rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 19. Net capital flows (quarterly data)**

<!-- Start of picture text --> billion USD % of GDP (right - hand scale) %<br>20 20<br>15<br>15<br>10<br>10<br>5<br>5<br>0<br>0<br>-5<br>2002 2003 2004 2005 2006 2007 2008 2009 2010<br>-5 -10<br><!-- End of picture text --> Note: net capital flows = net financial account. Source: authors’ calculation.

**Figure 21. Structure of gross capital inflows (% of GDP)**

<!-- Start of picture text --> FDI Portfolio Other<br>%<br>8,00<br>7,00<br>6,00<br>5,00<br>4,00<br>3,00<br>2,00<br>1,00<br>0,00<br>-1,00 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>-2,00<br>Source: own calculation.<br>Figure 23. Liquidity ratios in banks with assets<br>above 200 million PLN<br>STLR LTLR<br>1,8<br>1,6<br>1,4<br>1,2<br>1<br>0,8<br>0,6<br>0,4<br>0,2<br>0<br>01.200903.200905.200907.200909.200911.200901.201003.201005.201007.201009.201011.2010<br><!-- End of picture text --> Source: own calculation.

**Figure 23. Liquidity ratios in banks with assets above 200 million PLN**

Notes: STLR: short-term liquidity ratio; LTLR; long-term liquidity ratio, i.e. ratio of coverage of illiquid and limited liquidity assets with regulatory capital and external funds. Source: KNF.

**Figure 20. Liabilities and claims to parent banks**

<!-- Start of picture text --> Liabilities Claims (right - hand scale)<br>zl billion zl billion<br>135 40<br>130 35<br>125 30<br>120 25<br>115 20<br>110 15<br>105 10<br>100 5<br>95 0<br>12.2009 01.2010 02.2010 03.2010 04.2010 05.2010 06.2010 07.2010<br><!-- End of picture text --> Source: KNF.

**Figure 22. Domestic and cross-border loans for nonfinancial sector (quarterly data)**

<!-- Start of picture text --> 90 % domestic loans cross - border loans (right - hand scale) % 16<br>89 14<br>88 12<br>87 10<br>86 8<br>85 6<br>84 4<br>83 2<br>82 0<br>2004 2005 2006 2007 2008 2009 2010<br>Source: JEDH and NBP.<br><!-- End of picture text -->

**Figure 24.  Currency structure of new housing loans to households**

<!-- Start of picture text --> Zloty CHF Euro USD<br>100 %<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2008 2009 2010 2011<br>Source: KNF.<br><!-- End of picture text --> 33
zero.

Third, we analyze data from the randomized experiment designed by Fafchamps et al. (2014), which gave cash or in-kind grants of about $120 to a sample of Ghanaian microenterprises. As before, we focus on the sub-sample of male-owned enterprises, as the showed no effect on business for women. At program significant ownership baseline, all firms are located either in Accra, the capital of Ghana, or in the nearby industrial city of Tema. We define migration as any move outside the Greater Accra Region. Panel F indicates that the intervention significantly increased business ownership by 4 percentage points in the first year after treatment, and by about 9 percentage points three years after. The program shows negative (although not statistically significant) impacts on migration. The migration-self-employment elasticities suggest that every one-percentage change in self-employment causes a 0.04 percentage points reduction in migration after one year, and a 0.14 percentage points decrease after three years.

Lastly, we consider the Youth Enterprise With Innovation in Nigeria (YouWin!) competition launched in 2011 in Nigeria and studied by McKenzie (2017b). The program provided a four-day business plan preparation course, and then grants of about $50,000 to winners randomly selected among a pool of semi-finalists. Panel G of table 3 reports the related ITT estimates. We observe large and sustained effects on self-employment, which reach 31.5 percentage points after three years, and is still 19.6 percentage points five years post application. We find that participants in the treatment group are about 2 percentage points less likely to migrate after three years (from a control group average migration rate of about 6%), and 3.6 percentage points less likely to have moved at the 5-year endline (control group mean of 8%). We estimate that each percentage point increase in self-employment reduces migration between 0.06 and 0.15 percentage points.

Table 4 shows that the conclusions drawn in the previous sections remain unchanged if we look at the effects of each program on any employment (and at migration-any-employment elasticities) instead of self-employment.

# _Meta-analysis_

A simple average of the 16 different ITT estimates of migration from the seven studies in table 3 gives an average effect of -0.007. Four of the estimates show significant negative impacts on migration, while there is only one positive estimate, from the Uganda YOP intervention, that is itself reversed to a significant negative effect over a longer time period. The results therefore show an average tendency for programs that promote self-employment to lower migration, but that the size of the effect is 16
# 1 Introduction

Like other developing regions in the post-World War II era, the demographic transition in the Middle East and North Africa (MENA) region has given rise to rapid labor force growth. But with an average growth rate above 3 percent since the 1960s, no other region comes close to the magnitude and persistence of MENA’s labor market pressures. And while employment growth was relatively strong in the 1970s, it failed to keep pace with the expansion of the labor force during the 1980s and 1990s. As a result, MENA recorded some of the highest unemployment rates among developing regions in the 1990s. Recent estimates indicate that unemployment rates range from about 2.3 percent in the United Arab Emirates to close to 29.8 percent in Algeria (see World Bank (2004)). For the region as a whole, the unemployment rate is currently estimated at 15 percent of the labor force.<sup>1</sup> Based on current trends in job creation, the prospects for absorbing new entrants into labor markets and those currently unemployed are rather bleak. With the labor force growing in the present decade at 3.4 percent per annum, the average unemployment rate for the region could reach 22 percent by 2010.

Low output growth is often the “proximate” cause for the rise in unemployment. Following the oil bust in the late 1980s, the region experienced a weak recovery in the 1990s due to the protracted pace of policy reforms. But there are also structural reasons for unemployment that were not addressed by policymakers in the past decade. MENA countries exhibit rigidities in educational systems, wage setting, and regulatory regimes due to the dominance of the public sector in labor markets. Although rates of human capital accumulation have remained steady, MENA has reaped less than its potential in terms of economic growth and job creation. Government legislation on hiring and firing, minimum wages, and on collective bargaining agreements as well as employment guarantees in the public sector have interfered with the efficient functioning of the labor market.<sup>2</sup> In normal times, the impact of

> 1Youth unemployment ranges from 37 percent of total unemployment in Morocco to 73 percent in Syria, with a simple average of 53 percent for all countries for which data are available. Except in Jordan and Lebanon, first-time job seekers make up more than 50 percent of the unemployed (ILO 2003).

> 2See for instance Pissarides (1993) and Pritchett (1999). In countries where wage levels fell in real terms during the 1990s, educated and experienced workers were affected the most, leading to a degradation of skills. Despite this, recent estimates suggest that returns to education are generally higher in the public sector than in the private sector at nearly all education levels but the university level (Assaad (2002) and World Bank (2004)).

3
Appendix 3. List of financial crises (continued)

||Banking crisis|Currency crisis|Debt crisis|
|---|---|---|---|
|Niger|1983|1994|1983|
|Nigeria|1991; 2009|1983; 1989; 1997; 2016|1983|
|Norht Macedonia<br>|1993|||
|Pakistan||1972||
|Panama|1988||1983|
|Papua New Guinea||1995||
|Paraguay|1995|1984; 1989; 2002|1982|
|Peru|1983|1976; 1981; 1988|1978|
|Philippines|1983; 1997|1983; 1998|1983|
|Poland|1992||1981|
|Romania|1998|1996|1982|
|Russian Federation|1998; 2008|1998; 2014|1998|
|Rwanda||1991||
|Sao Tome & Principe|1992|1987; 1992; 1997||
|Senegal|1988|1994|1981|
|Serbia||2000||
|Seychelles||2008|2008|
|Sierra Leone|1990|1983; 1989; 1998|1977|
|South Africa||1984; 2015|1985|
|South Sudan||2015||
|Sri Lanka|1989|1978||
|Sudan||1981; 1988; 1993; 2012|1979|
|Suriname||1990; 1995; 2001; 2016||
|Syrian Arab Republic||1988||
|Tajikistan||1999; 2015||
|Tanzania|1987|1985; 1990|1984|
|Thailand|1983; 1997|1998||
|Togo|1993|1994|1979|
|Trinidad & Tobago||1986|1989|
|Tunisia|1991|||
|Turkey|1982; 2000|1978; 1984; 1991; 1996; 2001; 2018|1978|
|Turkmenistan||2008||
|Uganda|1994|1980; 1988|1981|
|Ukraine|1998; 2008; 2014|1998; 2009; 2014|1998; 2015|
|Uruguay|1981; 2002|1972; 1983; 1990; 2002|1983; 2002|
|Uzbekistan||2000; 2017||
|Venezuela, RB|1994|1984; 1989; 1994; 2002; 2010|1982; 2017|
|Vietnam|1997|1972; 1981; 1987|1985|
|Yemen, Rep.|1996|1985; 1995||
|Yugoslavia, The former SFR|||1983|
|Zambia|1995|1983; 1989; 1996; 2009; 2015|1983|
|Zimbabwe|1995|1983; 1991; 1998; 2003||

Note: Years of crises are taken from Laeven and Valencia (2018), while the list includes currency crises in 2017 and 2018, which are computed with data on nominal exchange rates from the IMF and the methodology described in Laeven and Valencia (2018).

42
3

# I. The Effects of Development on Health

# State of the Art

Statistical analyses of the determinants of life expectancy and other measures of mortality across nations, mostly by demographers, provide insight into the effects of development (defined here as improvements ir. the material welfare of the reference population) on individua, health. For example Preston (1980) found that per capita income and adult literacy, but -.t a measure of calorie availability, were closely associated with life expectancy both in 1940 and 1970. However, only half the increase in life expectancy over the intervening 30 years could be explained by changes in the values of those variables. The rest was apparently attributable to changes in public health technology, general improvements in transportation and communication, and possibly changes in the distribution of income or access to health services that were favorable to health. Wheeler (1980) similarly found an increase in life expectancy between 1960 and 1970 that could not be fully explained by such variables as per capita income, adult literacy. population per doctor and population per nurse.

These cross-national studies establish associations between various indicators of development and health, but cannot inform us about whether more development actually causes better health (either on average for entire communities or for those individuals whose higher income or better education is measured as "more development"). An example of our ignorance of the causal sequence emerges indirectly from the studies noted above: although per capita income is associated with or correlated with low mortality (poorer countries
outcomes ��� associated with residence in the city. As cities offer better labor market � � outcomes and better schooling outcomes than rural areas, we assume that �� �� and ��� ��� for any city _k_ . We also assume that �� ����, implying that paying for an urban school is within the affordable choice set of migrants. These assumptions on the cost and quality of urban schooling are supported by both qualitative and quantitative studies on China (see Chen and Feng, 2013; Goodburn, 2009; and Lai et al., 2014). The migration cost from rural area � to city � is denoted ��,�.<sup>12</sup> We assume that the worker has a linear utility of the form where � is household disposable income, � is the child’s human capital, and � the child’s vector of non-schooling outcomes. ���, �� is a function which increases in both � and the components of � and captures the rate at which the child’s schooling and non-schooling outcomes translate into expected future income and/or the parental valuation of the child’s well-being from schooling and non-schooling outcomes. Note that we do not assume any particular specification for function _g_ and allow for any level of complementarity between schooling and non-schooling outcomes.<sup>13</sup> Also note that our setting with a single school fee in each city is compatible with a more complex framework that would have migrants choose a school within a distribution of heterogeneous schools in each city that each produces different levels of human capital at different costs.<sup>14</sup>

> 12 For simplicity and without any loss of generality, we assume that the cost for a household to migrate to a given city is the same whether or not the worker brings his child along with him. �

> 13 For example, choosing a CES specification ���, ����∝�� ��1 ���������� where W(.) is a welfare index associated with non-schooling outcomes would allow us to consider all cases from perfect substitutability to perfect complementarity depending on the value of �.

> 14 To see this, assume that the distribution of schools in each city � is represented by a continuum of schools producing human capital ����� for ������� , where �� is a concave education production function �� ��� increasing in �. It is easy to see that if the condition ��� . �� �1 holds, parents who bring their children to city _k_ will optimally choose the cheapest and lowest human capital producing school in the city. This condition is likely to hold if (i) financial inputs do not have a strong impact on the production of education or (ii) returns to

8
# TELECOMMUNICATION REFORMS, ACCESS REGULATION, AND INTERNET ADOPTION IN LATIN AMERICA'

Antonio Estache, The World Bank and ECARES Marco Manacorda, Queen Mary College and CEP, London School of Economics Tommaso M. Valletti, Imperial College and CEPR

## 1. **INTRODUCTION**

Latin America is betting on the potential "new economy" productivity payoffs from telecommunications reform. It wants fast improvements in the quantity and quality of its information and communications technology. Chile's Telecommunication Subsecretariat optimistic growth target estimate issued in mid 2000 for fixed line, mobile phone services and internet products for the next decade is quite illustrative.<sup>2</sup> By 2010, it expects fixed phone penetration to more than double to reach 49%, mobile penetration to triple to 60% and internet access to quadruple to about 50% of the population.<sup>3</sup> Similar statements made by key policymakers from Argentina to Mexico suggest that this optimism is shared throughout the region. The underlying assumptions are: (1) the liberalization of the sector has progressed enough to allow the countries of the region to now make the most of cheaper technologies and lower costs of access to web enabled telecommunications technologies and services; (2) once supply has increased enough, most policymakers expect demand to follow quickly thanks to the diffusion of the new technologies throughout the region which requires very little input from the government besides ensuring the liberalization of the telecoms sector and (3) this should significantly contribute to close the gaps between the poorest and the richest across and within countries. 4

> We are grateful to M. Celani, E. Engel, L. Guasch, A. Galetovic, B. Mueller and P. Noumba for useful feedback on an earlier version. The views expressed in this paper are however our own and should not be attributed to any of the institutions we are affiliated with. Any mistake is obviously ours and ours only.

> 2 Latincom (2000), "Chile's optimism greeted with skepticism", October 6'h, p. 12.

> 3 For comparison purposes, currently, 41% of the US population has a computer.

> _4_ Some authors have serious doubts that these benefits will be evenly distributed and talk about the risks of a digital divide among nations without an explicit government intervention. See Norris (2000) and Rodriguez and Wilson (2000). This is not addressed in this paper.

1
Table 8: General-Equilibrium effects of deeper trade integration for EAP countries, year 2017.

||||_Intensi_<br>_PT_|_ve Margin_<br>_A_<sup>_CF L_</sup><br>_Deep_||_Extensiv_<br>_PTA_|_e Margin_<br><sup>_CF L_</sup><br>_All_|
|---|---|---|---|---|---|---|---|
|Country Name|Iso3|Within <br>∆Export<br>(1)|EAP<br>∆GDP<br>(2)|Within EAP <br>∆Export<br>(3)|plus ROW<br>∆GDP<br>(4)|Within EAP<br>∆Export<br>(5)|, Cluster #1<br>∆GDP<br>(6)|
|Australia|AUS|8.66|1.12|10.87|1.39|2.07|0.25|
|Burma|MMR|13.95|0.10|14.96|0.10|-0.16|0.00|
|Cambodia|KHM|3.58|2.51|3.41|2.36|0.23|0.13|
|China|CHN|2.38|0.09|2.83|0.10|2.11|0.08|
|Fiji|FJI|2.23|1.49|3.29|1.98|5.48|4.74|
|Hong Kong SAR, China|HKG|-0.49|-0.71|-0.27|-0.59|3.35|6.22|
|Indonesia|IDN|8.14|1.19|9.33|1.31|-0.05|-0.01|
|Japan|JPN|1.92|0.14|2.63|0.17|10.85|0.84|
|Korea, Rep.|KOR|4.74|0.81|8.88|1.53|2.89|0.59|
|Lao PDR|LAO|10.37|2.54|11.06|2.62|-0.16|-0.03|
|Macao SAR, China (Aomen)|MAC|0.09|0.14|0.12|0.04|7.63|8.28|
|Malaysia|MYS|3.79|2.79|4.22|2.99|0.38|0.19|
|Mongolia|MNG|0.13|0.26|-0.02|0.14|3.67|4.36|
|New Zealand|NZL|7.39|1.37|7.49|1.38|-0.36|-0.07|
|Papua New Guinea|PNG|2.19|2.27|3.11|2.92|4.32|6.40|
|Philippines|PHL|2.69|4.59|2.89|4.69|1.06|1.03|
|Singapore|SGP|2.38|3.71|3.26|5.25|1.24|1.19|
|Thailand|THA|4.97|2.70|5.55|2.89|0.67|0.37|
|Tonga|TON|6.65|1.27|6.32|1.21|12.34|2.14|
|Vietnam|VNM|5.35|2.70|5.90|2.87|0.43|0.20|
|EAP||3.14|0.35|4.11|0.44|3.02|0.26|
|RoW||-0.19|-0.03|0.24|0.07|-0.08|-0.02|

_Note_ : percent change compared to the baseline in total exports and GDP of participating countries.

27
25 Weak investment may help explain the low trade elasticity for the post-Great Recession period that we documented in Section III.<sup>19</sup> However, the changing composition of demand cannot on its own explain the decline in the long-run world trade elasticities throughout the 2000s. If this channel were driving the changing long-run relationship between trade flows and GDP, the world trade elasticity in the pre-Great Recession period should have been increasing, as the share of investment in aggregate demand was rising, the share of public consumption was falling and private consumption remained stable. As discussed in Section III, this is not what we find in the data.

# **E.   Changes in Protectionism**

Finally, a change in protection seems to be a natural candidate for the lower responsiveness of world trade to income, as an increase in trade barriers in the 2000s could have contributed to lower growth of world trade (and particularly so in the aftermath of the Great Recession). However, the recorded increase in protectionism since the outset of the crisis has not been substantial. The WTO trade restrictiveness indicators –capturing border measures such as tariff increases, import licenses, or new customs controls- show a modest increase in the share of world trade covered by new import restricting measures since the Great Recession (Figure 13). For 2013, this share was at 1.3 percent for all WTO member and observer countries. World Bank data on Temporary Trade Barriers (TTBs), such as antidumping and countervailing duties, provide a similar picture.

One simple way to test the role of changing protection on the dynamics of trade and income relationship is to augment equation (1) to include a variable capturing the level of trade barriers. As a measure of protectionism, we use the World Bank’s TTBs dataset that offers a

> 19 Boz et al. (2014) provide evidence consistent with this hypothesis. They use the model estimates of Bussiere et al. (2013) that are based on an “import-intensity adjusted” demand, which gives higher weight to components of demand with higher import content such as investment. They show that the model predictions are close to actual trade growth for a set of advanced economies during the global trade slowdown.
Table D1: Correlates of attrition across phases

|||Depen|dent varia|ble:||
|---|---|---|---|---|---|
||Started r<br>preferenc|evealed<br>es rides|Started <br>assi|randomize<br>gnment rid|d car<br>es|
||(1)|(2)|(3)|(4)|(5)|
|Low education (middle school or less)|-0.014|0.074*|-0.014|0.074*|0.000|
||(0.056)|(0.044)|(0.056)|(0.044)|(.)|
|Young (18-25 years-old)|-0.067|-0.016|-0.067|-0.016|0.000|
||(0.057)|(0.045)|(0.057)|(0.045)|(.)|
|Single|0.056|0.003|0.056|0.003|0.000|
||(0.052)|(0.042)|(0.052)|(0.042)|(.)|
|Employed|-0.067|-0.032|-0.067|-0.032|0.000|
||(0.054)|(0.042)|(0.054)|(0.042)|(.)|
|High self-reported socio-economic status|0.046|0.042|0.046|0.042|0.000|
||(0.078)|(0.061)|(0.078)|(0.061)|(.)|
|Take-up at zero opportunity cost||0.000<br>(0.000)||0.000<br>(0.000)|0.000<br>(.)|
|Any take-up with positive opportunity cost|||||0.000<br>(.)|
|Constant|0.750***<br>(0.069)|0.833***<br>(0.066)|0.750***<br>(0.069)|0.833***<br>(0.066)|1.000<br>(.)|
|Observations|371|296|371|296|226|
|Regression sample mean||0.843||0.843|1.000|

_Note:_ Unit of observations is the rider. Sample in columns (1) and (3) include all participants. Sample in columns (2) and (4) are restricted to riders who completed at least 5 zero cent opportunity cost rides. Sample in column (5) includes only riders who completed at least 5 positive opportunity cost rides. Standard errors in parentheses.
(or low) value. If those sectors represent only a small fraction of total trade flows, it would be misleading to impose an extreme value of time costs to all HS4 categories within the given GTAP sector.

We take a different approach to deal with the missing values. Specifically, we follow Minor and Hummels (2013) and replace missing values at the HS4 level by the average of non‐missing ones within any given GTAP sector. Doing so enables us to use trade flows of all HS4 / HS2 categories in the construction of weights. We clarify our procedure with the numerical example developed above. Consider the case where Hummels and Schaur (2013) were not able to estimate τ<sup>���</sup> , for example because the U.S. data do not feature any import of this category. In such a case, we create an average value τ<sup>���</sup> computed as the average “value of time” of all non‐missing HS categories within the GTAP sector. If the world consists of only our two countries 1 and 2, the formula for τ<sup>���</sup> would be simply: We then replace missing values in our data set with those averages and obtain: _Table 9: Disaggregated trade flows between country 1 and country2 in sector GTAP_1_

|**Origin**|**Destination**|**GTAP Sector**|**HS Category**|**Τ**|**Trade Flow**|
|---|---|---|---|---|---|
|Country 1|Country 2|GTAP_1|HS_1|τ<sup>HS1</sup>|5|
|Country 1|Country 2|GTAP_1|HS_2|τ<sup>HS2</sup>|20|
|Country 1|Country 2|GTAP_1|HS_3|τ<sup>���</sup>|10|
|Country 2|Country 1|GTAP_1|HS_1|τ<sup>HS1</sup>|30|
|Country 2|Country 1|GTAP_1|HS_2|τ<sup>HS2</sup>|10|
|Country 2|Country 1|GTAP_1|HS_3|τ<sup>���</sup>|5|

Using this procedure enables us to create “value of time” for each GTAP sector and origin‐destination pairs that contain at least one HS category that is traded. Of course, country pairs that do not trade at all a given GTAP sector cannot be associated with any “value of time”. We then build the ad‐valorem time barrier by simply multiplying the shipment time (computed using the network analysis described in Section 3) and the value of time aggregated from HS2 estimates derived above. It is important to keep in mind that for a given shipment time, the ad‐valorem time barriers feature sectoral heterogeneity: some sectors are highly time sensitive (for example perishable products) while others are not. Table 10 presents the percentage decrease in total trade barrier, as a percentage change from the initial value.

22