rafmacalaba/datause-encoder-data huggingface dataset:
|**Table 17: Exploring the Presenc**|**e of Omitted Variabl**<br>**Category of Ad**|**e Bias in Base OLS**<br>**ditional Covariates**<br>|**Regression Results U**<br>**Integrated to the Ba**<br>|**nderlying the Mean D**<br>**se Regression**<br>|**ecomposition South**|**, Female**<br>|
|---|---|---|---|---|---|---|
|_VARIABLES_|_Base South_|_Manager_<br>_characteristics_|_Plot_<br>_characteristics_|_Geospatial_<br>_characteristics_|_Crop fixed effects_|_Community_<br>_characteristics_|
|Female manager=1|||||||
|Age|0.0467|0.0747*|0.0408|0.0355|0.0205|0.0725|
||(0.0376)|(0.0426)|(0.0379)|(0.0378)|(0.0364)|(0.0440)|
|Age Squared|-0.000549|-0.000720*|-0.000506|-0.000461|-0.000300|-0.000733*|
||(0.000339)|(0.000389)|(0.000341)|(0.000348)|(0.000320)|(0.000386)|
|Years of schooling|0.0202|0.0220|0.0203|0.0131|0.0286|0.0245|
||(0.0187)|(0.0201)|(0.0194)|(0.0200)|(0.0174)|(0.0197)|
|Manager is nonmuslim†|||||||
|Manager has nonfarm activity†|0.136|0.159|0.0884|0.128|-0.0130|0.125|
||(0.192)|(0.199)|(0.190)|(0.193)|(0.184)|(0.202)|
|Household female adult size (age 12-60)|-0.127**|-0.0848|-0.134**|-0.104|-0.149**|-0.0888|
||(0.0647)|(0.101)|(0.0641)|(0.0648)|(0.0640)|(0.0665)|
|Household male adult size (age 12-60)|-0.00777|0.0305|-0.0201|-0.0257|0.0184|0.0148|
||(0.0652)|(0.108)|(0.0675)|(0.0647)|(0.0640)|(0.0624)|
|Child dependency ratio|-0.208|-0.177|-0.240|-0.214|-0.255|-0.0154|
||(0.182)|(0.236)|(0.182)|(0.180)|(0.186)|(0.196)|
|Log [GPS-Land Area (HA)]|-0.828***|-0.820***|-0.841***|-0.829***|-0.925***|-0.737***|
||(0.285)|(0.284)|(0.286)|(0.271)|(0.315)|(0.253)|
|Log [GPS-Land Area (HA) Squared]|-0.0220|-0.0159|-0.0273|-0.0234|-0.0416|-0.00469|
||(0.0536)|(0.0542)|(0.0538)|(0.0529)|(0.0581)|(0.0495)|
|Plot distance to household|0.00190***|0.00198***|0.00196***|0.00192**|0.00150***|0.00145***|
||(0.000505)|(0.000609)|(0.000560)|(0.000820)|(0.000464)|(0.000518)|
|Access to extension†|0.312|0.367|0.133|0.757|0.694|0.566|
||(0.481)|(0.485)|(0.507)|(0.471)|(0.502)|(0.510)|
|Grows cash crop†|0.0821|0.0500|-0.0668|0.0140|0.715**|0.111|
||(0.292)|(0.295)|(0.494)|(0.305)|(0.305)|(0.288)|
|Only one crop on plot †|<br>-0.679**|<br>-0.738**|<br>-0.629**|<br>-0.468*|<br>-0.0725|<br>-0.601*|
|Robust standard errors in parentheses. *** p<|(0.281)<br>0.01, ** p<0.05, * p<|(0.294)<br>0.1. State fixed effect|(0.281)<br>s not displayed. † indi|(0.266)<br>cates dummy variable|(0.295)|(0.319)|

54
**Figure 7. Urban Housing and Transportation Expenditures (of total expenditures)**

<!-- Start of picture text --> LBR<br>BFA<br>MOZ<br>TZA RWA<br>UGA<br>CMR CIV<br>NGA GHAZMB KEN<br>NER MWI<br>ETH<br>SLE SEN AGO<br>STP<br>COM<br>0 .1 .2 .3<br>housing expenditures<br> country Fitted values<br>.1<br>.08<br>.06<br>transportation expenditures .04<br>.02<br><!-- End of picture text --> This section explored household expenditures on housing, food, and transportation in order to understand how households allocate spending across countries and income quintiles. The examination of expenditures found very high levels of spending on food in SSA relative to other regions, indicating a pattern consistent with lower levels of development. The highest income quintile in SSA still spends a larger percent on food than the average household in Korea did in 1975. This level of spending was evident across all country income groups, but dropped considerably in the upper two income quintiles for all countries. The clear negative relationship between food and housing expenditures by country further suggests that expenditures on housing are unlikely to increase unless high shares of food expenditures decrease and free up household spending.

- **3 Housing Conditions in SSA: A continental challenge with evident subregional variation**

The previous section suggested that household expenditures in SSA are indicative of its stage of development: households in countries at initial stages of development end up spending most of their income on food. As incomes rise, household are able to shift some of their income away from food expenditures and into housing. In this section we focus instead on the quality of housing in the region and uncover challenges between urban and rural areas and across income quintiles. In particular, the analysis draws a regional picture of the presence or absence of housing amenities as well as a broader view of housing shortages. This approach unbundles the critical housing issues affecting households in SSA by identifying housing shortages and the level of deprivation in each country. This is a commonly used technique to elicit households’ housing constraints. For example, the UN defines slums in terms of deprivations in order to have standard indicators and track progress for programs like the Millennium Development Goals (UN-Habitat 2009). Additionally, basic residential infrastructure coverage is often used as a way to measure housing and living conditions (Angel 2000; Gulyani and Basset 2010). While previous studies have highlighted the challenges related to adequate housing provision, stressing the problem of informality in SSA (see Box 3 for a discussion), this is the first study to calculate measures of 9
The welfare expressions (14) and (15) form the basis for our quantitative welfare analysis of trade reform. In order to conduct such an analysis, we need information about the income parameters _μ_ , _σε_<sup>2,and∆</sup><sup>_σ_andthepreferencesparameters</sup><sup>_β_and</sup><sup>_γ_.Ourempiricalanalysis</sup> provides estimates of the income parameters. We estimate an average value of a quarterly variance, _σε_<sup>2,ofof</sup><sup>_._008(averagedacrossindustriesandover time), andthisisalsothevalue</sup> we use in all welfare calculations reported below. Similarly, we choose a quarterly growth rate _μ_ = _._ 005 to match the average growth rate in aggregate real income in Mexico over the relevant sample period. For the preference parameters, we choose a quarterly discount factor of _β_ = _._ 99 and a degree of risk aversion of _γ_ = 1 (log-utility) for the baseline economy. These values for the preference parameters are in line with the values used in the macroeconomic literature (Cooley (1995)). However, we also report the welfare results for a higher degree of risk aversion ( _γ_ = 2).

We conduct the following exercises. Starting from a tariff level of _τ_ = _._ 10, which is roughly the average tariff level in our data set, we consider the welfare consequences of reducing the tariff level to _τ_<sup>_′_</sup> = _._ 05. Our empirical analysis in section III suggests that this tariff reduction has two effects for industries with high import penetration. First, there is a short-run effect that leads to an increase in income risk for one year (four quarters), and in this section we evaluate the welfare cost of this short-run effect. Second, there is an “interaction effect”, and we report the welfare cost corresponding to this effect as follows. We compute the welfare cost of a short-run increase in income risk following a real exchange rate appreciation of ten percent with the tariff level also at ten percent, and then compare this welfare cost of the same exchange rate appreciation with the cost that obtains when the prevalent tariff level were five percent instead. Finally, we consider the welfare effect of a change in income risk due to a downturn in the economy, with the growth rate of GDP lowered by five percent, and again see how this is altered if the tariff level were lowered by five percent.

31
|Direct exports 10% or more of sales Y/N|0.048<br>(0.031)|0.083<br>(0.052)|
|---|---|---|
|Foreign ownership Y/N|0.005|-0.006|
||(0.030)|(0.056)|
|Establishment has checking or savings account Y/N|-0.018|0.038|
||(0.022)|(0.038)|
|Manufacturing Sector Y/N|0.004|0.018|
||(0.035)|(0.063)|
|Constant|1.901|6.321|
||(4.019)|(7.138)|
|Year Fixed Effects|YES|YES|
|Firm Fixed Effects|YES|YES|
|Number of observations|25,337|25,712|
|Adjusted R2|0.364|0.401|

Note: *** p<0.01, ** p<0.05, * p<0.1. Regressions use survey weights. Standard errors are clustered at the country level Next, we examine the intensive margin of investment, namely, the amount of a firm’s investment when public debt rises. The dependent variable is log of investment in current USD.<sup>3</sup> To avoid taking log of zero investment, we add one unit (USD 1) to the investment variable. Table 3 reveals that when public debt rises, firms invest significantly less. Real investment by firms drops by 2.4 percent for each 1 percentage increase in central government debt. The decline is larger in equipment investment (-2.3%), although the drop in land building investment is quite substantial (-0.9%). Note that firm fixed effects are included in the estimations.

**Table 3: Public Debt and The Intensive Margin of Investment**

|**Panel**|**Log of A**|**mount Investm**|**ent (+1)**|
|---|---|---|---|
||**Total**<br>**Investment**|**Land and**<br>**Building**|**Equipment**|
|Central Gov debt, % of GDP (t-1)|-0.024***|-0.009***|-0.023***|
||(0.006)|(0.003)|(0.005)|
|Log of real GDP per capita|-2.851|-2.896*|-2.371|
||(3.791)|(1.714)|(3.759)|
|Log of age of firm|-0.098|-0.040|-0.098|
||(0.313)|(0.130)|(0.295)|

> 3 To the extent that the year fixed effects capture the USD inflation, the use of current USD is equivalent to real USD.

11
The ultimate goal of VAT reforms is to have a broad based tax that will cover most goods and services. Exemptions and reduced rates should be kept at a minimum and classified in broad categories as opposed to long lists of items defined at the sub‐product level. For example, all medicines, medical equipment, food, books and periodicals can be exempt. Provision of exemptions or reduced VAT rates for inputs and machinery used by certain types of firms is hard to justify on policy grounds. These incentives would make little difference to the ultimate tax burden of firms as VAT on such purchases is creditable. If the objective is to relieve exporting firms of the cash flow and compliance burden, then the solution lies in improving VAT administration and provision of prompt refunds to exporters. Multiple cascading indirect taxes should be replaced by a single levy. If properly implemented, the announced reforms of indirect taxes in Bangladesh and India could offer useful lessons for other countries in the region. Sound evaluation of the design and impact of these reforms will be necessary.

Simpler and broad based corporate and VAT taxes will be easier to administer and comply with. Tax systems that have similar rates and rules for most taxpayers reduce opportunities for evasion and encourage voluntary compliance. Reform of tax administration can make compliance less costly. Priorities for reform include streamlining business processes, reducing processing times, cutting the number of annual tax payments (which is currently among the highest in the world), making greater use of IT systems (including online filing) and providing taxpayer education services. Countries that frequently change tax rules and procedures should consider instituting a cap on the number of SROs that can be issued throughout the year. Several measures can be taken to better detect fraud and non‐compliance, including through regular risk based audits and use of third party information (e.g. from banks, customs, land registration records) to prevent tax evasion.

Broadening the tax base and strengthening public administration will improve revenue collection. This will create space for reducing corporate income tax rates (that are currently higher than in other developing regions) without sacrificing essential public spending. Lower rates and more predictable tax policy and administration will increase investment attractiveness of South Asian countries. Tax reforms will have a stronger impact on business and investment growth if implemented as part of a wider reform package to improve the regulatory environment.

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What conclusions emerge from the work summarized above?

> O At the national level, high per capita income is not in itseef critical to better health. This is perhaps the single most important conclusion.

Though some minimal level of income is almost surely a prerequisite to maintaining good health, that level is fairly low. Above that level, other factors matter as much if not more.

- O Much improvement in health has occurred as a result of development in .he broadest sense, not because of improvements in personal health services.

The cross-national studies suggest that in the first two or three decades of the postwar period, improvements in the cachnology of disease control, in transportation and communication, and in food distribution during emergencies,11 were important contributors to better health. Many of these factors required no change in health systems in developing countries to bring about lower mortality, and little or no change in Individual attitudes or behavior. In this sense, development in the broadest sense has and should continue to contribute to better health.

On the other hand, the relative power of such factors as these, that do not rely on changes in individual behavior (or rely on simple one-time acts In the Cebu study, community-level data on prices, service availability and quality of health care, as well as access to transportation, and so on have been collected. For examples of use of such data, see Gertler and van der Gaag (1988), Akin et. al. (1985), and Cebu Study Team (1990).

Unfortunately even these prices may ns: be immune from endogeneityl Rosenzweig and 'Wolpin (1989) explore the possibility of migration in renponse to community prices, which -- if it occurs -- raises questions aoout the exogeneity of such "prices." For a further comment on this and other problems of methodology in econometric studies of household behavior, see fn. below. 11 These factors, not included in the analyses, are assumed to explain the Ounexpected' improvements in life expectancy reported by Preston and by Wheeler.
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- In all strategies, the FCB funds initial scale-up payments and other financial instruments do not become available or used until the total amount of available FCB has been exhausted. The available FCB was set equal to the 2015 FCB of US$50 million. Variations in the amount of funds available through the FCB were also considered, details of which can be found in Annex 6.

- Under Strategy B, insurance starts to pay out once the FCB of US$50 million has been exhausted, and stops paying out once scale-up payments reach US$455 million (the 1 in 30 year PSNP scaleup contingent liability). All expenditure between these two points is fully insured, therefore the maximum insurance payout is US$405 million (US$455 million less US$50 million). The insurance premium for this defined coverage was determined based on an assumed market-based insurance pricing multiple.

- Under Strategy C, the amount of available budget reallocation was assumed to be US$100 million. This assumption has been made with reference to the recent budget reallocation by the government of Ethiopia of approximately US$70 million for drought response. Budget reallocations of larger amounts are likely to be more costly and to incur greater hurdle rates<sup>10</sup> .

- In all strategies it is assumed that the HRD (humanitarian response) acts as financier of last resort, only used after all other financial instruments have been exhausted. In practice HRD allocations are uncertain but for this analysis we assume that they can be relied on.

Economic and other financial assumptions in respect of the financial instruments are required for the analysis, details of which are included in Annex 3.

# **4 Main Findings**

## **Base Case Scenario**

The cost of each risk financing strategy in each of the 5,000 simulated scenarios was determined using the formulae for opportunity cost derived in Clarke et al. (2016). In each of the 5,000 years the average cost is calculated as well as the cost at different return periods (events with different probabilities of occurrence). For the purpose of this paper we report the average, 1 in 5 year and 1 in 30 year cost of each strategy.

The average expenditure to be financed is US$139 million, covering an average of 2.9 million additional beneficiaries per year, and the average cost of financing this liability ranges from US$175 million to US$ 230 million (Figure 4.1 and Table 4.2).

> 10 US$70 million was the highest reallocation known at the time of this paper being written. Recent data in MarchApril 2016 suggest that the Government of Ethiopia may be committing to reallocate as much as US$700 million from the oil reserve for future drought response, though the hurdle rate of this reallocation is unknown.

8
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