rafmacalaba/datause-encoder-data huggingface dataset:
as the proxy for expected net gain in absolute terms. When ∆ _yi_ is included as a regressor in the vector **x** _i_ , one potentially faces the problem of a contaminated regressor. The applicable explanatory variable is expected net gain while the variable used is a noisy realization of it. We are thus facing a problem analogous to the one of errors in variables in a linear regression model. The extent of the estimation bias induced by this problem depends of course on how accurate individual expectations are. If individuals can perfectly predict the actual income change, the use of ∆ _yi_ as explanatory variable is valid. The wider realized gains are distributed around expected gains, the more severe the bias introduced by using ∆ _yi_ .

There is yet another reason for why ∆ _yi_ is only a noisy measure of the expected income change. Individual shocks may not be i.i.d. in each month, but rather be correlated. For example, if a fisherman falls unexpectedly sick for an extended period of time right after purchasing a fibre boat and this reduces his ability to go fishing, ∆ _yi_ underestimates his expected gains.

For both of these reasons, we will experiment with two specifications in the empirical analysis. One where ∆ _yi_ is included in the **x** _i_ vector without modification and one where, in the spirit of the two stage least squares model, ∆ _yi_ is first regressed on a vector of instruments and its predicted values ∆<sup>�</sup> _yi_ say, are used instead. Indirect evidence for the “noisiness” of ∆ _yi_ is provided by the fact that our estimates of ∆ _yi_ are negative for one fifth of those households for which both catamaran and fibre sales data are available. For these households, individual rationality seems to be violated as they adopt although they expect smaller profits from the new technology.

To give an idea of how income change and adoption time are empirically related, Figure 2 plots _t_<sup>_∗_</sup> _i_<sup>over ∆</sup><sup>_yi_.If ∆</sup><sup>_yi_is an accurate measure of expected gains, unconstrained</sup> economic efficiency dictates that all households which realize a positive income change adopt immediately while those with a negative ∆ _yi_ never adopt. When funds available to the fishing village are limited, constrained economic efficiency dictates that households which realize a positive income change adopt in decreasing order of ∆ _yi_ . While there is some negative correlation between _t_<sup>_∗_</sup> _i_<sup>and∆</sup><sup>_yi_(thecorrelationcoefficientequals-0.04),</sup> this relationship is apparently rather weak. This finding may provide a lower bound on the 20
# * **Are We Confusing Poverty with Preferences?**

Bart van den Boom<sup>1</sup> , Alex Halsema<sup>1</sup> and Vasco Molini<sup>2</sup> 1 Center for World Food Studies, VU University Amsterdam, The Netherlands 2 The World Bank _Keywords_ Poverty; revealed preferences; heterogeneous preferences; Mozambique; Ghana; food Engel-curve JEL classification: C23, D31, N36

* The authors acknowledge financial support from the World Bank. Earlier drafts of this paper were presented at the CSAE conference in Oxford.  We thank Kathleen Beegle and Justin Sandefur for comments on an earlier version of the manuscript. Of course, we are the sole responsible for all possible errors the paper may contain.
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The livestock oriented equations are direct applications of microeconomic theory. Animal stocks are specified as a function of producer prices for livestock commoditles, crop price guarantees, Input prices and lagged stocks. Productlon of livestock commodities Is specifled as a functlon cf producer prices, feed crop price guarantees, animal stocks and lagged productlon. Per capita consumption of livestock commodities Is determined by consumer prices for livestock commodities, total per capita consumption, reative price for food and lagged per capita consumption of the livestock commodity. Nex exports of livestock commoditles are specified as a funcl:ton of producer prices and animal stocks In the US. Market clearing for livestock cormoditles Is accomplished by determning the price which equates quantity demanded with quantity supplied.

# IV The FAIMC;'-

The FAIRMODEL of the U.S. macroecono,ny contains 128 equations In total, wlth 30 stochastic equatlons and 98 Identitles. The model consists of six sectors: household, firm, Yinancial, tederal government, state atnd local government and foreign. It is designed to simulate a variety of alternative lJ.S. macroeconomic policy scenarlos. The FAIRMODEL is described in detail in Fair (1984). In the scheme of Figure 1, the FAIRMODEL generates solution values of llnkage variables that are transferred to a Mexican macroeconomic model and the USAGMKTS model. For the analysis presented In this paper, lack of an adequate macroeconomic model for Mexico means that the path of effects from U.S. macropolicy vla the Mexican macroeconomy to Mexican agricultural markets does not exist. Instead, linkage variables from the Mexican macroeconomy to MEXAGMKTS are fixed at
and lenders make, their behavior can be expected to change. One way to see whether governance improves with greater transparency is to look at how transparency affects access by firms to finance through its impact on corruption. BDL (2005) conduct an empirical investigation into the impact of supervision and private monitoring on corruption in lending. They use the index of supervisory practice and an index of private monitoring used in this paper. In this paper, I extend their analysis and ask whether economic transparency affects corruption in bank lending after having accounted for supervisory and regulatory practices in the financial sector. Though it is not possible to simultaneously investigate all the channels through which economic transparency may affect financial sector outcomes, I investigate whether reducing corruption in bank lending may be one of the ways in which bank portfolios may improve. **_Theoretical Justification_** In most of the finance literature, information asymmetries are of two types: (a) lack of information on borrower type and limited ability to monitor borrower actions lead to adverse selection and moral hazard; and (b) insufficient checks and balances (incentives and penalties) on bank owners/directors and supervisors, and the presence of moral hazard, lead to poor bank portfolios and bank failures. Usually, the literature has focused on how improving information asymmetries between (a) regulators and depositors on the one hand and bank management /owners on the other, or (b) lenders and borrowers can improve bank portfolios. The primary information asymmetry that this paper focuses on is that between the state and private agents. But this information asymmetry has consequences for how lenders and borrowers behave. The government knows the economic situation but either for political reasons (for example, the desire to hide bad 11
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|**Table 4: Regression results for log PM2**<br>|**.5 concentratio**<br>|**n (mg/m**<sup>**3**</sup>**) in**<br>|**kitchens**<br>|
|---|---|---|---|
|**Explanatory variables**|**(1)**|**(2)**|**(3)**|
|_Fuel and Stove use_||||
|Household uses wood and traditional stoves|0.305**|0.288**|0.273*|
||(2.33)|(2.23)|(2.11)|
|Household uses wood and improved wood stoves|−0.023|−0.350|−0.075|
||(−0.09)|(−0.15)|(−0.33)|
|Household uses charcoal and improved stoves|0.190|0.143|0.136|
||(1.43)|(1.13)|(1.08)|
|Household uses ethanol|−0.856**|−0.701**|−0.722**|
||(−4.96)|(−4.25)|(−4.40)|
|_Kitchen characteristics_||||
|Open Kitchen|-|−0.030|−0.020|
|||(−0.30)|(−0.21)|
|Airy Kitchen|-|−0.124|−0.131|
|||(−1.09)|(−1.15)|
|Kitchen with permeable roof|-|−0.058|−0.048|
|||(−0.36)|(−0.30)|
|Kitchen with permeable wall|-|0.270|0.297|
|||(1.03)|(1.10)|
|Size of kitchen (Sq. ft.)|-|−0.020**|−0.020**|
|||(−4.96)|(−4.91)|
|Size of kitchen vent (Sq. ft.)|-|−0.055|−0.060|
|||(−1.24)|(−1.37)|
|_Weather characteristics_||||
|Strong Wind|-|-|−0.299|
||||(−1.46)|
|Dry day|-|-|−0.089|
||||(−0.76)|
|Rain-free Day|-|-|−0.113|
||||(−1.04)|
|Warm Temperature|-|-|−0.072|
||||(−0.80)|
|R<sup>2</sup>for the regression model|0.219|0.265|0.280|
|Number of HHs (groups)|335|335|335|
|Number of observations|868|868|860|

Note: Figures outside the parentheses are regression coefficients and in the parentheses are t-statistics based on robust standard errors. *=statistically significant at 10% level, **= statistically significant at 5% level or better. Regressions additionally control for survey rounds and locations, which are not reported. Source: World Bank household survey (2009-2010) Table 4 and Table 5 report the regression results for PM2.5 and CO respectively. In each case, column (1) presents results for a model that includes alternative fuel and stove use variables only. Column (2) retains the fuel and stove use variables and adds controls for kitchen characteristics. Column (3) presents results for all relevant variables: fuel and stove use along with kitchen characteristics and controls for ambient environment. It should also be noted that unobserved factors in this case may be ventilation behavior of the households: length of time the households keep doors and windows of kitchen open.

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# _4.1.2 Manufacturing_<sup>_9_</sup>

With regard to manufacturing, see Table 3, AGOA liberalises 1,249 tariff lines for nonLDCs on top of the 3,116 lines given duty free preferences under the GSP. These additional preferences amount to 14 percent of the total number of manufacturing lines. Again, the impact for LDCs is more limited due to the prior liberalization of many lines under the GSP. AGOA liberalises an additional 199 lines compared to the 4,223 lines already liberalized under the GSP. The third and fourth columns of the table show the importance of the liberalization of clothing products under AGOA which adds an additional 557 tariff lines. This creates a significant difference in the benefits available to Sub-Saharan African LDCs under AGOA.

**Table 3: Liberalization of Manufacturing Products under AGOA  – The Number of Tariff Lines Liberalized**

||**Without appar**|**el benefits**|**With appar**|**el benefits**|
|---|---|---|---|---|
||**Non-LDC**|**LDC**|**Non-LDC**|**LDC**|
|**Total Lines**|8660|8660|8660|8660|
|**Total GSP**|3116|4223|3116|4223|
|**GSP**|3116|3116|3116|3116|
|**GSP LDC**|...|1107|...|1107|
|**_GSP LDC but not AGOA_**|...|57|...|57|
|**AGOA**|1249|199|1806|756|
|**Clothing (HS61, 62)**|...|...|557|557|
|**Duty-Free**|2773|2773|2773|2775|
|**Lines Excluded from AGOA**|1522|1465|965|911|
|**Main sectors containing products**<br>**excluded from Preferences**|Leather products, Tex|tile and textile ar|ticles, Headgear, Gla|ss and glassware|

Data source: USITC dataweb For manufactures, about 16 percent of the dutiable lines are excluded from preferences for the countries that receive clothing benefits, while for those countries that have not been granted the clothing benefits duties must be paid on 25 percent of the dutiable lines in the US tariff schedule. The key products excluded from tariff preferences are textile

> 9 It is important to note that cotton (which is part of chapter 52) is treated as an agricultural product by the WTO but is included under manufacturing in this paper. Raw cotton is subject to a tariff rate quota and safeguard measures in the US. This means that in chapter 52 there is a ‘General Note 15 product’. In fact there is a special clause in general note 15 such that imports of upland cotton do not count against the tariff quota, but this is irrelevant to the main cotton producers in Africa. Here again we believe that inclusion of this tariff line as an AGOA product is misleading so we exclude it.

11
# **c. Science**

|**c. Science**<br>Estimated effect of public spending on educa<br>**Variable**|tion on inequalit<br>|y of opportunity:<br>|Dc= α+ βspe|**Depen**<br>ndingc+ Xcθ+|**dent variable: Ine**<br>εc|**quality index in s**|**cience**||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Public expenditure per pupil in primary as|-0.6571***|-0.5530***|-0.4555***|-0.4342***|||||||||
|a % of GDP per capita (circa 2004)|(0.1497)|(0.1391)|(0.1172)|(0.1389)|||||||||
|Public expenditure per pupil in secondary|||||-0.4642***|-0.3663***|-0.2523***|-0.2085**|||||
|as a % of GDP per capita (circa 2010)|||||(0.1193)|(0.1045)|(0.0883)|(0.0980)|||||
|Public expenditure per pupil in tertiary ed.|||||||||-0.0166|-0.0377|0.0104|0.0323|
|as a%of GDPper capita(circa 2002)|||||||||(0.0487)|(0.0442)|(0.0270)|(0.0219)|
|Constant|18.8734***|27.7268***|18.9268***|18.6990***|18.3396***|26.7638***|14.5209***|14.4152***|<sup>7.6102***</sup>|20.3795**|* -0.2346|0.7754|
||(2.9899)|(3.2987)|(4.3168)|(4.5122)|(3.2020)|(3.7592)|(4.5735)|(4.6944)|(1.9999)|(4.6286)|(5.3334)|(4.9362)|
|**Average years of schooling (year 2000)**|No|Yes|Yes|Yes|No|Yes|Yes|Yes|No|Yes|Yes|Yes|
|**Gini index for income (circa 2002)**|No|No|yes|Yes|No|No|yes|Yes|No|No|yes|Yes|
|**GDPper-capita(US$, PPP, 2002)**|No|No|No|Yes|No|No|No|Yes|No|No|No|Yes|
|**_R2_**|0.4132|0.6260|0.6611|0.6626|0.3514|0.5341|0.5505|0.5836|0.0033|0.2483|0.4711|0.5331|
|**Observations**|46|45|45|45|48|47|46|46|44|44|44|44|

Robust standard errors in parentheses. * Significant at ten percent; ** significant at five percent; *** significant at one percent. Note: Data on average years of schooling comes from Barro and Lee (2013), we restrict to individuals above 25 years of age; data un public expenditure and GDP per-capita comes from UNESCO’s Institute for Statistics to guarantee homogeneity; the data on Gine comes from Milanovic’s compilation of Gini indexes. Source: Authors’ calculations based on data from the OECD, PISA 2012, UNESCO, Barro and Lee (2013), and Milanovic: <u>http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/0,,contentMDK:22301380~pagePK:64214825~piPK:64214943~theSitePK:469382,00.html</u> 14
steps also included measures to avoid energy subsidies reemerging if oil prices rebound— automatic pricing mechanisms or full energy price liberalization have been common (for example, China, Côte d’Ivoire, India, Jordan, Madagascar, Mozambique, Mexico, Thailand, Ukraine; Asamoah, Hanedar, and Shang 2017; Beylis and Cunha 2017).<sup>19</sup>

# **6.3 Remaining challenges**

Some of these policies have yet to bear fruit. Notwithstanding fiscal and energy subsidy reforms in energy exporters, fiscal break-even prices—the oil prices at which government budgets are balanced—in almost all energy-exporting EMDEs exceed current prices, often by considerable margins. Energy subsidies still represented an average of 4 percent of GDP as of 2018 among energy-exporting EMDEs, many of which implemented reforms 2014-16 (Figure 10). In 2019, the share of commodity exports in total goods exports remained as high now as in 2013, before the last oil price plunge. The recent oil price plunge may provide further momentum to proceed with planned reforms and deepen them once the immediate health crisis subsides.

Energy importers, in contrast, could take advantage of lower energy prices to lower subsidies— which averaged over 2.5 percent of GDP in 2018—and utilize these resources to finance urgent health care needs. In energy exporters and importers alike, there is an opportunity to put in place reforms now that are non-binding in the short term but address long-standing inefficiencies and fiscal costs in the long term.

**_Fiscal space generated by subsidy reforms._** Replacing energy subsidies with expanded and bettertargeted social safety nets, coupled with structural reforms, can improve fiscal positions while supporting low-income households.<sup>20</sup> Policies to reduce subsidies can help promote growth because fiscal savings generated by lower subsidies can fund productivity-enhancing education and infrastructure. For example, in Egypt, fiscal savings from the energy subsidy reforms were redirected towards social spending (ESMAP 2017b). These policies can also foster low-carbon transition and promote green energy (Monasterolo and Raberto 2019; Mundaca 2017). For energy-

> 19 In Mozambique, the elimination of fuel subsidies, the introduction of an automatic fuel price adjustment, and increased tariffs on electricity and public transportation, contributed to the 2 percentage points of GDP narrowing of the primary fiscal balance between 2016 and 2018.

> 20 For details, see Coady et al. (2017, 2019); Guénette (2020); Stocker et al. (2018); and World Bank (2014, 2015a, 2015b).

22
rural) immediately prior to the last move is the same type of residence than in 1994<sup>1</sup> . This gives us the type of residence in 1994 of the respondent sibling. We use this information, and the assumption that siblings share common characteristics and background to determine whether the other siblings have a rural or an urban background.

The Rwanda 2000 DHS has one major drawback for the present analysis: it does not contain the ethnicity of the respondent. The Government of Rwanda no longer allows the registration of ethnicity, thereby complicating our analysis. It is namely generally acknowledged that the 1994 genocide targeted mainly Tutsi. Econometrically, this is the equivalent of an omitted variable problem. A variable, ethnicity, determines the probability to die and we cannot include it in the analysis. We address this problem in Section 5.

# **4. Demographic and Socio-economic Distribution of Excess Mortality in 1994**

Our analysis proceeds in two steps: first, we provide descriptive statistics about trends in mortality for different categories (by gender, birth cohort and socio-economic status) as illustrated in figures 1-5. In a second stage, we will verify the robustness of the findings from the descriptive trends in a multivariate regression framework.

Our results indicate that the excess mortality of 1994 dramatically affected the population composition of Rwanda. Figure 1 very simply plots, without any adjustment for the number and the age of individuals at risk in each year, the number of deaths reported among siblings in each year. It illustrates very clearly that excess mortality was heavily concentrated in 1994.

> 1 As a robustness check, we also relaxed this assumption and performed the analysis on the subsample for which we had certainty about their rural/urban location before 1994. The results were very similar.

10
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In these simulation experiments we assume for simplicity that the change in the average military spending ratio in each region is spread over the whole period 1986-90, 1/ and that after reaching its new level in 1990 the military ratio remains constant thereafter. The stylized paths of the changes in military spending ratios that occurred in each region during the late 1980s are illustrated by the solid lines in the top panels of Figure 1. The numerical parameters of the model are our panel estimates from Tables 5 and 6. Z/ Table 7 and Figure 1 (solid lines) summarize the simulation results for each of the seven regions. Line 4 indicates the change in the investment ratio that results from the shift in the military spending ratio in each region. For example, in the Asian developing and North African countries, which had the biggest cuts in their military spending ratios over 1986-90, the resulting increase in investment is nearly 0.7 percent of GDP; in the Middle East investment rises by 0.25 percent of GDP; in the industrial countries and the developing countries of the Western Hemisphere<sup>the rise is</sup> about 0.15 percentage points.

1/ Specifically, we assume that the natural logarithm of the military spending ratio declines linearly over this period.

_2/_ To implement the simulation we first substitute equation (2) into equation (1) to obtain the reduced-form relationship between the military spending ratio and the growth path. From this reduced-form equation we obtain the deviation of the simulated growth path for each region owing to the change in the military spending ratio from the path that<sup>would have</sup> prevailed if this exogenous change had not occurred (for a detailed explanation see Appendix II). Note that since ours is a long-run model the simulations trace the dynamic effects of this change on regional levels of _capacity_ output. We are not interested in the short-run Keynesian multiplier effects of military spending cuts, since these affect<sup>actual</sup> output _relative to_ capacity output.
# Economic Development and Female Labor Participation in the Middle East and North Africa:

# A Test of the U-Shape Hypothesis

_Paolo Verme_<sup>_1_</sup>

**Keywords:** Labor market, participation models, fertility, economic transformation, female education. **JEL:** F66; J13; J16; J21.

> 1 World Bank. The author is grateful to Manuel Arellano, Lidia Ceriani, Abdoul Gadiry-Barry, Jamal Guennauoi, Daniela Marotta, Ernest Sergenti and to participants to a seminar held at the World Bank in May 2014 for useful discussions and suggestions.