rafmacalaba/datause-encoder-data huggingface dataset:
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# **4. Oil Price Effects**

Oil prices have fluctuated quite markedly over the period, as have the prices of other commodities. In particular, oil prices were nearly three times as high in real terms on average over the period 2005–13 as in the years 1996–2005. In tables 9 and 10, we explore whether the relationship between fiscal aggregates and fuel exporting is different in the two halves of the data period, by including a dummy variable that is equal to one from 2005 onward, and equal to zero in the early years, together with an interaction term between this dummy and the fuel exporter dummy. The time trend that was included in earlier tables is omitted.

In table 9 the coefficient of the 2005-13 dummy indicates that resource-poor countries are estimated to have received significantly more tax revenue and also to have spent significantly more in 200513, with the revenue effect being larger, so their fiscal balance improved on average by 0.8 percent of GDP. The coefficients of the fuel exporter dummy are similar to those in table 1, except that expenditures of fuel exporters are estimated not to have increased in the second period as expenditures of resource-poor countries have. The revenues of fuel exporters have also increased less than the revenues of resource-poor countries, although not significantly so.

Table 10 suggests that fuel exporters spent 4.9 percent of GDP more on government consumption and 1.7 percent of GDP more on government investment than resource-poor countries in the years 1996–2004, but there was a shift in the second period, when fuel exporters spent only 1.3 percent (= 4.9 – 3.6) of GDP more on consumption and 2.8 percent (= 1.7 + 1.1) more on investment. This implies that in 2005–12 the extra revenues of fuel exporters (+9.0 percent of GDP) were overwhelmingly saved (fiscal balance +5.0 percent of GDP and investment +2.8 percent of GDP).

An interesting question is how fiscal variables in fuel-exporting countries react in the short term to changes in the real price of oil. Table 11 shows that revenue is strongly correlated with the real oil price, as one might expect, but that expenditure reacts only with a lag. Consequently the fiscal balance improves in the first year when the oil price rises. Table 12 looks at oil price effects on different types of expenditure. Both capital and consumption expenditure increase significantly in the year after an oil price increase. A second lag of the oil price is not significant (results not shown). These results are consistent with the analysis of surges in natural resource revenues in IMF (2012), which reports that savings increase more than investment, particularly for short-lived surges. If investment can be undertaken efficiently in developing countries, it is likely to have a high return, and a substantial increase in public investment may be an optimal response to a resource boom (Berg _et al_ ., 2013).

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pattern of macroeconomic fluctuations, and the bulk of it focuses on the channel of firms (Bernanke and Gertler 1989; Greenwald and Stiglitz 1993; Aghion et al. 2010).

The early literature predicts that financial development reduces macroeconomic fluctuations (Bernanke and Gertler 1989; Greenwald and Stiglitz 1993). More recent literature suggests that the nature of relationship between financial development and macroeconomic volatility can be nonlinear (Aghion et al. 2004) and may depend on several factors, such as the composition of short-term and long-term investments in the economy (Aghion et al. 2010).

# _The Model_

Consider a closed economy that is populated by a continuum of infinitely lived households and firms, both of measure unity. There exists a fraction λ of households with no access to banking or other instruments to save. These consumers, who may be referred to as non-Ricardian households, are liquidityconstrained and unable to save or borrow to smooth consumption. They have no assets and spend all their current disposable labour income on consumption in each period.

Labour supply is inelastic as no labour-leisure choice is made by the representative household. Emerging economies are characterised by large size of informal employment where average hours of work are found to be higher than that in the formal sector employment (Blunch et al. 2001; International Labour Organization 2012). For instance, studies found that informal sector workers worked on average fifteen hours more than their counterparts in the formal sector (Blunch et al. 2001).<sup>2</sup> Hence, in an emerging economy setup, it is reasonable to

> 2 In India, more than 90% of the workforce and about 50% of the national product are accounted for by the informal economy (Report of the Committee on Unorganised Sector Statistics 2012). According to National Sample Survey Organisation (2004–05), of the total workers, 82% in the rural areas and 72% in the urban areas are engaged in informal sector. In terms of absolute numbers, out of the total 465 million people employed in the formal and

11
# Figures and Tables

_Table 1 Main outcomes of interest_

|_Soci_|Outcomes Name<br>_o‐economic outcomes – s_|Details<br>_urvey based_|
|---|---|---|
|1|Employment index|Standardized weighted average of the number of hours spend on wage<br>employed activities in the past 7 days, (log) cash wage received in the past<br>7 days, (log) outstanding wage from the past 7 days, (log) total wage in past<br>7 days, number of activities on wage employment in the past 7 days,<br>number of hours spend on self‐employed activities in past 7 days, (log) self‐<br>employed cash earnings in the past 7 days, (log) self‐employed in‐kind<br>earnings in the past 7 days, (log) outstanding earnings from the past 7 days,<br>(log) total self‐employed earnings in the past 7 days, number of self‐<br>employed activities in the past 7 days, total number of employees, (log)<br>business revenue during the past 4 weeks, (log) business sales yesterday,<br>(log) aggregated business costs in the past 4 weeks|
|2|Consumption index|Standardized weighted average of the number of different food items<br>consumed in the past 7 days, (log) total food expenditure in the past 7<br>days, (log) value of self‐produced food in the past 7 days, (log) expenditure<br>on non‐food items in past 1month, (log) expenditure on assets in past 1<br>month|
|3|Savings, investment<br>and debt index|Standardized weighted average of having or sharing a formal bank account,<br>currently saving any money, (log) amount held at bank account, (negatively<br>coded) number of formal loans received, (negatively coded) other debt,<br>(negatively coded) number of informal loans received in the past 1 month,<br>(negatively coded) (log) total amount of formal loans, (negatively coded)<br>(log) total amount of informal loans, business ownership, participation in<br>training during the past 12 months, number of trainings done in the past 12<br>months|
|4<br>_Psyc_|Business skills index<br>_hological and behavioral_|Standardized weighted average of frequency of visiting competitors,<br>frequency of asking customers about other products they would like to be<br>sold, frequency of setting sales targets, frequency of comparing targets to<br>performance, frequency of recording purchase and sales, knowledge of the<br>business register, knowledge of fees to register a business at cashier’s<br>office of the Business Register, knowledge of operating license from State<br>government, knowledge of inspections from payam authorities, knowledge<br>of taxes, knowledge of bribes (rashua), knowledge of paying an<br>intermediate person to take care of taxes, registration of company name at<br>business register, registration at cashier’s office of the Business Register,<br>obtainment of operation license from the State government, experienced<br>inspection by payam authorities, payment of formal taxes, payment of<br>bribes (rashua), payment of intermediary person to take care of taxes<br>_outcomes_|

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“the financial management system which the Norwegians chose to trust functioned very badly.” In this case, trust may have been inefficiently high, but more often it is likely to be too low.

The Paris Declaration’s high numerical targets for several indicators of aid harmonization and alignment, to be achieved by 2010, reflect a view that donors currently exhibit too little trust in recipient countries’ systems, even taking their flaws into account. The use of country systems can readily be portrayed as a prisoners’ dilemma game among donors, with trust as an efficient but non-equilibrium outcome. Standardization of aid management procedures could substantially reduce transactions costs for recipients, at a relatively small cost to each donor. Taking procurement as an example, an individual donor’s first preference would be for all other donors to be bound by a set of harmonized regulations that did not favor any particular donor country’s contractors, but to remain free itself to use its own procurement rules. Any other single donor would have the same preference, so the equilibrium outcome is non-harmonization. In the absence of any enforcement mechanism, a donor has an incentive to “defect” and use its own procurement systems. However, a donor would prefer a harmonized set of rules binding all donors, including itself, to the fully non-harmonized outcome if the savings in transactions costs to recipients were sufficiently high.

Moreover, it is generally acknowledged that using recipients’ aid management systems strengthens them, while avoiding them undermines them, by diffusing accountability and fragmenting policy and planning processes (OECD, 2009b; Mokoro Ltd. 2008a). When donors bypass country systems they often staff their own parallel aid management systems by “poaching” the most talented government officials. Trusting in country systems rather than bypassing them therefore increases the productivity of public funds including aid, but an individual donor typically will under-invest in strengthening country systems, as the benefits accrue mostly to other donors and in the future while it bears the full (and immediate) costs.

The Paris Declaration, along with its follow-up “Accra Agenda for Action” in 2008, can be viewed as an agreement among donors acknowledging these collection action problems, and creating a modest level of peer pressure to “cooperate” rather than “defect” on the optimal outcome of increased investment in the strength of country systems.<sup>2</sup> The OECD-DAC, in cooperation with the UN and World Bank, conducts “Paris Declaration Monitoring Surveys” that measure progress toward numerical targets for harmonizing aid and aligning it with country systems. The DAC’s periodic “peer reviews” of the aid systems of donor countries now include sections assessing progress towards better-harmonized and better-aligned aid.

> 2 International donor conferences on harmonization bring senior managers of aid agencies “in close contact with colleagues from other agencies, pushing them to align with recognized international best practice and not be seen as laggards” (de Renzio, 2005: 11).

6
considerably more vulnerable than others. In a country where dependency on men ─ first one‘s father and then one‘s husband ─ is embedded in the law and culture, one might expect shocks such as losing a father or becoming a widow to be closely associated with worse living standards and current poverty. The paper presents empirical evidence that confirms this hypothesis. However, the paper also presents evidence suggesting that the disadvantages and poverty faced by widows have longer lasting deleterious welfare and growth consequences by way of their dependents, even when the widow remarries.

Drawing on the literature, the following section details the various forms gender differentiation within and across households can take and how this may result in heightened vulnerability. In this context, the role and existence of mitigating social safety nets in Mali is also discussed. Sections 3 and 4 then turn to the quantitative data for this study with analyses of the 2006 Enquête Légère Intégrée Auprès des Ménages (ELIM) and Demographic and Health Survey (DHS) for 2006, respectively. As one way to get at these issues with existing household level data, Section 3 first examines female headed households and whether they are poorer than male headed households and under what circumstances. This leads me to focus on a specific shock, namely widowhood. Section 4 first addresses the possibility of selection into widowhood and presents evidence that this is not an important source of bias. Using the DHS, the paper then provides evidence that gender differentiation has a particularly significant and negative effect for ever-widowed and less-so for ever-divorced women. A final section concludes.

# **2. Understanding the sources of vulnerability for women in Mali**

Welfare relevant gender differences can be identified along a number of dimensions, including: legal protection, individual endowments, preferences, constraints stemming from social norms (for example, on options outside the household), and access to and control over household and community assets and resources. Such gender differences within the household also interact with age in influencing welfare outcomes, including inequality between men and women and the welfare effects of shocks.

<u>Legal protection</u> in Mali and in many Sahelian West African countries privileges men. Inheritance and marriage continue to be controlled by customary law under which individual rights and particularly women‘s rights are often denied in favor of preserving tradition (Wing 3
|**Table 1: Ba**|**se Regression Res**|**ults, OLS**|||
|---|---|---|---|---|
|Dependent variable:_Employment Growth_|**(1)**|**(2)**|**(3)**|**(4)**|
|Overall Corruption (CI avg.) * Financially|-1.374***|-1.363***|-1.268***|-1.260***|
|Constrained (CI avg.)|(0.460)|(0.440)|(0.445)|(0.445)|
|Overall Corruption (CI avg.)|0.836**|0.820***|0.783**|0.775**|
||(0.329)|(0.317)|(0.321)|(0.321)|
|Financially Constrained (CI avg.)|1.228|1.380|1.338|1.344|
||(1.708)|(1.751)|(1.758)|(1.765)|
|Initial Employment (logs)|-5.760***|-5.608***|-5.872***|-5.874***|
||(0.348)|(0.325)|(0.322)|(0.328)|
|Age of Firm (logs)||-2.138***|-2.033***|-2.034***|
|||(0.296)|(0.294)|(0.294)|
|Manager Experience (logs)||0.103|0.011|0.022|
|||(0.273)|(0.275)|(0.274)|
|Exports (proportion of sales)||1.324|1.095|1.099|
|||(2.157)|(2.189)|(2.198)|
|Foreign Ownership (% ownership)||0.024***|0.022**|0.022**|
|||(0.009)|(0.009)|(0.009)|
|Multi-firm (dummy)||1.819**|1.745**|1.733**|
|||(0.705)|(0.715)|(0.709)|
|Female Manager (dummy)|||-1.230***|-1.230***|
||||(0.444)|(0.443)|
|Power Outage (logs)|||0.193|0.200|
||||(0.124)|(0.122)|
|Informal Competition (dummy)|||-0.859*|-0.832*|
||||(0.448)|(0.435)|
|Firm Bought Assets (dummy)|||3.022***|3.043***|
||||(0.440)|(0.455)|
|Time Tax (%)||||0.004<br>|
|||||(0.015)|
|Courts: Obstacle Severity||||0.012|
|||||(0.213)|
|Crime Losses (% of sales)||||-0.014|
|||||(0.046)|
|Taxes: Obstacle Severity||||-0.161<br>(0.166)|
|Labor Laws: Obstacle Severity||||0.002|
|||||(0.174)|
|Industry fixed effects|Yes|Yes|Yes|Yes|
|Country fixed effects|Yes|Yes|Yes|Yes|
|Number of observations|41,203|41,203|41,203|41,203|
|R-squared|0.167|0.177|0.186|0.186|

Standard errors in brackets. All standard errors are Huber-White robust and clustered on the country times industry level. Significance is denoted by *** (1%), ** (5%), * (10%). All regressions use a constant term (not shown).

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Table A.1: Summary Statistics

||Mean|Median|standard Dev.|Minimum|Maximum|
|---|---|---|---|---|---|
|Real annual Wage (Rs/year)|57065|44871|48982|421|1576665|
|% Area Under LDO|0.097|0.048|0.122|0.002|0.626|
|Travel Time to Large City (hour)|2.479|1.883|2.348|0.078|15.182|
|Area LDO*Travel Time|0.350|0.087|0.617|0.001|4.947|
|Area of DSD (0000 sqkm)|0.015|0.011|0.013|0.002|0.087|
|% area >1000 feet of elevation|0.090|0.000|0.232|0.000|1.000|
|Gabalon's Malaria Endimicity Index|3.407|1.480|3.410|0.340|11.500|
|Plasmodium Vivax (000 cases)|0.040|0.009|0.107|0.000|0.959|
|Plasmodium Falciparum (000 cases)|0.013|0.004|0.030|0.000|0.368|
|% of lab. Forc. w. education>= primary|0.675|0.674|0.151|0.293|0.961|
|% of area under inland water bodies|0.066|0.000|0.157|0.000|0.960|
|Land quality (% Excellent )|0.040|0.000|0.148|0.000|1.000|
|Land quality (% Very good)|0.044|0.000|0.146|0.000|0.990|
|Average Slope (%)|11.303|8.420|9.283|0.186|33.100|
|Log( Education Level (yr))|2.037|2.303|0.683|0.000|2.833|
|Log(Age)|3.604|3.638|0.289|3.045|4.174|
|Male|0.693|1.000|0.461|0.000|1.000|
|Married (yes=1)|0.756|1.000|0.430|0.000|1.000|
|Christian (yes=1)|0.055|0.000|0.229|0.000|1.000|
|Muslim (yes=1)|0.033|0.000|0.179|0.000|1.000|
|Buddist (yes=1)|0.782|1.000|0.413|0.000|1.000|
|Moor (yes=1)|0.032|0.000|0.177|0.000|1.000|
|Estate (yes=1)|0.150|0.000|0.357|0.000|1.000|
|Manufacturing (yes=1)|0.233|0.000|0.423|0.000|1.000|
|Unskilled Services (yes=1)|0.192|0.000|0.394|0.000|1.000|
|Skilled Services (yes=1)|0.287|0.000|0.453|0.000|1.000|
|Share of agri in total employment(%)|0.333|0.331|0.211|0.008|1.000|
<!-- Start of picture text --> _<br>c<br>2005 2010 2015 2020<br>year<br>——— (mean) log Pow er Spending ——— (mean) log GDP<br><!-- End of picture text --> <!-- Start of picture text --> =<br>2cs<br>o<br>»ry<br>2005 2010 2015 2020<br>year<br>——— (mean) log Road Spending ——— (mean) log GDP<br><!-- End of picture text -->
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agriculture in the domestic economies of SSA and the expansion of the nontraded service sector between the two periods (see table (1.3)). Similarly, the average ratio of external debt service to exports increased sharply from an average of 9.6% in 1973-81 to average 16.7% for the 1982-85 period (table (1.2)), the stock of external debt io GDP ratio also rose from 39% in 1980 to 69% in 1987. In short the story of economic performance in Africa is summed up by Oyejide (1990), There is very little debate regarding Africa's poor economic performance and the Long-term nature of the decline in living standards, particularly during the 1980's. But controversy continues to surround the issue of which factors are responsible for the crisis." One important interpretation of the causes behind the declining shares of agriculture and the general worsening positions of the economies of SSA as described above, emphasizes the role of domestic economic policies, especially real appreciation and real overvaluation.<sup>1</sup> ' The evidence from Sub-Saharan Africa (see table (1.2)) points to episodes of dramatic real appreciation over the seventies and the first half of the 80s. The real appreciation is clearly related to the high rate of fiscal expansion and the increaced domestic absorption (negative of the resource balance) where both indicators respectively rose by 2.1 and 1.4 percentage points of GDP between the first two periods (table (1.2)). On the face of this expansive macroeconomic policies, SSA experienced severe negative external shock, where the value of this aggregate shock turned from a small but positive average of 0.1 to a negative at -5.3. The terms of trade component of this shock also reflect the same effect, with the TOT index declining from 106.3 in 1973-81 to 91.6 in 1982-85. Also external finance available to SSA dropped by more than 50% over the two periods. Therefore, as the RER is appreciated, the major fundamentals call for an equilibrium depreciation of the RER. Hence, it is likely that the economy wide-structure of t'A frequently cited analysis of the economic crisis of SSA representing this tradition is the World Bank's Berg Report (World Bank (1981)).
Going beyond the number of countries and looking at the overall trade balances for groups of countries, Sub-Saharan Africa, as a group, has a trade deficit in raw foods which is slightly more than 1 percent of their imports. Middle-income SSA is food surplus but lowincome SSA is a net food importer.

Unlike other low-income countries, being oil exporter or having civil conflict does not make that much different in net food imports of low-income SSA countries as a percentage of their imports. All three groups have deficits of about 3 percent of their imports.

**Table 6: African Trade Balance for Raw Food by Income Group Country**

||Net E<br>|xports/Imp<br>|orts($mill<br>|ion)<br> <br>|Net Exp<br>|orts as % o<br>|f All Goods<br>|Imports<br>|
|---|---|---|---|---|---|---|---|---|
|CountryGroup (No. of Country)|1980/81|1990/91|2000/01|2004/05|1980/81|1990/91|2000/01|2004/05|
|Sub-Saharan Africa, All (47)|-137|231|14|-1,329|-0.3|0.5|0.0|-1.1|
|SSA Middle-income (8)|733|737|1,109|1,257|5.0|4.9|3.9|2.5|
|SSA Low-income (39)|-869|-506|-1,094|-2,586|-2.5|-1.7|-2.4|-3.4|
|SSA Oil Exporters (6)|-566|-210|-682|-1,333|-2.9|-1.9|-4.0|-4.0|
|SSA Civil Conflict States (5)|-35|-62|-60|-178|-0.7|-1.1|-1.0|-2.5|
|Other SSA Low-income(28)|-269|-234|-353|-1,074|-2.4|-1.8|-1.5|-3.0|

Notes: Food is defined as raw food in SITC Revision 2, excluding all cash crops, processed food and seafood (see detailed classifications in Appendix Table 1).

The classification of country groups is provided in Appendix Table 4.

** Gabon is also in oil exporters, we added it to the middle-income group, not to create another group for just one country.

Source: Based on partner data from UN COMTRADE Statistics.

The picture shown above does not however mean that SSA would definitely lose under global reforms. The continent is a significant agricultural exporter that tends to export other agricultural products and import primarily grains. Table 7 shows the number of SSA countries that are net exporters of agricultural commodities.

**Table 7: African Country Classifications by All Agricultural Trade**

||No. of Co<br>|untries in All Agricul<br>|tural Trade<br>|
|---|---|---|---|
|CountryGroup|Net Exporter|Net Importer|Total|
|Middle-income **|6|2|8|
|Low-income|26|13|39|
|Oil Exporter|4|2|6|
|Civil Conflict States|2|3|5|
|Other Low-income|20|8|28|
|Sub-Saharan Africa,total|32|15|47|

Notes: All agriculture is defined as all raw food, cash crops and agric raw materials in SITC Revision 2, excluding processed food and seafood products (see detailed classifications in Appendix Table 1).

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