rafmacalaba/datause-encoder-data huggingface dataset:
_(Table C.7 continued from previous page.)_

||(0.01)|(0.01)|(0.01)|
|---|---|---|---|
|HH income|-0.00|-0.00|-0.00|
||(0.00)|(0.00)|(0.00)|
|Muslim|-0.34|-0.54**|-0.31|
||(0.23)|(0.25)|(0.23)|
|Socioeconomic background|-0.02|-0.03|-0.03|
||(0.02)|(0.03)|(0.02)|
|Household head|0.14|0.07|0.21|
||(0.17)|(0.30)|(0.17)|
|Wife’s share in HH income|-0.05|-0.18|-0.04|
||(0.14)|(0.16)|(0.14)|
|Nr Children|0.01|-0.01||
||(0.04)|(0.05)||
|Age gap of spouses|-0.00|-0.00||
||(0.01)|(0.01)||
|Marriage duration|-0.00|-0.01||
||(0.01)|(0.01)||
|Brought assets in marriage|-0.12**|-0.11*||
||(0.06)|(0.06)||
|Spousal education difference|0.00|0.00||
||(0.01)|(0.01)||
|Constant|0.36|0.77**|0.56*|
||(0.36)|(0.38)|(0.31)|
|N|488|427|488|
|p-value of F-statistic|0.41|0.28|0.32|
|Missing indicators|Yes|No|No|
|Onlymarried|No|Yes|No|

Notes: Standard errors in parentheses. In column (1), to include the variables only defined for married women and include currently unmarried respondents, I follow two steps. First, I set these variables to zero if they are missing and, second, I include dummy variables in the regression that indicate whether the variables are missing. Column (2) does not make these adjustments and hence only includes married respondents. Column (3) includes only covariates defined for all respondents. * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01 77
# **Table 2: Descriptive statistics of selected drivers on nominal and real lending interest rates, spreads, and net interest margin**

## Median values by tercile

||le|Nominal|Lending<br>|NIM|||Macro|-fiscal||hd|Bank<br>bk|sector<br>|<br>Non-||Busi<br>Cdit|ness<br>Rule of<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||ci||<br>-deposit|||||||Overea|3 an|NPLs|<br>||re|law|
||Ter|Rate<br>(%)|<br> <br>spread<br>(%)|<br>(%Earning<br>Assets)|Inflation<br>(%)|<br>St. Dev.<br>inflation<br>(%)|<br>Public<br>Debt<br>(% GDP)|<br>GDP per<br>capita<br>(US dollar)|<br>Policy<br>rate<br>(%)|costs<br>(% Total<br>Assets)|<br>concentrtion<br>(% Bank<br>system assets)|<br>(%Total<br>Gross<br>Loans)|<br>interest<br>income<br>(% Total<br>Income)|<br>Priv.<br>credit<br>(%GDP)|<br>Bureau<br>Coverage<br>(% Adults)|<br>[-2.5<br>(weak) to<br>2.5<br>(strong)]*|
|Nominal|T1|7.1|4.2|3.7|2.3|1.2|44.0|5,792.0|3.2|3.4|69.8|7.5|18.2|49.7|0.0|-0.147|
|lending<br>interest|T2|12.2|6.9|5.1|6.6|1.6|42.8|3,132.8|6.0|3.6|73.0|5.7|16.1|31.4|1.8|-0.521|
|rate|T3|19.2|10.5|7.1|8.5|2.1|42.2|1,676.0|9.6|5.5|68.7|6.1|16.6|15.1|3.1|-0.632|
|Lending-|T1|7.5|3.7|3.5|3.6|1.4|39.4|5,056.2|4.9|3.0|66.7|4.7|17.9|43.0|2.0|-0.453|
|deposit|T2|12.1|6.9|5.4|5.2|1.5|43.2|3,625.9|6|3.9|62.4|8.8|14.6|32.3|0.8|-0.324|
|spread|T3|19.5|12.0|6.4|6.6|2.0|44.3|1,959.2|7.4|5.1|78.3|6.9|18.0|15.0|0.0|-0.801|
|Net|T1|8.9|4.8|3.0|3.8|1.4|49.9|4,678.2|3.6|2.3|72.6|5.5|17.2|46.4|0.0|-0.436|
|Interest|T2|11.4|6.5|5.0|5.0|1.4|39.7|3,862.2|5.5|4.2|62.4|6.2|18.3|31.4|3.3|-0.427|
|Margin|T3|17.6|9.1|7.9|7.6|2.2|42.2|1,624.8|7.2|5.8|78.3|7.2|15.8|19.3|2.6|-0.635|
|Real|T1|7.0|3.7|4.1|4.2|1.6|42.2|3,285.5|4.5|3.8|66.3|5.5|20.3|26.3|0.7|-0.468|
|lending<br>interest|T2|10.7|6.7|5.6|5.3|1.5|42.2|3,548.8|4.9|4.1|64.9|7.6|14.4|39.1|1.8|-0.262|
|rate|T3|19.2|10.7|6.6|6.6|2.0|48.1|2,886.1|6.3|4.7|75.8|6.4|15.8|21.3|2.2|-0.500|

Note: Terciles calculated from country average values 2003-2017 of the variable (i.e., nominal interest rate, spread, NIM, or real interest rate), and sorted from the lowest to the highest levels. T1 shows the lowest tercile, and T3 the highest tercile. Data for drivers is median values by tercile.

*The rule of law indicator captures perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence. For more details, see Kaufmann et al. (2010)

Source: IMF-IFS, World Bank Finstats 2019, World Bank Governance Indicators, World Bank Doing Business 41
|||Fact|or|
|---|---|---|---|
||Global<br>(1)|Advanced<br>(2)|Emerging Infows<br>(3)|
|VIX_s|-0.278<sup>_∗∗_</sup>|-0.554<sup>_∗∗_</sup>|-0.322<sup>_∗∗∗_</sup>|
||(0.107)|(0.217)|(0.0635)|
|_N_|25|25|25|
|_R_<sup>2</sup>|0.271|0.385|0.268|
|BAA10YM_s|-0.287<sup>_∗∗∗_</sup>|-0.463<sup>_∗∗_</sup>|-0.209|
||(0.104)|(0.209)|(0.133)|
|_N_|36|36|36|
|_R_<sup>2</sup>|0.412|0.384|0.097|
|GZ_s|-0.285<sup>_∗∗∗_</sup>|-0.473<sup>_∗∗∗_</sup>|-0.236<sup>_∗∗∗_</sup>|
||(0.0642)|(0.169)|(0.0845)|
|_N_|36|36|36|
|_R_<sup>2</sup>|0.406|0.400|0.123|
|UncD_s|-0.319<sup>_∗∗∗_</sup>|-0.519<sup>_∗∗_</sup>|-0.316<sup>_∗∗∗_</sup>|
||(0.0648)|(0.219)|(0.0726)|
|_N_|29|29|29|
|_R_<sup>2</sup>|0.415|0.394|0.298|
|RiskavD_s|-0.295<sup>_∗∗_</sup>|-0.564<sup>_∗∗∗_</sup>|-0.332<sup>_∗∗∗_</sup>|
||(0.109)|(0.186)|(0.0582)|
|_N_|29|29|29|
|_R_<sup>2</sup>|0.354|0.465|0.329|
|UncD_s|-0.221<sup>_∗∗∗_</sup>|-0.268|-0.176<sup>_∗_</sup>|
||(0.0480)|(0.177)|(0.0911)|
|RiskavD_s|-0.154<sup>_∗_</sup>|-0.393<sup>_∗∗_</sup>|-0.220<sup>_∗∗∗_</sup>|
||(0.0839)|(0.152)|(0.0592)|
|_N_|29|29|29|
|_R_<sup>2</sup>|0.472|0.527|0.383|

HAC estandard errors, Newey-West 4 lags, in parentheses _∗ ∗∗ ∗∗∗ p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01 Table 7: Regressions of the factors on different measures of risk (plus a constant, not shown). All variables are expressed in first differences. BAA10YM is Moody’s U.S. corporate BAA spread, GZ is the Gilchrist and Zakraj ̃sek (2012) corporate bond spread index, and UncD and RiskavD respectively are the uncertainty and risk aversion measures constructed by Bekaert et al. (2019). All the risk measures have been rescaled to have unit standard deviation, so that their coeficients are comparable.

43
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16
field of development economics. The World Bank was accustomed to making ex-ante evaluations of the “soundness” of projects – soundness being the jargon of those times – and was satisfied as long as its loans were repaid by the recipient countries. Of course, the usually prolonged relations between the Bank and client countries made it natural for the Bank’s officers to consider and discuss previous experiences, and a few evaluational studies of specific Bank projects had been promoted by the Bank in the early 1960s. Yet no systematic evaluation of Bank-financed projects or assessment of their success (or lack thereof) had been put into place. The Bank had no independent evaluation function in its organizational chart, and its first project evaluations were implemented by operational staff.

When Albert Hirschman – a prominent economist at Columbia University and a founding father of development economics in the 1950s – proposed joining forces with the Bank to study some general elements of project appraisal, he was venturing into almost completely unexplored territory. Hirschman showed the centrality of project appraisal for the design and implementation of effective development projects, thereby becoming a major contributor, as we will see, to the shift in development economics from broad development theories to more focused analyses of what happens at project level. His groundbreaking volume, _Development Projects Observed_ (1967a), emerged from his study of a series of World Bank projects in the mid-1960s, and served as the first attempt to establish project evaluation as a standard practice in the development field (Picciotto 1994; Willoughby 2003). Yet his emphasis on the intrinsic uncertainty of knowledge derived from project evaluation, as well as his reluctance to make his reflections more “operational,” complicated his relationship with the Bank. The Hirschman study, for which Bank staff and management had such high expectations, soon disappointed most of them. Furthermore, the Bank all but ignored the final outcome of his project, the 1967 book, and especially disliked its first chapter, “The principle of the hiding hand,” which was widely circulated after appearing as a separate article in Daniel Bell’s and Irving Kristol’s journal _The Public Interest_ (Hirschman 1967a and 1967b). In particular, Hirschman’s insistence on uncertainty as a structural element in the decision-making process did not fit in well with the operational drive of Bank economists and engineers.

This paper is based mostly on previously undisclosed archival material from the World Bank Archives and the Albert Hirschman papers held at Princeton University. It will tell the story of the genesis, development, and conclusion of Hirschman's collaboration with the World Bank, and discuss its relevance for the postwar history of development. The study of these dynamics 3
move. Among the alternatives, Frankel (2008) and Calvo (2008) have discussed the role of the real interest rate; Wolf (2008) and Svensson (2008) have mentioned the importance of shifts in global supply and demand. Further, Krugman (2008) has argued that the increase in oil prices, providing an incentive to produce biofuels, is responsible for the increase in food prices. Little effort, however, has so far been devoted to disentangling these hypotheses from an empirical standpoint.

In this paper, we attempt to progress the empirical evidence on primary commodity prices along different dimensions. First, we examine the extent and nature of price co-movements between primary commodities.<sup>1</sup> In order to perform our empirical analysis we exploit the information embedded in annual historical prices. Specifically, we analyze 24 commodity price series observed for more than 100 years of data from 1900 to 2008. Such a low frequency should reduce the noise to signal ratio and largely eliminate the influence of speculation on commodity prices, allowing us concentrate on “fundamental” price co-movements. We first diagnose the overall co-movement in the panel, using the test statistic suggested by Ng (2006) and then apply the Bai and Ng (2004) Panel Analysis of Nonstationary and Idiosyncratic Components (PANIC) to identify potential common factors in commodity prices. These methods are attractive since they include statistical tests of co-movement, taking account of the time series properties of the data. Our findings highlight a sizeable degree of correlation in the data and detect the existence of a common factor.

We next investigated the relationship between commodity prices and macroeconomic determinants. Using a Factor Augmented Vector Auto Regression (FAVAR) approach,<sup>2</sup> we relate the identified common factor in commodity prices to

> 1 This mirrors much recent empirical work on co-movement of economic variables, for example Kose et al. (2003), Monacelli and Sala (2009) and Ciccarelli and Mojon (2010).

> 2 Such an approach has been applied by Bernanke et al. (2005) in the examination of US monetary policy.

3
where IR (respectively ID) is the interest rate on central bank financing (respectively domestic-currency denominated bank deposits), IB the nominal rate of return on government bonds, IDF the domestic interest rate on foreign-currency deposits held in the domestic banking system by each category of household h, FDh, GBB<sup>Ttotalgovernmentbondholdingsbycom-</sup> mercial banks, DLB (respectively FLB) borrowing from the central bank (respectively abroad), and DDh domestic-currency deposits by household h.

We assume that there are four categories of households in the economy. Rural households, identified with the sub-index A, consist of all workers employed in the rural sector. Informal sector households, identified with the sub-index I, consist of all the (unskilled) workers employed in the informal economy. Formal sector households, identified with the sub-index F , consist of all workers (skilled and unskilled) employed in the formal sector, both public and private. For all three groups, income is based on the return to labor (salaries), distributed profits, government transfers, remittances from abroad, and interest receipts on holdings of financial assets (net of borrowing from domestic banks). The fourth group consists of profit earners, identified with the sub-index E, whose income comes from firms’ net earnings in the rural and formal private sectors, profits of commercial banks, interest on deposits, and government transfers.

Profits from rural production are assumed to be distributed in proportion 0 < shpA < 1 to rural households and 1 − shpA to (urban) profit earners. Using (52), income of rural households is given by where 0 < γh < 1 is the portion of total government transfers (TRH) each household h receives, so that<sup>P</sup> h<sup>γ</sup> h<sup>=1,andDDhdomestic-currencyde-</sup> posits in domestic banks by households h. Rural and informal sector households are assumed not to hold foreign-currency deposits, either domestically or abroad.

To capture the fact that firms in the informal urban sector tend to be small, family-owned enterprises, we assume that households in that sector own the firms in which they are employed. Using again (52), income of the informal sector households is given by where REMIT measures the foreign-currency value of the flow of remittances 26
WPS4194 <mark>P</mark> OST <mark>-C</mark> ONFLICT <mark>T</mark> RANSITIONS <mark>W</mark> ORKING <mark>P</mark> APER <mark>N</mark> O <mark>. 8</mark>

# **Oil and the Propensity to Armed Struggle in the Niger Delta Region of Nigeria**

Aderoju Oyefusi Department of Economics and Statistics, University of Benin, Nigeria E-mail address: aderojuoyefusi@yahoo.com World Bank Policy Research Working Paper 4194, April 2007 _The Post-Conflict Transitions Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about post-conflict development (more information about the PostConflict Transitions Project can be found at http://econ.worldbank.org/programs/conflict_ ). _An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in these papers are entirely those of the authors. They do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org ._ This paper originated from a PhD dissertation carried out by the author in the Department of Economics and Statistics, University of Benin, Nigeria. The author is grateful to Paul Collier; Anke Hoeffler; Indra De Soysa; M. Reynal-Querol and Akin Iwayemi for extensive comments, to participants at the CSAE 2006 Conference, University of Oxford and the PIDDCP research project workshop, Oslo, June 19-20, 2006 for comments on earlier drafts, and to the African Economic Research Consortium (AERC), Nairobi, for financial support.

1
<mark>Bussolo, M. and Lopez-Calva, L.F., 2014.</mark> _<mark>Shared Prosperity: Paving the Way in Europe and Central Asia</mark>_ <mark>. World Bank Publications.</mark> <mark>Cancho, C., Dávalos, M.E., Demarchi, G., Meyer, M. and Sanchez-Paramo, C., 2015. Economic mobility in Europe and Central Asia: exploring patterns and uncovering puzzles. World Bank Policy Research Working Paper No. 7173.</mark> <mark>Cancho, C., Davalos, M.E. and Sánchez-Páramo, C., 2015. Why so gloomy? perceptions of economic mobility in Europe and Central Asia. World Bank Policy Research Working Paper No. 7519.</mark> Cancho, Cesar A.; Kiso, Natsuko; Corral, Paul. 2016. Welfare Impact of Macroeconomic Events: The Case of Georgia and Azerbaijan in the Aftermath of the 2015-16 Currency Devaluations. Background Paper for South Caucasus Programmatic Poverty Assessment TA FY16.

<mark>Chetty, R., Hendren, N., Kline, P. and Saez, E., 2014. Where is the land of opportunity? The geography of intergenerational mobility in the United States.</mark> _<mark>The Quarterly Journal of Economics</mark>_ <mark>,</mark> _<mark>129</mark>_ <mark>(4), pp.1553-1623.</mark> <mark>Cohen, G.A., 1989. On the currency of egalitarian justice.</mark> _<mark>Ethics</mark>_ <mark>,</mark> _<mark>99</mark>_ <mark>(4), pp.906-944.</mark> <mark>Corak, M., 2013. Income inequality, equality of opportunity, and intergenerational mobility.</mark> _<mark>The Journal of Economic Perspectives</mark>_ <mark>,</mark> _<mark>27</mark>_ <mark>(3), pp.79-102.</mark> <mark>Dang, H.A. and Lanjouw, P.F., 2013. Measuring poverty dynamics with synthetic panels based on cross-sections.</mark> Policy Research Working Paper Series 6504. The World Bank, Washington DC <mark>Dworkin, R., 1981. What is equality? Part 2: Equality of resources.</mark> _<mark>Philosophy & Public Affairs</mark>_ <mark>, pp.283-345.</mark> European Bank for Reconstruction and Development. 2016. Life in Transition: A Decade of Measuring Transition. European Bank for Reconstruction and Development.

<mark>Ferreira, F.H., Messina, J., Rigolini, J., López-Calva, L.F., Lugo, M.A., Vakis, R. and Ló, L.F., 2012.</mark> _<mark>Economic mobility and the rise of the Latin American middle class</mark>_ <mark>. World Bank Publications.</mark> <mark>Ferreira, F.H. and Gignoux, J., 2013. The Measurement of Educational Inequality: Achievement and Opportunity 1.</mark> _<mark>The World Bank Economic Review</mark>_ <mark>,</mark> _<mark>28</mark>_ <mark>(2), pp.210-246.</mark> <mark>Fields, G.S., 2000. Income mobility: Concepts and measures.</mark> <mark>Fleurbaey, M., 2008.</mark> _<mark>Fairness, responsibility, and welfare</mark>_ <mark>. Oxford University Press.</mark> <mark>Hertz, T., Jayasundera, T., Piraino, P., Selcuk, S., Smith, N. and Verashchagina, A., 2007. The inheritance of educational inequality: International comparisons and fifty-year trends.</mark> _<mark>The BE Journal of Economic Analysis & Policy</mark>_ <mark>,</mark> _<mark>7</mark>_ <mark>(2), pp.1-46.</mark> 41
42 Tablo 4.13 (Con't.)

|C.198S<br>im<br>|~~~SENEGAL|ESE<br>OTrHER AFR.|NON-AFRr|
|---|---|---|---|
|Top Management|7,972|6,130|20,812|
|Top Technicians and<br>Mfiddle Management|<br>4,474|4,431|15,437|
|Technicians and<br>Skilled Workers|1,677|1,993|11,624|
|Workers and<br>Apprentices|_950_|_855_|410|

Values are in 1,000 of Francs CFA. Source: 80 Enterprise sample The more striking case is the growth in average compensation of non-African technicians and skilled workecs (95 percent) as compared to that of similarly skilled Senegalese (36 percent) and other Africans (62 percent). In this category of workers, this diversity in compensation increases led to a jump in she ratio of the non-African earnings to Senegalese earnings from 4.8 to 6.9 and in the ratio of non-African to other African earnings from 4.8 to _5.8_ between 1980 and-1985. Hence, although Senegalization in the labor market has taken place in terms of increased employment of the Senegalese in more skilled positions, the gap between the per employee compensation of non-Africans and Senegalese has been widening. It should be noted, however, that because of the substantial exodus of the non-Africans, the mean compensation per employee in the two lowest skill groups may be quite sensitive to the characterist'-s of the few remaining nonAfrican workers. Moreover, an evaluation of the merits of the emerging earnings differentials would require information on the relative productivity of individuals in the three national groups.
The effect of the level of migration on remittance flows seems rather ambiguous if we look at the results from the OLS estimation (Table 3). However, if we instrument the migration variables, the coefficient uniformly becomes greater and significant (Table 4). According to our OLS estimates, if the number of migration increases by one percent, this would increase remittance flows by about 0.4 percent (column 3 of Table 3), while it is found to increase remittances by about one percent based on the IV estimation results (column 3 of Table 4). This seems to support our argument that the endogeneity issues need to be addressed in the estimation. Note that if we instrument both the migration and financial development variables, the impact of the level of migration on remittances is somewhat attenuated. Based on these results, a one percent increase in the number of migrants would result in a 0.7 percent increase in remittance flows (column 3 of Table 5). This implies that an increase in the level of migration would decrease remittances sent by each migrant, which is likely to be due to family reunification effect. Similar patterns are also observed when using the ratio of the number of migrants to the population size as our migration variable (columns 7-10 of Tables 3-5).

The impact of the financial sector development of recipient countries is not as clear as in the case of the migration level (Tables 3-5). When controlling for the endogeneity bias, the coefficients become greater, but significant only marginally (Column 1, 2, 5 and 6 of Table 5). The effect of the financial sector development on remittances seems positive as claimed in the existing literature, but further work seems needed to examine its significance in determining remittance flows.

As far as the education level of migrants is concerned, its effect is not always significant. However, when it is significant, the difference between the ratio of people with tertiary education among migrants and the ratio among the working population of home country adversely affects remittance flows. In other words, the higher the education level of migrants relative to the population in their home countries, the lower the amount of money sent home. This in turn underlines the notion that if migrants come from relatively 10