rafmacalaba/datause-encoder-data huggingface dataset:
# **The “Resource Curse” in MENA? Political Transitions, Resource Wealth, Economic Shocks, and Conflict Risk**

Michael Ross Kai Kaiser Nimah Mazaheri<sup>*</sup> <u>JEL Classifications:</u> O57 - Comparative Studies of Countries, O53 - Asia including Middle East, P5 - Comparative Economic Systems, P48 - Political Economy; Legal Institutions; Property Rights; Natural Resources; Energy; Environment; Regional Studies

* UCLA, World Bank, and Univ of Washington/WBI.  This paper was commissioned as a background paper to the World Bank‘s MENA Social Development Regional Study _Reducing Conflict Risk and Supporting Peaceful Transition_ .  Special thanks goes to Jorge Araujo and Alan Gelb for comments on previous versions of this paper, and participants comments for an initial version presented at a MENA Region presentation and Economist Forum at World Bank in Washington, DC March and April 2011.  Particular thanks goes to Lorena Viñuela for her significant role in helping shape the paper and comparative insights from Latin Americas democratic transition.  All views and opinions expressed in this paper are those of the authors.  All remaining errors are our own.

<mark>i</mark>
– _Comprehensiveness_ Existing studies record immigrants in a limited set of destination countries (OECD countries in addition to a few selected non-OECD destinations). By expanding the number of destinations to cover all countries in the world, we provide a comprehensive picture of international human capital mobility. Furthermore, we are able to quantify total emigrant stocks, _Eg,s,t_ for all the countries of the world, since we present comprehensive migration matrices. For example, compared to the set of OECD destinations, the total number of adult migrants identified in 2000 increases from 59.3 to 111.9 million.

– _Natural-based_ We are able to refine our definition of emigration rates. Instead of dividing the number of migrants by the corresponding labor force at origin (which includes immigrants), we divide it by the natural labor force, i.e. the number of individuals born in the origin country (which excludes immigrants). Our emigration rates thus differ from those computed in previous studies, _Eg,s,t/_ ( _Lg,s,t_ + _Eg,s,t_ ) as we do not need to proxy the natural labor force _Ng,s,t_ at the denominator with _Lg,s,t_ + _Eg,s,t_ . This makes a substantial difference in countries with large levels of immigration, especially at the higher skill level.

_Net vs Gross_ – We are able to identify the size and skill structure of adult immigration and emigration stocks from all countries, including for those in the developing world. In previous studies, immigration data were only available for OECD member states and selected nonOECD countries. Complete emigration data were simply unavailable. Furthermore, data only referred to gross immigration. Since we have both immigration and emigration numbers for all countries, we can compare the entries and exits of workers and compute comparable net migration balances for college graduates and less educated workers for all nation states 29
**Table 6. Regression Results on Farmer Yield Over-Estimation**

_Dependent_ _<u>Variable: Log of Plot-Level Ratio of Self-Reported to Sub-Plot Crop Cutting Yield</u>_

||||||**MAPS I**|||||
|---|---|---|---|---|---|---|---|---|---|
|_Decile_|_10_|_20_|_30_|_40_|_50_|_60_|_70_|_80_|_90_|
|_Plot Characteristics_||||||||||
|Log Plot Area (GPS, ha)|-0.359*|-0.221**|-0.341***|-0.234**|-0.317***|-0.499***|-0.365***|-0.296**|-0.856***|
|Production Heaped<sup>†</sup>|0.658***|0.351***|0.273**|0.267**|0.370***|0.311*|0.258|0.507***|0.684**|
|Log Plot Distance from Dwelling (GPS, km)|0.120|0.023|0.037|-0.011|0.032|0.111|0.173*|0.016|0.007|
|Pure stand†|0.172|0.073|0.050|0.131|0.169|0.423**|0.070|-0.014|0.073|
|% Enumerator-Assessed Damage in Crop Cut Sub-Plot|0.002|0.002|0.004*|0.006**|0.009***|0.012***|0.017***|0.015***|0.023***|
|<br>Log Intercropping Seeding Rate (=100 for Pure stand Plots)|-0.568*|-0.110|-0.044|-0.209|-0.012|-0.134|0.099|-0.147|0.055|
|Cover Crops Present Prior to Planting†|0.074|-0.099|0.060|-0.041|-0.052|0.088|-0.125|-0.100|-0.279|
|Log Maize Seed Planting Rate (Kg/Ha)|0.189|0.122*|0.060|0.124*|0.220**|0.182|0.209*|0.240**|0.172|
|Organic Fertilizer Application†|0.653|-0.059|0.255|0.518|-0.406|-0.232|0.241|-0.046|-0.945*|
|Inorganic Fertilizer Application†|-0.218|-0.032|-0.098|-0.015|-0.056|-0.056|-0.077|0.042|-0.154|
|Log Household Labor Days|-0.075|-0.026|-0.040|-0.019|-0.068|0.024|0.120|0.085|0.067|
|Log Hired Labor Days|0.066|0.052|0.105|0.153**|0.277***|0.283***|0.325***|0.287***|0.260|
|No Hired Labor†|-0.065|-0.012|0.129|0.328|0.832**|0.784*|0.970**|0.673|0.258|
|% Deviation of Seasonal (May-June) Rainfall from Long-Term Mean|0.016|0.000|0.007|0.017|0.016|0.028|0.043**|0.033*|0.007|
|Harvest Reported in More Than One Condition†|0.794***|0.362***|0.313**|0.299**|0.430***|0.425**|0.339*|0.231|-0.013|
|Household Characteristics||||||||||
|Wealth Index|0.098|-0.029|0.053|0.081|0.036|0.124|0.029|-0.087|-0.205|
|Agricultural Asset Index|0.091|0.094*|0.058|0.017|-0.100|-0.165|-0.079|0.020|0.050|
|Dependency Ratio|0.062|0.010|-0.042|-0.082|-0.083|-0.140*|-0.115|-0.151**|-0.124|
|<br>Log Household Size|0.108|0.120|0.082|0.086|0.143|0.044|-0.258|-0.177|-0.110|
|HH Cultivates More Than One Maize Plot|-0.133|-0.227**|-0.190|0.026|0.042|-0.001|-0.113|-0.143|-0.383|
|Manager Characteristics||||||||||
|<br>Manager = Respondent†|-0.283|-0.107|0.001|0.011|0.029|0.119|-0.056|0.151|-0.285|
|Received Crop-Production Related Extension Services†|0.086|0.174*|0.046|0.060|0.138|0.018|-0.028|-0.016|0.157|
|Female†|-0.280|-0.004|-0.081|-0.040|-0.182|-0.223|-0.257|-0.163|-0.249|
|Log Age (Years)|0.172|-0.011|0.127|-0.026|-0.036|-0.081|0.025|0.221|0.403|
|Log Years of Education|0.061|0.024|-0.024|-0.004|-0.043|-0.109|-0.082|-0.060|-0.098|
|Round II Indicator (=1)†||||||||||
|<br>Constant|-2.638|-1.814*|-2.684**|-1.983*|-3.501**|-3.590*|-4.744***|-3.692**|-4.308|
|**Enumerator Dummies?**|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|**Random Effects?**|**-**|**-**|**-**|**-**|**-**|**-**|**-**|**-**|-|
|**N**|440|440|440|440|440|440|440|440|440|
|**R**<sup>**2**</sup>|0.156|0.191|0.165|0.201|0.253|0.289|0.273|0.261|0.221|

_Continued next page_
availability of evidence about the epidemic and the use of that information when formulating the local response.<sup>31</sup> The first was conducted by the MPS with the 36 DTSs, as part of an effort to evaluate the PEN 2008-2011.<sup>32</sup> None of the 16 DTSs that responded to the survey reported having conducted elaborated specific studies about the prevalence and epidemiological profile of HIV/AIDS on their local population during the years the PEN was in use. The second survey was conducted in August 2011 by the MPS with those same DTSs in the context of the Study.<sup>33</sup> It revealed that all DTSs but one had generated studies about the epidemic in their territories. However, a number of DTSs reported that they had not identified the most vulnerable groups, and other DTSs included a long list of various social groups—or even the entire population—as a vulnerable group.

Similarly, the degree of knowledge that EPSs have of the epidemic among their ensured populations is variable—as reflected by the survey conducted by the MPS among the EPSs.<sup>34</sup> Some EPSs have produced their own indicators to monitor persons registered in the care programs, to analyze the quality of data reported by IPS and SIVIGILA, to assess the information flow with the Observatorio Nacional, and other variables.

In any case, these initiatives have sought to improve the information about the insured people for managerial and accounting purposes rather than for elaborating the HIV/AIDS epidemiological map to inform future strategic and clinical decisions. Furthermore, only four of the 16 responding EPSs reported having set up initiatives for data collection and technical reports elaboration (PEN activity A-66, linked to goal 12).

# **_(b) Programmatic Framework: National Level_**

At the national level, Colombia has been developing the necessary normative to sustain the programmatic framework of the HIV/AIDS response. The country has passed laws, decrees and agreements that: (i) regulate the handling of HIV, AIDS and

> 31 The response rate to both surveys was low—not exceeding 40% of the sample—which requires taking the data with caution.

> 32 MPS (2011)  Evaluación del Plan de Respuesta Nacional ante el VIH y Sida 2008-2011, Bogotá: MPS.

> 33 Please see Annex 1. Eight Departments and two Health District Secretaries answered the survey.

> 34 MPS (2011)  Evaluación del Plan de Respuesta Nacional ante el VIH y Sida 2008-2011, Bogotá: MPS.

30
benefits exist and are often generous. Its exclusion as an alternative to self-employment may bias results. For OECD countries with traditional unemployment benefits, the variable takes the value of the duration of unemployment benefits which Nickell (1997) found this to be the most important variable for explaining levels of unemployment. For Latin America and other countries, we calculate the standard severance pay package given the mean tenure (or predicted if unavailable).

Youth (Youth): We include one demographic measure as well, the share of the working population found between the ages of 16 and 20. The model implicitly assumes homogeneous work forces across countries. This is clearly not true as the share of young workers is much higher in Latin America than in the OECD countries. This variable is most relevant to the turnover regressions where traditionally young workers have higher rates of turnover as they shop around for careers. But it may also have a similar interpretation in the self-employment regressions.

Latin Dummy (LA): We include a dummy for being from the Latin American region. In theory, this may capture any difference between OECD economies and the region, including labor legislation."<sup>5</sup> Ideally, we would like to eliminate the significance of this variable by including the labor market variables that it may be proxying for.

# **_3.2 Results_**

The results must be interpreted with caution. First, we have at best 40 observations and in our most courageous moments, only 17. This in some cases can make the results sensitive to the countries included. Second, the data are not uniform. M[ost of the OECD variables were gleaned from presumably consistent publications of that organization while the Latin variables were individually extracted from not necessarily consistent survey data. The LAC dummy may pick up these data discrepancies.

Despite these potential pit-falls, the regressions prove surprisingly robust and consistent _15_ We included a squared income term as well but it was never significant. This does not, however preclude more exotic non-linear functions.

15
**Figure 3.** Electricity Emission Intensity

<!-- Start of picture text --> Water Distribution<br>Posta l services<br>Agriculture<br>Air transport<br>Manu f .-Pla stics<br>Manuf.-Chemicals<br>Retail<br>Manu f .-Apparel<br>Manu f .-Machinery<br>Construction<br>Water transport<br>Storage/wa rehouse<br>Manu f .-Me tal products<br>Land t ransport<br>Wholesale<br>Manuf.-Cement<br>Manu f .- Editing and recording<br>Manu f .-O thers<br>Vehic l e/ f u el sales<br>M anuf.-Textiles<br>0 .2 .4 .6 .8 1<br><!-- End of picture text --> Note: The bar plot shows the average emissions intensity from electricity consumption by economic sector. Emissions intensity is computed as total emissions by electricity consumption divided by value added. Data are from DGII and SIEN and are relative to 2018. Dotted blue lines refer to standard deviations within sectors.

**Figure 4.** Fuels Emission Intensity

<!-- Start of picture text --> Air transport<br>Land t ransport<br>Manu f.-Cement<br>Water tran sport<br>Stora ge/warehouse<br>Agriculture<br>Posta l services<br>Ma nuf.-Textiles<br>M anuf.-Chemicals<br>Manuf.-Metal products<br>Vehicle/fue l sales<br>C onstruction<br>Manuf.-Plastics<br>Manuf.-Machinery<br>Wholesale<br>Manuf . -Apparel<br>Manuf.-Others<br>Manuf.-Editing and recording<br>Water Distribution<br>Retail<br>0 10 20 30<br><!-- End of picture text --> Note: The bar plot shows the average emissions intensity from fuels consumption by economic sector. Emissions intensity is computed as total emissions by fuels consumption divided by value added. Data are from the combined sources DGII and SIEN and are relative to 2018. Dotted blue lines refer to standard deviations within sectors.

**Existing carbon pricing and fuel taxes.** Our methodology for estimating emissions at the firm level lies on the assumption that firms within sectors face the same price of fuel and electricity. While this may be a strong assumption in some countries, it does seem to apply fairly well in the case of the firms in our sample. Fuel prices are announced weekly by the Ministry of Industry, Commerce, and SMEs (MICM);

14
|n|n©|0|0)||0||
|---|---|---|---|---|---|---|
||<br>CA>0|<br>CA<0|CA<0|CA>0|CA<0|CA<0*|
|Elasticities|||||||
|of|0.0251|0.0251|0.0636|0.07|0.07|0.07|
|B,|0.0414|0.138|0.130|0.43|0.43|0.86|
|Bs|-0.324|-0.324|-0.343|-0.38|-0.38|-0.38|
|By|-0.357|-0.357|-0.228|-0.32|-0.32|-0.32|
|B,|-1.846|-1.846|-1.324|-1.8|-1.8|-1.8|
|ays|0.0733|-0.102|-0.0978|0|-0.1|-0.18|
|Va|0.208|0.208|0.185|0.2|0.2|0.2|
|Ve|0.263|0.263|0.148|0|0|0|
|ve|LA17|1.117|2.082|4.96|4.96|4.96|
|é|3.181|3.181|2.184|5.15|5.15|5.15|
|Impacts|||||||
|By|0.02|0.06|0.03|0.31|0.29|0.53|
|B,|0.04|0.10|0.07|0.34|0.32|0.58|
|ve|0|0.05|0.10|0|0.22|0.55|
|Va|0|0.04|0.02|0|0|0|
|Ps|0|0.01|0.01|0.11|0.15|0.51|
|ot|0|0|0.01|0.09|0.16|0.74|
|TOTAL|0.06|0.28|0.23|0.85|1.14|21|

Notes: Impact measures the prowth response (m percentage points) to a 10 percent of GDP increase in the saving ratio. The elasticities and impacts shown are for the full sample. *: based on alternative values for Se, yr and &
Wang Q., 2010. “Effective policies for renewable energy—the example of China's wind power—lessons for China's photovoltaic power”, Renewable and Sustainable Energy Reviews, Volume 14, Issue 2, February 2010, Pages 702-712, ISSN 1364-0321, 10.1016/j.rser.2009.08.013.

Wang X., 2012. “Maximizing Leverage of Public Funds to Unlock Commercial Financing for Clean Energy in East Asia”. World Bank.

Wang X., Wu Y., 2013. “China Renewable Energy Development Story”. World Bank, forthcoming.

Zhang, H., L. Li, et al. (2011). "Comparison of renewable energy policy evolution among the BRICs." <u>Renewable and Sustainable Energy Reviews</u> **15** (9): 4904-4909.

Zhao, Y., K. K. Tang, et al. (2013). "Do renewable electricity policies promote renewable electricity generation? Evidence from panel data." Energy Policy **62** (0): 887-897.

37
Policy Research Working Paper 9463

# **Abstract**

This study aims to provide a quantitative and integrated analysis of long-term structural transformation and labor productivity growth in Malaysia. Using data from the Department of Statistics Malaysia from 1987 to 2018 and decompositions that take account of the static and dynamic efficiency gains from labor reallocation, it documents that Malaysia has undergone structural transformation from an agriculture-driven to a services-driven economy. However, in contrast to common perceptions, the country’s impressive growth in output per capita over the past three decades can largely be attributed not to its structural transformation but instead to sustained growth in within-sector labor productivity. At 3 percent, the contribution of between-sector reallocation of labor to growth in output per capita in Malaysia has been relatively low. Accordingly, together with efforts to spur the more productive reallocation of labor across sectors and positively affect the employment rate, the main policy challenge for Malaysia going forward will be to achieve sustainable labor productivity growth within various sectors.

This paper is a product of the Social Protection and Jobs Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at aabdurrahman@worldbank.org and aschmillen@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. 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 this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
- 32 -

at a stumpage price of 15.3 Kwacha per solid m<sup>3</sup> . As a result of this calculation the Government accepted the Bank's recommend4tion that the stumpage price for fuelwood would be increased to 3.9 MK/m3 in March 1986 and #fter that at 15 percent annually in real terms until a fee of 15.3 MK/m' is achieved by the target date of 1996.

Table 2.5: MALAWI SECOND WOOD ENERGY PROJECT: STUWAGE CALCULATION FOR GOVERNMENT PLANTATION (3,000 ha at LIlongwe) b/

|Year|Costs|Benefits|
|---|---|---|
||('000MK)/|('OO0m3Solid)|
|1||541.9-|
|2||392.6-|
|3||447.6-|
|4||469,6-|
|5||487.9-|
|6||469.7-|
|7||152.322|
|8||85.436|
|9-12|52.5<br>per year|40 per year|
|13||52,525|
|14||52.542|
|15-18|52.5<br>per year|46 per year|
|19||52.522|
|20||52.536|
|21-24|52.5 per year|40 per year|
|25||52.518|
|26||52.530|
|27-30|52.5 per year|33 per year|

PDV of costs (at 12%) * 2194.1 Discounted Yield (at 12%) * 143.3

- Solid m3 Stacked m3

- Stumpage Fee to Cover Replacement Cost * 2194.1 a 15.3 mk c or 10.7 mk 143.3

- January 1986 Stumpage Charge * 2.9 mk or 2.0 mk c/

- a/ Base costs as of January 1986, Including physical contingencies. Area planted over 6 year period with a rotation of 6 year. Year 1, 303 ha; Year 2, 494 ha; Years 3-6, 551 ha per year.

- b/ The Lilongwe plantation has been used to calculate the stumpage rates, because the average above ground merchantable mean annual Incre.wnt (MAI) over 4 rotations of 12 m3 solid/ha Is considered typical. The assumed average MAI per rotation Is In m<sup>3</sup> /ha 12.1; 13.9; 12.1 and 10.0 respectively.

c/ January 1986 exchange rate USSI a 1.80 Malawlan Kwacha (WC).
Consistent with other studies, adequate housing seems to be a higher priority for female-headed HH, particularly if incomes rise beyond the median income (e.g., UN-Habitat 2014). Femaleheaded households may also be more likely to use their house as an informal business, a contributing factor explaining their better housing adequacy outcome.

Next, we disaggregate by education of the head of household.<sup>17</sup> Within the sample of 64 emerging economies, the difference in terms of housing adequacy between high educated and low educated HHs is most pronounced in Burkina Faso, where 89 percent of educated HHs have adequate housing compared to only 41 percent of HHs with heads having below secondary education. Burkina Faso HHs with low educated HH heads have a 40 percent likelihood to live in adequate housing.

We also find housing adequacy to vary by occupation of the head of HH. HHs where HH heads are employed in the agriculture sector are significantly more likely to have inadequate housing compared to their peers in the services or industry sector.<sup>18</sup> The difference between employees in the agriculture sector – a highly informal sector – and the service sector is most pronounced in Angola, Zambia, and Nicaragua.

Differences in terms of likelihood for adequate housing are also observable within the three biggest cities / provinces in Brazil, India, Indonesia, and Nigeria. Differences are most pronounced in Nigeria. Overall, only about 55 percent of HHs live in adequate housing conditions. About 87 million people in Nigeria live in inadequate housing. Within Nigeria’s three biggest cities, 7 million people in Kano, 3.8 million in Lagos, and 4 million in Kadua live in inadequate housing conditions. In contrast, in Brazil, there are almost no differences between the three largest cities. Overall, about 10 million people across the country are currently in need for adequate housing. Brazil’s HH survey is one of the few surveys that provides information on ethnic origin. The difference between indigenous groups, Afro-Americans, and others is small but significant. Indigenous peoples are less likely to live in adequate housing (93 percent), compared to AfroAmerican peoples (95 percent) and Whites (95 percent).

# **5. Estimating the Stock and Flow Housing Deficit**

The main issue in developing an empirical estimate of the housing deficit is conceptualizing what a stock or flow housing deficit precisely encompasses. The computation of the _AHI_ presents the

> 17 Research has shown that adequate housing is strongly associated with better educational outcomes, particularly in children as it is associated with lower numbers of disruptive moves, evictions, or crime (e.g., WHO 2018). Yet, the direction of the association can also be reverse: education of HH heads can also have an impact on housing adequacy as more educated HHs might place higher importance to adequate housing conditions, or as more educated HHs might have more financial means or information to access housing finance. 18 This may also reflect that HHs employed in the agriculture sector are more likely to live in rural areas.
we find that younger firms are less likely to use a line of credit or bank finance and more likely to use informal sources of financing.

Next, we find that our results are robust to excluding all transition countries (i.e. all Business Environment and Enterprise Performance (BEEP) surveys), in which privatization history might contaminate the firm age. In other words, firms that have been privatized may appear as being new/young, i.e. they might have been recently reregistered, while in fact they are “revamped” old firms. Our results are also robust to excluding high-income countries and only considering low and lower middle income countries.

In separate regressions we reproduced our results on firms that report having a single establishment, which eliminates from the sample firms with a domestic or foreign parent that might provide financing. Again, we obtain robust results for age and single proprietor dummy.

Finally, in Table 9 we examine the smaller subsample of firms where an individual or family is the largest shareholder. Our main results are robust. Furthermore, for this sample of firms we can test the impact of (i) the owner also being the manager and (ii) having a female principal owner. We find that after controlling for other firm characteristics, owner/manager is only significant in predicting less usage of a line of credit. However, we find that firms with female principle owners are significantly less likely to use bank financing and significantly more likely to use informal finance. Furthermore, as shown in column 5, the interaction of firm age and female ownership is significantly negative, suggesting that female owners of young firms are significantly more likely (relative to male owners) to use informal financing. Although we leave a 19