rafmacalaba/datause-encoder-data huggingface dataset:
51

# China

The United Nations assumes no net international migration for China, but the World Bank's assumption of net emigration is much more reasonable. The only quantitatively important immigration into China has consisted of refugee flows (primarily of ethnic Chinese) from Vietnam. There are currently around 285,500 VIetnamese refugees in China (U.S. Committee for Refugees, 1988:46), but most of these arrived in China in 1978 and 1979. Net emigration of 150,000, as previously proposed by the World Bank, seems low giver. the large outflow to the United States and the continuing outflow of illegal migrants to Hong Kong. I would recommend a net level of emigration of 250,000 for 1980-1985 and 1995-2000 with slightly higher emigration in 1985-1995. Emigration to the United States, which is the primary destination for Chinese migrants, has been quite constant at around 25,000 anrually since 1982 when Taiwan was given a separate quota and the figures for mainland China were reported separately for the first time. This situation is likely to be unstable in the near future. It is likely that emigration to Hong Kong will increase as 1997 approaches. It is estimated that over 25,000 illegal immigrants from China enter Hong Kong each year and legal immigration will undoubtedly increase in the next 10-15 years as well.

With respect to the sex ratio, in recent years there have been slightly more females than males moving to the United States annually (see Table 20). Illegal immigration to Hong Kong is heavily male dominated, however. Nevertheless, the sex ratio of net emigrants previously used by the World Bank is too high, especially for 1985-2000. If we assume that three-quarters of illegal immigrants to Hong Kong are males and take the actual sex distribution of Chinese immigrants to the United States, then the overall sex ratio would be about 160. I would suggest this as a maximum sex ratio for the 1980-1985 period. If anything, the sex ratio should decrease somewhat after 1985.

Table 20. Immigration to the United States from Mainland China

|Fiscal Year|Males|Females|
|---|---|---|
|1982|12,149|13,469|
|1983|11,430|12,581|
|1984|11,132|12,231|
|1985|11,748|13,039|
|1986|11,812|13,294|
|1987|12,345|13,496|

Source: Immigration and Naturalization Service, Statistical Yearbooks, 1982-8b

# The Philippines

Between 1980 and 1984, 215,504 Filipinos legally immigrated to the United States alone, more than 20,000 moved to Canada, and more than 16,000 moved to Australia. Substantial numbers also moved to Europe and to other
Fonseca, 2015; Sorsa et al., 2015; Klasen et al., 2021; Lopez-Acevedo et al., 2021a; LopezAcevedo et al., 2021b). In contrast, our paper explores different data sets for Guatemala and is thus able to differentiate the impact of a wide set of factors.

Second, we explore nationwide data and within-country dispersion by relying in the two most recent population census collected by the Guatemala National Institute of Statistics (INE, in its Spanish acronym). Differences across more than 1.5 million female household heads or spouses in active age in 333 municipalities and 22 departments in Guatemala, between 2002 and 2018 are empirically explored. Furthermore, the 2018 census is combined with unique survey and administrative data at the subnational level. In contrast, most studies exploring within-country variation in FLFP typically rely on household or labor force surveys alone.

Third, our empirical specification accounts for differences across the country in migration and rates of crime and violence. These are well known drivers of FLFP and development more broadly in the Central America region. On the one hand, the number of Guatemalan who live abroad in 2019 reached 6.9% of the overall population (Instituto Guatemalteco de Migración, 2021). Remittances are, therefore, a very important driver of consumption and growth in the country and likely impact the women’s decision to participate in the labor market (Amuedo-Dorantes and Pozo, 2006; Antman, 2015). On the other hand, Guatemala is a country with high levels of crime and violence, and particularly of gender-based violence. In 2015, female homicides reached 7.6 per 100,000 females, well above the averages of 2.2 worldwide, 3.8 in Mexico or 4.4 in Colombia (World Bank, 2021). On the other hand, the percentage of women aged 15-49 years old subject to physical or sexual violence in the last 12 months in Guatemala was 8.9 in 2015, below the rates of countries such as Mexico (9.5 in 2016) and Honduras (11 in 2012), but well above the rates of El Salvador (6.7 in 2014), and some European countries such as France (7 in 2012) and Germany (5 in 2012) (World Bank, 2021).

We closely relate to Bhalotra and Fernandez (2021). Exploring Mexican census data, they assess the role of different drivers of FLFP between 1960 and 2015.<sup>4</sup> During this period Mexico’s FLFP rose from 13% to 47%. Changes in women’s education and in the occupational structure of employment account for the change. But the increase in FLFP of the 1990s is an exception as it remains largely unexplained. The authors conjecture that unobservable factors such as changes in social norms regarding marriage or fertility could be driving these changes. In our paper we can observe proxies of social norms, attitudes towards women and of social policies and we explore their role to explain the within-country variation in FLFP.

> 4 Bhalotra and Fernandez (2021) propose as supply side drivers of FLFP: education, marital status, and fertility. Demand-side drivers include sector and occupational structure of employment at the municipality level.

4
Source: WB, Doing Business, 2006. Lower numbers indicate more flexibility.

- For a country such as Timor-Leste with very limited domestic capital, a negligible indigenous industrial base and dependence on technology-intensive industry such as oil, the degree to which policy stimulates investment and trade is unusually important. Figure 3 shows an index of protection provided under local law to foreign investors, and indicates that Timor-Leste provides weak protection to foreign investors relative to regional comparators.

<!-- Start of picture text --> Figure 3: Investor protection index<br>10<br>9<br>8<br>7<br>6<br>4 5<br>3<br>2<br>1<br>0<br>Timor-L PNG Fiji Thai Malay Sing Brazil Philip Indon Austral<br><!-- End of picture text --> Source: WB, DB, 2006. Higher numbers = more protection.

- as businesses become formal and grow, they become increasingly dependent on their ability to enforce contracts with business partners, workers, and creditors/debtors.  The time and cost of contract enforcement is therefore a critical factor in the incentives that businesses face.  Comparative findings on this question are presented in Figure 4.  Timor-Leste is a clear outlier in terms of both time and costs involved in enforcing contracts, being dramatically higher on both counts than all regional comparators.

<!-- Start of picture text --> Figure 4: Time and cost of contract<br>enforcement, 2004<br>1200 200<br>180<br>1000 160<br>800 140 Time<br>120<br>600 100<br>80 Cost<br>400 60<br>200 40<br>20<br>0 0<br>Source: WB, DB, 2006.<br>Time (days)<br>Cost (as % of debt)<br>Timor-L PNG Fiji Thai Malay Sing Brazil Mozam Indon Austral<br><!-- End of picture text --> 11. In summary, it is clear that macroeconomic shocks, inherited structural challenges and in some areas current policies are major factors in any discussion of labor markets in Timor-Leste. Even the most smoothly functioning labor markets would have struggled to generate sustainable employment in the face of such conditions. This is even more the case when the young and growing population structure of Timor-Leste is taken into account (see below in LM discussion). However, the challenging context within which employment is taking place makes the need for employment-promoting policies within the labor market even more important, as Timor-Leste can not afford the luxury of letting sustained high growth overcome any shortcomings of labor and related policies. The following section discusses LM developments within Timor-Leste which have taken place in the face of the challenging macro environment.

7
discrimination are somewhat widely used. Based on the sample data from 45 telephone operators in 18 African countries, more than 70 percent of mobile operators employ twoor three-zone peak load pricing (Table 4).<sup>16</sup> Fixed-line operators are less likely to rely on peak pricing; 4 out of 10 operators are using a flat rate scheme. Note that a three-zone tariff includes peak, off-peak, and off-off-peak periods. The last is usually applicable for midnight to very early morning.

About 80 percent of mobile operators take advantage of termination-based discriminatory pricing between on- and off-net calls. All fixed-line operators differentiate the unit rate of calling to fixed-line terminations from the rate of calling to mobile terminations. This is natural because of additional access costs between the two networks. However, some mobile operators offer the same rate for intra- and inter-mode connections; they typically charge a high monthly fee and apply a flat per-unit rate to all calls, independently of their terminating networks. The adoption rates of inter-mode discrimination are 77 percent and 70 percent for prepaid and contract-based mobile carriers, respectively.

<u>Table 4. Adoption Rates of Pricing Mechanisms in Selected African Countries</u>

||Fixed<br>(10 operator|-line<br>sinsample)|Prepaid<br>(35 operator|mobile<br>sinsample)|Contrac<br>(33 operato|t mobile<br>rsinsample)|
|---|---|---|---|---|---|---|
|||Diffusion||Diffusion||Diffusion|
||Obs.|<br>rate (%)|Obs.|<br>rate (%)|Obs.|<br>rate (%)|
|Peak load pricing 1/|||||||
|Flat rate|4|40.0|5|14.3|9|27.3|
|Two-part|6|60.0|17|48.6|14|42.4|
|Three-part|0|0.0|13|37.1|10|30.3|
|Two-part pricing (monthly charges)|10|100.0|3|8.6|21|63.6|
|Business discount|2|20.0|5|14.3|14|42.4|
|Friend discount|1|10.0|10|28.6|10|30.3|
|Tie-in arrangement|0|0.0|1|2.9|11|33.3|
|On/off-net discrimination 2/|...|...|29|82.9|26|78.8|
|Inter-mode discrimination 2/ 3/|10|100.0|27|77.1|23|69.7|

1/ Based on on-net per unit rates.

2/ Based on peak prices.

3/ Relative to on-net per unit rates.

Source: Author’s calculations based on data collected through telephone operator websites.

While the two-part tariff is common for fixed-line telephone services, in mobile telecommunications the absence of monthly charges is a typical feature of prepaid services. About 65 percent of contract-based mobile operators use a two-part pricing scheme. Table 5 shows the levels of monthly and per-unit costs in the region. Many

> 16 When a telephone operator has more than one billing plan/package, the cheapest one is selected. See the details in Section V.

15
<!-- Start of picture text --> 4000 243 312 298 8096<br>3500 -— xz -_ 000<br>Wi r | | 6947<br>3000 mz #000 6220<br>2500 6000 | 5230 5404<br>2000 SOOO =<br>1500 | 3079 3351 3498 3500 3504 a A000 4144<br>1000 + 3000 |<br>500 |<br>2000<br>() 1 1<br>2008 2009 2010 2011 2012 2013 1000<br>Employed Nationals mTM Employed Foreign 0 2008 2009 2010 2011 2012 2013<br><!-- End of picture text -->

<!-- Start of picture text --> 11.4 a0.2<br>0 2<br>CSS<br>20 60.0<br>=Sadiola *Sitakilly **Sanso %Fourou Il Gouandiaka =Sadiola &Sitakilly :*Sanso Fourou i Gouandiaka<br><!-- End of picture text -->
# **3.1 Child Laborers**

Table 5 below presents the figures for the time allocation of individuals aged 10 to 17 living in S ̃ao Paulo City in 2010 for male and female individuals, separately. There are around 1.4 million individuals in this category and the majority attend school. In fact, 85.4% (86.6%) of boys (girls) attend school only; and 6.2% (5.4%) of the boys (girls) divide their time between school and work. Thus around 92% (93%) of boys (girls) attend school. Conversely, 2.5% (1.7%) of boys (girls) work only; and 5.9% (6.3%) of boys (girls) neither work in the labor market nor attend school. Summing up those that work only and those that work and attend school, the incidence of child and adolescent work among boys is 8.7% and among girls it is 7%. Note that of all boys working in the labor market, 71% attend school, and for girls the figure is 76%. On the other hand, of all boys attending school, 6.8% work in the labor market, and of all girls attending school, 5.8% work in the labor market.

# **3.2 Students**

Our data encompass students enrolled in the 6<sup>_th_</sup> _,_ 7<sup>_th_</sup> and 8<sup>_th_</sup> grades at S ̃ao Paulo municipal schools. According to the _2010 Educational Census_ from the Brazilian Ministry of Education, there are around 580,000 students enrolled in these grades in the City of S ̃ao Paulo in 2010. Table 6 shows their distribution across grades and school systems. Of all of them, 49.6% are enrolled in the public municipal schools, 31.5% are enrolled in public state schools and 18.8% are enrolled in private schools and these proportions are similar for all grades. Since our data are of municipal school children only, we observe roughly half of the 6<sup>_th_</sup> _,_ 7<sup>_th_</sup> and 8<sup>_th_</sup> grade students in S ̃ao Paulo City, a population of around 290,000 students.

The IBGE Demographic Census has information about the type of school system in which the student is enrolled as well. It classifies schools as public or private but does 11
countries and the Worldwide Governance Indicator for 2012, reporting results from the latter in the table below.<sup>11</sup> The estimates do not provide much support for the notion that the standard measures of economic policy and performance were strongly associated with the extent of tapering. GDP growth, the budget deficit, public debt, level of reserves and the governance indicator do not exert a significant impact on the exchange rate. In contrast, financial market size, the increase in current account deficit and the extent of real exchange rate appreciation are still associated with the subsequent exchange rate impact.

Some additional results are worth mentioning. The one “macroeconomic fundamental” that shows up as significant in Table 6 is the inflation rate in the prior period. Inflation is, of course, one mechanism through which a country can experience real appreciation. So this coefficient may be picking up the same financial-market effects that we identified before.<sup>12</sup> That the indicator for the exchange rate regime enters negatively and significantly here is not surprising. It simply tells us that countries that pegged their currencies suffered less depreciation in the summer of 2013. More interesting will be whether they also saw less (or more) movement in their reserves and equity prices. Finally, there is some sign that countries which tightened their capital controls in the prior period experienced more currency depreciation when talk turned to tapering. It may be that these were the countries with the greatest perceived vulnerability (where policy makers responded by tightening controls – the change in controls was partly endogenous, in other words) and that perceived vulnerability translated into actual vulnerability. But if so, there is no sign that controls had a moderating effect starting in May.<sup>13</sup> Table 7 reinforces the conclusion that controls alone were ineffectual as a macroprudential device. There, where the dependent variable is weighted average of the change in the exchange rate and change in reserves, the capital control measures lose their significance.

Note also that the exchange rate regime has no separate significant impact on the change in reserves (although it does continue to register significantly in Table 7) and no significant impact on the change in the equity price index.

> 11 We also estimated the standardized coefficients to compare the coefficients of various variables quantitatively. These show that the coefficient of the size of financial markets is the largest followed by the coefficients of real exchange rate and current account deficit.

> 12 Note however that the real exchange rate remains significant in the equation where inflation is included, so this cannot be all that occurring.

> 13 It also could be that our measure of controls is imperfect – that we are not picking up further changes in their incidence and extent in the first four months of 2013, since we use 2010–12 data.

13
14 Figure 8: Impact of oil intensity of the economy and exchange rate on the change in vulnerability in the developing world by region, 1999–2008 <!-- Start of picture text --> East Asia and Pacific<br>100 Europe and Central Asia<br>90<br>90 86 Latin America and the Caribbean<br>Middle East and North Africa<br>80 75<br>South Asia<br>70 63 65 Sub-Saharan Africa<br>60 53 55<br>50<br>50 44<br>42<br>40<br>40<br>30 25<br>19 19<br>20 14 16 14<br>11<br>10<br>0<br>Declining oil intensity of GDP Offset exceeding 20% Appreciating exchange rate<br>% of countries in each region<br><!-- End of picture text --> _Sources:_ Table A1.4 and author’s calculations.

Corresponding results for 1999–2009 can be found in appendix 1, figure A1.1. The general trends were broadly comparable to those from 1999–2008.

# **3.2 Retail prices and pass-through coefficients**

Retail prices of gasoline and diesel in January 2012, converted to current U.S. dollars, are shown in figures 9–10. Predictably, the countries with the lowest prices were all major hydrocarbon producers, where upstream oil revenue is effectively used to cross-subsidize downstream fuel prices. República Bolivariana de Venezuela stands out as having exceptionally low prices, less than a tenth of the next lowest-price country. Malawi had the highest retail prices for both fuels, although this is in part because the value of its currency had been kept artificially high for quite some time; since the devaluation in May 2012, the currency has depreciated by more than two-thirds. Turkey, Uruguay, and Senegal are other developing countries with high retail prices. The United States had one of the lowest gasoline prices among the sampled countries, reflecting the economies of scale in the market, its high efficiency, and its low fuel taxation: at the national level, the excise tax on gasoline has been frozen at a mere 4.9 U.S. cents per liter since 1993, and total federal and state taxes and fees amounted to 12.9 U.S. cents per liter in January 2012 (API 2012). Further, January 2012 also saw one of the lowest margins (sum of distribution and marketing costs and margins) in a decade.<sup>3</sup>

> 3 Based on the difference between retail prices and spot prices for conventional regular gasoline using price data from the U.S. EIA available at www.eia.gov/petroleum/data.cfm#prices.
Table 7: The role of bank branches from the trade-partner country: IV, before and after the crisis

|Crisis? (Yes=1)|-0.645*|-0.707**|-0.595*|-0.707**|-0.594*|-0.707**|0.114|
|---|---|---|---|---|---|---|---|
||(0.349)|(0.347)|(0.350)|(0.347)|(0.348)|(0.347)|(0.367)|
|Financial|11.014***|9.998***|2.486|9.998***|10.147***|9.998***|2.685|
|development ( <sup>_Loan_</sup><br>_GV A_<sup>)</sup>|(1.922)|(1.890)|(1.921)|(1.890)|(1.882)|(1.890)|(1.914)|
|Financial<br><br>|-5.905*** <br>|-5.219***<br>|0.320<br>|-5.219*** <br>|-5.255*** <br>|-5.219***<br>|0.430<br>|
|development ( <sup>_Loan_</sup><br>_GV A_<sup>)</sup> <sup>_×_ Cri-</sup>|(1.551)|(1.531)|(1.545)|(1.531)|(1.523)|(1.531)|(1.550)|
|sis?||||||||
|TP bank? (Yes=1)||0.312***||||0.312***||
|||(0.085)||||(0.085)||
|TP bank? _×_||-0.436***||||-0.436***||
|Crisis?||(0.070)||||(0.070)||
|Share(TP branches)|||0.081***||||0.068***|
||||(0.007)||||(0.007)|
|Share(TP branches)|||-0.042***||||0.092***|
|_×_ Crisis?|||(0.004)||||(0.012)|
|Regional TP bank?||||0.312***||||
|(Yes=1)||||(0.085)||||
|Regional TP bank?||||-0.436***||||
|_×_ Crisis?||||(0.070)||||
|Share(Regional TP|||||-0.003**||-0.004***|
|branches)|||||(0.001)||(0.001)|
|Share(Regional TP|||||-0.006***||-0.033***|
|branches) _×_ Crisis?|||||(0.001)||(0.003)|
|Log(Sub-regional|-1.265***|-1.118***|-0.515**|-1.118***|-1.118***|-1.118***|-0.449*|
|GVA)|(0.251)|(0.247)|(0.245)|(0.247)|(0.246)|(0.247)|(0.244)|
|Log(Importer GVA)|0.541***|0.506***|0.782***|0.506***|0.509***|0.506***|0.723***|
||(0.094)|(0.094)|(0.100)|(0.094)|(0.094)|(0.094)|(0.099)|
|Log(Distance)|-1.306***|-1.302***|-1.287***|-1.302***|-1.296***|-1.302***|-1.255***|
||(0.164)|(0.164)|(0.163)|(0.164)|(0.164)|(0.164)|(0.162)|
|Log(Sub-regional|0.092***|0.089***|0.068**|0.089***|0.085***|0.089***|0.004|
|GVA) _×_ Crisis?|(0.027)|(0.027)|(0.027)|(0.027)|(0.027)|(0.027)|(0.029)|
|Log(Importer GVA)|0.066***|0.082***|0.064***|0.082***|0.083***|0.082***|0.080***|
|_×_ Crisis?|(0.007)|(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|
|Log(Distance)|-0.113***|-0.124***|-0.106***|-0.124***|-0.133***|-0.124***|-0.126***|
|_×_ Crisis?|(0.020)|(0.020)|(0.020)|(0.020)|(0.020)|(0.020)|(0.020)|
|Regional (exporter) FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country (importer) FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean dep. var.|1.02|1.02|1.02|1.02|1.02|1.02|1.02|
|F stat|75|63|67|63|64|63|61|
|Kleibergen-Paap LM test p-|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|value||||||||
|Hansen J stat p-value|0.000|0.000|0.137|0.000|0.000|0.000|0.300|
|R<sup>2</sup>|0.553|0.554|0.558|0.554|0.554|0.554|0.562|
|N|37440|37440<br>16|37440|37440|37440|37440|37440|

The dependent variable is the _Log_ ( _Exportit_ ) expressed in 100,000 constant USD. Robust standard errors clustered at the pair level are reported in brackets. The symbols ***,**,* represent significance at the 1%,
MALI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE But the adjustment of power tariffs present to cover costs represents an enormous challenge for policy makers in Mali. They currently average about $0.20 per kilowatt-hour—already among the highest in the region (figure 16). Regardless, they are not high enough to recover operational costs, let alone the full cost of power. Furthermore, given the pressing demand for power, the country is moving from low-cost hydro generation to more expensive thermal generation to meet short–run demand. It seems inevitable that tariffs will eventually need to be increased to sustain the sector’s financial viability and that subsidies and parallel financial schemes will need to be established to keep power affordable and the utility financially viable.

**Figure 16. Power prices in Mali are among the highest in Africa**

<!-- Start of picture text --> 50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Average Tariff<br>US cents per KWh<br>Liberia Chad Burkina Faso Cape Verde Senegal Mali Uganda Kenya Congo, Rep. Rwanda Benin Niger Cote d'Ivoire Namibia Madagascar Cameroon Ghana Botswana Mozambique Lesotho Tanzania South Africa Ethiopia Zimbabwe Nigeria Malawi DRC Zambia<br><!-- End of picture text --> Source: Briceño-Garmendia, Cecilia and Maria Shkaratan 2010.

The emerging policy challenge of underpricing is directly linked to the generation mix that determines such a high cost of power. Manantali demonstrates the importance of a diverse generation mix. While underpricing has remained significant during the last 10 years, during the period 2003–04 the availability of cheaper hydropower from Manantali kept costs low, and as a result the financial burden of underpricing fell slightly. Nevertheless, demand growth for power soon caught up with the additional generation capacity provided by Manantali. Three years after the plant began operation, the expansion of power provision from thermal-based generation made Mali even more vulnerable to the spike in international oil prices. Operational costs doubled between 2002 and 2008. Tariffs did not adjust accordingly, and consequently underpricing reached historical levels.

Underpricing is not the only inefficiency hampering the financial health of EDM. Distribution losses remain extremely high—over 22 percent of electricity production. The financial cost of distribution losses was almost 0.8 percent of GDP in 2008, or just below 25 percent of EDM revenues. Actions to address EDM’s distributional losses would relieve some financial pressure and contribute to reducing the utility’s supply costs.

31