rafmacalaba/datause-encoder-data huggingface dataset:
of a political equilibrium, and that the incentives for policy makers may not be in favor of fiscal reform. It is thus important to disentangle elements related strictly to capacity in terms of existing resources and those related to potential political constraints. This paper proposes a simple exercise to distinguish how these factors influence the ability of countries to achieve the first Sustainable Development Goal of eradicating extreme poverty by 2030. In this paper we propose, first, an accounting exercise to assess whether it is feasible for countries to eliminate poverty using only domestic resources, implementing simple re-distribution rules (which this papers calls the _arithmetics_ of DRM). We then argue that the concentration of resources in the hands of fewer individuals in the society may hinder the ability to effectively implement such redistribution rules –both from the revenue side and from the social spending side. We propose a new way to approximate the concentration of political influence in a country linked to the unequal control over resources– which may thwart the efforts to eliminate poverty solely through domestic resource mobilization (which this papers calls the _politics_ of DRM). We apply the new methodologies to the most recent surveys available for over 120 developing countries.

The rest of the paper is organized as follows. Section 2 provides a brief review of the literature on merits of DRM as well as the constraints to increasing DRM in terms of both the taxable capacity and the political equilibrium. Section 3 proposes a framework for measuring the arithmetics and the politics of DRM. Section 4 describes the data utilized in the exercise. Section 5 presents the results of the analysis. Section 6 concludes.

# **2 Beyond capacity: Unpacking the constraints to increasing domestic resource mobilization**

While domestic resource mobilization (DRM) has recently garnered attention in the international dialogue, its importance for both economic and state development has long been acknowledged. As Schumpeter asserted almost a century ago, “the fiscal history of a people is above all an essential part of its general history” (Schumpeter, 1918). In addition to serving as an essential source of funding for governments to invest in delivering public goods and services, DRM can also reduce dependency on aid donors, thereby enabling countries to pursue long term national priorities, and it can strengthen the responsiveness of institutions by enhancing the accountability of governments to their citizens (see, for instance, Bhushan and Samy, 2015).

The fiscal capacity of countries to pursue development objectives is often analyzed in terms of the dichotomy between domestic resource mobilization versus foreign development assistance. This dichotomy or complementarity– has been studied, mainly, from the perspective of the potential shift of accountability from citizens in recipient countries to 2
- 41 -

Table 15: Mean Percentage of Total Instruction Time Devoted to Selected Subjects in the Primary School Curricula of Less-Developed Countries by Gross Primary Enrollment Rate, 1960s and 1980s (Constant Cases)

||Gr|oss Primary|Enrollmen|t Rate:|
|---|---|---|---|---|
||Less tha|n 80 %|Greater|than 80 %|
||(N=3|4-36)|(N=3|4-38)|
||1960s|1980s|1960s|1980s|
|Combined Lang.|38.0|35.2|34.0|32.1|
|National Lang.|26.1|25.5|24.4|22.9|
|Local Lang.|0.2|1.4|1.7|1.6|
|Foreign Lang.|2.2|2.5|1.4|2.0|
|Official Lang.|9.9|5.6|5.4|4.5|
|Mathematics|17.0|18.8|16.3|17.8|
|Science|6.1|7.2|7.4|8.2|
|Comb.Social Science|7.7|8.2|8.5|8.1|
|Social Studies|1.6|4.4|2.7|5.3|
|History|2.5|1.5|2.3|1.0|
|Geography|2.7|1.6|2.3|1.0|
|Civics|0.9|0.6|1.1|0.7|
|Comb.Moral/Religion|6.6|4.7|5.3|5.6|
|Religion|6.4|4.7|3.8|3.8|
|Moral Ed.|0.4|0.3|1.4|1.4|
|Aesthetic Ed.|9.3|11.1|10.6|9.5|
|Music|4.0|4.2|4.1|3.9|
|Art|5.4|6.8|6.5|5.6|
|Physical Ed.|7.3|8.4|6.5|6.3|
|Hygiene|0.6|0.6|2.2|1.8|
|Comb.Voc.Ed.|5.3|3.3|7.8|7.0|
|Manual Training|2.3|1.7|2.6|2.9|
|Agrculture|0.8|0.7|2.7|1.9|
|<br>Domestic Science|2.0|0.9|2.4|1.7|
|Vocational Ed.|0.1|0.0|0.6|0.5|
|Business|0.0|0.0|0.0|0.0|
|Other|1.8|2.3|2.2|2.9|
# **2. Related Literature**

While the relationship between international trade and the skill premium has been widely studied, the literature has focused mostly on trends before and after episodes of trade-liberalization.<sup>5</sup> As far as we know, the relationship between fluctuations of RTS and the cyclical component of exports has not been studied. Much research has been done on the relationship between RTS and business cycles, and some has been conducted on the relationship between trade and income volatility. But the empirical connection between the cyclical behavior of RTS and exports has not been established.

There is no clear relationship between returns to schooling and the business cycle, either theoretically or empirically. Some studies have found a pro-cyclical response of the skill premium, while others have found a counter-cyclical response. Furthermore, some studies have found a lagged relationship instead of a contemporaneous response.

As mentioned by King (1980), counter-cyclical returns to schooling are supported by theories proposed by Oi (1962) and Reder (1955). Oi (1962) and Mincer (1991) argue that if firms invest in job-specific capital for highly educated workers they have an incentive to retain these workers and lay-off less educated workers during downturns. In Reder (1955), this cyclical behavior is explained by a quality adjustment of the workers over business cycles. During booms, when job applicants become scarce, employers tend to lower hiring standards, thus reducing the wage skill premium. During recessions, when applicants for a given job are often plentiful, skilled workers crowd out unskilled workers.<sup>6</sup> Kniesner _et al._ (1978) found a positive relationship between the unemployment rate and the (crosssectional) rates of returns to schooling, using the United States National Longitudinal Survey from 1966 to 1970. This countercyclical relationship is also found by King (1980), using the same survey for the years 1968 and 1971.

On the other hand, contrasting evidence of a pro-cyclical relationship has also been found. For instance, Ammermueller _et al._ (2009) (using the German Microcensus from 1996-2003) found an inverse relationship between regional unemployment rates and returns to schooling. Likewise, Ziliak _et al._ (1999) used data from the U.S. Panel Study of Income Dynamics (PSID) from 1971-1990 and found that RTS are pro-cyclical for whites but counter-cyclical for blacks.

To explain the pro-cyclical relationship, Ammermueller _et al._ (2009) argue that if the wages of less skilled workers are covered by collective agreements while highly educated workers negotiate directly with their employer, then the wages of skilled workers might be more sensitive to the business cycle than those of unskilled workers. Consequently, the wage premium between skilled and unskilled workers might narrow in recessions. In line with this hypothesis, Raisian (1983) found that skilled workers exhibit

> 5 See for example Goldberg and Pavcnik (2007)

> 6 For empirical evidence on this quality adjustments see Gautier et al (2002), Teulings and Koopmanschap (1989) and Devereux (2004).

4
OECD Imports: Diversification of suppliers and quality search* Olivier Cadot<sup></sup> Céline Carrère<sup></sup> Vanessa Strauss-Kahn<sup>§</sup> Keywords: Import diversification, International trade, OECD, JEL classification codes: F1, O11

> * Research on this paper was supported by a grant from the World Bank. Without implicating them, the authors would like to thank Daniel Lederman, William Maloney, Marcelo Olarreaga, and an anonymous internal reviewer for useful comments. We are grateful to Madina Kukenova for her excellent research assistantship on a previous version of the paper.

>  World Bank, HEC Lausanne, CERDI, CEPR and CEPREMAP.

>  CERDI-CNRS, Université d‘Auvergne.

> § ESCP-Europe and CEPII
# **4.3. COUNTRY FINANCE AND GOVERNANCE INDICATORS**

Private credit to GDP ratio is used to proxy of financial sector development (Table 9 in the Appendix). This is in line with the literature which has generally utilized private credit to GDP ratios to proxy for financial sector development (Beck, 2003; Braun, 2003; Classens, Stijin, and Laeven, 2003; Fisman and Love, 2003a). Financial indicators are worse in MENA than in highincome countries, and are lower than expected given their income levels (Figure 4). For example, the private credit to GDP ratio in high-income countries is 117.2 compared to only 49.25 in MENA.

In the area of governance, property rights index is used as a proxy, which has also been used by Claessens and Laeven (2005), and Hur, Raj, and Riyanto (2006). Global Competitiveness Reports offer a wide range of scores ranging from 1(worst) - 7(best) on a country’s development. These scores are strongly correlated with income levels. High-income countries score the best across all topics, in most cases at least one-point higher than any other income group. Low-middle-income countries also have the lowest GCI scores in the group. The GCI property rights index in MENA is higher in than non-MENA countries, but much lower than in high-income countries.

A second proxy for governance is the set of World Governance Indicators (WGI), developed by the World Bank (See Kauffman, Kraay, and Mastruzzi, 2009). World Governance Indicators values are normalized between -2.5 and 2.5 with a higher value being associated with better governance. The WGI Voice and Accountability indicator captures the quality of interaction between citizens and government and is particularly relevant for the MENA region, as well as being instrumental towards promoting a stable environment for production and investment (World Bank, 2003a). Among all six WGI indicators, the MENA region averages the lowest in this measure. Voice refers to the voice of the citizens and their freedom of expression, choice in government, and ability to demand action from those in power. Accountability refers to the accountability of the government and refers to the presence of sanctions whether through voting, institutional oversight, or oversight from a free media.

13
period up to 2001. However, ever since, exports as a share of GDP have leveled off (Figure 5), while imports have continued to rise (Figure 6). This is clear when considering their change over time: From 1980 to 2001, exports increased by 21.1 percentage points of GDP, while imports rose by 13.7 points. From 2001 to 2017, exports declined by 0.2 percentage points as a share of GDP, while imports grew by a further 7.2 percent of GDP. Turkey is a net oil importer, and as Figure 7 shows, its net oil import bill is sizeable and has grown, much of which has been due to higher oil prices post-2000. However, net energy imports rose from 2.6 percent of GDP prior to 2002 to 4.3 percent, only accounting for a small proportion of the total increase in imports over that period.

Figure 4: Components of the current account (percent of GDP) <!-- Start of picture text --> 10%<br>5%<br>0%<br>-5%<br>-10%<br>-15%<br>Goods Services Primary & secondary income CAB<br>Source: Central Bank of Turkey, IMF World Economic Outlook<br>Figure 5: Exports of goods and services (percent of GDP)<br>30%<br>Goods Services<br>25%<br>Total Poly. (Total)<br>20%<br>15%<br>10%<br>5%<br>0%<br>Source: Central Bank of Turkey, IMF World Economic Outlook; author’s estimates<br>1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> 7
curriculum and teacher training, significant positive improvement in TIMSS started to appear in 2003, especially in science. Continuous significant improvement has been noticed for female students (see Figure 1).

Figure 1: Jordan’s Performance in the Trends of International Mathematics and Science Study <!-- Start of picture text --> 560<br>540<br>520<br>science All<br>500 science private<br>480 science public<br>460 science male<br>science female<br>440<br>420<br>400<br>1999 2003 2007<br><!-- End of picture text --> Figure 2: Jordan’s Math Performance in the Trends of International Mathematics and Science Study <!-- Start of picture text --> 520<br>math All<br>500<br>math private<br>480<br>math public<br>460<br>440 math male<br>420 math female<br>400<br>1999 2003 2007<br><!-- End of picture text --> 4
Bradshaw, S., Chant, S., and Linneker, B. (2017a). Knowing gendered poverty in the global south: A protracted path to progress? Retrieved from _Spazio Filosofico_ , 20 (2), 265–286 <u>http://eprints.lse.ac.uk/id/eprint/85657.</u>

- Bradshaw, S., Chant, S., and Linneker, B. (2017b). Gender and poverty: What we know, don’t know, and need to know for Agenda 2030. _Gender, Place & Culture,_ 24(12), 1667–1688. https://doi.org/10.1080/0966369X.2017.1395821

Breman, J. (2010). ‘A poor deal’ _Indian Journal of Human Development_ , 4(1): 133-42.

Buvinic, M., Lycette, M. and McGreevey W.P., (eds.) (1983). _Women and Poverty in the Third World_ . Baltimore, Maryland: The Johns Hopkins University Press.

- Burzynska, K. and Contreras, G. (2020). ‘Gendered effects of school closures during the COVID-19 pandemic,’ _Lancet_ 395(10242): 1968.

- Callander, E. J., Schofield, D. J., and Shrestha, R.N. (2012). ‘Capacity for freedom: A new way of measuring poverty amongst Australian children’, _Child Indicators Research_ , 5(1), pp. 179–98.

- Croft, T. N., Marshall, A. M. J., and Allen, C. K., et al. (2018). _Guide to DHS Statistics._ Rockville, Maryland, USA: ICF. Retrieved from: https://www.dhsprogram.com/pubs/pdf/DHSG1/Guide_to_DHS_Statistics_DHS -7.pdf.

- Currie, J. and Moretti, E. (2003). ‘Mother’s Education and Intergenerational Transmission of Human Capital. Evidence from College Openings’, _Quarterly Journal of Economics,_ Vol. 118, No. 4: 1495-1532.

- De Lannoy, A., Frame, E., Koka, P., Leibbrandt, M. (2016b). ‘Multidimensional youth poverty: Estimating the youth MPI in South Africa at ward level’, A Southern Africa Labour and Development Research Unit Working Paper 189. Cape Town: SALDRU, University of Cape Town.

30
Deindustrailization, which progressed in Russia in the 1990s,<sup>27</sup> is a second factor that might endogenously shift the distribution of abilities to strip. Given Russia’s vast natural resources, in ten years Russia will still be a major exporter of natural resources. A bad situation—characterized by a high relative return to stripping—could worsen because the value of man-made capital (machines, buildings, equipment) tends to deteriorate more quickly than that of natural resources under a stripping/low-maintenance strategy. This process may shift the distribution of types, among those who still control assets, toward those with greater ability to strip. In that case, the incentives to strip would be increasing over time and the prognosis for the rule of law would be worse than our model might suggest.<sup>28</sup> But there are plausible factors that would hasten the transition to the rule of law. The model assumes only two possible activities: building value and stripping assets. If, in addition, there is a subsistence activity (“gardening”) that agents could undertake in the nonrule-of-law state,<sup>29</sup> then ultimately that activity would be adopted by everyone as the asset base was depleted. In the long run, no one would oppose a transition to the rule of law.

For simplicity, the model assumes constant returns to scale to investment. If, as in 27 The value of fuel and mineral exports as a fraction of total exports in Russia rose from 53.2 to 60.6 percent between 1996 and 2000, as the value of exports in manufacturing fell and the value of natural resource exports rose (World Bank, Statistical Information and Management Analysis).

28 Arguing slightly in the other direction is that the extractive industries still need to finance the investments required for resource depletion, so that there is a need for a minimal rule of law to elicit these funds.

29 It has to be assumed, moreover, that individuals cannot simultaneously “strip” and engage in this standalone activity, or at least that the increase in value from the rule of law in the stand-alone activity exceeds the decrement in value in the asset stripping activity.

33
**Plc,Planning, and Research**

**International Commodity MarketsI**

In recent years, fluctuations in such macroeconomic variables as interest rates and exchange rates appear to have significantly affected primary commodity prices.

Could primary commodity price indices be used as an indicator of inflation? What was the impact on commodity prices of announcements about the money supply, inflation, and economic activity?

Palaskas and Varangis studied the relationship between commodity prices and various macroeconomic variables, They focused particularly on interest ratv,because of the important role they play in the portfolio adjustment model, in which investors move between commodities, bonds, and money as interest rates change.

Their tests did not rule out the hypothesis that there is a measurable, long-nmn cquilibrium between real interest rates and real commodity prices. Changes in real interest rates significantly affect prices on metals, minerals, nonfuel commodities, and agricultural raw materials - as represerted by the World Bank's commodity price indices.

Their tests rejected the hypothesis of a longrun relationship between real commodity prices and either consumer prices or the money supply (as represented by U.S. M2 plus dollar holdings in foreign central banks).

Causality tests show that commodity prices in nominal terms (except for metals and minerals) strongly affect consumer prices (as represented by the weighted consumer price index of the G-7 countries), but consumer prices do not affect commodity prices. And some groups of commodity prices can be reliable indicators of movements in consumer prices.

Causality tests also show that changes in the money supply cauise changes in commodity prices, but commodity prices do not affect thc moneY supply. Nor is it possible to nuantify the relationship between money supply .,.id commodity prices.

Palaskas and Varangis use co-integration techniques, error-correction modeling, and causality tests to analyze the relationships between macroeconomic variables and commodity prices.

Using an error correction model to specify and estimate the relationship between real interest rates and real commodity prices provides equations using other macroeconomic variables which have good forecasting abilities on commodity prices, such as industrial production, exchange rates, and the price of oil.

This paper is a product of the Intemational Commodity Markets Division, International Economics Department. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Dawn Gustafson, room S7-044, extension 33714 (54 pages with graphs and tables).

The PPR Working Paper Series disseminates the findings of work under way in the Bank's Policy, Planning, and Research Complex. An objective of the series is to get these fimdings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official policy of the Bank.

Produced at the PPR Dissemination Center
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Appendix Table 10: Action plan phytosanitary tasks

||All<br>phyto<br>tasks|Planter<br>phyto<br>tasks|Spouse<br>phyto<br>tasks|Planter phyto<br>tasks<br>with sole<br>responsibility|Spouse phyto<br>tasks<br>with sole<br>responsibility|
|---|---|---|---|---|---|
|Couples' Training|0.04|-0.01|0.01|0.01|0.02***|
||(0.04)|(0.03)|(0.02)|(0.02)|(0.01)|
|Observations|927|927|927|927|927|
|Mean for individual training|0.13|0.09|0.02|0.06|0.00|

* p<0.1 ** p<0.05 *** p<0.01.

Note: For all variables, individuals who did not take part in the training had missing values, which were recoded as zeros. Baseline control variables in all regressions: Age of household head, Household size, Number of adults, Number of children, Dependency ratio, Married planter, Polygamous relationship, Matrilineal ethnicity, Planter has IGA, Asset index, Credit constrained, Share of plots owned, Average distance to plots (minutes), Number of plots, Average plot size.

# Appendix Table 11: Action plan fertilizer tasks

||All fertilizer<br>tasks|Planter<br>fertilizer<br>tasks|Spouse<br>fertilizer tasks|Planter<br>fertilizer tasks<br>with sole<br>responsibility|Spouse<br>fertilizer tasks<br>with sole<br>responsibility|
|---|---|---|---|---|---|
|Couples' Training|0.03|0.01|0.01|0.01|0.02***|
||(0.03)|(0.02)|(0.01)|(0.02)|(0.01)|
|Observations|927|927|927|927|927|
|Mean for individual<br>training|0.10|0.07|0.02|0.05|0.00|

Robust and clustered standard errors in parentheses.

* p<0.1 ** p<0.05 *** p<0.01.

Note: For all variables, individuals who did not take part in the training had missing values, which were recoded as zeros. Baseline control variables in all regressions: Age of household head, Household size, Number of adults, Number of children, Dependency ratio, Married planter, Polygamous relationship, Matrilineal ethnicity, Planter has IGA, Asset index, Credit constrained, Share of plots owned, Average distance to plots (minutes), Number of plots, Average plot size.

4