rafmacalaba/datause-encoder-data huggingface dataset:
## **2.4 Aggregation over Items**

Our empirical analysis uses measurements computed by aggregating over a number of food items. The definitions above can be adjusted to allow for _I_ items: where _Yi_<sup>_∗_is usual consumption of item</sup><sup>_i_= 1</sup><sup>_, . . . , I_,</sup><sup>_Y∗≡_�</sup> _i_<sup>_Y_</sup> _i_<sup>_∗_is (total) food consumption, and</sup><sup>_b∗_</sup> _i_<sup>=</sup> _Y_<sup>_Y_</sup> _<u>i</u>_<sup>_<u>∗∗</u>_</sup> is the budget share allocated to the item.

Even after aggregation, acquisition from diaries remains an unbiased estimate of usual consumption _Y_<sup>_∗_</sup> . Specifically, a generalization of the frequency of purchase model to the case of a vector of items still yields equation (2) for total food acquisition _Y_<sup>_d_</sup> , as we show in the Online Appendix. We also show there that, despite the aggregation across items, severely misleading conclusions about inequality from diary data are still possible. For example, assuming that acquisitions are independent across goods and occur at the same rates _Ni_<sup>_∗_’sforallhouseholdsyields:</sup> The lower bound can be significantly larger than one depending on assumptions about the correlation of consumption across items. For example, if consumption is independent across items and goods are acquired once a week ( _Ni_<sup>_∗_= 1),thevarianceof</sup><sup>_Yd_mustbeatleasttwiceaslargeasthevarianceof</sup><sup>_Y∗_.</sup> Equation (2) may still be valid if survey participation affects the purchasing behavior of respondents, or with misreporting. For example, recorded cash outlays tend to be higher at the beginning of the diary period (in the first day in particular; see Lyberg and Kasprzyk, 2004, and Silberstein and Scott, 2004). If this pattern arises because of antedated expenditures that would normally take place later in the same week, equation (2) is still valid. Moreover, acquisition _Y_<sup>_d_</sup> is still an unbiased estimate of _Y_<sup>_∗_</sup> under more general forms of manipulation or misreporting (e.g., overspending on goods linked to social status or underreporting due to an embarrassment motive) that result in any zero-mean, possibly non-classical, disturbance term at the right hand side of _Y_<sup>_d_</sup> in (3).

# **3 Data, Descriptives and Graphical Analysis**

## **3.1 The Iraq Household and Socio-Economic Expenditure Survey**

Iraq is an upper middle income, resource-rich, fragile, and conflict-affected country facing development challenges in line with those of far poorer countries. The median education level in 2012 was primary schooling. While infant mortality rates remain below the norm for similar countries, a third of Iraqi children aged 0-5 are stunted, 58 percent of adult males of working age are employed, and only one in 8
affect metals prices through the parameters �� and �� . Industrial production is positively correlated with metals prices (�� �0). In the estimation, the prior distributions of �� and �� are restricted to be centered at median values of 0.1 and -0.1. These values were taken from a literature survey of 32 studies (Appendix Table 2.1). The model was estimated using monthly data over the period 1991–2017.

While the identification strategy is more flexible and offers more plausible estimates of short-term oil supply and demand elasticities, results tend to confirm the conclusions of earlier studies— namely, that an oil price decline driven by a favorable supply shock can be expected to support global industrial production over time, while a price decline resulting from a drop in demand is associated with a subsequent slowdown in global activity. On average, since the early 1990s, oilspecific supply and demand shocks have almost equally explained the forecast error variance of oil prices (Appendix Table 2.2). During the 2000s, the model suggests that demand shocks played a major role in driving oil price fluctuations, in line with other findings (Baumeister and Peersman 2013; Kilian and Hicks 2013). However, the relative importance of supply factors was substantially higher during the 2014–16 oil price plunge.

# **Appendix Table 2.1. Short-term oil demand and supply elasticities: descriptive statistics of survey of the literature**

||Demand elasticity|Supplyelasticity|
|---|---|---|
|Minimum|-0.92|-0.16|
|Maximum|-0.03|0.27|
|Median|-0.14|0.13|
|Mean|-0.21|0.11|

Note: Statistics in the table are based on 32 studies that estimates oil supply and demand elasticities (Caldara, Cavallo, and Iacoviello 2016).

# **Appendix Table 2.2. Oil price forecast error variance decomposition at selected horizons over the period 1991–2017**

|Percent|Oil supplyshock|Oil demand shock|
|---|---|---|
|After 6 months|39|40|
|After 12 months|38|39|

Note: results are based on a Bayesian SVAR including (in log difference) global oil production, oil price, global industrial production and metal price. Numbers in the table are posterior median variance decomposition at the indicated horizons.

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Table D2: ANCOVA Regressions by Head-of-Household Gender [Dependent Variable: Food Insecurity] <!-- Start of picture text --> Fractional Index 3 Fractional Index 4<br>Ethiopia Malawi Ethiopia Malawi<br>-1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1<br>Effect on Food Insecurity Effect on Food Insecurity Effect on Food Insecurity Effect on Food Insecurity<br>Nigeria Nigeria<br>-1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1<br>Effect on Food Insecurity Effect on Food Insecurity<br>Mild Moderate Severe Index Mild Moderate Severe Index<br>HHI 1 HHI 2<br>Ethiopia Malawi Ethiopia Malawi<br>-1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1<br>Effect on Food Insecurity Effect on Food Insecurity Effect on Food Insecurity Effect on Food Insecurity<br>Nigeria Nigeria<br>-1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1<br>Effect on Food Insecurity Effect on Food Insecurity<br>Mild Moderate Severe Index Mild Moderate Severe Index<br>53<br><!-- End of picture text --> _Note:_ The figure plots ANCOVA regression results with region and round controls and standard errors clustered at the household level (see Equation C.1). We display coefficients for the interaction of lagged income diversity indices (Indices 3-6) and a head-of-household gender indicator. Male-headed households serve as the comparison group. Horizontal lines represent 95 percent confidence intervals.
employment (figure 2). It is also worth noting that jobs have grown relatively slowly in MP; according to World Bank estimates, while the size of the working age population in the state (not in school) grew by about 6.6 million people between 2005 and 2012, the number of workers expanded by only 0.7 million over the same period, suggesting that not enough jobs were available for everyone of working age. Women were more adversely affected: about 2.2 million female workers dropped out of the workforce, even as 2.9 million men joined, resulting in an aggregate expansion in the number of workers by 0.7 million, a trend that we witness in other low-income states and in India as a whole.<sup>1</sup>

**Figure 1: Female labor force participation rates in urban MP is close to the Indian average**

<!-- Start of picture text --> Urban Female Labor Force Participation: 2011<br>45% 39.1%<br>40%<br>35%<br>30%<br>25% 19.3%<br>20% 15.2%<br>15%<br>10% 5.9%<br>5%<br>0%<br>Source : NSS, 2011-12<br>Figure 2: Employed urban women tend to be in regular jobs or be non-farm self-<br>employment<br>90 Urban employment, 2011‐12 85.5<br>80<br>70<br>60<br>50<br>40<br>30.5<br>27.5<br>30 23.7<br>20 13.1<br>10 5.9 5.6 1.1 5.1 1.9<br>0<br>Regular N‐F Self‐empl F Self‐empl Casual Out of LF &<br>unempl<br>female male<br>Bihar Jharkhand Uttar Pradesh Assam Haryana Uttaranchal Punjab Rajasthan Delhi Madhya Pradesh Jammu & Kashmir D & N Haveli Gujarat Chandigarh West Bengal Lakshadweep Odisha All India Arunachal Pradesh Pondicherry Maharastra Nagaland Manipur Karnataka Andhra Pradesh Goa, Daman & Diu Tamil Nadu Himachal Pradesh Kerala Meghalaya Chattisgarh A & N Islands Tripura Mizoram Sikkim<br>Percent<br><!-- End of picture text --> _Source:_ NSS, 2011-12 3
Table C.8: National Estimates of Effect of Years of Schooling on Number of Children Born - Census

||**Number of**<br>**Children Born**<br>(OLS)|**Years of**<br>**Schooling**<br>(First Stage)|**Number of**<br>**Children Born**<br>(Reduced Form)|**Number of**<br>**Children Born**<br>(2SLS)|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Years of Schoolingizy|-0.120<br>(0.012)<br>[0.000]||||
|Add’l Years of Free<br><br> <br>||0.120<br>|-0.060<br>||
|Schooling<br>�<br>_I_<sup>_F P E_</sup><br>_zy_<br>�||(0.037)|(0.017)||
|||[0.002]|[0.001]||
|Add’l Year of MTI||0.201|-0.011||
|No Script Change||(0.052)|(0.011)||
|�<br>_I_<sup>_MT I−T_</sup><br>_zy_<br>�||[0.000]|[0.321]||
|Add’l Year of MTI||-0.081|-0.001||
|with Script Change<br><br> <br>||(0.024)|(0.011)||
|�<br>_I_<sup>_MT I_</sup><br>_zy_<br>�||[0.002]|[0.894]||
|_I_<sup>_F P E_</sup><br>_zy_<br>_× I_<sup>_MT I−T_</sup><br>_zy_||-0.003|0.005||
|||(0.003)|(0.001)||
|||[0.318]|[0.001]||
|_I_<sup>_F P E_</sup><br>_zy_<br>_× I_<sup>_MT I_</sup><br>_zy_||0.005<br>|-0.008<br>||
|||(0.003)<br>|(0.001)<br>||
|||[0.062]|[0.000]||
|�<br>Years of Schoolingizy||||-0.297<br>(0.144)<br>[0.039]|
|First Stage F-Statistic||9.72||9.72|
|Number of Clusters|60|60|60|60|
|N|180,243|180,243|180,243|180,243|

_Source_ : Author’s analysis based on data from the Ethiopian census of 2007. _Note_ : The dependent variable is either years of schooling in column 2 or number of births in the other three columns. � Years of Schooling _izy_ is the reported years of schooling from the data; Years of Schooling _izy_ is the predicted level of schooling, instrumented with the free primary education (FPE) intensity measure _Izy_<sup>FPE</sup> , two mother tongue instruction (MTI) intensity measures _Izy_<sup>MTI-T</sup> and _Izy_<sup>MTI</sup> , which denote the measures for MTI regions without and with script change, respectively, and the interactions for regions in which two interventions occurred. All samples include women in birth cohorts from 1970 to 1988. All regressions include birth year and zone fixed effects and zone-specific linear trends. Standard errors are clustered at the zone level and shown in parentheses; _p_ -values are shown in square brackets.

A.24
their efficiency and adjust their scale of operations accordingly. Following Evans (1987), the basic empirical model is a general growth function _g_ in size and age: where _St’_ and _St_ are the size of a firm for the period _t’_ and in period _t_ , respectively, and _At_ is the age of the firm in period _t_ . This functional relationship can be moderated through a set of investment climate variables _IC_ : The equation thus suggests the following regression framework: where the dependent variable corresponds to the average annual growth rate, _d_ stands for the number of years over which the growth rate is measured, and _a_ and _b_ are the coefficient vectors.

The partial derivates of growth with respect to size and age allow testing for alternative theories of firm growth. Learning models of firm growth such as Jovanovic (1982) suggest that these should be negative. In line with Evans (1987), higher order expansions of the logarithmic expression for firm size and age, and an interaction term between size and age are included in the regression. The higher order expansions (squared and cubic forms) highlight the variation of the growth-size (age) relationship over the size (age) distribution of firms. The basic framework also incorporates six regional dummies and a dummy for enterprise participation in the formal sector. A basic regression is run without investment climate constraints on average real sales and employment growth as a first step (in a second step, in the following sections, we add to this regression selected investment climate constraints; results are documented in the appendix).

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need, the family can attempt to smooth their consumption and other life-cycle outcomes following a disaster. Thus to supplement income following a negative shock to income and asset wealth as a result of exposure to a natural disaster, there may be an increase in the number of children to ensure that the future income stream is not compromised – conditional on the child bringing great benefit to the household than cost.

In this study, three large-scale earthquakes are the focus. The earthquake in India occurred on January 26, 2001 in Gujarat. According to EM-DAT data (Centre for Research on the Epidemiology of Disasters (CRED) 2008), 20,005 people were killed as a result of this earthquake and nearly 6.5 million people were affected. The earthquake in Pakistan on October 8, 2005 occurred in North West Frontier in the north-west region of the country. It had a devastating effect and killed 73,338 people and 5.1 million people were affected. The earthquake in Turkey hit on August 17, 1999 in Izmit in the western region of the country. It killed 17,127 people and affected 1.3 million people. Each of these earthquakes led to large-scale loss of life and property. Publicity surrounding each of the earthquakes was widespread and images of the damage were televised around the world and remain imprinted in the viewers’ minds.

Data on earthquakes since 1900 (Centre for Research on the Epidemiology of Disasters (CRED) 2008) indicate that India, Pakistan and Turkey are exposed to the risk of the earthquake events. What remains unanticipated is the magnitude of the earthquake and the exact location of the earthquake, and the timing of the event. Studies that consider the fertility response to natural disasters often account for risk (Cain 1981; Cain 1983; Cain 1986; Pörtner 2008): the seasonality of flooding and hurricanes makes the concept of risk the key instrument. In the case of earthquakes, which are neither seasonal nor predictable in magnitude and epicenter, the concept of a shock is the dominant instrument.

In India, according to the EM-DAT data, the 2001 Gujarat earthquake was the largest recorded in terms of casualties since data are available in 1900. The second largest was in 1905 in Kangra, some 1000km north of Gujarat along the Himalya that killed 20,000 people. Gujarat is subject to seismic activity, but it is not along the Himalaya mountain range which is the region most vulnerable to seismic activity. The last large earthquake in Gujarat was in 1956, more than a 5
both skilled and unskilled products. If anything, the price increase for unskilled products is 0.3 percentage point more compared to the price increase for skilled products.

To further address the above concern of different consumption baskets for rich and poor households, we run an alternative specification where we restrict attention to cheap products. We define cheap bar-codes at the product category level _c_ as follows Thus, a UPC _i_ belonging to category _cat_ and selling in county _j_ is considered cheap if its price is smaller than the median price of similar UPCs in the same category _cat_ . Since product prices, and hence their ranking within a category are sensitive to the introduction of a new establishment, we perform this categorization by considering only the pre-announcement prices for a specific deal _d_ . Table 4 shows that the results are virtually identical for these two types of goods.

Third, to make sure that our results are not driven by the consumption of outlier or luxury items, we first restrict our sample to only commonly available products. If a UPC is sold in more stores than the median number of stores among all UPCs within its category in the years prior to the deal, that product is considered common. Table 5, Panel A reports the results. Coefficients across all four specifications remain stable, positive, and significant. Next, we restrict our sample to only grocery stores.<sup>13</sup> The coefficient in our preferred specification (Column 4) in Table 5, Panel B is virtually identical to the result obtained from our main specification.

Fourth, it is possible that building an MDP could make a county fundamentally more attractive for subsequent development projects, and at the same time lead to rising cost of living. To address this concern, in Table 5, Panel C we restrict our sample to only first-time winner markets. Again, the results remain stable.

Lastly, the onset of the global financial crisis in 2007 depressed house prices and affected local demand considerably during the period 2007-2012.<sup>14</sup> One potential concern with our results is that it may capture the differential response of prices to household demand shocks stemming from house price shocks and household wealth fluctuations (Stroebel and Vavra,

> 13The IRI data also contains product prices from pharmacy stores, such as CVS.

> 14See, for example, Mian and Sufi (2011); Mian et al. (2013); Mian and Sufi (2014).

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# **Box 2 . A Functional Approach to Financial Intermediation**

# **Function 1: Payments**

A financial system provides a payments system for the exchange of goods and services.

# **Function 2: Pooling of Funds**

A financial system provides a mechanism for the pooling of funds to undertake large-scale indivisible enterprise.

# **Function 3: Resource Transfer**

A financial system provides a way to transfer economic resources through time and across geographic regions and industries.

# **Function 4: Coping with Risk and Uncertainty**

A financial system provides a way to manage uncertainty and control risk.

# **Function 5: Pricing**

A financial system provides price information that helps coordinate decentralized decision making in various sectors of the economy.

# **Function 6: Asymmetric Information and Incentive Problems**

A financial system provides a way to deal with asymmetric and incentive problems when one <u>party to a financial transaction has information that the other party does not.</u> Source: Merton (1995).

As noted, PCM involves ‘crowding in’ of private capital to projects that would not receive it in the absence of MDB interventions (i.e., additionality). While each of the five structures discussed below offers potential benefits for mobilizing private finance across multiple functions, some are better suited to specific functions than others. For example, public-private partnerships are well suited to pooling funds for large-scale investment, while guarantees can be beneficial for managing uncertainty and controlling risk. Syndicated loans can be viewed as beneficial for both pooling funds and addressing both asymmetric information and uncertainty/risk. Sustainable financial contracts may be especially effective in mitigating asymmetric information and incentive problems. Finally, climate and banking regulations and policies can exert influence on the transfer of economic resources across geographic areas and sectors.

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- 11 -

production (Figure 4).<sup>11</sup> Consequently, in drought years the country had to import 500 to 2,000 GWh of energy or 10 to 50 percent of total power consumption at unfavorable international prices, with approximately 10 percent of demand still left unmet.

Figure 3. Installed electricity generation capacity, 2006 <!-- Start of picture text --> 2.0<br>1.8<br>1.58<br>1.6 1.45<br>1.36<br>1.4<br>1.2 1.14 1.13<br>1.0 0.87 0.91 0.96<br>0.76<br>0.8<br>0.6 0.53<br>0.4<br>0.2<br>0.0<br>Figure 4. Electricity production from hydroelectric sources, 2005<br>98.7<br>100<br>80<br>60<br>51.3<br>42.9<br>39.7<br>40 34.0<br>21.5<br>20 14.8<br>9.9 8.4<br>2.9<br>0<br>Electricity installed capacity per capita (kW)<br>Albania Bosnia and Herzegovina Bulgaria Croatia Macedonia, FYR Romania Serbia Czech Republic Greece Slovak Republic<br>Share of hydropower generation (% of total)<br>Albania Bosnia and Herzegovina Bulgaria Croatia Macedonia, FYR Romania Serbia Czech Republic Greece Slovak Republic<br><!-- End of picture text --> Source: _World Development Indicators_ (WDI), Energy Information Administration database and IPA (2009).

Figure 4. Electricity production from hydroelectric sources, 2005 Sources: _WDI_ , Energy Information Administration database and IPA (2009).

# **_Demand_**

From the industrial demand point of view, Bulgaria, Macedonia and Serbia seem to be energy-intensive economies. In general, energy demand increases proportionally with economic development, but how much energy is required to produce one unit of output—

> 11 For instance, see Fida _et al._ (2009).
specific time-varying regressors in explaining the differences across countries and years. Table 4 gives the regression results.

Column 1 in Table 4 presents the underlying Africa-wide time trend between 1990 and 2013 based on a regression of the log of the odds ratio on time. The statistically significant trend is equal to a 0.4% annual increase in the share of population living in FHH when evaluated at the mean share over the entire sample. The next regression (column 2) additionally controls for factors or their proxies that the literature emphasizes as determinants of the preponderance of female headed households. These are log GDP per capita (based on 2005 PPP at constant 2011 international $); the agricultural share of GDP as a proxy for local employment opportunities and incentives for migration; HIV prevalence (%); the urban population share (%); whether the country has experienced a serious conflict over the last ten years (PRIO dataset); the Muslim population share (%); and the female labor force participation rate.<sup>23</sup> Note that all of these variables are time varying and country specific.

None of these explanatory variables account for the trend. However, the HIV prevalence and the Muslim share, with positive and negative effects respectively, are statistically significant correlates of the dependent variable (column 2). Our full specification in column 3 adds average years of schooling, life expectancy and average age of adults all separately for men and women, as well as women’s mean age at first marriage.<sup>24</sup> Strikingly, the time trend vanishes upon inclusion of these demographic and human capital population attributes. On closer inspection, it appears to be specifically the combination of rising age at first marriage and higher education levels that eliminates the positive trend in the FHH share. Given the high correlation between these two traits, it is difficult to say which is more important. Higher age at marriage clearly allows more schooling for girls but it also has an important independent explanatory effect. It can be expected to have a different trajectory and evolve through time and across countries differently to the other variables. Indeed, the newly entered demographic and education variables are all highly correlated both across genders and among themselves. However, they each clearly have an independent effect since in some form or the other, they retain individual significance.

The separate explanatory power of each of these factors can be better seen in column 4 of Table 4 which, based on various tests of the estimated coefficients, presents our preferred dataconsistent specification, as a restricted version of the full model given in column 3. We can’t

> 23 See Appendix, A1 for details on data definitions and sources. 24 These controls are calculated from the DHS surveys. See the Appendix.

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