rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 4. Share of total value added accounted for by the top decile firms, 2008– 17**

<!-- Start of picture text --> 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Year<br>25<br>20<br>Percentage<br>15<br>10<br><!-- End of picture text --> _Source:_ World Bank staff calculations based on SBS data set.

# **Figure 5. Share of employment accounted for by the top decile firms, 2008–17**

<!-- Start of picture text --> 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Year<br>18<br>16<br>14<br>Percentage<br>12<br>10<br><!-- End of picture text --> _Source:_ World Bank staff calculations based on SBS data set.

**These top decile firms are also typically smaller, less productive, and less likely to invest in intangible assets than the rest of the firms.** As highlighted in the methodological section of this paper, the increase of markups cannot be unequivocally interpreted as a decline in competition in a given market; it can reflect firms’ efficiency gains or adoption of qualityupgrading strategies. To explore this aspect, a simple econometric analysis was applied to capture performance differences between the group of firms that is leading the markup increase (the top decile firms) and the rest of the firms, based on the markup distribution. The following specification was adopted: a a a 0 1T + η + ρ + γ ,                             (8) ccjjiijj iijj jj jj cc ccjjiijj where the dependent variable reflects different firm characteristics (of firm CCha ssa aa = δδ + δδ TT_dda aaddaa + μμ _i_ that operates in

_i_ that operates in sector _j,_ county _c,_ at year _t_ ) to be compared. Three dependent variables are tested: size (measured in terms of the number of full-time employees), productivity (measured as TFPR discounted for markup<sup>16</sup> ), and intangible assets (measured as a dummy that takes the value of 1 for firms having a positive value of intangible fixed assets<sup>17</sup> ). The key explanatory variable is T , a dummy that takes the value of one firm if _i_ is among the top decile of the markup distribution in year _t_ —while η _t_ , ρ _j_ , and γ _c_ are, respectively, time, NACE 4-digit sector, iijj and county fixed effects. The parameters of equation (8) are estimated by OLS (or Probit TT_dda aaddaa regression maximum likelihood, when the dependent variable is the intangible assets dummy), always using heteroscedasticity-robust standard errors. Results displayed in table 3, columns 1 and 2, suggest that firms in the 90th percentile of the markup distribution are, on average, 46.8

> 16 TFPR discounted for markup is computed as TFPR-markup. In this regard, it is not a perfect measure of the true technical efficiency, but just a proxy of productivity that accounts for market share and the effect of materials elasticity, while other factors, such as the effect of product quality or innovation, are not considered. 17 Value of intangible assets in the SBS data set is defined as value built on (a) R&D expenditure and (b) franchise, patents, and other similar rights and values.

15
two views. The challenge for the analyst is to incorporate both types of views into testable hypotheses. While it is relatively straightforward to include economic opportunities and constraints, demographic and regional characteristics, into an empirical analysis of time use, it is quite difficult to pinpoint the exact variables that capture social norms or values that also influence intra-household time use.'<sup>0</sup> However, in so far as social norms and values stay unchanged over a relatively short period of time, an analysis of time use using panel data can control for some of these differences in norms by controlling for unobserved differences in time use.

In this paper I explore the determinants of time use of boys and girls in Peru. The paper takes advantage of the fact that unlike in previous studies in the literature, the availability of panel data from Peru allows me to determine the adequacy of the economic model in describing intra-household time use. If economic variables are found to not influence time use significantly, then it can be argued that the variations could be from differences in other factors such as social norms and roles. " The three activities that children engage in are modeled separatelyhousework, schooling and labor. It is already common in the literature on female labor supply/time allocation to model time spent in housework as yet another activity that can respond to economic and demographic environment of the household. In this paper housework is taken as one of the two components of children's labor. The availability of panel data allows me to test the effects of changes in the household's opportunities and constraints (health and sickness, changes in employment, access to infrastructure and life-cycle and demographic characteristics) on children's time use by gender. The following hypotheses are of interest: interpretation would argue that boy-girl time-use responds to economic changes as much as do other behavioral outcomes such as consumption.

> **10** Dummies for ethnicity or the relationship of the individual to head have been used to capture the effects of social roles and norms on adult time use (see Fafchamp and Quisumbing, 1998; Kevane and Wydick, 1998). Such variables may be inadequate in capturing the effects of social norms, particularly in crosssection data.

Of course it could also be due to specification error.

3
pessimistic view that coffee farmers use windfall profits entirely for consumption. The evidence rather suggests that coffee farmers invest into alternative crops and non-agricultural activities. In addition, the price boom appears to have strong multiplier effects that tend to be concentrated in coffee-growing regions.

These results are obtained by analyzing three household surveys for the years 1992/93, 1995/96 and 1999/00. The empirical strategy adopted to identify the effects of coffee price changes on households’ welfare consists of comparing the performance of different groups. The most basic null hypothesis tested here is that, controlling for other factors, households more dependent on coffee farming should be more intensively affected by changes of coffee prices. Due to data quality limitations, going beyond measuring this correlation and towards proving causation has been difficult. In particular, since we rely on an ex-post assessment of changes in household welfare based on the surveys, we cannot isolate the impact of changes of coffee world prices from that due to the domestic coffee market liberalization; we can only capture their combined effect. And given that we do not use panel data, changes shown for particular household groups may reflect both inter-group movements and changes for initial group members.<sup>1</sup> The remainder of the paper is structured as follows. After discussing briefly how important coffee price shocks were in relation to other factors, we deal with the transmission of international price changes to Ugandan coffee farmers. We then move on to examine the changes in the performance of different households and the possible factors behind them. The final section puts the findings of the paper into a broader policy context.

# **The Impact of Coffee Price Changes on Incomes and Poverty: Existing Evidence**

Most analyses of the links between movements of export crops prices and poverty have to deal with two sets of problems. First, it is usually very difficult to isolate price changes from other shocks and, second, changes in international prices are seldom fully transmitted to farmers and this partial pass-through can vary by region, crop, market structure, size of the farm, etc. These two sets of issues are also relevant for the case of coffee in Uganda. This

> 1 There is a panel of 1200 households for the years 1992/93 and 1999/2000 – a sub-sample of the sample used in our analysis. Yet, we prefer to work with the cross-sections, as (a) the panel does not cover the boom year 1995/96, and (b) the panel would yield too small sub-groups that are of interest to our analysis.

4
standards. Specifically, the z-scores are defined as the standard for well-nourished children. For example, the height-for-age z-score for a child _i_ in age and gender group _c_ is constructed as where _Hic_ is the measured height of the child and _MedianHc_ and σ c are the age- and genderspecific median height and the standard deviation of the height, respectively, of well-nourished children. A risk indicator is falling two standard deviations below this z-score measure. In round one, 18% of children in the sample were malnourished, compared with 32% in round two. In urban areas, stunting increased from 11% to 21% between rounds, whereas in rural areas, it increased from 20% to 58%.

The independent variable of interest used in this study is the level of public expenditure in each region. The total public expenditure is defined as the summation of investment and current expenditure. The unit of measurement is 100 million soles (approximately 33 million dollars). During the period analyzed (2002–6), total per capita government expenditure increased by 34%<sup>17</sup> .

# _III. RESULTS_

The following section describes the different data sources used in this study and presents the results. It includes explanations of the different pathways through which public spending improves child well-being and the possible exclusion mechanisms at work.

> 17 Statistics from the Ministry of Economics and Finance (www.mef.gob.pe).

12
Financial Regulation and Government Revenue Table 3: Regulation and Profits: Yearly Variation (Million Real Birr)

|Variable|Pre-Tax|Bank Profts (R|eal Birr)|
|---|---|---|---|
||Pre-Policy<br>|||
|Year 2009|110.3***||(35.68)|
|Year 2010|137.6***||(32.98)|
|Year 2011|164.5***||(36.15)|
||Post-Policy|||
|Year 2012|172.2***||(34.53)|
|Year 2013|179.5***||(33.08)|
|Bank FE||Yes||
|Observations||52||
|Adjusted _R_<sup>2</sup>||0.904||
|Mean Dep. Var.||126.2||
|SD Dep. Var.||96.42||

_Note:_ This table reports OLS estimates. The unit of observation is bank level and bank fixed effects are included. Robust standard errors are in parentheses. Profits are extracted from banks’ individual annual profits and are reported in their before-tax expression in million birr; it is a continuous variable. The mean and standard deviation are reported in the final two rows. We regress over the available years to verify the average profit evolution in the banking industry, which (as expected) markedly declines after 2011. All quantities are in millions of real birr, at their 2010 value. The adjusted _R_<sup>2</sup> of these regressions is also reported. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

of the policy, the amount of NBE bills (in black) went from zero to roughly 500 million birr in April 2011 and kept growing steadily afterwards. At the same time, safe assets (which include NBE bills) increased one-to-one with this variable. This shows that the policy was successful in leading Ethiopian banks to become safer with the introduction of the policy, and banks did not substitute away from one type of liquidity (bank-to-bank deposits, cash, etc.) to NBE bills, but rather increased the overall aggregate.

Last but not least, we have referred to this policy as a macroprudential regulation as well as a “financial regulation tax”. Indeed, we find that bank profitability suffers in the aftermath of this. For this purpose, we collect data from bank annual reports and run a similar regression to equation (2): The difference is that while balance-sheet variables (NBE bills, safe assets) are measured monthly, the profits are only measured yearly. Analogously to the previous subsection, we report the coefficients _βy_ of equation (3) in Table 3 and plot the yearly coefficients in Figure 2. By inspecting both elements, 12
# **Figures**

Figure 1. Historical and projected world population, 1950-2050, and 10 rural-urban populations (FAOSTAT, 2008). Figure 2. The relationship between meat consumption and per capita 11 income in 2002. National per capita income based on purchasing power parity (PPP). Data from World Bank (2006) and FAO (2006), figure from Steinfeld et al. (2006). Figure 3. Per capita availability of meats, 2000 and 2050, reference 17 run. Source: IFPRI IMPACT model simulations. Figure 4. Land-use change (food crops, pastureland, and biofuel 21 crops) globally, 2000 to 2050, reference run. Rosegrant et al. (2009), IMAGE model. Figure 5. Average cereal yields on a regional basis, 1961 to 2004. 30 Data from FAOSTAT, graphic from WDR (2008). Figure 6. Estimated changes in land use from 1700 to 1995. Data 38 from Goldewijk (2001), graphic form Steinfeld et al. (2006). Figure 7. Change in forest areas excluding regrowth, 2000, 2025, 39 and 2050, as estimated by the IMAGE model. Graphic from Rosegrant et al. (2009). Figure 8. Development of global biodiversity 1700-2050 in mean 45 species abundance in various natural biomes. From GLOBIO 3, graphic from Rosegrant et al. (2009). Figure 9. Biodiversity development for the world, and contribution 46 of stress factors to the decline in the reference run. From GLOBIO 3, graphic from Rosegrant et al. (2009).

4
**Table 3 – Subjective Income Satisfaction and Yitzhaki (1979) and Silber-Verme**

# **(2010) Deprivation Indices**

|||**Yitzh**|**aki RD**|**Silber-**|**Verme09**||
|---|---|---|---|---|---|---|
|||Coeff.|z-stat|Coeff.|z-stat|Obs.|
|United Kingdom|1991|-1.271|(2.51)*|-2.74|(4.81)**|5128|
||1992|-0.861|-1.64|-1.886|(3.08)**|4787|
||1993|-0.647|-1.29|-1.969|(3.33)**|4590|
||1994|-1.44|(2.80)**|-3.12|(5.30)**|4656|
||1995|-2.125|(4.13)**|-2.732|(4.66)**|4568|
||1996|-2.358|(4.19)**|-3.93|(6.25)**|4833|
||1997|-1.757|(3.23)**|-2.735|(4.46)**|4826|
||1998|-1.768|(3.01)**|-3.092|(4.74)**|4821|
||1999|-2.381|(4.20)**|-3.797|(6.10)**|4710|
||2000|-1.007|<br>-1.68|-2.266|<br>(3.51)**|4613|
|Germany-West|1991|-7.216|(8.34)**|-5.923|(6.26)**|7327|
||1992|-8.293|(8.96)**|-5.359|(5.60)**|7359|
||1993|-7.384|(8.00)**|-5.571|(6.12)**|7229|
||1994|-8.596|(9.31)**|-7.705|(7.77)**|7083|
||1995|-8.256|(7.58)**|-6.344|(5.40)**|7419|
||1996|-7.185|(7.24)**|-5.746|(5.57)**|7348|
||1997|-6.729|(7.40)**|-5.452|(5.86)**|7221|
||1998|-7.154|(7.49)**|-6.735|(6.55)**|8109|
||1999|-7.453|(6.84)**|-6.479|(5.51)**|7820|
||2000|-7.459|(6.20)**|-7.656|(5.36)**|7590|
|Germany-East|1992|-5.282|(9.24)**|-5.213|(8.15)**|3570|
||1993|-6.132|(10.23)**|-6.363|(9.71)**|3396|
||1994|-6.312|(7.67)**|-7.167|(10.01)**|3339|
||1995|-4.102|(6.36)**|-4.975|(7.64)**|3247|
||1996|-5.304|<br>(7.76)**|-4.361|<br>(6.33)**|3218|
||1997|-5.481|(8.11)**|-4.392|(6.18)**|3148|
||1998|-5.456|(8.10)**|-5.347|(7.22)**|3318|
||1999|-5.359|(7.62)**|-5.473|(7.59)**|3260|
||2000|-6.49|<br>(10.10)**|-5.761|<br>(7.17)**|3239|
|Poland|1994|-18.74|(20.17)**|-10.968|(13.53)**|5775|
||1995|-30.692|(26.30)**|-17.627|(16.93)**|8908|
||1996|-30.652|(26.25)**|-16.569|(15.73)**|9084|
||1997|-17.9|<br>(19.73)**|-10.942|<br>(14.31)**|5831|
||1998|-15.962|(18.24)**|-8.705|(11.24)**|5679|
||1999|-17.032|(19.94)**|-9.628|(12.55)**|5695|
||2000|-17.273|(20.27)**|-8.361|(10.79)**|5689|
|Hungary|1992|-13.993|(18.15)**|-7.82|(10.55)**|4609|
||1993|-10.909|<br>(16.28)**|-5.643|<br>(9.00)**|4093|
||1994|-12.269|<br>(18.46)**|-5.224|<br>(9.02)**|3841|
||1995|-10.856|(17.45)**|-5.47|(9.36)**|3393|
||1996|-10.402|(17.57)**|-5.379|(9.41)**|2963|
||1997|-9.132|<br>(14.93)**|-3.378|<br>(5.63)**|2348|

Source: Elaborated from CHER database. Dep. Var. = Income satisfaction. Ordered Logit estimations. Z- stat in parenthesis. (*) significant at 5% level; (**) significant at 1% level.

27
high, medium or low impact group following the classification in Vavra (2020), which is detailed in Table A.1. In our simulations, we assume that firms in high impact sectors (e.g. restaurants, accommodation) experience a 100% revenue loss during the lockdown, firms in medium impact sectors (e.g. manufacturing) experience a 50% revenue loss, and firms in low impact sectors (e.g. education, essential retail) experience a 20% loss.<sup>6</sup> Based on the sectoral composition of the economy and our impact category classification, countries experience an average loss in sales during the lockdown between 38% (Ethiopia) and 59% (Costa Rica). When weighted by firms’ sales, the average output loss varies less and is slightly lower, as larger firms tend to be in lower-impact sectors.

At baseline, at least 70% of firms are profitable in all but one country. The average profit margin (counting loss-making firms as having zero profits) ranges from 4% to 16%, and does not exhibit a clear correlation with a country’s GDP per capita. This is relevant, as the profit margin indicates the relative drop in profits that firms can sustain without incurring losses.

In terms of the composition of costs, material costs are the largest component and represent 41% of total costs on average. Labor costs account for 21% and fixed costs for 37% of all costs on average.<sup>7</sup> Cost shares vary across countries, which will be relevant for interpreting our results. In particular, reported labor cost shares are lower in poorer countries – presumably due to labor informality – and material cost shares are correspondingly higher. Figure A.1 shows that the cost shares also vary by firm size (turnover) and profit margin. Profitability is increasing in firm size. The labor cost share and the fixed cost share are decreasing across firm-size deciles, while the share of material inputs is increasing.<sup>8</sup> This means that larger firms can better adjust to the demand shock, somewhat mitigating the shock’s aggregate impact.

To confirm the accuracy of the cost share information, we compare our data to the cost shares observed in the World Bank Enterprise Survey, which contains the breakdown of costs into material, labor and fixed costs for the manufacturing sector. As Figure A.3 shows, the cost shares for the manufacturing sector in the two datasets are similar in levels and in slopes across firm-size deciles.

> 6 We implicitly assume that the sectors gaining from the pandemic, e.g. retailers of cleaning products, refrigerators and teleconferencing equipment are sufficiently small and specific so that there is no sector group that experiences an overall revenue increase.

> 7The Albania data do not have the breakdown of costs into the different components, and the Ethiopia data do not feature a distinction between labor costs and material costs. We hence impute the cost shares in these cases with the average from the

> 8Within the top decile, the profit margin continues to increase, but the cost shares are fairly stable (Figure A.2).

6
# Table 8: Who Pays R&D Costs? Percent Distribution of R&D Among

Industry, Universities, and Public Research Institutes, 2001

|R&D cost bearers<br>R&D carriers|Government|Industry|Non-Japanese|Total|
|---|---|---|---|---|
|Industry|1.4|98|0.5|100|
|Non-profit & public|||||
|Research institutes|90.6|9.3|0.1|100|
|Universities|50.9|49.1|0|100|
|Total R&Dspending|21|78.6|0.4|100|

Source: Statistics Bureau, Statistics on Japan’s Science and Technology Research, Tokyo. 2003. <u>http://www.stat.go.jp/data/kagaku/4-03.htm</u> Table 9: Percent Distribution of R&D Spending by Sectors and Research Categories*, 2001

||Basic<br>Research|Applied<br>Research|Development<br>Research|Total|
|---|---|---|---|---|
|Total Spending|14.6|23.4|62|100|
|Industry|5.8|20.4|73.9|100|
|Non-profit & public Research|||||
|Institutes|28.3|26.7|44.9|100|
|Universities|53.5|37.5|9|100|

*Based on R&D spending on natural science only.

Source: Statistics Bureau, _Statistics on Japan’s Science and Technology Research_ , Tokyo. 2002.

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- 2 -

# **I. INTRODUCTION**

Infrastructure procurement is still a challenging task for governments, not only because public works are expensive, but also because the nature of the works is often highly complicated and customized. Competitive bidding became prevalent among public procurement systems throughout the world, but how to design the auction mechanism remains difficult in practice, especially when bidder participation is endogenously determined. Typical are large-scale electricity projects. Governments or public entities, namely auctioneers, may be able to increase or decrease the number of bidders at will, but at certain expense.

The endogeneity of bidder participation is a complex issue in auction theory as well as its applications. As is well known, competition is the single most important factor toward achieving economic efficiency and anti-corrupt allocation of an object through competitive bidding. Even in the traditional fixed- _n_ approach where the number of bidders is assumed fixed and commonly known by bidders, the equilibrium bid is expected to decrease with competition.<sup>1</sup> In the infrastructure procurement, it is of particular concern that the realized degree of competition is often limited, regardless of whether it is contracted out through a traditional method or public-private partnership (PPP) arrangement (e.g., Foster, 2005; NOA, 2007; Estache and Iimi, 2008a).<sup>2</sup> Estache and Iimi (2008a) confirm the importance of intensifying competition to contain infrastructure procurement costs, but in a static sense. The previous analysis presumes that

> 1 In the following sections of this paper, a government procurement auction is considered unless otherwise mentioned. Thus, bidder’s cost of procuring the contracted-out work is a piece of unknown private information. The best contractor is a firm with the lowest possible cost parameter.

> 2 According to the Private Participation in Infrastructure (PPI) database, the top 10 percent largest firms— defined by the number of transactions that each company obtained—were awarded about half of total infrastructure PPP contracts.
# **Is Increasing Inorganic Fertilizer Use in Sub-Saharan Africa a Profitable Proposition? Evidence from Nigeria**

Lenis Saweda O. Liverpool-Tasie* Department of Agricultural, Food and Resource Economics, Michigan State University 211B, Agriculture Hall, 446 W. Circle Drive, East Lansing, MI, 48824 USA <u>lliverp@msu.edu</u> Bolarin, T. Omonona Department of Agricultural Economics University of Ibadan, Ibadan, Nigeria Awa Sanou Department of Agricultural, Food and Resource Economics, Michigan State University East Lansing, MI, 48824 USA Wale Ogunleye Department of Agricultural Economics University of Ibadan, Ibadan, Nigeria

*   corresponding author

Key words: Fertilizer profitability, Maize, Marginal physical product, Nigeria, Africa JEL codes: Q12, Q18, D24 <u>Acknowledgements: We acknowledge and appreciate financial support for this work from the</u> Bill and Melinda Gates Foundation. We also appreciate support from Luc Christiaensen, Amparo Palacios-Lopez, Jonathan Kaminsky and the rest of the World Bank’s “Agriculture in Africa— Telling Facts from Myths” Project team. We are most grateful to Christopher Barrett, Nicole Mason, Megan Sheahan, Luc Christiaensen and an anonymous reviewer for their comments on earlier versions. Any views expressed or remaining errors are solely the responsibility of the authors.

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