rafmacalaba/datause-encoder-data huggingface dataset:
In Figure 1, we have also added, for reasons that will become obvious, the seven largest new member states of the European Union, the latest set of countries to sustain rapid growth at middleincome levels and—in the case of many of them—attain high-income status.

The key point is that unlike the EA5 high growth economies, the middle-income countries in all three other groups—developing East Asia, Central Europe and Latin America—have shown divergent experiences. The best performers have continued to grow rapidly, with several Eastern European and Latin American countries graduating to become high-income economies in the 2000s, while the worst performers have grown slowly or stagnated, appearing to be trapped in middle-income.

**Figure 1: Per capita income levels, East Asia, Latin America and Central Europe**

<!-- Start of picture text --> 35,000<br>Maximum/Minimum of<br>East Asia 5 high-income<br>30,000 East Asia 5 developing<br>Latin America 5<br>EU 8 new members<br>25,000<br>20,000<br>15,000<br>10,000<br>5,000<br>0<br>1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2013<br><!-- End of picture text --> Source: Maddison (2003), Bolt and van Zanden (2014) and Conference Board Total Economy Database.

Note: The figure shows the development in the range of per capita incomes within four groups of economies: the highincome East Asia Five (Hong Kong, China; Japan; Korea, Republic; Singapore; and Taiwan, China: maximum and minimum), middle-income East Asia Five (China; Indonesia; Malaysia; Philippines; and Thailand: maximum and minimum), the large, middle-income Latin America Five (Argentina; Brazil; Chile; Colombia; and Mexico: maximum and minimum), and EU Eight New Members (Bulgaria; Croatia; Czech Republic, Hungary; Poland; Romania; Slovak Republic; and Slovenia: maximum and minimum). In the last group, prior to 1985, data for Czechoslovakia are shown, instead of Czech and Slovak Republics.

It was against this theoretical and empirical background that we came up with the term “the middleincome trap” to describe economies that were being “squeezed between the low-wage poorcountry competitors that dominate in mature industries and the rich-country innovators that 3
**Table 2. Drivers of trends in the skill content of jobs (from t-1 to t).**

|**Labor supply factors**|**Labor demand and structural change**|
|---|---|
|‐<br>Education:<br>o Share of adults with tertiary<br>education (change, from t-1 to<br>t)<br>‐<br>Gender:<br>o Female employment share<br>(change, from t-1 to t)<br>‐<br>Age structure:<br>o Working-age population (% of<br>total population) (change, from<br>t-1 to t)<br>o People 65 years or older (% of<br>total population) (change, from<br>t-1 to t)|‐<br>Level of development:<br>o GDP per capita PPP (log, t-1)<sup>3</sup><br>‐<br>Economic growth<br>o GDP per capita growth (from<br>t-1 to t)<br>‐<br>Sectoral structure<br>o Industry Value added (% of<br>GDP) (change, from t-1 to t)<br>o Services Value added (% of<br>GDP) (change, from t-1 to t)<br>‐<br>Technology<br>o Internet users per 100<br>inhabitants (change, from t-1<br>to t)<br>‐<br>Trade<br>o Imports (% of GDP) (change,<br>from t-1 to t)<br>o Exports (% of GDP) (change,<br>from t-1 to t)|

> 3 We use the lagged level of GDP to capture different trends in the skill content of jobs by level of economic development.

19
<!-- Start of picture text --> FIGURE 2: SPEED AND SCALE OF E-PROCUREMENT ADOPTION12<br><!-- End of picture text --> <!-- Start of picture text --> 100% 100% 100% 100% 100% 100% 100% 100%<br>100% 90% 94% 96%<br>90% 87%<br>80%<br>70%<br>60% 55%<br>49%<br>50%<br>40% 37%<br>30%<br>20% 14%<br>10% 5%<br>0% 0% 0% 1%<br>0%<br>2011-2012 2012-2013 2013-2014 2014-2015 2015-2016 2016-2017 2017-2018<br>BWDB LGED RHD<br><!-- End of picture text --> Source: Data collected from procuring entities and e-procurement system

# 3. THEORETICAL FRAMEWORK

20. Well-functioning public procurement systems are meant to achieve value for money, ensure open and fair access for bidders and run efficiently. We consider these three distinct sets of outcomes—(i) economy; (ii) access and (iii) efficiency—separately, while recognizing their interdependence. In this section, we define these concepts and identify the main mechanisms through which e-procurement can affect them. In the following section, we will map these concepts to specific indicators.

# 3.1 OUTCOME DEFINITIONS

21. “Economy” or “value for money” for a given quantity of products refers to the quality of goods or works obtained for a given procurement price. Accordingly, economy can increase because a given quality is achieved at a lower price or because a higher quality is achieved at a given price or both.

22. “Access” in the context of public procurement captures the extent to which the procurement process de facto ensures open and fair competition among bidders. “Limited access” refers to a situation in which public procurement contracts are allocated and managed in ways that benefit a closed network of firms and government officials, while intentionally restricting access to others. This definition applies North, Wallis, & Weingast's (2009) concept of “limited access orders” to the context of public procurement. Limiting access typically involves bending explicit rules and principles of good public procurement. Such a conceptualization is very close to political science concepts that are recently gaining prominence such as quality of government as impartiality (Rothstein & Teorell, 2008), and particularistic governance regimes

> 12 A granular version of this graph showing the transition of each procuring entity has been included in Appendix 9.8.

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<!-- Start of picture text --> ou<br>SD 10% In percent of British GDP until 1914,<br>5 ° in percent of US GDP thereafter<br>=<br>2<br>o<br>&<br>a<br>&<br>hel<br>fe)<br>x<br>ge<br>=<br>3<br>fe)<br>fi<br>e<br>oO<br>0<br>1790 1815 1840 1865 1890 1915 1940 1965 1990 2015<br>[SSS Advanced country recipient rs) Developing country recipient<br><!-- End of picture text -->
having access to water and sanitation, the necessary increase in education appears no less ambitious than the required improvements in water and sanitation infrastructure.

**Table 8: Investment in Education vs. Investment in Water and Sanitation**

||**Liv**|**es saved by**|**1000 live bi**|**rths**|**%**|**contributi**|**on to MDG**|**4**|
|---|---|---|---|---|---|---|---|---|
||Counter-<br>factual|Counter-<br>factual|Counter-<br>factual|Counter-<br>factual|Counter-<br>factual|Counter-<br>factual|Counter-<br>factual|Counter-<br>factual|
||I|II|III|IV|I|II|III|IV|
|SSA|11|31|6|31|0.146|0.434|0.087|0.444|
|Other Countries|4|14|2|16|0.103|0.380|0.069|0.427|

_Notes:_ Counterfactual I: scenario basic improved water and sanitation. Counterfactual II: scenario high end improved water and sanitation. Counterfactual III: scenario primary education for mothers. Counterfactual IV: scenario secondary education for mothers.

# **4.3 Cost-Effectiveness Analysis of Investments in Water and Sanitation**

Even though the potential contributions of investment in water and sanitation towards achieving MDG 4 and reducing child mortality appear large, the analysis presented in section 4.2 does not provide any insights into the cost-effectiveness of the suggested policy options. To address this issue, we compute costs per life-year saved, and compare it to the countries’ annual GDP per capita. According to the WHO, a health intervention is considered as costeffective if the costs per life-year saved are smaller than current GDP per capita of this country.<sup>18,19</sup> In Table 6, we measure mortality effects in terms of the fraction of children from each cohort that die before the age of 5. To establish the absolute number of deaths saved per year we first multiply the child deaths per 1000 prevented with the size of the cohort born in the baseline (see equation (4) and Table 7). To get the number of life-years saved per year we have to multiply the number of deaths prevented times the standard life expectancy at the age of death (75 years). Future life-years are discounted at 3%, so that 75 years of life expectancy translate into about 30 life-years in net-present-value terms (see equation (5) and Table 7)<sup>20</sup> . Since water and sanitation infrastructure protects more than one birth cohort, the estimated number of life-years saved needs to be multiplied with the (discounted) number of years the

> 18 http://www.who.int/choice/costs/CER_thresholds/en/index.html

> 19 While it may appear strange from a humanitarian perspective to differentially value child lives across countries, the simplifying assumption underlying this proposition is that the minimum value of one life-year saved for the society is the output the person would have produced in a given year.

> 20 For reasons of simplicity we assume that all children do not die before the age of 5. Our estimates are therefore a rather conservative estimate of life-year saved, the difference to an exact consideration would however, be marginal. A discounted life expectancy of 80 years would for example “only” translate into 31 life-years in netpresent-value terms.

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if they are built to code.<sup>2</sup> These spillovers or externalities are absent in more sparsely populated rural areas where damages to smaller sized and dispersed dwellings will cause less or no collateral damage.

# _Exposure_

The main reason why urban risk is large and increasing is the rise in exposure. Although urbanization statistics suffer from a lack of standard definitions of what should be considered ‘urban’, the assumption of half the world’s population living in cities seems realistic. Urban populations are growing in practically all developing countries. About 40-60 percent of this growth can be attributed to natural growth, i.e., fertility of urban dwellers (Montgomery 2009). The remaining growth is due to urban expansion and migration, reducing the share of rural residents except where rural fertility is vastly larger.

The latest UN urban population estimates suggest that, globally, urban population exceeded rural population for the first time in 2008 (UN 2008). In less developed regions, this threshold is expected to be reached by 2019. This continuing urbanization process will lead to an increase of exposure of people and economic activity in hazard prone urban areas. Although we can only speculate about the global distribution of disaster damage in cities today and in the future, newly available geographically referenced data yield some estimates of urban exposure to natural hazards. A recent global hazard analysis generated a comprehensive database of hazard events during 19752007 from observed data and of event probabilities from geo-physical models (ISDR 2009). We combined this hazard information with city-specific population projections

> 2 It should be noted that well-documented evidence for such externalities is quite scarce. Their importance is usually taken as given: “A building collapse may create externalities in the form of economic dislocations and other social costs in addition to the economic loss suffered by the owner. The owners may not have taken these consequences into account when evaluating specific mitigation measures. Consider the following example. A building toppling off its foundation after an earthquake could break a pipeline and cause a major fire, which would damage other homes that had not been affected by the earthquake in the first place. “Kuenreuther and Roth (1998). See also <u>www.quakesmart.org/index.php?option=com_content&view=article&id=92&Itemid=209. But some</u> experiences have been documented: “As shown by research on the Great Hanshin-Awaji Earthquake, including that conducted by the Architectural Institute of Japan, Architectural Institute of Japan (1997), houses with inferior earthquake-resistant quality triggered large negative externalities in the neighborhood. For example, broken fragile houses blocked transportation networks, thereby preventing effective fire fighting and, by severing lifelines, they made recovery more difficult.” (Nakagawaa et al. 2007).

5
overall participation rate for European Union countries<sup>5</sup> is 100%, whereas the participation rate of non-EU ECA countries is substantially lower (64%).

**Table 3: Share of countries participating in PISA, by cycle and region**

||||PISA|cycle|||
|---|---|---|---|---|---|---|
||I<br>(2000/01)*|II<br>(2003)|III<br>(2006)|IV<br>(2009/10)*|V<br>(2012)|VI<br>(2015)|
|East Asia and Pacific|20.0%|21.6%|24.3%|32.4%|35.1%|35.1%|
|Europe and Central Asia|50|48.2|64.3|66.7|64.9|68.4|
|Latin America and Caribbean|12.8|7.7|15.4|28.2|19.5|26.8|
|Middle East and North Africa|4.8|4.8|19.1|28.6|23.8|38.1|
|North America|66.7|66.7|66.7|66.7|66.7|66.7|
|South Asia|0|0|0|12.5|0|0|
|Sub‐Saharan Africa|0|0|0|2.1|0|0|

_Assessment capacity._ Prior assessment experience appears to increase PISA participation (table 4). Participating in an ILSA appears to build assessment capacity; the share of countries participating in PISA after having previously participated in IEA’s TIMSS assessment is higher than that of countries that had not participated in TIMSS previously. For all PISA cycles, countries that had participated in a recent TIMSS assessment had higher rates of participation in PISA as compared with countries that had not previously participated in TIMSS. This advantage was relatively consistent over time; the higher advantage for prior TIMSS participants was 50 percentage points in 2000, and 54 percentage points in 2015. The advantage from TIMSS participation may arise from the similarity between the two assessments in the administrative skills required, so that countries that participated in TIMSS would already understand the administrative demands of PISA participation.

**Table 4: Share of countries participating in PISA, by cycle and previous assessment “capacity”**

||||PISA|cycle|||
|---|---|---|---|---|---|---|
||I*|II|III|IV*|V|VI|
||(2000/01)|(2003)|(2006)|(2009/10)|(2012)|(2015)|
|International Large‐Sc|ale||||||
|In TIMSS|60.5%|51.2%|59.6%|62.7%|64.3%|73.3%|
|Not in TIMSS|9.8|11.13|17.7|22.2|13.8|18.7|
|Regional|||||||
|<br>In Regional|0|0|24.0|7.1|25.0|21.1|
|<br>Not in Regional|21.0|20.4|27.42|35.4|31.0|36.7|

5 The EU countries are: Austria, Belgium, Bulgaria, Croatia, Republic of Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden and the UK.

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Europe and the former Soviet Union country groups in the relationship between income inequality and per capita GDP. While there was no association between GDP per capita level and changes in inequality in Eastern Europe, Ivaschenko found a significant U- shaped relationship (the increase in inequality was smallest among middle-income countries) between the two variables for the former Soviet Union countries.

A study by Ivanova (2006) highlighted the effect of government policies on inequality using evidence from Hungary, Poland, and Bulgaria. She shows that government policies prompted by the trend towards liberalization and privatization, such as reducing social spending, limiting access to social assistance through strong selectivity and conditionality criteria, and introducing market-regulated (fee-based) access to many social services, have had profoundly negative impact on socioeconomic equality and contributed to inequality’s getting embedded in the transforming societies. According to this study, inequality was not only a byproduct of macroeconomic policies, but also a natural outcome of the particular model of society chosen by the transition economies, for instance, the choice of the minimalist safety-net approach as opposed to the universalistic welfare-state approach of the European social market economies.

While inequality increased in the transition region overall, country specific studies provide a clearer trend of changes in inequality within each country. A study of Poland’s income distribution before and during the transition (Keane and Prasad 2002) reveals significant increases in inequality as measured by wages from formal employment. Keane and Prasad also find that the reallocation of workers from a public sector with a compressed wage distribution to a private sector with much higher wage inequality, accounts for the bulk of increased earnings inequality during transition. They highlight the role that increased social transfers had in limiting the increase in inequality.

The unemployment benefits, pensions, family and child allowances that provided economic protection for the most vulnerable citizens prior to the transition underwent major transformations. Giammateo (2006) looked at the impact of state transfers (and taxes) and market oriented reforms on gross and disposable income inequality.

4
8% in Kenya and Tanzania between 1993/94 and 1998/99 (although Ghanaian firms had an exit rate of just over 3% per annum), and Gebreeyesus (2005) finds annual exit rates of 22% in Ethiopia from 1996-2003. In terms of slightly more dated data, Roberts (1996) finds annual exit rates of 11.1% in Colombia from 1977-85; and Tybout (1996) reports exit rates of 10% in Chile from 1979-85. The entry rate is also fairly low compared to other developing countries. Shiferaw (2009) reports annual entry rates of approximately 20% for Ethiopia from 1996-2002, Roberts (1996) finds entry rates of 12.2% in Colombia from 1977-85, and Tybout (1996) finds entry rates of 6% in Chile from 197985, however entry rates were likely lower than normal in this latter study as the sample period covered a major recession.

Interestingly, firm exit and firm entry tend to move in the same direction through time, with rates decreasing until 1994, and then showing an upward trend after this point. This is a somewhat surprising result given that a priori, there are strong reasons to expect them to move in opposite directions (e.g. a recession increases the firm exit rate and decreases the firm entry rate) (Bartelsman et al. 2004).<sup>10</sup> Furthermore, while net entry is positive in every year, the gap between entry and exit rates has narrowed considerably since 1992.<sup>11</sup> Figure 1 also shows that the growth rate of the manufacturing sector has been extremely volatile in the period under examination; however there does not tend to be a clear relationship between economic conditions and firm entry or exit.<sup>12</sup>

> 10   One reason the pattern can vary can depend on the extent to which entrepreneurship becomes a means of coping with negative shocks.  That the Moroccan data has no minimum cutoff makes it more likely to capture this effect that country datasets that only capture firms with 10 or 20 or more employees.

> 11 The increase in exit rates after 1994 may be linked to greater competitive pressures as exposure to international markets expanded and as privatization efforts were undertaken (World Bank Country Assistance Strategy (2001)).

> 12 This interpretation is consistent with the findings of Haddad, de Melo and Horton (1996) who found no significant correlation between rates of real output growth and entry and exit rates for Morocco from 198489.

9
land sales creates an ideal experimental ground to assess how land rental markets respond to rising real wages. The analysis differentiates across farmers since the theory predicts possible scale advantage among relatively large farms when they face wage growth. With the expansion of off-farm employment, some farmers are predicted to increase their labor supply to nonagricultural sectors, and/or rent out their land to other farmers (Bowlus and Sicular, 2003). In contrast, some farmers are likely to increase their operational size by renting in land from other farmers, and to start using machines through purchasing machines or renting machine services to substitute for labor. The analysis highlights land endowment in two dimensions: farm size and fragmentation.

In all the econometric estimations, first differences are taken to wipe out unobserved fixed error components, which could lead to bias in the cross-sectional estimation. The key explanatory variable in the first-differenced form is village-level real wage growth separately computed for agriculture and nonagriculture work (see details on the construction of the wage variables in Section 3.3). Labor can be imperfectly substitutable between agricultural and nonagricultural work due to differences in the required skills, and so we use the village-level wages for the two sectors. Furthermore, we use the proportion of nonagricultural income and migration rate, both computed at the village level, to represent potential labor shortage in agriculture.

The paper is organized as follows. Section 2 describes wage growth and mechanization recently observed in Chinese agriculture. Section 3 explains the panel data collected in six provinces in China, with two rounds in 2000 and 2008. Section 4 describes the empirical strategy. The empirical findings are summarized in Section 5, followed by concluding remarks in Section 6.

# **2. Wage Growth and Mechanization in China**

China’s economy has maintained its high annual growth rate of GDP, roughly at 10%, for more than four decades. In 2013, GDP per capita reached nearly US$6,629 (NSBC, 2014). Among other factors, off-