rafmacalaba/datause-encoder-data huggingface dataset:
_Africa’s Macroeconomic Story_ _Guinea’s_ large current account deficit, estimated at almost 40 percent of GDP in 2012-14, is mainly due to imported capital goods used to expand mining production, as in the case of Sierra Leone. A significant expansion in mining exports (projected to increase from 25 percent of GDP in 2012 to 50 percent in 2016) is expected to reduce the current account deficit to just 5 percent of GDP by 2016. The expansion in mining projects is largely financed by foreign direct investment, expected to reach 35 percent of GDP in 2014, having relatively minor implications for the external debt burden. External debt is set to decline modestly relative to GDP and fall sharply relative to exports.

**Guinea, Balance of Payments, 2012-16**

||**2012**|**2014**|**2016**|
|---|---|---|---|
|_Percent of GDP_||||
|**Current account**|**-38.8**|**-39.7**|**-5.0**|
|Trade balance - goods|-24.9|-25.6|6.7|
|_of which: mining projects_|4.3|0.3|30.6|
|Imports of goods|51.5|52.8|44.8|
|_of which: mining projects_|20.7|24.9|19.0|
|Exports of goods|26.7|27.2|51.4|
|_of which: mining projects_|25.0|25.2|49.6|
|Current transfers|2.3|2.1|1.4|
|_Official_|_0.3_|_0.4_|_0.0_|
|**Capital account balance**|**1.4**|**1.1**|**0.6**|
|**Financial account**||||
|Direct and other private investment|23.0|30.4|2.0|
|_of which: mining projects_|28.0|35.4|9.0|
|Net public borrowing|0.6|0.1|1.1|
|**External debt (nominal)**|**19.0**|**18.3**|**13.4**|
|Present value|14.9|13.8|9.8|
|_percent of exports_|_53.2_|_49.0_|_18.6_|

_Source:_ IMF Country Reports No. 12/295 (Oct. 2012) and No. 12/301 (Oct. 2012).

_Note:_ estimates for 2012; projections for 2014 and 2016.

_Niger’s_ current account deficit is projected to narrow from an estimated 26.5 percent of GDP in 2012 to almost 10 percent in 2016, as mining exports (uranium and oil) increase and imported capital goods decline. In this case, however, foreign direct investment is expected to decline significantly. .Official transfers, project grants and net public borrowing provide the bulk of external resources.

**Niger, Balance of Payments, 2012-16**

||**2012**|**2014**|**2016**|
|---|---|---|---|
|_Percent of GDP_||||
|**Current account**|**-26.5**|**-13.5**|**-10.6**|
|Trade balance - goods|-11.7|-3.6|-0.7|
|Imports of goods|36.5|30.3|29.5|
|_of which: capital goods_|_13.5_|_10.2_|_10.0_|
|Exports of goods|24.8|26.7|28.8|
|_of which: uranium and oil_|_15.4_|_17.4_|_18.5_|
|Current transfers, balance|4.6|4.3|4.0|
|_of which: public, net_|_3.5_|_3.1_|_2.9_|
|**Current transfers**|**6.5**|**6.4**|**6.3**|
|_Official_|_5.9_|_5.8_|_5.7_|
|**Financial account**||||
|Foreign direct investment|15.1|1.1|-1.5|
|Public borrowing,net|4.7|5.2|5.1|

_Source:_ IMF Country Report No. 12/109 (May 2012).

_Note:_ estimates for 2012; projections for 2014 and 2016.

43
# **1.  Introduction**

Wages of incumbent workers are seldom cut, even in the face of large negative shocks. During the last few years, a growing body of literature using micro data has documented the importance of downward wage rigidities in several countries and over a range of time periods. In the US, clear signs of resistance to nominal wage cuts are found in all studies (see among others Kahn, 1997; Altonji and Devereux, 2000, and Lebow et al. 2003). More recently, a comprehensive cross-country study conducted in the framework of the International Wage Flexibility Project (IWFP) has demonstrated the existence of downward rigidity in real wages in addition to nominal wages in many European countries (Dickens et al. 2007, 2008).

Understanding the relative flexibility of labour costs is essential for a better understanding of the working of the economy at the macro level. From a monetary policy perspective, the adjustment of marginal costs to economic shocks determines the slope of the Philips curve in New Keynesian Models (Galí and Gertler, 1999). From a labour perspective, understanding the links between wage rigidities and unemployment was emphasised by Layard et al. (1991), and most of the empirical micro literature on wage rigidities retained this subject as the main motivation for the analysis.<sup>1</sup> However, even if base wages are rigid, does such wage rigidity necessarily imply rigid labour cost structures? Firms have other margins of adjustment beyond base wages to manage their wage bills, including the adjustment of flexible pay components such as bonuses or fringe benefits, the adjustment of labour costs via reorganization of production, or using labour turnover as a tool to adjust labour costs to changes in economic activity. These other margins have hardly been studied in the existing literature.

This paper broadens the discussion of the relative rigidity of wages to include the flexibility of other adjustment mechanisms that involve the use of labour inputs. Using a unique survey from a large sample of European firms, we are able to identify the incidence of the following labour cost-saving strategies: reduce or eliminate bonus payments; reduce non-pay benefits; change shift assignments or shift premia; slow or freeze rate at which promotions are filled; recruit new employees at lower wage level than those who left voluntarily; and encourage early retirement to replace high wage employees by entrants with lower wages. The paper makes three contributions to the literature. First, we document comparable information on labour cost adjustment practices beyond base wages for a large set of EU countries and sectors. This allows us to discuss the relative importance of each individual strategy across countries characterised by different sets of laws and institutions governing their labour

> 1 See Goette et al. (2007) and the references therein.

4
In our model and simulation, the ability of education systems to add-value and increase the skills for a large proportion of a country’s ability distribution determines whether automation has a positive or negative effect. Countries in the middle of development are most at risk if education quality is low because of (1) their reliance on middle-skill labor in a relatively large formal sector and (2) because the informal sector still presents a viable alternative for middle-skill labor to exit. A developed country with poor education quality is less at risk because the informal sector is not a viable alternative to middle-skill labor.

# **EMPIRICAL APPROACH**

Until automation takes hold to the large extent that many expect in the coming decades, we cannot empirically identify how countries will be affected. We can, however, examine the employment characteristics and education quality of countries to identify which countries our model predicts are most likely to be negatively affected and the fears of automation realized.

Our empirical approach is as follows. First, we test the underlying assumption of the model that educational attainment and skills are positively associated with employment that is complementary to automation capital. In doing so, we identify how different types of education and different types of skills associate with this type of employment. Second, we test whether the model correctly predicts the relationship between economic development and the three types of employment in our model: that which is complimentary to automation capital, substitutable by automation capital and informal. Doing this identifies which countries exhibit the employment characteristics which our model predicts are risk factors for automation. Third, we study how international student assessment outcomes relate to these risk factors to identify which countries are most likely to be negatively affected by automation, according to our model. We also examine secondary-level vocational programs which are often viewed in developing countries as an important approach to preparing youth for the labor market.

_Identifying automation-substituting and automation-complementing labor sectors_ To identify employment that is automation-substituting and automation-complementing, we use the Frey and Osborne (2013) estimates of an occupation’s probability of being automated in the 18
# **Using National Statistics to Increase Transparency of Large Land Acquisition: Evidence from Ethiopia**<sup>**¶**</sup>

Daniel Ali, Klaus Deininger, Anthony Harris World Bank, Washington DC <u>dali1@worldbank.org, kdeininger@worldbank.org, aharris3@worldbank.org</u> JEL Classification: O13, Q12, Q15 Keywords: large scale land acquisition, commercial farms, productivity, farm size distribution, Africa, Ethiopia

> ¶The research leading to this paper would not have been possible without the initiative by Hashim Ahmed, Office of the Prime Minister and Head Economic and Policy Analysis Unit, Government of Ethiopia, the active collaboration by the Ethiopian Central Statistical Agency, especially Biratu Yigezu, Habekristos Beyene, and Ahmed Ebrahim, and support by Alemayehu Ambel, Andrew Goodland, Firew Bekele, Asmelash Haile, Lars Moeller, and Lou Scura. Insightful comments from Gero Carletto, Luc Christiaensen, Issa Faye, Thea Hilhorst, Bart Minten, James Opio-Omoding and seminar participants at the African Development Bank and the 2015 Annual Bank Conference on Land and Poverty greatly helped to improve the quality of the paper. Funding from DFID and the German Government is gratefully acknowledged.
13 shipped to major OECD markets.<sup>9</sup> To place the importance of these tariff<sup>rates in perspective, the table</sup> shows total 1991 OECD imports of each product group from the FSU. Appendix Tables 1<sup>through 3</sup> provide more detailed three-digit SITC trade and tariff statistics for manufactured goods, agricultural<sup>raw</sup> materials and foods, and ores, minerals and nonferrous metals.

Table 5 shows FSU exports faced relatively high OECD tariffs in several important sectors. The republics paid an average import duty of about 15 percent on food exports of approximately $900 million to the EC, Finland, and Japan with several product sub-groups (fruit, sugar, and beverages) having average tariffs ranging from 23 to 40 percent. Import duties on some manufactured products like leather, clothing, and footwear in Japan, or clothing in tne EC, Finland, Sweden and the United States averaged between 11 and 30%. However, the major adverse effects of OECD duties would be almost certainly due to FSU products often being required to pay considerably higher tariffs than similar (competing) goods exported by other countries.

Overall, tariffs in the United States and Japan averaged about 5.2% on FSU exports and EC duties averaged roughly two percentage points higher. These rates range from about 70 to 90% higher than the average tariff on all imports in these three markets.'° The underlying tariff-line level data also show the FSU **often** faced considerably higher import duties than those paid by other (competing) exporters of the same product due to extensive OECD preferences that differentiate among sources of 9From GATr records incorporated into the World Bank-UNCTAD "Software for Market Analysis and Restrictions to Trade" (SMART) data base. All reported tariffs are the average of the MFN or special<sup>preference</sup> duties that ame applied to imports. See World Bank (1992, Appendix C) for a description of the SMART<sup>data base</sup> and model. The EC announced it will grant GSP treatment to the Republics in 1993 as "an exceptional tempra measure. Due to the uncertainty associated with the longer term application of<sup>GSP tariffs we show MFN rates</sup> in the following tables. Also, key products like steel, fish and textile and clothing exports are excluded<sup>from the</sup> EC scheme. Finally, Laird and Yeats (1987, p. 95) show the EC GSP has only a modest impact on<sup>tariff levels</sup> reducing the average MFN duty on manufactures (excluding chemicals)<sup>from 8.1 to 6.4 percent.</sup> 10 After accounting for special preferences, Laird and Yeats (1987:94-95) estimate that the average tariff on all Japanese and US imports was 3.1 and 3.4%, respectively. The lower average Japanese duty was due<sup>largely</sup> to the relatively high share of very low tariff crude materials (i.e., metal ores, nonmetallic<sup>minerals, unrefined</sup> petroleum, etc.) in total imports. The overall EC(1O) tariff was estimated to be even lower (2.5%)<sup>due to the</sup> extensive EC preferences.
<!-- Start of picture text --> =>. aot e 7 wet— oT,TO ~<br>et ee<br>cet! ao” aa<br>om — i *<br>. Looe .<br>cy . x 1 ef. coee - es<br>er an<br>phe<br>- ~ Ay ae a<br>ra r -<br>fe<br>oi<br>r eee eT<br>i) 2 4 6 8 10<br>pctile<br>——_ 1993 —-—- 2006<br>Dash lines represent confidence intervals at the 5% level.<br><!-- End of picture text -->
**Figure 1** Evolution of Unemployment Rate, Monetary Poverty, HDI, and the Cyclical Component of Real GDP per Capita in LAC

<!-- Start of picture text --> the Cyclical Component of Real GDP per Capita in LAC<br>120 -165<br>-145<br>110<br>-125<br>-105<br>100<br>-85<br>90<br>-65<br>-45<br>80<br>-25<br>70 -5<br>15<br>60<br>35<br>55<br>50<br>75<br>40<br>Unemployment Rate Monetary Poverty 1/HDI Real GDP per Capita - Cyclical Component (right axis)<br>Index (2003=100)<br>Index (2003=100, inverted axis)<br>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 --> Notes: Poverty line of 5.50 dollars per person a day (2011 PPP). Averages for LAC are weighted by population. LAC aggregate based on 15 countries for which microdata are available: Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, the Dominican Republic, El Salvador, Ecuador, Honduras, Mexico, Panama, Paraguay, Peru, and Uruguay. In each year and for all indicators, the sample is restricted to the countries for which data on monetary poverty is available. The cyclical component of real GDP per capita is defined as the cycle over the trend.

Sources: Authors’ estimations based on SEDLAC (CEDLAS and World Bank), Human Development Report Office (United Nations), and WEO (October 2018).

17
# **Shared Prosperity and the Mitigation of Poverty: In Practice and in Precept**

# **Kaushik Basu**

Senior Vice President and Chief Economist The World Bank Washington, DC 20433 and C. Marks Professor Department of Economics Cornell University Ithaca, New York 14853 For correspondence: kbasu@worldbank.org

**Acknowledgements and disclaimer** : The paper is written in my individual capacity and should not be read as an official statement of the World Bank. In working on this paper I have benefited from discussions with a large number of people and, at the risk of omission, would like to thank Alaka Basu, Shaohua Chen, Tito Cordella, Vivian Hon, Pete Lanjouw, Tamara McGavock, Richard Morgan, Ambar Narayan, Gunilla Olsson, David Rosenblatt, Jaime Saavedra, Jan Svejnar, and  Eric Verhoogen. I would also like to thank Sandra Gain and Merrell Tuck-Primdahl for valuable editorial suggestions. Earlier versions of the paper were presented as the President’s Invited Lecture at the ISI World Statistical Congress in Hong Kong, August 30, 2013, and as a plenary lecture at the Human Development and Capabilities Association Congress in Nicaragua, September 10, 2013. On 11 November, 2013, it was presented in New York, at UNICEF and as the Global Economic Governance Lecture at Columbia University, New York on 11 November 2013. I am grateful to the participants for the many useful comments, suggestions and some loaded questions.
higher debt ratio (especially short-term debt ratio) than firms with good governance. Kirch and Terra (2012) analyze firm-level data from five South American countries and conclude that the institutional quality of a country has a significant positive effect on the level of long-term debt in a firm's financial structure. Using the ownership structures of French firms, Ben-Nasr, Boubaker, and Rouatbi (2015) find that firms with multiple large shareholders tend to have debt with shorter maturity. Awartani, Belkhir, Boubaker, and Maghyereh (2016) show that stronger rule of law, better regulatory effectiveness, better legal protection of creditors, and more developed financial intermediaries are associated with greater use of long-term borrowing by Middle East and North African firms. In contrast with these region- and country-specific studies, our study exploits the richness of international data to investigate the role of cross-country variation in institutional features that mediate the role of governance on debt maturity.

<mark>Our paper contributes to the three strands of the literature, by showing that the governancedebt maturity relationship depends on the larger institutional environment and in particular the strength of investor protection. By examining cross-country differences in legal origin, we are able to address some of the endogeneity concerns associated with examining debt maturity and governance. The rest of the paper is organized as follows. The next section describes the data and methodology. Section 3 presents the empirical results, including baseline regressions that show the effect of governance on short-term debt, the role of investor protection on the relationship between governance and debt maturity, instrumental variables regressions that are aimed to address endogeneity concerns and a number of robustness tests for the alternative definitions of the governance variables. Section 4 concludes the paper.</mark>

# **2. Data**

This section provides detailed information about the sample of firms, data sources and how we construct our variables. Our discussion starts with sample selection. Then, we present the construction of the governance and board measures used in our study, as well as the measures of country level institutional quality. Finally, we present the construction of the short-term debt measure and firm level controls.

6
ients of insurance payouts are more likely to purchase insurance the following year. Section 4 searches for evidence of a number mechanisms by which this could take place. Section 5 discusses a number of behavioral explanations for the results. Section 6 concludes and offers policy recommendations.

# **2 Index Insurance and Customer Data**

## **2.1 Context: BASIX Policies**

In this analysis I study monsoon rainfall index insurance policies underwritten by the insurance company ICICI-LOMBARD and sold by BASIX, a microfinance institution based in Hyderabad. The policies insure against excess or deficit rainfall, and are calculated based on rainfall measured at a stated weather station. By basing payoffs on just rainfall, the policies should have low monitoring and verification costs, and also should be free of adverse selection and moral hazard. These attributes make policies inexpensive to create and administer, which allows them to be sold in small quantities and priced at levels affordable for poor farmers. BASIX’s policies are designed to pay out in situations where adverse rainfall would cause a farmer to experience crop loss, and are therefore calibrated to the water needs of local crops.

BASIX policies are divided into three phases, which are meant to roughly capture the three phases of the growing season: planting, budding/flowering, and harvesting. If cumulative rainfall is too low or high in any of these phases, the crop’s output is potentially damaged and the farmer could suffer a loss. The policies are designed to start when farmers first start planting, which depends itself on rainfall. Therefore, the policies have a dynamic start date which means that Phase 1 begins on the day that cumulative rainfall since June 1 reaches 50mm or on July 1, whichever comes first. Each phase generally lasts 35-40 days. During this time, rainfall data is collected daily at a designated weather station, and payouts are calculated using the cumulative rainfall over the phase.

A phase of coverage is defined by three parameters: “Strike”, “Exit”, and “Notional”. Deficit policies begin to pay out when the rainfall drops below the level of the Strike, and gives its full payout when it falls below the Exit. In between, it pays the Notional amount of rupees for each millimeter below the Strike.

In 2006 and 2007, all rainfall insurance contracts sold by BASIX included three phases, with the first two protecting against deficit rainfall, and the third protecting against excess rainfall. In 2005 the policies all had three phases, but each phase protected only against deficit rainfall. Table 1 presents a sample contract, from Nizamabad district in the state of Andhra Pradesh.

Given the policy parameters we can see how the payouts will evolve according to rainfall. Figure 1 shows the payout schedule for phase II of the above policy. There is no payout when rainfall is above the strike, which is 125mm. Then as rainfall decreases the payout increases linearly until rainfall reaches the exit of 40mm, then jumps to the policy limit of Rs 1000 once rainfall falls below 40mm.

4
# **3. Models of Inflation**

A quick glance at the literature points to three classes of models for inflation determination.<sup>12</sup> The first view envisions inflation as a cost-push phenomenon in the context of a long-term constant mark-up over costs. The second view considers inflation mainly as a monetary phenomenon and links changes in monetary variables to those in prices. Finally, the third view envisages inflation as stemming from real factors, imbalances between aggregate demand and supply in particular. A review of the empirical studies suggests that the first type of model has been frequently employed in emerging market economies, while the other two have been typically—but not exclusively—used in industrial countries. The next section will provide a brief discussion of these three classes of models.

# **3.1 Mark-up Models**

Goodfriend (1997) provides a comprehensive discussion of the theoretical underpinnings of the mark-up model. These types of models, in which the price level is determined by costs and a given mark-up, take the following form: where P, μ, W, E, P<sup>*</sup> stand for the domestic level of prices, the mark-up over costs, wages, the nominal exchange rate, and the level of foreign prices, respectively.<sup>13</sup> The above equation arises from the maximization problem of a firm that faces a demand curve

> expectations upwards, which would lead to increases in wages and non-tradeable goods prices, and thus bringing about inflation and monetary base growth.

> 12 This section draws largely on Bailliu et al. (2002).

> 13 EP* is a measure of foreign prices expressed in Turkish lira.

12