rafmacalaba/datause-encoder-data huggingface dataset:
17

# **Fiscal Adjustment and “Shared Prosperity”**

The World Bank has recently advocated the notion of “shared prosperity” as a major policy goal in order to address the shortcomings of standard measures of economic development (Word Bank, 2013). The World Bank’s shared prosperity indicator is defined as the growth rate of the income of the bottom 40 percent of the income distribution. Contrary to indicators of economic development from national accounts (such as GDP growth), improvements in share prosperity require growth to be inclusive of the less well-off segments of the population (Basu, 2013).

We examine the links between fiscal policy at the state-level and shared prosperity in Brazil<sup>9</sup> . The results obtained when regressing the first difference of the log income of the bottom 40 percent on our baseline control variables are presented in Table 4. Since the dependent variable is now a growth rate, we modify Equation 2 and include the lagged level of the log of the income of the bottom 40 percent as a control variable to capture convergence effects (see equation 3). Time dummies were also included, but not a time trend.

The results are mixed for the main variable of interest. The coefficients for changes in the cyclically-adjusted primary balance are positive, but not statistically significant in fixedeffects regressions. Nevertheless, the coefficients are highly significant for all GMM specifications (with semi-elasticities ranging from 1.4 to 1.6). Diagnostic tests do not indicate that cross-sectional dependence is a problem for these models. Overall, except for the convergence term, the other control variables included do not seem to present a robust statistically significant association with our shared prosperity measure, which indicates that exploring the correlates of shared prosperity at the sub-national level remains an interesting avenue for further research.

Summing up, in all specifications presented in this section, a tighter fiscal stance at the subnational level is negatively associated with inequality and positively associated with our measure of shared prosperity. Nevertheless, in what concerns the shared prosperity regressions, the coefficient of the change in the cyclically-adjusted primary balance is not statistically significant in fixed-effect specifications. In none of the specifications the tighter fiscal stance is linked to a deterioration in inequality.

> 9 As one of the twin goals of the World Bank, shared prosperity is measured by the growth of the income per capita of the bottom 40. In this paper we present this measure at the state level (bottom 40% of the state) to provide a finer lens on the reading of shared prosperity in a large federal country such as Brazil. The World Bank benchmark for shared prosperity in Brazil remains at the national level.
|Country|_AR_(1)|_AR_(2)|_MIDAS_|_MIDAS_ with|_MIDAS_ with|
|---|---|---|---|---|---|
|||||_AR_(1)|_AR_(2)|
|Brazil|0.041|0.038|0.009|0.009|0.011|
|Chile|0.024|0.024|0.022|0.019|0.019|
|Colombia|0.010|0.011|0.011|0.011|0.011|
|Mexico|0.009|0.008|0.007|0.007|0.007|

Table 2. Comparison for predicting the industrial production growth rates (m/m) by using the Root-mean-square error ( _RMSE_ ). Note: The data sample ranges from February 2018 until February 2020 for _AR_ regressions while January 2019 until February 2020 for MIDAS regression.

# **6 Conclusion**

A novel database is used to generate high frequency forecasts of economic activity in the wake of COVID-19. The World Bank and IMF have revised growth estimates significantly downward during COVID-19. The health and economic policy responses and subsequent economic outcomes are very uncertain. To reduce some of this uncertainty this paper details the use of daily mobility and air quality data to predict movements in industrial production, which is typically used to assess within-year movement of GDP growth. The database includes Google’s Community Mobility Report data, air quality data and OECD industrial production data.

Estimation proceeds in three steps: (i) lagged mobility data are patched with air quality data; (ii) the mobility data are then combined to extract a common Mobility Index via Kalman filtering; and finally (iii) a MIDAS approach nowcasts industrial production from the smoothed Mobility Index. The results can be updated daily. This paper illustrates its use for a set of Latin American countries.

The Mobility Index is compared to a standard auto-regressive forecast model. The 14
_COTE D’IVOIRE: Competitiveness, Cocoa, and Real Exchange Rates_

# **Evolving Trade Patterns Over Time**

**Figure 4: Distribution of Traditional Major Export Destinations Over Time**

**Cote D'Ivoire Trade: Percentage of Total Exports To Major Developed Trade Partners**

<!-- Start of picture text --> EEC<br>France<br>80%<br>Netherlands<br>USA<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Annual - 1960-2003<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002<br>Percent of Cote d'Ivoire's Total Exports<br><!-- End of picture text -->

**Figure 5: Distribution of Imports From Traditional Trade Partners Over Time**

**Cote d'Ivoire Trade - Distribution of Imports by Major Developed Trading Partners**

<!-- Start of picture text --> 90%<br>EEC<br>80%<br>France<br>Netherlands<br>70% USA<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002<br><!-- End of picture text --> 6
Narshingdi (8 monitoring points), and rural areas of Jessore (16 points) and Rangpur (11 points). Average outdoor concentrations in the three locations are 36, 48, and 62 ug/m<sup>3</sup> , respectively. The overall average for 35 monitoring points is 50 ug/m<sup>3</sup> , which we have adopted as our estimate of outdoor pollution for this exercise.

In the previous paper, we have shown that pollution generated in cooking areas diffuses almost immediately into living areas.<sup>3</sup> As a result, pollution in both areas exhibits strong pollution cycles in response to fuel combustion for cooking. Drawing on information from continuous, 24-hour monitoring of PM10 in 27 households in Narshingdi, Figure 2 displays a typical daily pollution cycle as a 24-hour plot of the ratio of the hourly mean PM10 concentration to the daily mean concentration.<sup>4</sup> Its distinguishing features include two peaks during morning and evening cooking times, when pollution rises to over 3 times the daily average, and extensive periods in the afternoon and evening when pollution is substantially lower than the daily average. Daily indoor pollution cycles are also reflected in outdoor ambient cycles, as many houses emit cooking smoke. Figure 3 illustrates a typical ambient cycle, drawn from 24-hour monitoring at 7 points in rural villages of Jessore and Rangpur.<sup>5</sup> We combine mean indoor and outdoor pollution concentrations with the hourly ratios in Figures 2 and 3 to produce Table 3, which provides hourly estimates of PM10 in cooking areas, living areas and outside the house.

> 3   For illustrations of the close relationship, see Dasgupta, et al. (2004), Figure 2, p. 12.

> 4   Of course, households differ significantly in the timing of daily peaks; some have three rather than two, and there are also significant differences in peak levels and change gradients.  Figure 2 represents the central tendency in the observed patterns for 27 households..

> 5   This cycle is clearly different from the cycle in Figure 2, for two apparent reasons.  First, the outdoor cycle reflects the combined effect of fuel-burning in many households, whose daily cycles reach peaks at different times, and at different PM10 emissions intensities.  However, this does not explain the large difference in the relative size of the two outdoor peaks, as compared with rough parity for the indoor peaks.  Since the outdoor pattern is reproduced across several widelyseparated monitoring points, we hypothesize that different atmospheric conditions lead to more pronounced accumulation and duration of suspended particulates in the evening.  Alternatively,  preparation of evening meals may cluster more tightly in time, leading to more concentrated loading of PM10 in the evening.  Future research may shed more light on this phenomenon.

8
_Low-Income Countries’ Access to Private Debt Markets_ (figure 19). Only the most credit worthy borrowers will be willing and able to issue bonds under such dire market conditions.

# **Figure 18 Emerging Market Sovereign Bond Spread, 1998 to 2008**

<!-- Start of picture text --> Basis points<br>1250<br>1000<br>750<br>500<br>250<br>0<br>1998M1 2000M1 2002M1 2004M1 2006M1 2008M1<br><!-- End of picture text --> _Source:_ JPMorgan EMBI Global Composite Index.

**Figure 19 Emerging Market Corporate Bond Spreads, Jan. 2007 to Dec 2008**

<!-- Start of picture text --> Basis points<br>2000<br>Investment grade<br>1500<br>Non-investment grade (BB)<br>1000<br>500<br>0<br>Jan-07 May-07 Sep-07 Jan-08 May-08 Sep-08 Jan-09<br><!-- End of picture text --> _Source:_ JPMorgan Corporate emerging market bond indexes (CEMBI).

Prospects are expected to improve significantly over the longer term, however. New creditors have emerged as important sources of finance. Several managed funds and private equity firms have developed expertise to invest in frontier debt and equity markets. Assets held by sovereign wealth funds will continue expand, particularly if commodity prices rebound, which would provide an additional source of financing for equity and debt securities issued by frontier markets. Also, assets managed by institutional investors (notably, pension, insurance and mutual funds) in developing and advanced 24
- 21 -

# Table 5

# Comparative Analysis of the Premium on Discount Charged by Selected European Countries on Iron and Steel Exports to Associated African Countries

|Year|Average Premium<br>Belgium 2/|or Discount C<br>France 3/|harged Associate<br>Portugal 4/|d Countries_<br>United<br>Kingdom 5/|
|---|---|---|---|---|
|1962-63|20.7|36.9|12.7|4.0|
|1964-65|21.2|21.8|37.3|8.8|
|1966-67|25.7|21.0|25.6|14.4|
|1968-69|19.1|23.9|29.9|12.'|
|1970-71|15.2|16.7|43.7|l3.v|
|1972-73|18.0|18.6|18.7|15.5|
|1974-75|26.4|8.1|42.9|9.9|
|1976-77|35.3|-3.6|6/|22.5|
|1978-79|37.0|26.1|6/|15.1|
|1980-81|17.1|20.9|6/|19.2|
|1982-83|25.5|8.6|6/|36.5|
|1984-85|16.0|36.2|6/|37.9|
|1986-87|31.5|66.5|6/|53.0|

- 1/ Based on the four and five digit SITC products listed in Appendix Tables 3 through 13. The average premium or discount has been calculated relative to the average unit value for each product paid by other developing countries.

- 2/ Burundi, Rwanda and Zaire comprise the as!iociated country group.

- 3/ See Table 3 for a list of countries classified as French associates.

- 4/ Angola and Mozambique comprise the associated country group.

- 5/ Countries classified as United Kingdom associates are Kenya, Uganda, Tanzania, Sudan, Nigeria, Cambia, Sierra Leone and Ghana.

- 6/ From 1976-77 to 1986-87 the premiums on Portugal's exports ro:.se dramatically and averaged over 120 percent. It appears likely that the hostilities in Angola were a major factor causing the large increase in premiums over those which prevailed during 1962-63 to 1974-75.
long-term financing and negatively with short-term debt. The variable construction procedures that are discussed in this section are available in Table A3 of the Appendix.

We should also note that some of the variables control for the factors that affect firms’ demand for short-term debt. These variables aim to capture the factors that lead shareholders, managers or directors to prefer short-term financing over the alternative methods of financing. However, as documented by Custodio, Ferreira and Laureano (2013), supply-side factors are also important drivers of the equilibrium debt maturity levels. _deposits_ , _bonds_ , _stock_market_cap_ and _stock_market_turnover_ are the variables that are likely to capture the supply-side factors driving the availability of capital to firms.

The descriptive statistics of the variables are shown in Table 2. To limit the effect of outliers on our results, we winsorize all variables, except for the governance variables, at their 1<sup>st</sup> and 99<sup>th</sup> percentiles. As Panel A shows, the share of short-term debt in overall debt has a mean value of 32.1% for our sample firms. This figure is lower than the one reported in Fan et al.’s (2012) sample, which is 47%. While it is useful to compare the distribution of this key variable with prior findings, we should also note that our sample differs from theirs in a number of dimensions, including sample period as well as the coverage of firms and their countries.

**Table 2. Descriptive Statistics**

This table presents the various descriptive statistics of variables used. These are the number of observations, mean values, standard deviations, minimums, maximums, 25<sup>th</sup> percentiles, medians, and 75<sup>th</sup> percentile values. The definitions of the variables are in the Appendix. We winsorize all variables at the 1% level except the governance variables. We use one-year lagged values of firm and country-level control variables.

|**Panel A. Dependent**<br>**variables**|**Obs.**|**Mean**|**Std. Dev.**|**Min.**|**25**<sup>**th**</sup>**perc.**|**Median**|**75**<sup>**th**</sup>**perc.**|**Max.**|
|---|---|---|---|---|---|---|---|---|
|_std_|28,591|0.321|0.322|0.000|0.045|0.208|0.518|1.000|
|_leverage_|33,034|0.225|0.225|0.000|0.035|0.178|0.338|1.144|
|**Panel B. Governance v**|**ariables**||||||||
|_gov44_|40,019|0.607|0.094|0.297|0.538|0.605|0.674|0.953|
|_gov_board_|40,019|0.55|0.126|0.150|0.450|0.545|0.640|0.917|
|_gov_indep_|40,019|0.645|0.276|0|0.429|0.750|0.875|1|
|_size_eff_|39,962|2.145|0.692|1|2|2|3|3|
|_ceo_chair_|35,504|2.125|0.921|1|1|2|3|3|
|**Panel C. Firm-level co**|**ntrols**||||||||
|_size_|33,604|6.430|2.418|0.722|4.804|6.541|8.022|12.299|
|_asset_tangibility_|32,690|0.225|0.228|0.000|0.037|0.147|0.343|0.884|
|_asset_turnover_|32,066|0.916|0.783|0.000|0.327|0.772|1.263|3.898|
|_roa_|32,775|-0.046|0.296|-1.902|-0.010|0.022|0.062|0.298|
|_mb_|32,684|1.968|1.730|0.654|1.081|1.374|2.097|12.187|

**Panel D. Country-level controls**

15
With youth unemployment above 25 percent in recent years, a top priority for governments is to create an environment where employment expands, especially for the growing young population (Figure 6).<sup>10</sup> This is a key issue of concern, as young people, especially those from the lower- and middle-income households often lack the resources to be able to cope with unemployment. Youth benefit more from aggregate unemployment reductions, but also appear to suffer more during down-turns. Youth unemployment and the lack of quality jobs for graduates are often cited as key motivations for protest.<sup>11</sup> Experience has shown that youth unemployment contributes to social instability and unrest. Unemployment (and a feeling that growth has left out large segments of the population) is at the heart of the discontent associated with the “Arab Spring” in several countries in the region. Female employment to population ratios are the lowest in the world by a wide margin.

Figure (6): Youth Unemployment Rate in Different Regions (15-24) <!-- Start of picture text --> 30<br>24.4<br>25<br>20 18.5 18.8<br>16<br>15.2<br>15 12.3 13.1<br>11.3<br>10.1<br>9.1<br>10<br>5<br>0<br>East Asia South Asia Sub-Saharan World OECD & LAC S.E. Asia & Central/SE Middle East North Africa<br>Africa EU Pacific Europe &<br>CIS<br><!-- End of picture text -->

**_Source:_** Freund (2012).

Simultaneously the region records the highest female unemployment rates at 18 percent; and the lowest female labor force participation. For example, the female unemployment rate in Egypt is 23 percent, in Tunisia 17 percent, and in Morocco, 10 percent in 2011.<sup>12</sup> On average, over the past decade, more than two out of three women did not participate in the labor market, this is due to societal traditions, which make it much harder for women to participate in the labor market outside their homes. These low aggregate labor force participation rates are thus predominantly driven by limited female participation. Although countries have invested in women in their education and health care services, countries have not benefited sufficiently from this productive human capital through a gender-neutral playing field for entrepreneurship and employment.

# **B. Employment Dynamics in the Arab Region**

Access to employment and income-earning opportunities is a vital issue for the Arab region. It is difficult to find reliable longitudinal data on firm dynamics in the Arab region, but a rough observation of enterprise survey data suggests a different pattern from that of international experience.<sup>13</sup> A large amount of formal job creation occurs in large firms and within governments (2007-2011) (Figure 7). In multiple countries, the amount of job creation in large

> 10 Youth defined as those between the age of 15–24.

> 11 See, for example, The Economist, _Bottom of the pyramid: Job-hunting lessons for young Egyptians_ , September 10, 2011. 12SaharNasr, and Douglas Pearce (2012).

> 13Stone, A, and Lina, T. Badawy. _SME Innovators and Gazelles in MENA: Educate, Link, Train, Certify, Compete!_ (MENA Knowledge and Learning Quick Note #43, September 2011, World Bank).

11
# **Inflation Dynamics and Food Prices in an Agricultural Economy: The Case of Ethiopia**<sup>a</sup>

JOSEF L. LOENING DICK DUREVALL YOHANNES A. BIRRU World Bank University of University of Sussex Gothenburg Keywords: Agriculture, Cointegration analysis, Ethiopia, Exchange rate, Money demand, Food prices, Forecast, Inertia, Inflation.

JEL classification: E31, E37, E52, O55.

a We would like to thank Hashim Ahmed, Arne Bigsten, Robert Corker, Astou Diouf, Paul Dorosh, Karen Mcconnell-Brooks, Jiro Honda, Deepak Mishra, Paul Moreno-Lopez, Rashid Shahidur, Eleni Gabre-Madhin, Francis Rowe, Stephen O'Connell, Cristina Savescu, Patricia Seex, Emily Sinnott, and Zaijin Zhan. We would also like to thank participants during seminars in Ethiopia in October 2008, and in January and April 2009 at the Economists’ Forum in Washington DC for useful comments and discussions. The views expressed in this paper are our own and should not be attributed to any the people mentioned above or to the institutions we are affiliated with. Comments are welcome at <u>dick.durevall@economics.gu.se, jloening@worldbank.org, or y.a.birru@sussex.ac.uk.</u>
administrative centers already had been transformed into top-of-the-line commercial and office development complexes, laid out around newly cleared downtown park space.

Relocation of state-owned enterprises (SOEs) and municipal administrative centers has not been without problems. A 2012 study by the World Bank found that relocation at sites far from the city center had contributed to urban sprawl and carbon generation. Meanwhile, many of the central- and provincial-owned SOEs have set up real estate development subsidiaries of their own and became property speculators. A study published by the National Bureau of Economic Research (US) found that central-government SOEs accounted for 51% of all land purchased at auction in Beijing in 2010, up from zero in 2003, and that they paid higher prices than private developers for comparable land. The national government in 2011 had to promulgate regulations forbidding central-owned SOEs from engaging in property development, except for the 14 SOEs that have this as their principal mission.

# **LESSONS FROM INTERNATIONAL EXPERIENCE:  PART I**

Four lessons stand out from this Part I review of international experience:

# **Importance of Having a Policy That Encourages Active Management of Public Land Assets and Unlocking of Economic Value**

India does not have a central policy, or provide policy guidance, regarding management of public land assets. It does not encourage examination of public land hoarding, or encourage agencies to explore ways to unlock land values. Worse, at the center level, India currently has a policy that freezes any type of alienation of center land—whether by sale, leasing, contribution of property to joint venture developments, or pledges of land values to support infrastructure investment. The “freeze” on all public-land activity has been imposed by Cabinet, in order to provide time for development of a Cabinet-level policy governing public land management and public land disposition.

Preparation of such a Cabinet policy would seem to be a top priority. It has the potential not only to unlock center-controlled public lands, but to serve as a model for states and urban local bodies. The statement of policy principle can be simple—perhaps along the lines of Australia’s statement that the purpose of public land ownership is to enhance public service delivery, and that government is not in business to be a permanent property owner. The policy then could clarify the prerequisites and safeguards for land disposition and specify the conditions under which decisions about land use will be delegated to individual departments or agencies.

# **Value of Public Land Inventories**

Informed decisions about the disposition of public lands will require reliable inventories of public landholdings--by owning agency, location, current and planned use, size, and (for 21
20

# **Figure 3 Coffee Marketing Chain in Costa Rica**

<!-- Start of picture text --> Coffee Producers<br>Producers Mills<br>sell cherry may<br>directly to vertically<br>integrate<br>Cooperative or Private Domestic or Private Multinational<br>Processing Mills<br>Export Market Domestic Market<br>Mills sell Mills Mills sell Mills<br>processed vertically processed vertically<br>coffee integrate coffee integrate<br>directly to into directly to into<br>Exporters Roasters<br><!-- End of picture text --> Law 2762 regulates the selling process: Farmers are asked to produce their identity papers with every deposit of cherries at a mill and they must receive a receipt for it. In this way, there is a record of all coffee sold to a mill. After processing the cherries, mills must then register all sales contracts for green coffee, whether for export or domestic sale, with Icafé. They must register the sale of coffee even if the mill is also the exporter or roaster. The quantities from the registered sales contracts can then, if necessary, be verified by the recorded (receipted) quantity of coffee sold by farmers to mills. In theory, this verification can control for mills trying to sell coffee that was purchased ‘under the table’ from (illegal) middlemen. According to Law 2762, mills should also provide agricultural input services such as limited credit and/or input acquisition facilities to all producers, although in practice, this only occurs for large producers.

Several factors contribute to a competition for volume of cherries among mills in the Costa Rican coffee market, so that at first glance, the reasons for hypothesizing imperfect competition at the farm-gate in Costa Rica are not apparent. First, in the general context of an international market characterized by a move towards last minute production by roasters requiring large and ready volumes of coffee, mills and exporters in Costa Rica, as elsewhere, benefit from size. Second, by centralizing the processing of coffee off-farm, mill processing in Costa Rica takes place in industrial plants that clearly have a cost-minimizing efficiency point of production. In actual fact, a recent study identifies that most processing mills in Costa Rica are operating on the increasing returns to scale (IRS) portion of their long run average cost curves (Mosheim 2002). This simply supports the oft-made observation that Costa Rica has an over-capacity in coffee processing. Finally, in the years with binding ICA quotas, Icafé allocated Costa Rica’s quota to mills according to the average size of the last two harvests, providing further incentive to compete for volume of coffee cherries.