rafmacalaba/datause-encoder-data huggingface dataset:
In order to identify which cities fall in this category of competitive cities, we follow the characterization presented in the World Bank Report (2016) and take a look at how cities perform in three key variables: GDP growth, job creation, and productivity. These key variables show how cities are performing and are good measures to pinpoint competitive cities for the following reasons. Cities with a higher GDP growth rate are usually better positioned to attract firms and foreign direct investment. They are often full of potential opportunities for their habitants, also attracting more people. An increasing demand for jobs clearly plays a central role in attracting workers and contributes to maintain city development, while an increasing productivity helps attract qualified workers by offering better compensation.

More specifically, we look at the difference between city and national values for GDP growth, job creation, and productivity. We take a city’s GDP per capita growth and for each year, subtract the national GDP per capita growth to get how much the city is growing with respect to the national values. We follow the same logic to calculate job creation and productivity relative indicators. For job creation, we use city and national yearly employment growth rates. To measure productivity, we use city and national gross value added per worker, per year.

By focusing on these three variables, and how they compare to the national indicators, we can identify cities that are performing above average. This analysis can shed light on what makes such cities that are “above the norm” different from others. It can also define typologies of cities that can help identify possible paths that new growing cities may follow in the future. This descriptive analysis will stop short from identifying any causal relations. Even though this exercise will not be able to answer causality questions, looking at correlations and city typologies can still be informative and a good starting point to understand city performance, challenges, and future problems that may come as a consequence of rapid growth.

We then move forward and compare cities’ performance for the whole period of analysis, between 2006 and 2015. We construct a yearly average by city for each of the three variables, and use this annual average to identify which cities are outperforming their nation, on average, throughout this period. Because we are using an annual average for a considerably long period of 10 years, we may be smoothing out the growth of some cities if they experience higher rates in the last five years for example. Although this can underestimate these cities’ performance and fail to identify them as competitive, we think that analyzing these variables for a longer period has the advantage of recognizing those cities that have been able to remain competitive for a sustained period of time. We then look at cities’ performance on each of these variables separately.

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proportionally more poor people under 25 and more non poor over 25 years old; this is unsurprising since youth are over-represented among the poor (World Bank, 2006).

The distribution of workers across employment states before the transition is very similar across countries. Figures 1, 2 and 3 present the distribution of individuals in Argentina, Brazil and Mexico across initial states. Separating by age, individuals younger than age 19 are mainly studying before a transition is made, as would be expected. Between 19 and 24, they are mostly working in the formal sector, studying or out of the labor force. The only difference between countries is in the case of Argentina, where the informal sector is relatively more important vis-a-vis the formal sector as an initial state. Above 25, pre-transition workers are mainly self-employed, with a formal job or out of labor force. Overall the non-working population exceeds the working population for age 24 and younger in Argentina but that is only the case for those ages 18 and younger in Brazil and Mexico.

The three countries in the study demonstrate similar sectoral “life-cycle” profiles. Among workers in all three countries, the youngest workers are primarily informal wage employees (Figures 4-15). But by the late-teens/early 20s, the formal wage sector takes over as the primary employment state. Finally, we see the propensity of self-employment to monotonically increase with age, peaking in the late 40s, or into the late 60s in some country-gender cases. These patterns emerge for men and women and for poor (1<sup>st</sup> income quintile) and non-poor (5<sup>th</sup> wage quintile), though the levels differ across these groups. For example, the non-poor have a much lower participation in the informal wage sector than the poor and the poor never reach levels of formality that surpass those of informal wage employment. Nonetheless, the general pattern persists.

# **4. Transition and Duration Results**

For each age group-country, we estimate the transition matrix, propensity to move matrix, and the duration matrix. We also estimate these three matrices for each age groupcountry-gender and age group-country-wealth level, giving us a total of 45 matrices per country. Rather than presenting 135 matrices, we limit the presentation to key transition 11
# _MiDES:_ New Data and Facts from Local Procurement and Budget Execution in Brazil<sup>∗</sup>

Ricardo Dahis<sup>1,5</sup> , Bernardo Ricca<sup>2</sup> , Thiago Scot<sup>3</sup> , Nathalia Sales<sup>4</sup> , and Lucas Nascimento<sup>3,5</sup>

> 1Monash University

> 3Development Impact Evaluation (DIME), World Bank

4PUC-Rio

> 5Data Basis

_Keywords_ : Subnational Public Finance; Procurement, Budget Execution _JEL Codes_ : H50, H72

> ∗Dahis: ricardo.dahis@monash.edu; Ricca: bernardoogr@insper.edu.br; Scot: tscot@worldbank.org; Sales: nathalia.msales@gmail.com; Nascimento: lnascimentomorei@worldbank.org. The authors acknowledge generous support from the World Bank DEC Research Support Budget (RSB).
5 tend to grow at a slower rate<sup>in comparison to countries that begin with</sup> a more developed financial market<sup>,other things remaining constant. This</sup> is because underdeveloped<sup>financial markets are usually characterized by</sup> poor ability to allocate<sup>and manage capital efficiently: screening</sup> different investment opportunities<sup>for those most likely to yield high</sup> returns, monitoring firms<sup>in a way to ensure that they live up to their</sup> potentials, for example [see<sup>Greenwood and Jovanovic (1990), Towneend</sup> (1983), Stiglitz (1991)1. The<sup>literature on financial marketn and growth</sup> is extensive, starting with<sup>the work of Goldsmith (1969), McKinnon (1973),</sup> and Shaw (1973). Goldsmith<sup>(1969) argued that financial development</sup> "accelerates economic growth<sup>and improvea economi</sup> performance to the extent that it facilities<sup>the migration of funds to the best user, i.e.,</sup> to the place in the economic<sup>system where the funds will yield the highest</sup> social return" [p.400J.

This paper is organized<sup>as follows. Section 2 examines the</sup> relationship between policy<sup>and development of financial markqts in some</sup> fifty developing countrias using<sup>annual average data for the period 1965</sup> to 1989. Simpls correlations confirm<sup>that repressive financial policies</sup> ar& associated with shallow<sup>financial markets and limited financial</sup> intermediation. Section 3<sup>reports estimates of cross-country long-run</sup> growth regressions. The evidence<sup>suggests that ;he initial level of</sup> financial development and the<sup>starting value of human capital stock are</sup> both positive and individually<sup>significant in explaining a country's</sup> subsequent growth rate. Since<sup>initial conditions ignore policy changes</sup> over time, section 4 examines<sup>the link between policy change and growth.</sup> Again, the evidence shows<sup>that policy reforms that foster financial</sup> development are pooitively associated<sup>with output growth. The robustness</sup> of the result is tested in<sup>several ways including its sensitivity to</sup> cha.iges in time period and use<sup>of alternative indicators of financial</sup> development. The sensitivity<sup>results reinforce the evidence that a country</sup> which starts with a more developed<sup>financial market and fosters financial</sup>
25 improvement was partly due to subsidized energy prices in 1990. An enterprise-level assessment of redirection of trade would shed some light on the potential for export growth in the near future. The available evidence indicates that enterprises which had specialized solely in exports to the FSU and other CMEA countries did not have much success in redirecting their exports to other markets. For instance, the driving force behind Poland's export expansion in 1990 were the SOEs which had earlier exposure to Western clients and whose previous involvement in intra-CMEA trade was not significant, as a recent World Bank study forcefully argues (Mueller, 1991).

# C. Im;act of the Switch from a Supply- to a Demand-Constrained Economy

The analysis in Sections III and IV of this study shows the dramatic difference in foreign trade performance between the most reformed Central European troika and the BaLlan countries. It suggests that countries which decontrolled prices as well as liberalizing their trade regime, bringing exchange rates closer to market-based levels, and introducing unified exchange rates, succeeded in increasing exports to the OECD. No matter how liberal the trade regime or how strong or weak the domestic currency, improvement in export performance was the result of a shift from administrative rationing to market clearing prices--i.e., to the transition from a supply- to a demand-constrained economy. Despite the liberalization of the.. .- eign trade regimes, Bulgaria and Romania were crippled by macroeconomic chaos and wavering microeconomic reforms and registered falls in exports.

By the end of 1991, external trade regimes had been dramatically liberalized in CEE-3 countries.' They had all adopted currency convertibility, following a similar set of steps including legalization (or quasilegalization) of black market activities, liberalization of domestic prices and unification of exchange rates. They had all introduced tariffs, rules of customs valuation and anti-dumping procedures which were more or less compatible with GATr standards. And in all CEE-3 countries firms could decide what they wanted to import and/or export, and they had the right to buy foreign currency at the official exchange rate in order to import goods or services from abroad. As a result, tariffs and exchange rates became effective trade policy instruments.37 Table 10 summarizes the institutional features of the convertibility and foreign trade regimes of the CEE-3 as at the end of 1991.

> 36 IroniCally, the only products for which quantitative restrions were initially maintained were those subject to quotas, VERs, etc. imposed on them by the EC, the United States and other trading partners (Rodrick, 1992). 37 Their impact on firms' behavior may be weakened by the extent to which SOEs operate under a softbudget constraint. This is still the case for many SOEs in all post-communist economies, especially for the large ones which, because of size, bave strong political clout.
20 We measure financial development with the ratio of private credit to GDP: again we prefer to work with a dummy variable (FD) that takes on a value equal to 1 if this ratio is larger than its yearly median value. When we add in our model the interactions between the financial development dummy variable and international reserves and external debt over GDP, the results on workers’ remittances are by large confirmed (Table 7, columns [1] and [3]). As to exchange arrangements, we use the measure constructed by Reinhart and Rogoff (2002) which is meant to be alternative to the standard classification published in the IMF’s _Annual Report on Exchange Rate Arrangements and Exchange Restrictions_ . More precisely, we start from Reinhart and Rogoff’ coarser classification that group countries according to 5 categories from “no separate legal tender” to “freely falling” and we further group them into 3 categories<sup>27</sup> . As shown in columns [2] and [4] of Table 7, the introduction of the interaction between the exchange rate arrangement dummy (ARR) on one side and international reserves and external debt on the other do not affect the results on remittances.

Table 8 proposes three other robustness exercises. The first one relates to country size: here we build a dummy variable (SMALL) that is equal to 1 for countries with less than 500,000 resident as of 2002. The second one splits countries according to their literacy rate, again below and above the median (LIT). The third one aims at separately identifying the effects of civil wars on financial instability. For this, we use the Armed Conflict Dataset which is a comprehensive new database of civil conflicts developed by the International Peace Research Institute of Oslo (Norway) and the University of Uppsala (Sweden)<sup>28</sup> . The database, which focuses only on politically motivated violence, allows us to define a dummy variable (WAR) which is equal to 1 if a country experiences in a given year a conflict with more than 1000 battle deaths. All the

> 26 From Docquier and Marfouk (2005)’s estimates on emigration stocks by educational attainment, it emerges a strong positive correlation across countries between emigrants and working-aged resident population in terms of average years of education.

> 27 The first category contains the most restrictive arrangements: from “no separate legal tender” to “pre announced crawling band that is wider than or equal to +/- 2%”. The second category includes the intermediate arrangements: “de facto crawling band that is narrower than or equal to +/- 5%”, “moving band that is narrower than or equal to +/- 2% (i.e., allows for both appreciation and depreciation over time)”, “managed floating” and “freely floating”. Following the suggestions by Reinhart and Rogoff, we leave the last item “freely falling” in a third separate category.
profile is a sequence _s_ = ( _si_ ) _i_ ∈ _N_ of vote strategies for all individuals. The truthful vote for an individual _i_ ∈ _N_ , denoted by _s_<sup>∗</sup> _i_<sup>(</sup><sup>_R_) = (</sup><sup>_s_∗</sup> _i_<sup>_pq_</sup> ( _R_ ))1≤ _p_ < _q_ ≤ _m_ ), is defined for all 1 ≤ _p_ < _q_ ≤ _m_ by: Let _s_ = ( _si_ ) _i_ ∈ _N_ be a voting strategy. For all _p,q_ with _p_ < _q_ , we denote by _N_<sup>1</sup> ( _s_<sup>_pq_</sup> ) = { _i_ ∈ _N_ ∶ _s_<sup>_pq_</sup> _i_<sup>= 1}thesetofindividualswhohaveranked</sup><sup>_xp_above</sup><sup>_xq_inthevotingstrategy</sup> _s_ , by _N_<sup>−1</sup> ( _s_<sup>_pq_</sup> ) = { _i_ ∈ _N_ ∶ _s_<sup>_pq_</sup> _i_<sup>= −1}thesetofindividualswhohaveranked</sup><sup>_xq_above</sup><sup>_xp_,</sup> and by _N_<sup>0</sup> ( _s_<sup>_pq_</sup> ) = { _i_ ∈ _N_ ∶ _s_<sup>_pq_</sup> _i_<sup>= 0}thesetofvoterswhoareindifferentbetween</sup><sup>_xp_and</sup><sup>_xq_.</sup> The social ranking under the voting rule _W_ induced by a voting strategy _s_ , which we denote by _F_ ( _s_ ), is given by: where _xpxq_ ∈ _F_ ( _s_ ) means that _xp_ is socially ranked above _xq_ , so he or she is ranked above _xq_ in the social ranking _F_ ( _s_ ); and _xp_ ∗ _xq_ ∈ _F_ ( _s_ ) means that _xp_ and _xq_ are socially incomparable, that is, neither of the two candidates is socially preferable to the other. Note that the third condition in our definition of _W_ rules out the possibility of social indifference between two candidates _xp_ and _xq_ . In fact, we cannot simultaneously have ( _N_<sup>1</sup> ( _s_<sup>_pq_</sup> ) _,N_<sup>0</sup> ( _s_<sup>_pq_</sup> ) _,N_<sup>−1</sup> ( _s_<sup>_pq_</sup> )) ∈ _W_ and ( _N_<sup>1</sup> ( _s_<sup>_qp_</sup> ) _,N_<sup>0</sup> ( _s_<sup>_qp_</sup> ) _,N_<sup>−1</sup> ( _s_<sup>_qp_</sup> )) = ( _N_<sup>−1</sup> ( _s_<sup>_pq_</sup> ) _,N_<sup>0</sup> ( _s_<sup>_pq_</sup> ) _,N_<sup>1</sup> ( _s_<sup>_pq_</sup> )) ∈ _W_ .

We assume a probabilistic tie-breaking rule in case of social incomparability between two candidates. More precisely, if _xp_ and _xq_ are socially incomparable (i.e., _xp_ ∗ _xq_ ∈ _F_ ( _s_ )), then _xp_ is socially ranked above _xq_ with some probability Λ _pq_ and _xq_ is socially ranked above _xp_ with probability Λ _qp_ = 1 − Λ _pq_ . This tie-breaking rule generalizes the lexicographical tie-breaking rule where the probability Λ _pq_ of _xp_ is always equal to 1 whenever _p_ < _q_ .

**Example 1 :** _Consider a ranked-choice election where N_ = {1 _,_ 2 _,_ 3} _, X_ = { _x_ 1 _,x_ 2 _,x_ 3} _, and the voting rule is simple majority rule. Under this rule, a tripartition_ ( _T_ 1 _,T_ 2 _,T_ 3) _is_

10
16 exception is a new set of estimates of nominal rates of protection since 1985 for a few key farm products in China, India, Indonesia and Vietnam (Orden et al. 2006).

Better PSE-type estimates of the extent of price distortions facing farmers and other producers would improve policy transparency directly by providing stand-alone indicators that are easily understood by participants in policy debate. If they show the relative contributions of different policy instruments to the aggregate PSE, they can also assist trade negotiators to select their priorities. And if they are provided in the right format they can supplement the GTAP protection database used by most global modelers, which currently relies mostly on applied tariff rates to represent distortions to agricultural prices in developing countries (see www.gtap.org).

Even though the latest protection estimates, for 2001, are a vast improvement over earlier estimates provided by GTAP, shortcomings remain. One is that there may be unused protection in such tariffs, for example due to duty exemptions and drawbacks (Ianchovichina 2004). It is also possible that nontariff import barriers such as quarantine restrictions or food quality standards exist such that the applied tariff understates the actual protection level. Indeed recent measures of the Trade Restrictiveness Index suggest that there is nearly as much import protection from nontariff measures as from tariffs (Kee, Nicita and Olarreaga 2006). Third, production or export taxes or non-tax export restrictions, or exchange rate distortions – all largely ignored in the GTAP protection database – may be still in place. A fourth is that value added taxes on agricultural products (and excise taxes on alcoholic beverages and tobacco) may be applied at the border on imported products but not – or not as fully – on domestically produced like products.<sup>6</sup> Informal (often illegal) taxes in cash or kind are often levied on farmers by
# Figure 5. EAP: Drivers of potential output growth

# A. Potential GDP growth

<!-- Start of picture text --> Percent<br>10 EAP EAP excl. China Median<br>8<br>6<br>4<br>2<br>0<br>2000-21 2000-10 2011-21<br><!-- End of picture text --> B. Contributions to potential GDP growth <!-- Start of picture text --> Percent<br>TFP Capital Labor Potential growth<br>10<br>8<br>6<br>4<br>2<br>0<br>EAP EAP excl. China<br>2000-21 2000-10 2011-21 2022-30 2000-21 2000-10 2011-21 2022-30<br><!-- End of picture text --> <!-- Start of picture text --> C. Investment growth D. Potential TFP growth<br>Percent EAP EAP excl. China Median Percent EAP EAP excl. China Median<br>14 4<br>12<br>10 3<br>8<br>2<br>6<br>4<br>1<br>2<br>0 0<br>2000-21 2000-10 2011-21 2000-2021 2000-2010 2011-2021<br>E. Secondary education attainment F. Working-age population growth<br>Percent Percent EAP EAP Excl. China Median<br>100 EAP EAP ex. China Median 6<br>5<br>80<br>4<br>60 3<br>2<br>40<br>1<br>20<br>0<br>0 -1<br>2000-20 2000-10 2011-20 2000-2021 2000-2010 2011-2021<br><!-- End of picture text --> Sources: Haver Analytics; Penn World Tables; UN Educational, Scientific, and Cultural Organization (UNESCO) Institute of Statistics; UN Population Prospects; World Bank; World Development Indicators database. Note: EAP = East Asia and Pacific; TFP = total factor productivity. GDP weights are calculated using average real U.S. dollar GDP (at average 2010-19 prices and market exchange rates).

A.C.-F. Bars show period averages of annual GDP-weighted averages. Markers show median of GDP-weighted averages of the six EMDE regions. Orange whiskers show min-max ranges.

A.B. Estimates based on production function approach. Sample includes 53 EMDEs, of which six economies are from EAP (China, Indonesia, Malaysia, Mongolia, the Philippines, and Thailand).

C.D. Sample includes China, Indonesia, Malaysia, Mongolia, the Philippines, and Thailand (where potential growth estimates are available for both investment growth and TFP growth measures for the period 2000-21). E. Period averages of simple annual averages. Percentage of population ages 25 and above that completed at least lower secondary education. “EAP ex. China” includes Indonesia, Malaysia, Mongolia, the Philippines, and Thailand.

F. Working-age population refers to population ages 15-64. Sample includes six EAP economies.
<!-- Start of picture text --> Figure 5. Effect of Public Vocational on Labour Force Participation among<br>Women<br>Old cohort Middle cohort Young cohort<br>0.20<br>0.15<br>0.10<br>0.05<br>0.00<br>-0.05<br>-0.10<br>-0.15<br>20 25 30 35 40 45 50 55<br>Average age in years<br>Notes: See notes to figure 4<br>Figure 6. Effect of Public Vocational on Unemployment among Men<br>Old cohort Middle cohort Young cohort<br>0.06<br>0.04<br>0.02<br>0.00<br>-0.02<br>-0.04<br>-0.06<br>-0.08<br>-0.10<br>20 25 30 35 40 45 50 55<br>Average age in years<br>participation<br>Estimated effect on probability of<br>unemployment<br>Estimated effect on probability of<br><!-- End of picture text --> Notes: See notes to figure 4 Notes: See notes to figure 4 21