * HI|0.139**|-0.082|0.182***|-0.027|0.149**|-0.081|0.186***|0.009|
||(0.066)|(0.059)|(0.060)|(0.052)|(0.072)|(0.069)|(0.061)|(0.058)|
|HL * LI|0.086|-0.100|0.230***|0.000|0.104|-0.099|0.230***|-0.015|
||(0.076)|(0.062)|(0.060)|(0.065)|(0.089)|(0.076)|(0.059)|(0.066)|
|Investment|0.102*|0.216***|||0.130**|0.236***|||
||(0.054)|(0.053)|||(0.063)|(0.065)|||
|High indebtedness|||-0.042|-0.045|||-0.042|-0.044|
||||(0.051)|(0.048)|||(0.051)|(0.052)|
|Formal dress|-0.003|-0.017|0.056|0.020|-0.011|-0.020|0.046|-0.011|
||(0.044)|(0.048)|(0.044)|(0.043)|(0.052)|(0.053)|(0.045)|(0.031)|
|R-squared|<br>0.124|0.217|0.177|0.198|0.150|0.246|0.185|0.217|
|**Observations**|349|349|377|377|349|349|377|377|
|Peru|121|121|150|150|121|121|150|150|
|Mexico|98|98|83|83|98|98|83|83|
|Ghana|130|130|144|144|130|130|144|144|
|**Means for control auditors**|||||||||
|Dep. Variable|<br>0.658|0.654|0.467|0.228|0.610|0.605|0.466|0.237|
|Peru|0.441|0.441|0.347|0.000|0.380|0.380|0.347|0.000|
|Mexico|0.463|0.447|0.398|0.398|0.343|0.327|0.398|0.398|
|Ghana|0.868|0.868|0.707|0.398|0.868|0.868|0.705|0.427|
|**P-value of test**|||||||||
|_HL * HI = HL * LI_|0.495|0.736|0.443|0.680|0.583|0.720|0.482|0.717|
|_HL = 0_<br>This table reports the results in Ta<br>matched to either the cheapest prod<br>disclosed verbally only. An observ<br>include country fixed effects, audit<br>without experience (neophyte) an<br>indebtedness) for credit audits. St<br>significance * p<0.10 ** p<0.05 **|0.127<br>ble 4 using all pr<br>uct offered by th<br>ation in the regr<br>or's gender, dum<br>d had preference<br>andard errors a<br>* p<0.01.|0.257<br>oducts offered.<br>e institution (co<br>ession is an au<br>mies for visits <br> for a transact<br>re clustered at|0.001<br>Products offer<br>lumns 1-4) or<br>ditor-branch pa<br> to commercial<br>ion account fo<br> the auditor le|0.853<br>ed to auditors th<br>the most expensi<br>ir, each pair dur<br> institutions and <br>r savings audits<br>vel and are repo|0.135<br>at could not be<br>ve product (col<br>ing credit audit<br> provincial area<br> or a loan siz<br>rted in parent|0.419<br>matched in the<br>umns 5-8). The<br>s could have m<br>s. Control audi<br>e of 20 percent<br>hesis under coe|0.001<br>product marke<br>table focuses o<br>ultiple visits. <br>tors were assig<br> of household<br>fficient estima|0.936<br>t database, are<br>n information<br>All regressions<br>ned the script<br> income (low<br>tes. Levels of|
INTRODUCTION Xi

# _Vital Statistics Report_ and others are estimated from diverse data.

Future trends In life expectancy. Life expectancy is projected from year 0 to year t using a logistic function ovc.v time of the form e, = ko + k / {1 + exp [logit(eo) + rt]}, with logit(e0 ) = log, j(ko + k - eo) / (eo - ko)).

The logistic function is set to rise most rapidly from a level of 50 years or so and increasingly slowly at higher levels. The minimum (kl) for the logistic functions for both sexes is assumed to be 20 years, and the maxima (kI + k) are assumed to be 82.5 years for females and 75.8 years for males. The rate of change (r) for the logistic function is allowed to vary across countries, and for a given country to vary over time.

For the first quinquennium (1985-90), the rate of change (rJ) is estimatcd from the rate of change in the previous quinquennium (ro) and from the female secondary enrolmcnt ratio (so), using the equations = .00379 + .723 i, - .000n54 so for females, and = .01159 + .885 to - .000318 s. for males.

Percent urban is used in a few cases, with a different equation (see Bulatao and Bos 1989), where secondary enrolment is not available, and in fewer cases still the rate of change in the previous decade is used instead of the rate of change in the previous quinquennium when the latter appears to have been affected by exceptional circumstances. Limits are imposed on the rate of change for the first quinquennium (and for all other quinquennia), such that it cannot be greater than -.017 (which would give slow mortality decline) or less than -.053 (which would give rapid mortality decline).

For the second quinquennium, the rate of change is estimated as a function of the rate of change for the first quinquennium: r2 = -.007 + .8 rl.

The rate of change for the third quinquennium is estimated from the rate of change for the second quinquennium in the same manner. This equation allows rates of change to converge toward the uniform pattern imposed for subsequent quinquennia.

For these subsequent quinquennia for all countries, the rate of change in life expectancy is constant at -.035 for both sexes. At this rate, the annual increments to life expectancy vary by initial levels as indicated by the medium pattern in Table 1. Minimum and maximum increments corresponding to the slow and rapid limits imposed o n .ates of change in the first three quinquennia are also shown.

Future trends in infant mortality. i &.nt mortality is projected using a similar logistic function. The rate of change for each o' the first three quinquennia is obtained from the equation rt = .03 + .5 r,.,, with the restriction that this rate must be in the range [.024, .1301. These limits provide schcdi" of minimum and maximum annual dccrements to infant morl;.lity, varying ',; : -nit
# **II. RESULTS** FROM EcONOMETRIc **ANALYSIS**

The empirical analysis is conducted by jointly estimating equations (11) and (12) as a system, using an iterative seemingly unrelated regression (ITSUR) procedure. The underlying,system is nonlinear, and is primarily derived from the structure of the input demands, as represented in equation (11). The ITSUJR procedure estimates the parameters of the system, accounting for heteroscedasticity, and contemporaneous correlation in the errors across equations. As the cost shares sum to unity, _n-I_ share equations are estimated (where n is the number of production factors). The ITSUR estimates are asymptotically equivalent to maximum likelihood estimates and are invariant to the omitted share equation (Greene, 1993). All estimations were carried out with the MODEL procedure of the SAS system.

It is quite likely that due to heterogeneity in technology use, production efficiency, and managerial capacity among firms of different sizes, it may be limiting to group all firms in the same estimation process. Further, the benefits of location specific characteristics may be accrued more by smaller firms, who are relatively more dependent on access to buyers and suppliers, availability of ancillary services, inter firm non-technological externalities, and high quality infrastructure. In contrast, larger firms may be in a better position to internalize production of various intermediate goods, self-provide infrastructure, and stock higher inventories. As a result, they are relatively less dependent on location based amenities and characteristics. To make allowances for this heterogeneity, and test if in fact there are differences in production costs and the impact of economic geography across firms of different sizes, we classify firms into three categories: small, medium, and large. Small firms are defined as those with less than 50 employ24
created a Durable Solutions Secretariat (DSS) and adopted a national IDP policy alongside National Evictions Guidelines. This response aims to protect the rights of IDPs, ensure that any evictions are carried out in legal way, and provide alternative housing options (Refugees International, 2019).

Prolonged conflict has had far reaching effects, including the emergence of a large number female-headed households, with reports of nearly half of all households being female headed going back for nearly a decade (Food Security and Nutrition Analysis Unit [FSNAU], 2012; United Nations Development Program [UNDP], 2012). This is often attributed to the conflict related death of male family members or the inability of men to flee to certain areas (World Bank, 2014). As has been observed in other conflict settings, family splitting is also a strategy to cope with lack of economic opportunities (Brück & Schindler, 2009; Ibáñez, 2008). In Somalia sometimes men opt to return to their areas of origin and women and children are left behind with the intention of returning if and when the situation improves (World Bank, 2014).

Somalia ranks among the bottom dozen countries on the Women, Peace and Security Index, which measures women’s inclusion, justice, and security.<sup>4</sup> It has one of the lowest female labor market participation rates in Sub-Saharan Africa with only 43 percent of women in the labor force compared to 67 percent of men. However, women have always contributed to work on family farms. Gender roles have changed; increased numbers of female-headed households have led to changes in herd management practices and women’s greater involvement in livestock trade (ElBushra & Gardner, 2016). Currently, women are engaged in the informal sector and microenterprises and work in agricultural production and livestock activities. Women are heavily involved in crop production, provide more than 60 percent of labor in subsistence farming, and are mainly responsible for marketing fruits and vegetables for domestic consumption and account for 60-80 percent of domestic meat and milk trade, hides and skins, as well as petty trade (UNDP,

> 4 See Georgetown Institute for Women, Peace and Security/Peace Research Institute of Oslo “Women Peace and Security Index”. https://giwps.georgetown.edu/wp-content/uploads/2019/12/WPS-Index-2019-20-Report.pdf

8
SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 21. Sudan’s potential gains from greater operational efficiency**

|US$ million|ICT|Irrigation|Power|Transport|WSS|Total|Total as share<br>of GDP|
|---|---|---|---|---|---|---|---|
|Underrecovery of costs|—|n.a.|378|n.a.|15|393|1.84|
|Overstaffing|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|
|Distribution losses|—|n.a.|106|—|27|133|0.62|
|Undercollection|—|n.a.|0|n.a.|35|35|0.17|
|Low budget execution|0|n.a.|0|20|2|22|0.10|
|Total|0|n.a.|484|20|79|584|2.73|

_Source_ : Derived from Foster and Briceño-Garmendia (2009). _Note:_ WSS = water supply and sanitation; ICT = information and communication technology; GDP = gross domestic product. — = Not available. n.a. = Not applicable.

The power sector offers the largest potential savings—up to $584 million per year—if the operational inefficiencies of its utility were tackled. Undercharging for power costs Sudan about $380 million each year, or around 1.8 percent of the country’s GDP. If compared with the rest of Africa, where underpricing of power is commonplace, the National Power Corporation of Sudan (NEC) is doing better than utilities in other resource-rich countries on average, but is nevertheless a poor performer (figure 31).

**Figure 31. Underpricing of power and water in Sudan**

Financial burden of underpricing, as percentage of GDP <!-- Start of picture text --> 2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>Power Water<br>Sudan Resource Rich<br>Percenatage of GDP<br><!-- End of picture text --> _Source:_ Derived from Briceño-Garmendia, Smits, and Foster (2009).

The NEC’s 2004 distributional losses of 33.4 percent are three times the best-practice 10 percent benchmark, resulting in a staggering $106 million cost of inefficiencies.

In the water sector, average tariffs stand at $0.6 per cubic meter versus an estimated $0.69 per cubic meter average cost-recovery tariff. The macroeconomic burden of 0.007 percent of GDP is much lower than that for power, but is comparatively higher than that of other resource-rich countries.

Operational inefficiencies are also plaguing the water sector, and cost Sudan about $62 million a year, equivalent to 0.3 percent of GDP. This cost could be avoided by increasing the collection efficiency for

> <sup>43</sup>
by increases in the price of fuels. They also note that the NBR results are consistent with their general equilibrium results under the assumption where land is fixed, namely that it is not easy to change allocation of land between crops, which is likely to be the case in the short term.

In addition to the Arndt et al (2008) study a few other researchers have examined related aspects of the food price spike in the Mozambique context. Nhate, Massingarela and Salvucci (2013) examine the price transmission from international markets to Mozambique, the degree of integration across various markets in the country, and the political economy of the government policy responses to the 2007-08 price rise. They find that domestic prices for rice and wheat are only partially integrated with the international prices, and that the domestic prices remain high even after international prices had declined. Within the country, they find that the prices in the largest market in Maputo, the capital, transmit partially to some markets (Nampula), but not others (Beira).

Minot (2014) also undertakes a price analysis of agricultural commodities in 11 countries, including Mozambique. The analysis covers the period from January 2003 to December 2010, and includes beans, cooking oil, maize, millet, rice, and sorghum. The author notes that the retail prices in some of the markets experienced high volatility in the lead-up and during the food-price spike, particularly maize, though it is lower than in countries where the governments actively attempted to intervene in the markets in an attempt to stabilize prices.

# **3. Data and descriptive statistics**

# **3.1 Overview of the data**

This paper uses data from two main sources: (i) the 2014/15 round of the national household income and consumption expenditure survey, the Inquérito sobre Orcamento Familiar (IOF), conducted by Mozambique's National Institute of Statistics and (ii) farm level data from the 2015 Integrated Agriculture Survey (IAS) done by the Ministry of Agriculture and Food Security. A brief description of each data set and how they are used in the analysis is provided below. Later in this section we also discuss the data on market-level food prices, including trends across space and time.

12
proportions. The difficulty with the wage bill/revenue ratio was its sensitivity to changing coffee prices and its failure to capture other sources of public finance.<sup>18</sup> It might be argued that the ratio of wage payments to material and services purchases is the most direct expenditure index of changing factor proportions. However, this measure also faiis to account for changing relative prices. In Bolivia, national accounts suggest a substantial rise in complementary inputs as the wage/purchases ratio fell from 6.2 to 2.5 from 1982 to 1986. According to Klinov (1987), the ratio reflects the dominance of a decline it. wages relative to material prices. More detailed inspection of individual ministries reveals that the availability of complementary inputs per worker is little different that it was at the start of the period.1<sup>9</sup> Similarly in Ghana, the change in the ratio of the wage bill to expenditures on goods and services has been modest. However, purchases of grods and services in 1983 in real term3 equal less than half of the amounts in 1977, while actual employment more than doubled.<sup>20</sup> Factor proportions have worsened but available expenditure ratios often fail to capture these changes.

In sum, we do not have any benchmarks for when the ratio of government wages to GDP, government expenditures, revenues, or non-labor purchases becomes "excessive". In fact, because of changing relative prices -- rising material costs avd falling real wages -- available ratios may not capture the drift toward deteriorating factor proportions in government production. What seems to be called for is greater attention to how government pay and employment policies influence both the purchase of real inputs and the real labor time productively employing these inputs.

23
8 earning three times as much nonfarm income than women. Similarly, in Ruvuma, 58% of men and 28% of women earn non-farm income, though the amounts are on average smaller. More than 33% of men and 26% of women report having received unearned income, with the amounts reported by men on average twice as large as those for women. There is thus sufficient variation in the data to identify whether men and women spend earned and unearned incomes differently.

Tables 1 and 2 also show how much of the income is in cash and how much is in kind (mostly food produced for auto-consumption).<sup>18</sup> When markets are missing or when they are poorly developed (as in cash-constrained societies), trade becomes more costly and the nature of income (in kind or cash) may affect what it is spent on. While earned income is on average half in cash and half in kind in Kilimanjaro, in Ruvuma, cash income is on average 63 percent larger than in kind income. In both regions, a much bigger part of the unearned income is in cash.

Consumption is split up in food and nonfood expenditures. Food expenditures make up about 48% of overall spending in Kilimanjaro and 55% in Ruvuma. Given that the sample is from a low-income setting and following Bennett’s Law, non-staple food may still be considered a non-basic good and it is thus useful to divide food expenditures accordingly.<sup>19</sup> On average slightly more is spent on non-staple food than on staple food in Kilimanjaro (the richer province), while staple food spending exceeds non-staple food spending by 32 percent in Ruvuma. Alcohol and tobacco spending (a hedonic good) is considered separately. It is positive for 39% of households, making up between 2% and 80% of overall reported spending.

Non-food expenditures are categorized in spending on housing and appliances, transportation and communication, and clothing. The latter can be split up further in spending on men’s, women’s and children’s clothing. Housing expenditure accounts on average for 8% of total spending in Kilimanjaro and 5% in Ruvuma, with spending on the other two categories between one third and one half of that. Finally, three more“social”expenditure categories are considered: health expenditures, education expenditures (in effect an investment expenditure) and gifts given.

To study spending behaviors in rural China, the annual 2000-2004 household panel

> 18 Non-farm income in kind and in cash have been recorded separately in the questionnaire.  Crops and livestock sold make up cash farm income, with the remainder classified as in kind income (for auto-consumption and seeds).

> 19 Staple food includes bananas, grains, potatoes and cassava. Non-staple food includes expenditures on meat, dairy, and fruits and vegetables.
SDRFI may have different potential for shifting actual practice closer to best practice. It seems unlikely, however, that these different approaches would have much purchase on the gap between optimal and imperfect design, since that is a reflection of the whole political process.

In consequence, analysis of these issues could make comparisons from (at least) three different perspectives, the normative, the positive, and a plausible hybrid of the two. The normative approach would focus on some sort of optimal or best practice expenditure responses, whereas the positive would focus on what is likely to happen in practice. The hybrid approach would consider how instruments might be designed to shift actual practice closer to best practice. De Janvry (2014) is intended to examine this issue in depth, and will hopefully provide a fuller treatment of these issues.

It seems desirable to make some attempt at all three approaches. The gulf between best practice and actual practice can be very wide. In these circumstances, to focus on actual practice without any consideration of best practice would be unacceptable; however to focus on best practice without any consideration of actual practice would be unrealistic. To the extent that the gap between the two can be identified, to design an SDRFI strategy without attempting to at least partially close this gap would be inappropriate.

At least partial information on best practice responses should be obtainable from Post Disaster Needs Assessments. Information on what was actually done is more difficult, because of the informational difficulties already alluded to. One suggestion, made in section 7, is to use the case studies to pursue this issue.

# **3. Differential outcomes**

The focus of the research program is on the relative costs and impacts of different SDRFI strategies. This is quite different from, and more demanding than, the conventional approach to assessing the impact of a disaster. In the latter, the question is what the net impact of the disaster was, given whatever risk transfer and other mechanisms were actually in place, compared to what would have happened absent the disaster. In the present case the focus is on what improvements could be achieved by an appropriate choice of SDRFI, or possibly a set of alternative choices. Hence, the issue is one of differential performance under different choices. These differences in public expenditure outcomes will themselves be contingent on the income level of the country, its level of indebtedness, and on the type and scale of the disaster.

In some respects that might make the exercise easier than the conventional one, in others, harder. It may be easier to the extent that certain expenditures are common across different SDRFI options; in other words, even though they are impacted by the disaster, they are not impacted differentially under the different options considered. For expenditures that are not common in this sense, it may be harder, because identifying differences in differences is more demanding than simply identifying differences.

5
Cardiovascular disease, stroke, pulmonary disease and cancer are the leading urban killers in the middle income countries as they are in the high income ones. The sharp rise in these diseases and of diabetes and the projected increase through 2030 (see Table 5) is related to a number of factors. These include the increased intake of refined carbohydrates, processed foods and fats associated with a decline in the relative prices of foodstuffs, in the time needed to prepare food,<sup>50</sup> to sedentary living habits typical of urban living and urban sprawl, to cigarette consumption, and to pollution caused by urban transport and industry.

**Table 5 : Changes in Rankings for 15 Leading Causes of Death, 2002 and 2030 (Baseline Scenario)**

|Category|Disease or Injury|2002 Rank|2030Rank|
|---|---|---|---|
|Within top 15|Ischaemic heart disease|1|1|
||Cerebrovascular disease|2|2|
||Lower respiratory infections|3|5|
||HIV/AIDS|4|3|
||COPD|5|4|
||Perinatal conditions|6|9|
||Diarrhoeal diseases|7|16|
||Tuberculosis|8|23|
||Trachea, bronchus, lung|||
||cancers|9|6|
||Road traffic accidents|10|8|
||Diabetes mellitus|11|7|
||Malaria|12|22|
||Hypertensive heart disease|13|11|
||Self-inflicted injuries|14|12|
||Stomach cancer|15|10|
|Outside top 15|Nephritis and nephrosis|17|13|
||Colon and rectum cancers|18|15|
||Colon and rectum cancers|18|15|
||Liver cancers|19|14|

Source: Mathers and Loncar (2006).

> 50 The decreased time needed to prepare food and the greater reliance on processed foods e.g. French fries has meant that in the western countries, “groups who previously spent most time preparing food in the early 1970s – primarily married women – have increased most in weight” (Propper 2005, p.989).

29