rafmacalaba/datause-encoder-data huggingface dataset:
8 Application of these procedures yields the information base for estimating the female share of labor in crop production and its correlates. To obtain the national female labor share, all agricultural households are considered (both rural and urban). Estimates are weighted using the sampling weights and in accordance with the complex survey design (i.e. stratification and clustering) in each country, aggregated by each gender and then divided by the total crop labor time spent by adult household members in the country (or in a given category, when disaggregating within country across categories).

# **_Detecting possible reporting bias_**

As Bardasi et al. (2011) discuss, both the level of detail on employment questions and the type of respondent (proxy versus self) may affect the labor statistics collected and their comparability across countries. Here, the focus is not on the _levels_ of engagement, but rather on the relative engagement by men and women, i.e. the agricultural labor _shares_ . To the extent that potential over- or underreporting due to survey design issues affect male and female household members similarly (and in the same direction), survey design issues (including the use of recall data) should be less of a concern when estimating shares. Nonetheless, this does not hold automatically either.

Turning to the potential effect of the detail of the questionnaire first, based on a randomized household survey experiment in Tanzania, Bardasi et al. (2011) find a decline in male labor participation in agriculture and a (statistically non-significant) increase in female labor participation when a short as opposed to a detailed labor force participation module is used. The use of short modules would thus lead to an underestimation of the female labor share. Here, very detailed labor modules are used, with questions about the amount of time spent on each plot by
To summarize the discussion to this point, we are able to exploit three advantages over previous studies: (1) a large firm-level database drawn from extensive surveys which provide a precise measure of firm growth as well as valuable information on firm characteristics, (2) updated data on financial structure, which enables us to include a broader set of countries in our regressions than what was used in previous studies of bank-based versus market-based financial systems, and (3) a broader set of potential instruments, many of them tied directly to theoretical arguments about how endowments shape institutions, including financial ones.

Table 3 presents correlations between financial development indicators, potential instruments for financial development, and the firm characteristics that we include in the labor growth regressions. Table 4 presents regressions of financial development on firm characteristics and potential instruments, each added to the regression separately. The strongest correlations are negative ones between settler mortality and financial development (private credit/GDP; stock market capitalization/GDP). Next are the positive correlations between financial development and interpersonal trust. Note that the ethnic fractionalization measure was not strongly linked to financial development (results not in the table) and so we present only the trust measure in Tables 3 and 4.

Surprisingly, neither the legal origin variables nor our resource-based measures of endowments (commodities dummies, net oil exports) are significantly correlated with financial development. Though we tried a variety of variables, we found no strong positive relationships between grains and financial development, as we had expected from our reading of Engerman and Sokoloff, and thus we do not present the commodities dummies in the tables. We do present the net oil exports variable. Though it is not strongly correlated with our measures of financial development, it has some explanatory power for both variables (with t-statistics between 1.2 and 1.3). Similarly, though only English legal origin appears in the tables, we experimented with French legal origin and combinations of legal origins (e.g., non-English, non-French) and found no strong relationships with our measures of financial development.

For the most part, the regressions that control for firm characteristics in Table 4 confirm the correlations in Table 3. For example, private credit is significantly negatively linked to settler mortality and significantly positively linked to interpersonal trust. We also obtain the expected 15
coordinating the assistance effort. In particular, it is to be noted that, although aid coverage has reached many targets, it is still necessary to reach the poorest and most vulnerable people.

Moreover, Haitians have been showing quite remarkable levels of resilience so far, which leads us to think that they will devise unforeseen coping strategies to address the challenges that lay ahead. Aid efforts should definitely take this factor into consideration, tap into and build on these capacities, in order to deliver more targeted, effective and sustainable humanitarian and recovery forms of aid.

# **3. Methodological Framework**

Addressing the political economy of aid allocation often proves to be a major issue. Indeed, in the post-disaster context in Haiti—as in other countries—, it appears crucial to know whether assistance benefited more influential households. For instance, Francken et al. (2009) study the allocation of emergency assistance after cyclone Gafilo hit Madagascar in 2004, based on the USAID-funded survey conducted after the disaster. They find that, while assistance efforts also reached some untouched towns and villages, directly affected areas were more likely to receive aid. Also, they showed how donors’ assistance efforts were greater in richer areas and how mass media coverage made it more likely to receive assistance. Finally, areas which had been supporting the current government in previous elections received more assistance, and this assistance was often allocated to easily accessible communities, even when they had not been hit by the cyclone.

In the case of Haiti, CNSA (2010a) reports that food aid might not have been allocated in an equal and unbiased way, particularly when local boards had been entrusted with it or had simply taken it over by themselves.

Indeed, strict rules such as those described by Morris and Wodon (2003) are not necessarily implemented or applied when allocating assistance. In their model, assistance is used by the policymaker (be it a government or an agency) to equalize the level of post-disaster assets (including emergency assistance) of the poorest households with the assets level of the second poorest household, and so on... The policymaker may, however, choose to operate along different allocation patterns. One option is to give more weight to the losses incurred due to the disaster than to the level of pre-disaster assets, in connection with the idea that each household’s welfare situation is not only determined by its post-disaster assets level (satisfaction motive), but also by its post-disaster assets level as compared with its pre-disaster level (deprivation motive).

7
# **6. Tables**

_Table 1._ Main characteristics of selected household surveys in India

|**Survey name**|**Administering**<br>**agency**|**Number of**<br>**questions**<br>**about**<br>**consumption**|**Welfare**<br>**aggregate**|**Survey time**|**Description**|
|---|---|---|---|---|---|
|Consumption<br>Expenditure survey<br>(CES)2017/18|NSSO,<br>Government of<br>India|~ 400|MMRP|July 2017 to<br>June 2018|Withheld due to data quality concerns|
|Consumption<br>Expenditure survey<br>(CES)2011/12|NSSO,<br>Government of<br>India|~ 400|MMRP, MRP,<br>URP|July 2011 to<br>June 2012|Last available official consumption<br>survey round|
|Survey on social<br>consumption<br>(Social<br>Consumption<br>2017/18)|NSSO,<br>Government of<br>India|1|Usual monthly<br>consumption<br>expenditure of<br>the household|July 2017 to<br>June 2018|Education and health specific survey.<br>Sample: 113,823 households|
|Survey on social<br>consumption<br>(Social<br>Consumption<br>2014)|NSSO,<br>Government of<br>India|1|Usual monthly<br>consumption<br>expenditure of<br>the household|January to<br>July 2014|Education and health specific survey.<br>Sample: 65,932 households|
|Periodic Labor<br>Force Survey (PLFS)|NSSO,<br>Government of<br>India|1|Usual monthly<br>consumption<br>expenditure of<br>the household|July 2017 to<br>June 2018|Starts in 2017-18 to replace<br>employment-unemployment surveys.<br>Cross-sectional in rural areas and panel<br>in urban areas. Sample: ~56,000<br>households|
|India Human<br>Development<br>Survey (IHDS)|NCAER &<br>University of<br>Maryland,<br>Indiana<br>University and<br>University of<br>Michigan|52|MRP|Two and half<br>rounds: 2004,<br>2011 and<br>subsample<br>round in<br>2017|Household panel containing income and<br>expenditure questions. Sample: ~<br>41,500 households|
|Consumer<br>Pyramids (CP)|CMIE, private<br>data collection<br>agency|~ 80|Consumption<br>recall over last<br>three months|Starts in 2014<br>(and every<br>quarter since)|Starts in 2014. Household level panel<br>with three-monthly period recall.<br>Sample: ~174,000 households|

_Table 2._ Estimated poverty rate in 2017/18 ($1.90 per day poverty line) using survey-to-survey imputation methods, comparing different models

|**Sector**|**Model 1**|**Model 2**|**Model 3**|**Model 4**|
|---|---|---|---|---|
|National|8.70|8.75|8.61|8.47|
||[ 7.51, 9.90]|[7.32,10.19]|[7.37, 9.86]|[7.19, 9.76]|
|Rural|8.92|9.03|8.38|9.14|
||[7.36,10.48]|[7.18,10.88]|[ 6.80, 9.96]|[7.38, 10.9]|
|Urban|8.18|8.08|9.18|6.85|
||[6.34,10.01]|[6.07,10.09]|[7.43,10.94]|[5.50,8.19]|

21
23 Barring a profound change in vehicle fuels and fuel efficiency, or alterations in land use patterns that would shorten average travel distances per vehicle, emissions of carbon dioxide are likely to increase at about the same rate as vehicle fleets. More than half of vehicular carbon dioxide emissions will come from high-income countries for the foreseeable future. The production of other emissions (hydrocarbons, nitrogen oxides, etc.) are likely to vary dramatically across countries because emission control technologies vary. The fleets in high-income countries typically have much lower emission rates than the essentially uncontrolled fleets in low- and middle-income countries. However, this is changing, as many low- and middle-income countries are requiring emission controls for vehicles and are beginning to control the production of heavily polluting technologies such as two-cycle gasoline engines. The adoption of emission controls by most low- and middle-income countries is overdue because many of their large cities have the worst urban air pollution in the world, and the costs of vehicular emission controls have fallen over time.

Simple projections of motor vehicle fleets ignore changes in prices that may affect demand. The available evidence suggests that vehicle users react to price changes in ways that moderate the effects of price changes on vehicle ownership and use. Increases in gasoline prices encourage the purchase of more fuel efficient vehicles and reduce vehicle use somewhat. Increases in the price of new vehicles (for example, by means of purchase taxes on new cars) also increase the price of second-hand vehicles and extend the life of vehicles. This reduces the annual depreciation rate (a major component of the annual user cost of a capital good) and helps to offset the effect of the price increase. Thus price increases can reduce the demand for vehicle ownership and use, but demand is fairly inelastic with respect to price, partly because of the possibilities for compensating behavior. Therefore, price increases that are significantly larger than income increases will be required to produce substantial effects on vehicle ownership.

The distributional impact of taxes is often a policy concern. In low- and middleincome countries taxes on automobile ownership and use are progressive with income because it is the high income households that own cars.<sup>35</sup> This is not true in high income countries, where the vast majority of households own a car.

# _7.2 Vehicles and roads at the national level_

Road infrastructure has expanded with income at the national level across countries. Paved road length has been expanding at about the same rate as the number of motor vehicles, but total (paved and unpaved) national road length has expanded only about half as fast. High income countries now have less scope for expanding paved roads faster than Gordon Hughes, "the Incidence of Fuel Taxes: A Comparative Study of Three Countries," in D. Newbery and N. Stem, eds., _The Theory of Tcacation in Developing Countries_ (New York: Oxford University Press, 1987), pp. 533-559.
other features, this survey had the peculiarity of asking the households, regardless of tenure status, questions about the market rent and value of their dwelling. Thus, for every dwelling in the sample, we have estimates of both the value and the market rent of the unit, given by the household actually occupying that unit. This allows us to calculate individual rates of return for each unit, without facing the typical selection bias problems, each house serving as its own control.<sup>10</sup> Comparing the distributions of returns in the informal and formal sectors, we obtain the following results: (i) Rates of return are significantly higher in the informal sector, as predicted by the model. The mean rate of return in the formal sector is 6.7 percent, versus 8.1 percent in the informal sector. This corresponds to a 1.4 point average premium, or 22 percent premium in relative value. (ii) These figures imply a perceived risk on housing investment in the informal sector equivalent to an annual destruction rate ranging between 1 and 2 percent, depending on assumptions regarding the time horizon of housing investors. (iii) The two distributions of rates of return present highly idiosyncratic components, and traditional explanatory variables proxying either the strength of informal property right (Lanjouw and Levy, 2002), or lower perceived risks of eviction (Jimenez, 1984, Friedman, Jimenez and Mayo, 1988) have little power to explain the in individual rates of return between the formal and informal sector.

Our framework allows us to rationalize empirical findings of previous papers, which found that premiums for the formal sector, as measured by including a dummy for formal dwellings in otherwise classical hedonic equations, were always greater for prices than for rents. We show that the difference between the two premiums directly reflects the risks on housing investment in the informal sector and thus, should be positive. Moreover, from this difference in premiums we can estimate the risk faced by informal investors, as in our case. Calculations indicate that physical risk on informal investment was four to five times higher in Davao than in Manilla at the beginning of the 1980s.

Lastly, we try to understand what factors may drive the rental premium for the formal

> 10In a typical survey, the selection problem would arise from the fact that only the rents are known for the rental units, and the prices or estimated values for the dwelling occupied by their owners. A similar technique is used by Lanjouw and Levy (2002). In their paper, they used a survey where households living in untitled houses were asked to assess the value of their dwellings if it were titled.

5
|Bank<br> Bank<br> Bank<br> Bank<br>raser Institute<br>eritage Foundation<br> Bank<br> Bank<br> Bank<br> Development Indicators<br> on WDI<br>omhouse<br> on WDI<br> and Sturm (2011)<br>et al. (2001)<br> on Beck et al. (2001)<br> on Beck et al. (2001)<br> on Beck et al. (2001)<br>a et al. (2003)<br>r and Sturm (2012)|
|---|
|source<br>World<br>World<br>World<br>World<br>The F<br>the H<br>World<br>World<br>World<br>World<br>based <br>Freed<br>based <br>Moser<br>WDI<br>WDI<br>Beck <br>based <br>based <br>based <br>Alesin<br>Drehe|
|defnition<br>assessment of the quality of a country’s economic management<br>assessment of the quality of a country’s structural policies<br>assessment of the quality of a country’s policies for social inclusion<br>assessment of the quality of a country’s public sector management and<br>institutions<br>average of Economic Freedom of the World Index area 1, 3, 4 and 5<br>Index of Economic Freedom based on trade freedom, business freedom<br>and investment freedom<br>cumulative count of loans with sector codes corresponding to CPIA clus-<br>ter A and B<br>cumulative counts of conditions with theme codes corresponding to CPIA<br>cluster A and B<br>year trend<br>logarithm of GDP per capita, PPP<br>Net ODA and ofcial aid over GDP (international $)<br>measure for Political Rights<br>logarithm of one added to gross IDA disbursements, current million USD<br>dummy coded 1 if an IMF arrangement was signed<br>Total debt service, as % of GNI<br>logarithm of population<br>dummy coded 1 if an election occurred at t+1<br>number of years that separate year t from the nearest election<br>number of years between election in year t and the previous election<br>average annual per capita growth over the period 1996-2008<br>measure for ethnic fractionalization<br>average fraction of votes on key issues aligned with the G-7 over the<br>period 1995-2008<br>Table A.3: variable defnitions and sources|
|uster A<br>uster B<br>uster C<br>uster D<br><br> of cumulative loans<br> of cumulative conditions<br>DP per capita<br> GDP<br> Rights<br>oss IDA<br>ment with IMF<br>vice<br>pulation<br> cycle<br> frequency<br>annual per capita growth<br>actionalization<br>fraction of votes with G-7|
|variable<br>CPIA cl<br>CPIA cl<br>CPIA cl<br>CPIA cl<br>EFW<br>Heritage<br>number <br>number <br>year<br>log of G<br>aid over <br>Political<br>log of gr<br>arrange<br>debt ser<br>log of po<br>election<br>election <br>election <br>average <br>ethnic fr<br>average <br>33|
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# **Table 7—Model Elasticities**

|||Armington elasticities|||S|upply elastic|ities||
|---|---|---|---|---|---|---|---|---|
||Domestic vs Foreign|Foreign vs Foreign|Dixit-Stiglitz|Africa|SADC|China|EUR and ROW|USA|
|**Business Services**|||||||||
|Air Transport|||3.0|1.9|3.9|6.0|10.0|15.0|
|Communication|||3.0|2.5|5.2|8.0|13.4|20.0|
|Insurance|||3.0|3.3|3.3|3.3|3.3|10.0|
|Business Services nec|||3.0|2.5|5.2|8.0|13.4|20.0|
|Financial Services nec|||3.0|3.3|3.3|3.3|3.3|10.0|
|Transport nec|||3.0|1.9|2.3|2.6|3.3|10.0|
|Water Transport|||3.0|1.9|3.9|6.0|10.0|15.0|
|**Dixit-Stigliz Goods**|||||||||
|Chemicals Mineral and Metal Products|||6.8|1.9|3.9|6.0|10.0|15.0|
|Energy and Minerals|||11.6|3.3|3.3|3.3|3.3|10.0|
|Food Products|||5.1|3.3|3.3|3.3|3.3|10.0|
|Petroleum and Coal Products|||4.2|3.3|3.3|3.3|3.3|10.0|
|Other Manufacturing|||7.7|1.9|3.9|6.0|10.0|15.0|
|Textile and Apparel|||7.6|3.3|3.3|3.3|3.3|10.0|
|Wood and Paper Products|||6.3|3.3|3.3|3.3|3.3|10.0|
|**CRTS**|||||||||
|Agriculture and Forestry|2.5|30.0|||||||
|Other Services|1.5|30.0|||||||
|Trade|1.5|30.0|||||||
|Utilities|2.8|30.0|||||||

Source: Dixit-Stiglitz elasticities in services are based on estimates from Broda and Weinstein (2006). Dixit-Stiglitz elasticities in goods are the estimates of the elasticity of substitution for imports from different regions in the GTAP data set. Armington elasticities of substitution of domestic for foreign are from the GTAP data set. Armington elasticities of substitution of foreign for foreign are based on the estimates of Reidel (1988). Supply elasticities are based on the estimates of Schiff, Wang and Olarreaga (2002) and Schiff and Wang (2006).

68
probability distribution of shocks for each household. For each location, we can obtain the distribution of shocks from historical data on drought incidence. In our case we have monthly rainfall and temperature data for well-defined spatial grids from 1998 to 2012. Therefore we can obtain the historical distribution of shocks that a household is likely to encounter at a location. We exploit this knowledge in combination with our knowledge on the average effect of a shock to calculate for each location the “expected loss” occurring from exposure to weather shocks for each time period in our historical distribution of the drought measure. In some periods the drought measure is positive, and the expected loss will be zero. Based on this, it is possible to evaluate how many (if any) observations will fall below a specified outcome measure threshold in a hypothetical future period, thus indicating vulnerability rates at different risk-levels.

We apply this empirical strategy to measuring vulnerability to malnutrition for young children in West Africa. We use DHS data from five West African countries in order to capture the incidence and prevalence of underweight and stunting that can be attributed to droughts. In the next section we take up a discussion of the results.

# **5. RESULTS AND DISCUSSION**

Table 3 shows the first stage results – the impact of shocks. The dependent variables are the two most common measures of child malnutrition. Both variables are standardized relative to the global reference median for children of the same age. The shock variable is also normalized using the distribution over time and across space in our sample. The dependent variable is scaled by 100. Therefore, the results suggest that for a standard deviation change in shocks, stunting changes by around an eighth of a standard deviation. The impact on underweight is just slightly 18