rafmacalaba/datause-encoder-data huggingface dataset:
There is a visible spike at the minimum wage, suggesting that it truncates the wage distribution. The bite of the minimum wage is particularly pronounced in industries employing large numbers of low skilled workers, such as agriculture,<sup>hotels and</sup> restaurants, and trade. In these industries the incidence of<sup>minimumandsub-minimum</sup> wage employment ranges from 20 to 30 percent. However, these figures<sup>also indicate</sup> that some workers are paid less than the minimum wage.

Indeed, the average wage in the lowest wage bracket is significantly less than<sup>the</sup> minimum wage, implying that enforcement is weak.<sup>3</sup> 1 To the extent employers are<sup>able</sup> to circumvent the minimum wage regulation, it obviously does<sup>not harm employment.</sup> However (similarly as in the case of the employment protection regulations)<sup>it would be</sup> preferable to adopt a lower minimum wage with stricter<sup>enforcement,ratherthanthe</sup> other way round.

While probably limiting employment chances of less-sldlled workers,<sup>thehigh</sup> minimum wage in Lithuania has likely contributed to the<sup>fastpaceofenterprise</sup> restructuring and productivity improvements. By restricting adjustment<sup>of wagesto the</sup> fall in the demand for low-skilled labor, the high minimum<sup>wageinLithuania forced</sup> employers to close low-productivity jobs implying a rise in the<sup>average productivitylevel</sup> The high rate of job reallocation provides support to this view.<sup>If thisargumentis</sup> correct, then the high minimum wage would explain the apparent<sup>paradoxthathigh job</sup> turnover coexists in Lithuania with a stagnant unemployment<sup>pool.Job turnover is high,</sup> because employers have to close low-productivity jobs and replace them<sup>withhigher</sup> productivity jobs. The unemployment pool is stagnant since low-productivity<sup>workers</sup> due to the high minimum wage are locked out of employment (Boeri<sup>and Terrell,2001).</sup> To conclude, Lithuania has a modestly restrictive employment protection regulations but a relatively high minimum wage. However, the effects of<sup>legalnorms</sup> seem to be unevenly distributed across different segments of the economy.<sup>There isa</sup> flexible segment, consisting of small private non-unionized firms,<sup>where enforcementof</sup> regulations is weak and thus they hardly constrain employers ability to adjust

> **31** According to the employer based survey, the average<sup>wageinthe lowest wage bracket m1999was 330 Lits while</sup> the minimum wage was 430 Litas. Although the former<sup>figureis biased downwardsdue to theinclusion of part-time</sup> workers, still the discrepancy is substantial.

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# **Table 8A - Robustness Checks: Effect of Equality of Opportunity on Economic Development Human Opportunity Index - Average Mathematics, Reading and Science Outcomesfor Level 2 - PISA 2009** **<u>Controlling for Ethnic Fractionalization</u>**

|Regression<br>Instrument: Logof Wheat Sugar Sustainability|OLS<br>Dependent Variab|OLS<br>IV<br>le: Logaritm of Per Capita Income,Average 2005-2010|
|---|---|---|
|Index of Ethnic Fractionalization (0-Low to 1-high)|-2.505***|0.343<br>-1.075|
||(0.43)|(0.47)<br>(0.66)|
|Human Opportunity Index*||0.040***<br>0.028**|
|||(0.01)<br>(0.01)|
|constant|9.420***|6.942***<br>8.054***|
||(0.22)|(0.44)<br>(0.81)|
|R-sqr|0.158|0.577|
|Number of Observations|183|60<br>48|
|Wald F-statistics from first stage||13.591|
|**Controlling for Legal Origin**|||
|Regression|Dependent Variable: Lo|garitm of Per Capita Income,Average 2005-2010|
|Instrument: Logof Wheat Sugar Sustainability<br>|OLS|OLS<br>IV<br><br>|
|Human Opportunity Index*||0.038***<br>0.043***|
|||(0.00)<br>(0.01)|
|British Legal System|-0.344|0.658***<br>0.673**|
||(0.36)|(0.17)<br>(0.22)|
|French Legal System|-0.467|0.579***<br>0.626***|
||(0.32)<br>|(0.14)<br>(0.15)<br>|
|Germany Legal System|2.267**|0.692**<br>0.606|
||(0.72)|(0.22)<br>(0.33)|
|Scandinavian Legal System|2.653***|1.082***<br>0.975***|
||(0.72)|(0.22)<br>(0.25)|
|constant|8.246***|6.545***<br>6.252***|
||(0.26)|(0.20)<br>(0.35)|
|R-sqr|0.199|0.875|
|Number of Observations|142|55<br>48|
|Wald F-statistics from first stage||21.485|
|**Controlling for Share of Tropical Land**<br>Regression|Dependent Variable: Lo|garitm of Per Capita Income,Average 2005-2010|
|Instrument: Logof Wheat Sugar Sustainability|OLS|OLS<br>IV|
|% land area in geographical tropics|-1.927***|0.075<br>-0.237|
||(0.23)|(0.33)<br>(0.64)|
|Human Opportunity Index*||0.038***<br>0.031*|
|||(0.01)<br>(0.02)|
|constant|9.116***|7.133***<br>7.536***|
||(0.15)|(0.36)<br>(1.12)|
|R-sqr|0.315|0.58|
|Number of Observations|158|59<br>49|
|Wald F-statistics from first stage<br>||3.306|
|**Controlling for Latitud**<br>Regression|Dependent Variable: Lo|aritm of Per Capita IncomeAverage 2005-2010|
|Instrument: Logof Wheat Sugar Sustainability|<br>OLS|, <br>OLS<br>IV|
|latitude of country centroid|0.030***<br>|0<br>0.002<br><br>|
||(0.01)|(0.00)<br>(0.00)|
|Human Opportunity Index*||0038***<br>0034***|
|||.<br>.<br>(0.01)<br>(0.01)|
|constant|7.565***|7.179***<br>7.253***|
||(0.15)|(0.30)<br>(0.44)|
|R-sqr|0.207|0.58|
|Number of Observations|158|59<br>49|
|Wald F-statistics from first stage||17.839|
|**Controlling for Longitude**|||
|Regression|Dependent Variable: Lo|garitm of Per Capita Income,Average 2005-2010|
|Instrument: Logof Wheat Sugar Sustainability|OLS|OLS<br>IV|
|longitude of country centroid|-0.002|-0.003<br>-0.003**|
||(0.00)|(0.00)<br>(0.00)|
|Human Opportunity Index*||0.039***<br>0.036***|
|||(0.00)<br>(0.01)|
|constant|8.250***|7.154***<br>7.242***|
||(0.14)<br>|(0.29)<br>(0.37)<br>|
|R-sqr<br>Number of Observations|0.006<br>158|0.599<br>59<br>49|
|<br>Wald F-statistics from first stage||21.201|

* p<0.05, ** p<0.01, *** p<0.001

*Average Mathematics, Reading and Science Outcomes Level 2 - PISA 2009

28
between entrepreneurship and job creation is detectable only when purely self-employment and informal companies are excluded from the analysis (see Ghani, Kerr and O’Connell, 2011a and 2011c).

Turning our attention from the macroeconomic to the sectoral level, the empirical evidence concerning industrial dynamics also casts much doubt on the progressive potentialities of business start-ups. Firstly, survival rates for new firms are strikingly low: according to Bartelsman, Scarpetta and Schivardi (2005), who worked on data for ten OECD countries, about 20-40% of entering firms fail within the first two years of life, while only 40 to 50% survive beyond the seventh year (see also OECD, 2003, p. 145). The econometric evidence at the sectoral and microeconomic levels is largely consistent with this outcome; studies on different countries and different sectors reveal that more than 50% of new firms exit the market within the first five years of activity (see Dunne, Roberts and Samuelson, 1988 and 1989; Reid 1991; Geroski, 1995; Mata, Portugal and Guimaraes, 1995; Audretsch and Mahmood, 1995; Audretsch, Santarelli and Vivarelli, 1999a; Johnson, 2005)<sup>5</sup> .

Secondly, entry and exit rates are significantly correlated; this is one of the uncontroversial ‘stylized facts’ of the entry process according to Geroski (1995, p. 424), who pointed out that the “mechanism of displacement, which seems to be the most palpable consequence of entry, affects young, new firms more severely” (see also Baldwin and Gorecki, 1987 and 1991). Indeed, entry and exit rates have been found to be positively correlated across industries in both OECD countries (see Bartelsman, Scarpetta and Schivardi, 2005) and in DCs (see Bartelsman, Haltinwanger and Scarpetta, 2004<sup>6</sup> ).

This evidence opens the way to some considerations regarding the alleged role of entry as a vehicle for technological upgrading, productivity growth and employment generation. Consistently, one should be very cautious in seeing entrepreneurship measured as new firm formation as the main driver of development for a DC. If entry were indeed driven mainly by technological opportunities, growing sales and profit expectations, one would observe a

> economic, institutional, cultural and technological variables at the country level; obviously enough, this index turns out to be positively and strongly correlated with per capita GDP.

> 5For instance, Audretsch, Santarelli and Vivarelli (1999a) studied 1,570 new Italian manufacturing firms with at least one employee and tracked their post-entry evolution for six years. They found that hazard rates increased markedly during the first two years and then tended to decrease, with a final survival rate after 6 years of activity equal to 59.1%.

> 6 The authors used a sample of 22 countries (14 European, 6 Latin American, the US and Canada) and found that the correlation between entry and exit rates across industries in 1990 was positive and significant in the vast majority of cases (Bartelsman, Haltinwanger and Scarpetta, 2004, p.21, Table 6).

6
MALAWI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE reports covering different areas of infrastructure—ICT, irrigation, power, transport, WSS—and different policy areas including investment needs, fiscal costs, and sector performance.

This report presents the key AICD findings for Malawi, allowing the country’s infrastructure situation to be benchmarked against that of its African peers. Malawi is benchmarked against both low- and middle-income African peers that, respectively, provide close comparators and a more aspirational point of reference. Detailed comparisons will also be made with immediate regional neighbors in the Southern African Development Community (SADC).

Several methodological issues should be borne in mind. First, because of the cross-country nature of data collection, a time lag is inevitable. The period covered by the AICD runs from 2001 to 2006. Most technical data presented are for 2006 (or the most recent year available), while financial data are typically averaged over the available period to smooth out the effect of short-term fluctuations. Second, in order to make comparisons across countries, the indicators and analysis had to be standardized so that everything was done on a consistent basis. This means that some of the indicators presented here may be slightly different from those that are routinely reported and discussed at the country level.

# Why infrastructure matters

Over the decade 1997–2007, Malawi’s economy grew at an average annual rate of only 2.9 percent, placing it among the slower-growing nations on the continent. This performance falls well short of the sustained 7 percent per year needed to meet the MDGs. About 0.8 percentage points of southern Africa’s improved per capita growth performance during the 2000s can be credited to improved structural and stabilization policies (Calderón 2008), while 1 percent is related to improvements in the countries’ infrastructure platforms. In the case of Malawi, most of the boost was due to the ICT revolution, while power and roads contributed very little. Simulations suggest that if Malawi’s infrastructure could be improved across the board to the level of the African leader—Mauritius—annual per capita growth rates would be 3.5 percent higher than they are at present. All three of the infrastructure sectors considered could make substantial contributions to economic growth (figure 1).

2
# **7 Practical guidelines**

When adjusting for price differences, welfare analysts are called to make several choices: some are the subject of long-standing debate (which index should be used?), while others have attracted far less attention (should each expenditure component be adjusted by its own price index, or should a single, aggregate index be preferred? When estimating a spatial price index from the survey, should one use data that have been adjusted for inflation, or go with nominal expenditures?). This paper contributes to the second set of issues. The main aim of the analysis has been to clarify the implications of alternative deflation strategies on the welfare aggregate and, based on those implications, to reach some conclusion of which deflation strategy should be preferred. Below is an operational summary of the conclusions.

We start from a scenario that is common in practice, and that occurs after some work has gone into estimating the components of the nominal consumption aggregate. We have _i_ ) all elementary expenditures that make up the consumption aggregate, in nominal terms; _ii_ ) an exogenous set of temporal CPIs, so that there is a question of whether one should use the aggregate or the individual indexes (food and non-food, or even a finer breakdown by categories); _iii_ ) a need to adjust for spatial price differences, and to do so by estimating a price index based on survey data.

A step-by-step deflation strategy is as follows.

- 1) Before doing anything else, adjust all elementary expenditures for inflation. The temporal price adjustment should be performed first in order to be able to estimate the spatial deflator based on inflation-adjusted expenditures. This is especially important if the price dynamics during the survey period is sustained. Omitting this step would imply that the spatial price index would be estimated using distorted unit values and budget shares (section 5).

- 2) To adjust for inflation, the analyst may choose whether to use CPIs specific to each commodity group – food CPI for food expenditures, non-food CPI for non-food expenditures – which qualifies as a “multiple index” (MI) approach, or the total CPI for each and every expenditure, or a “single index” (SI) approach. We cannot conclude that SI provides a better estimate of the welfare measure than MI in _all_ situations (section 4), but there are some conditions in which this is the case, others where the two approaches cannot be ordered, none where MI is preferable to MI. For this reason, the general recommendation is to stick with a single index (SI).

- 3) Next, the analyst estimates a spatial price index based on survey data. The options may be limited here: a typical scenario is one where the analyst may be able to compute a food SPI, and one

37
# **Summary**

1. **The recent crisis.** Increases during 2007/08 in the prices of many major consumption commodities came as a shock to consumers and governments. Urban consumers, alarmed by jumps in the cost of their staple foods, participated in protests, often violent, that peaked at about the time world grain prices peaked, in the middle of 2008. Some demonstrations were serious enough to threaten to destabilize their governments. Millions of the world’s poor were forced to reduce their daily calorie consumption.

2. **The aftermath.** Grain prices have receded significantly from their 2008 peaks. But food prices remain high and volatile. As this forum indicates, the policy focus has switched from short-term tactics for crisis management to strategies to manage price volatility and assure that consumers worldwide not be denied access to the grain they need by chaos in world grain markets. Suggestions for global grain reserves have figured prominently in international discussions, including proposals for special emergency reserves, international reserves, and “virtual reserves” controlled via commodity futures and options trading.

3. **The charge.** At the suggestion of the European Bank for Reconstruction and Development, the Food and Agriculture Organization and The World Bank, this paper was drafted to provide a technical foundation for policy discussions about the appropriate role for public grain reserves and related policies in managing grain market volatility.

4. **Price volatility: recent evidence.** Before considering policy alternatives, it is obviously important to start with questions about the nature of the problem and its underlying causes. Are we witnessing the beginning of a new regime characterized by more volatile, if not higher, commodity prices?

**5. The long view.** A review of grain price histories reveals that the deflated prices of food grains have followed downward long-run trends, interspersed by episodes of steep price increases, followed by even more precipitous falls. **In the long view, the recent price spikes do not seem anomalous.** Relative to the other episodes experienced over the last 40 years, the real grain price volatility of the last few years has not been particularly high.

6. **What led to the recent price spikes?** Among the more **persistent changes** that set the stage for the grain price spikes of 2007/08 were sustained rapid increases in income in many countries, including China and India, which increased grain demand, especially for animal feeds. Public support for biofuel production was a large and persistent shifter of demand for maize and oilseeds, whereas funding of production-oriented crops research was neglected. By 2007, these factors could hardly have been surprises that could cause prices to jump. Their net effect was rather a progressive tightening of the aggregate supply-demand balance for major grains in the preceding years.

– iii –
these people end up staying at home, the final human capital level in the origin country might actually increase due to improved skilled migration opportunities. The important issue is that this likelihood of migration can neither be too high – too many people would leave and nobody would stay at home – nor too low – people would not invest in the their education. Using the above-mentioned bilateral skilled migration databases, Beine et al. (2008) show that the brain-gain effect can take place if the tertiary education level in the labor force is between 2 and 8 percent and the skilled migration level is not very high. The brain-gain effect has also been shown to exist using single-country survey data. For example, Batista et al. (2009) find significant brain gain in Cape Verde, specifically through increased education attainment of non-migrants. Similarly, using survey data in Moldova, Böhme (2012) demonstrates that migration positively affects parental aspirations for children’s education at the lower end of the human capital spectrum. In their survey of the ‘best and brightest’ in Tonga and Papua New Guinea, Gibson and McKenzie (2011) show that students who contemplated migration took additional classes. Chand and Clemens (2008) found that ethnic Indians were more likely to invest in their education relative to native Fijians after the 1987 coup, which led to violence against the Indian community and increased their desire to migrate.

The last concept to emerge in this literature is ‘brain waste’. The first type of brain waste occurs in an origin country if it overinvests in a specific type of human capital (e.g., engineers) and restricts labor movement. As a result, there will be high unemployment levels for those workers and the resources spent on them will be wasted. The source economy would theoretically benefit by allowing those individuals to 36
# TABLE 2 **EFmCTIVE** PROTECTION RATES **_(Z)_ IN MNUFJACTURING**

|Product|EPR La|Lear lb|
|---|---|---|
|Edible Oils|85|1988|
|Cotton Yarns|52|1986|
|Synthetic<br>Textiles|100||
|Plastic Products|37||
|Heavy Chemicals|68||
|Synthetic<br>Fibers/Resins|162||
|Iron/Steel<br>Products|72|1985/86|
|Castings/Forgings|72||
|Aluminum Products|-16|1986|
|Hand Tools|33|1987|
|Machine-Tools|21|1987|
|Non-Electrical<br>Machinery|64||
|Electrical<br>Machinery|42||
|Electronics<br>and Parts|92||
|Motor Vehicles<br>and Parts|19||
|Bicycles|24|1986/87|

/a Subsectoral EPRs are unweighted averages.

- jb Year to which pertain the large majority of observations. When no year is indicated, the observations are spread over 1986-1989.

- Note 1: 11 other subsectors with lese than 6 obeervations are not included in the table.

11. The levels of effective protection are generally high and suggest that the structure of protection grants Indian industry ample room for profits and/or inefficiency. A number of subsectors receive high effective protection: edible oils, heavy chemicals, iron/steel products, castings/forging, non-electrical machinery, electronics, and particularls **y** nthetic fibers/resins and synthetic textiles. Few other subsectors appear to have lower or insufficient effective protection: machine-tools, hand-tools, electrical machinery, motor vehiclee and parts, cotton textiles, and particularly aluminum products.

12. The EPR estimates are based on the comparison of value added in domestic and international prices, and effectively assume that capital costs are the same in all countries. Since most countries do not levy high taxes or QRs on capital goods, this assumption is a reasonable approximation. However, due to
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