rafmacalaba/datause-encoder-data huggingface dataset:
diversification into rural nonfarm employment and secondary towns is associated with poverty reduction, while agglomeration in mega cities is not. This holds both when considering the $1-day and the $2-day poverty head count rates. These effects are in addition to the poverty reducing effects of economic growth. In other words, were two countries to grow at the same rate, poverty would come down faster in a country following rural nonfarm diversification and secondary town development than in a country following rapid metropolization. Given that the results are controlled for differences in initial conditions (such as land inequality, institutional and political arrangements) through the inclusion of country specific dummies, this is a striking result.

Recall from the analytical exposition in Section 2 that the coefficient on the sectoral share can be interpreted as the impact of the sectoral transformation on poverty through the income distribution. The findings thus suggest that rural diversification and less concentrated urbanization lead to more inclusive growth patterns. This empirical regularity resonates with the findings from historical, comparative country case studies in East Asia (Otsuka, 2007). Taiwan, China, and South Korea experienced for example a similar per capita GDP growth of 7.1 percent between 1965 and 1990. Both countries also started at similar levels of inequality (a Gini of about 0.32). Yet throughout the subsequent decades inequality has been lower in Taiwan, China, and higher in South Korea. Taiwan, China’s economic development has been based on the development of more labor intensive small and medium enterprises located in rural and suburban areas, while South Korea’s development has been led by more capital intensive urban based, large enterprises.

The cross-country results also find support in recent micro-evidence from Africa. Tracking a representative sample of 3,301 rural individuals in Kagera, Tanzania, between 1991-94 and 2010, Christiaensen, De Weerdt and Todo (2013) find that the poverty headcount declined from 53 to 29.6 percent and that the number of poor people declined from 1,747 in 1991-94 to 979 in 2010. About half of the people who exited poverty did so shifting to rural nonfarm activities as their main occupation and/or by moving to secondary towns; about 30 percent escaped poverty while staying in agriculture and only 1 in 6 by migrating to one of 15
1.3 model 15

section). FDI Procedures measure the number of legal steps required before and after incorporation to start a wholly foreign-owned business. I construct the FDI Arbitration index by averaging indicators measuring i) the Ease of Arbitration Process and ii) the Extent of Judicial Assistance in resolving commercial disputes from the IAB data set. The Ease of Arbitration Process assesses whether there are restrictions on what the conflicting parties can or cannot do to resolve their dispute. The Extent of Judicial Assistance measures the role of domestic courts in assisting the process of arbitration and enforcing awards.

To control for the _general_ quality of institutions, I compute a composite indicator of five WGI developed by Kaufmann et al. (2010) with weights derived from Principal Components Analysis (PCA). The WGI variables are: i) the rule of law; ii) control of corruption; iii) regulatory quality; iv) political stability; and v) governmental effectiveness. Of the six WGI indicators, the one that I exclude is “voice and accountability” which is argued to capture citizens’ participation in selecting their government, as well as freedom of expression, freedom of association, and<sup>17</sup> The control for a general quality of institutions as measured by the WGI variables is important because I want to see whether FDI-specific regulations matter for FDI _over_ and _beyond_ the general quality of institutions. In other words, after controlling for the general quality of institutions, do FDI-specific provisions add any value?

Market size of the host country, proxied by GDP, is one of the most important determinants of horizontal FDI. Motives for vertical FDI are captured by GDP per capita (which proxies for average wage and the quality of infrastructure). Tariff

> 17 I concur with Thomas (2006) who argues that the concept of voice first articulated by Hirschman (1970) is not synonymous with accountability, freedom to select government, or other political freedoms. Nor is there a well-documented relationship between them. Overall, in terms of coverage of countries and topics, the WGI indicators are perhaps the most authoritative and widely used to assess the quality of institutions across countries over time. They are imperfect because they are a quantitative aggregation of perceptions (subjective data). For a summary of criticisms of these indicators and the response from the authors, see Kaufmann et al. (2007).
# **1. Introduction**

Economists and policy makers are interested in how employment gains or losses propagate through different sectors or regions. In recessions, job losses could form a vicious downward spiral, that is, initial job losses could cause further job losses and hence worsen the downturn. This concern was shared by many economists. Paul Krugman, for example, at the start of the Great Recession, argued that “ _r_ _<mark>ising unemployment will lead to further cuts in consumer spending. Weak consumer spending will lead to cutbacks in business investment plans. And the weakening economy will lead to more job cuts, provoking a further cycle of contraction</mark>_ <mark>” (New York Times, November 14, 2008). By the same token, the creation of new jobs could also generate demand for additional jobs. The argument for stimulus packages enacted during the Great Recession is partly based on this premise: new job creations and investment by the government could lead to new job gains and investment by the private sector. Since the Great Recession, many studies have been conducted to estimate fiscal multipliers, with wide-ranging results.</mark> This paper presents estimates of dynamic short-run employment multipliers within a U.S. county during 1998-2015. It also provides estimates for employment multipliers over the business cycle, in particular, during the boom time of 2002-2007, during the Great Recession of 2008-2010 and during the Recovery of 2011-2015. The plausibly exogenous source of employment changes is _a county’s tradable_<sup>_2_</sup> _employment changes that are only driven by aggregate shocks._ Since there are more than 3000 counties in the U.S, the national aggregate

> 2 Tradable industries refer to those industries that can sell their product across county lines. Since data on counties’ import and export are not available, we follow Mian and Sufi (2014) to define tradable industries as those that have international imports plus exports equal to at least $10,000 per worker, or if total exports plus imports exceed $500 million. Please see section 3 for more information.

2
|Table1: Share of Vehicle Usage and P|M10 Emissio<br>Two<br>wheeler|ns From Tra<br>Three<br>wheeler|nsport Secto<br>Car|r byMode<br>Taxi|Best Bus|Other<br>Diesel<br>vehicles|Total|
|---|---|---|---|---|---|---|---|
|Number of vehicles (2001)|440,517|101,914|344,870|62,447|3,391|76,424|1,029,563|
|Share of Vehicle km (2002)|20%|16%|29%|14%|4%|17%|100%|
|NEERI Emission factors (g/km)|0.21|0.21|0.27|0.27|3|3|-|
|Share of PM10(2002)|5%|4%|10%|5%|14%|63%|100%|

23
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# **1.  Introduction**

This paper presents the latest update of the Worldwide Governance Indicators (WGI) research project.<sup>1</sup> The indicators measure six dimensions of governance: voice and accountability, political stability and absence of violence, government effectiveness, regulatory quality, rule of law, and control of corruption. They cover 212 countries and territories for 1996, 1998, 2000, and annually for 2002-2006.<sup>2</sup> The indicators are based on several hundred individual variables measuring perceptions of governance, drawn from 33 separate data sources constructed by 30 different organizations. We assign these individual measures of governance to categories capturing these six dimensions of governance, and use an unobserved components model to construct six aggregate governance indicators in each period.

As in the past, we complement our estimates of governance for each country with margins of error that indicate the unavoidable uncertainty associated with measuring governance across countries. These margins of error have declined over time with the addition of new data sources to our aggregate indicators, and are substantially smaller than for any of the individual data sources. We continue to encourage users of the governance indicators to take these margins of error into account when making comparisons of governance across countries, and within countries over time. In particular, a useful rule of thumb is that when confidence intervals for governance based on our reported margins of error overlap in comparisons of two countries, or a single country over time, this suggests that the data do not reveal statistically (or for that matter practically) significant differences.

The margins of error we report are not unique to our aggregate indicators, nor are they unique to perceptions-based measures of governance on which we rely. Measurement error is pervasive among all indicators of governance and institutional quality, including individual indicators as well as ‘objective’ or fact-based ones -- if these

> 1 This paper is the sixth in a series of estimates of governance across countries.  Documentation of previous rounds can be found in Kaufmann, Kraay, and Zoido-Lobatón (1999a,b,2002), and Kaufmann, Kraay, and Mastruzzi (2004, 2005, 2006a,b).

> 2 A few of the entities covered by our indicators are not fully independent states (e.g. Puerto Rico, Hong Kong, West Bank/Gaza, Martinique, and French Guyana). A handful of very small independent principalities (e.g. Monaco, San Marino, and Andorra) are also included. For stylistic convenience all 212 entities  are referred to in this paper as “countries”.

1
and a the variance will be given by _var[y] = abeta/ [(abeta + bbeta )_<sup>_2_</sup> _(abeta + bbeta + 1)]._ Using the mean and variance of education observed from the households surveys we can solve for the two unknowns abeta and bbeta.

In a Fourth step we make use of all the parameters derived above to calibrate 5 of the remaining 6 parameters of the model: the rate of arrival of informal employment opportunities α; the job destruction rate of bad and formal δ and λ, respectively; the technology parameter of the good‐jobs sector production function A1 and, the migration cost (M). To do so we use five equations; these equations determine: i) the share of workers in the formal sector ii) the share of workers in the informal sector, iii) the average wage; iv) the duration of unemployment and v) the no migration condition. Our model gives very specific expressions for these quantities as functions of the parameters obtained in steps 1 through 3, and as a function of our unknowns: δ, λ, α, A1, and M. These expressions are also a function of m(θ), which we do not observe because we do not have data for vacancies. This means we need a sixth equation to solve for the unobserved parameters. We use the loss function value, which is a non‐linear function of the equations referred to in numerals i) to v). We find the values of δ, λ, α, M, A1 and m(θ) that minimize the difference between the observed value of the target statistics and the corresponding theoretical expression derived from the model. For example, if the share of workers in the informal sector in is 24.6%, and the expression for the share of workers in the informal sector is given by a function n0( δ, λ, α, A1, m(θ);Φ), then the model minimizes 0.246‐ n0(: δ, λ, α, A1, m(θ);Φ); where Φ represents the other parameters of the model that where estimated in the previous steps. A higher level of detail can be found in the annex.

In the final step we calibrate the cost of posting a vacancy c. To do so we use all the previously estimated parameters and the free entry condition. This condition states that in equilibrium the profit from posting an additional vacancy is zero. Once we have m(θ) we are able to solve for the implied level of vacancies v.

This calibration strategy means that our model is set to match earnings in all three sectors, the duration of unemployment and the share of working age population unemployed,
# **1. Introduction**

In 1998, 174 millions of the population in Latin America lived in poverty<sup>1</sup> . About 20.4 million people or 15 percent of Latin America’s poor were concentrated in the Northeast of Brazil. The Northeast Region of Brazil includes nine of the country's 23 states: Alagoas, Bahia, Ceará, Maranhão, Pernambuco, Paraíba, Piauí, Rio Grande do Norte and Sergipe. It covers about 1.5 million square kilometers, over 18 percent of Brazil's total area. In 1998, the population of the Northeast was 46 million or about 29 percent of Brazil's total population. Northeast GDP accounted for about 13 percent of Brazil's GDP and per-capita GDP in Northeast was only 46 percent of GDP in all Brazil. In 1999, the poverty rate, measured by per-capita income and the indigent poverty line, in the Northeast was about 44 percent compared to 22 percent elsewhere and still disproportionately rural<sup>2</sup> . In contrast, the four states in the Southeast which occupy only 11 percent of land area, accounted for 43 percent of total population and around 60 percent of Brazilian GDP. Finally, the poverty rate in São Paulo is 9 percent, hence less than a fifth of the northeastean poverty rate.

The disparity between the Northeast and the Center-South of Brazil goes back centuries. Several factors, including recurrent droughts, contributed to a rapidly growing socioeconomic gap between the two regions. The relative decline of the Northeast ceased only in the 1960s when the federal Government initiated broad-based measures to support development of the region. These measures helped stabilize the Northeast economy and modernize the industrial sector.

The Northeast of Brazil over the last ten years has on a per-capita basis been growing faster than Brazil as a whole. Estimating geometric growth rate from recently released GDP data from Contas Regionais, 1985-97, Carrizosa, Fiess and Verner (2001) find that during 1985 and 1997 per-capita GDP in the Northeast region increased by 3.7 percent while per-capita GDP in Brazil increased by 3.0 percent.

Poverty remains one of the biggest and most difficult problems in the Northeast region despite efforts to fight it by the local and federal governments and civil society. In

> 1 Wodon (2000).

> 2 In Brazil there does not exist an official poverty line. Different authors use different poverty lines. This paper uses a caloric intake poverty line (see section 2).

1
With the gradual technological shift towards cellular telephony now underway, however, SACU countries may “leapfrog” from fixed line to cellular telephony in the near future, leaving behind the low mainline teledensity––and appropriately so.

**_Affordability—pricing_** _._ **A 3-minute local call in U.S. cents is one of the most expensive in the world in the SACU region at 10 cents, largely driven by 15 cents in South Africa and 22 cents in Lesotho.** In contrast, the cost of a 3-minute call to the US is the lowest among the comparable groups.  It costs 265 cents in the SACU region, which is almost half the lowincome and SSA region and much lower than in the middle-income group.  The picture is similar when one looks at the cost of three off-peak minute cellular local call which costs 30 cents, compared to 54 cents in upper middle income countries and 42 cents in SSA.  South Africa and Namibia have the cheapest rates in the region with Swaziland and Botswana having the most expensive cellular rates.

**_Quality—technical._ The SACU average records more than twice as many faults as uppermiddle-income countries is more in line with low-income and SSA average.** Reported faults per 100 telephone mainlines (per year) are very high in the SACU region (54), with Lesotho (75) having the worst record and Botswana (37) being the best performer in the region.

**_Quality—perception._ In spite of the SACU region’s relatively poor showing in most of the categories in the ICT sector, commercial perceptions are more closely aliened to the better performing middle-income countries.** _Commercial perception of telephone infrastructures_ gets a rating of 4.9 compared to 5.6 in upper-middle-income countries and 4.9 in lower-middle-income countries, while _perception of availability of mobile telephone_ is even better at 6 which is matched by upper-middle-income countries and slightly higher than lowermiddle-income countries. Situation is also same in terms of _commercial perception of postal efficiency_ with the SACU region having a rating of 4.2 compared to 4.4 for upper-middleincome and 3.5 for lower-middle-income group. Commercial perceptions in SSA and lowincome countries were ranked lower than SACU across the board.

9
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32
**Table A.6. Macro SAM III for Guinea in 2016 (billions of current Guinean francs)**

||act‐prv act‐gov|com‐prv com‐gov|f‐lab<br>f‐cap|hhd<br>gov<br>row|tax‐act tax‐com tax‐imp ta|x‐exp tax‐dir c|ap‐hhd|cap‐gov cap‐row|inv‐prv|inv‐gov dstk<br>total|
|---|---|---|---|---|---|---|---|---|---|---|
|act‐prv||87,593||||||||87,593|
|act‐gov||6,061||||||||6,061|
|com‐prv|39,163<br>3,625|||49,974<br>16,357|||||4,535|5,307<br>0 118,962|
|com‐gov||||6,061||||||6,061|
|f‐lab|14,607<br>2,436|||0||||||17,043|
|f‐cap|32,008|||0||||||32,008|
|hhd|||17,043 29,583|2,977<br>0||||||49,602|
|gov||||2,065<br>811|1,814<br>5,132<br>2,241|0 1,650||||13,713|
|row||23,996|0<br>2,426|0<br>0||||||26,422|
|tax‐act|1,814|||||||||1,814|
|tax‐com||5,132||||||||5,132|
|tax‐imp||2,241||||||||2,241|
|tax‐exp||0||||||||0|
|tax‐dir||||1,650||||||1,650|
|cap‐hhd||||‐4,086||||6,688||2,602|
|cap‐gov||||4,675|||0|632||5,307|
|cap‐row||||9,253||||||9,253|
|inv‐prv|||||||2,602|1,933||4,535|
|inv‐gov||||||||5,307||5,307|
|dstk|||||||0|||0|
|total|87,593<br>6,061|118,962<br>6,061|17,043 32,008|49,602 13,713 26,422|1,814<br>5,132<br>2,241|0 1,650|2,602|5,307<br>9,253|4,535|5,307<br>0|

Note: Data in green cells are from calculations in Table A.5.

Source: Tables A.1‐A.5 and A.7.

‐29‐
**Table 1: Resource Endowments in MENA**

||**Economy & Po**<br>PC Income<br>(2008)|**pulation**<br>Pop<br>(millions)|**Resource De**<br>Per Capita<br>Resource Rent<br>(USD,2008)|**pendence**<br> <br>Rank|OGM<br>Rents/GDP<br>(%)|**Resource En**<br>Per Capita<br>Proven<br>Reserves<br>(USD,2005)|**dowments**<br>Sub-Soil<br>Assets/Total<br>(%,2005)|
|---|---|---|---|---|---|---|---|
||(i)|(vii)|(ii)|(iii)|(iv)|(v)|(vi)|
|N. Africa **Algeria***|4,974|34.4|2,112|10|42.5|13,293|43.9|
|Dijbuti|1,157|0.8|...|19|...|...|...|
|Egypt**|1,997|81.5|450|12|22.5|1,989|9.3|
|**Libya***|14,802|6.3|9,232|7|62.4|...|...|
|Morocco|2,769|31.6|3|18|0.1|71|0.2|
|**Sudan***|_1,404_|41.3|371|15|26.4|1,554|12.8|
|Tunisia**|3,955|10.3|397|13|10.0|1,051|2.2|
|Other<br>**Bahrain***|28,240|0.8|11,408|5|40.4|82,923|41.1|
|**Iran***|4,700|72.0|2,351|9|50.0|13,987|41.8|
|**Iraq***|2,817|30.7|2,468|8|87.6|...|...|
|Israel|27,652|7.3|105|17|0.4|253|0.1|
|Jordan|3,910|5.8|287|16|7.3|74|0.1|
|**Kuwait***|54,260|2.7|35,227|2|64.9|212,013|65.0|
|Lebanon|7,138|4.2|...|19|...|...|...|
|**Oman***|21,649|2.8|11,122|6|51.4|71,631|48.5|
|Palestinian Territory|...|-|...|19|...|...|...|
|**Qatar***|86,436|1.3|43,485|1|50.3|...|...|
|**Saudi Arabia***|19,152|24.8|13,445|4|70.2|86,620|59.3|
|**Syria***|2,649|20.6|660|11|24.9|4,657|22.9|
|**UAE***|58,272|4.5|21,717|3|37.3|118,111|33.8|
|**Yemen***|1,175|22.9|375|14|31.9|...|...|
|**MENA**|**17,455**|**406.7**|**8,623**|_na_|**37.8**|**43,445**|**27.2**|
|**Global Average**|**15,673**|**6,672.0**|**1,842**|_na_|**10.2**|**7,782**|**9**|
|_Source:_World Development Ind<br>_Notes:_* Net oil &gas exporters,|icators(WDI) (i,v<br>** Significant oil|ii),World Ba<br>andgas expor|nk Wealth of N<br>ts. Global aver|ations(WoN) <br>ages are unw|(ii,v,vii)<br>eighted.|||
|_~_UAE incorporates Abu|Dhabi,Ajman,D|ubai,Fujairah|,Ras al-Khaim|ah,Sharjah an|d Umm al-Quw|ain.||

# **_Autocracy to Democracy?_**

11. The MENA countries have long been distinguished by the absence of political inclusivity (Diamond 2010). Figure 2 displays global democracy trends by region since 1960; values on the x axis indicate the mean Polity scores of countries in the region. Polity is the most widely-used measure of democratic accountability in the field of political science. Notice that MENA has been either the most or second most undemocratic region in the world since 1960. Despite a slight democratizing trend since the late 1970s, the gap between MENA and all other world regions has grown larger. The lack of democracy, but more critically the extent to which MENA lags behind other regions, represent a special feature of the region in thinking about prospective transition dynamics moving forward. The way oil has interacted with political regimes in the past, and the way in which it may do so across particular country settings in the future, are important factors for the region meriting further scrutinity. While other regions have in the past experienced more of less rapid transitions to democracy, few would probably have predicted the on-set and speed of these transitions.

5
# **Figure 5 Inequality change under alternative infrastructure development scenarios**

- A. Catching up: halving the infrastructure level gap with other regions

<!-- Start of picture text --> West Africa LICs East Africa LICs South Africa LICs Central Africa LICs SSA UMCs<br>0.00<br>-0.01<br>-0.02<br>-0.03<br>-0.04<br>-0.05<br>Infrastructure Stock Infrastructure Quality<br>B. Keeping up: matching the rate of infrastructure growth of other regions<br>0.00<br>-0.01<br>-0.02<br>-0.03<br>-0.04<br>West Africa LICs East Africa LICs South Africa LICs Central Africa LICs SSA UMCs<br>Infrastructure Quantity Infrastructure Quality<br>Change in the Gini Coefficient<br>Change in the Gini Coefficient<br><!-- End of picture text --> 45