rafmacalaba/datause-encoder-data huggingface dataset:
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**TABLE 3.1**

Gross Value of OutPUt in Brazil and Mexico (1975) **and** the USA (1977) (national currencies)

|**-------------------------------------------------------**|Brazil<br>1975<br>(million<br>cruzeiros)|**__--------------------_**<br>Mexico<br>1975<br>(million<br> <br>pesos)|USA<br>1977<br>(million<br>dollars)|
|---|---|---|---|
|Total Manufacturing Output|782,698.5|480,048.2ab|1,358,526.4|
|Grain Mill Products|7,428.1|9,123.1|5,698.1|
|Sugar & Sugar Products|12,142.4|6,596.3|2,964.0|
|Malt**ard**Malt Beverages|3,429.0|10,973.8a|7,151.9|
|Tobacco**and**Tobacco Products|6,118.4|3<sup>,</sup>847 <sup>.4a</sup>|9,050.6|
|Textiles|29,420.5|17,909.9|22,486.5|
|Footwear**and**Leather Products|10,291.8|6,447.3|6,874.7|
|Pulp and Paper|10,731.1|14,607.1|21,828.7|
|Soap and Detergents|3,626.9|6,335.6|6,087.2|
|Paints|7,261.2|3,460.2<br>|6,629.7<br>|
|Agricultural Fertilizers|12,096.1|**4 '865-7ab**|<br>7,151.7|
|Petroleum Refining and Products|58,024.6|28,463.4|93,333.5|
|Tires and Inner Tubes|7,209.0|4,969.8|8,971.0|
|Cement|5,688.3|5,648.6|3,042.3|
|Bricks|6,041.1|1,636.9|1,637.4|
|Iron and Steel|51,525.3|32,836.9|50,582.0|
|Radio and TV Receivers|9,003.6|4,854.5|5,732.6|
|Motor Vehicles|57,791.5<br>---------------------------------|39,425.6<br>----|117,746.5|
|Total in our sample|297,828.9|202,002.0|376,968.4|
|**as**%of Total Manufacturing Output|38.05|42.08|27.75|

**a)** indirect taxes and subsidies are deducted (see table 2.3).

b) includes 25,004.7 million pesos (excl. indirect taxes **and** subsidies) for petroleum ref ining, which are not shown in the census Resumen General but taken from Sistema de Cuentas Nacionales de Mexico. Source: Figures for Brazil from Censo Industrial, figures for Mexico frcim Resumen General (except for figures mentioned under footnotes a) and b)), and figures for USA from the 1977 Census of Manufactures.
# **4.2 Capital Flow Cycles and Capital Flow Measures**

Next, we relate the use of various policy tools to the pace of capital inflows or outflows and during the episodes of sudden stops or sudden surges in capital flows. Following Eichengreen, Gupta and Masetti (2017) we identify episodes of sudden stops when capital inflows (FDI, portfolio equity and debt, and other inflows by nonresidents) as percent of trend GDP decline below the average of the previous 20 quarters by at least one standard deviation, when the decline lasts for more than a quarter, and when flows are two standard deviations below their prior average in at least one quarter.<sup>22</sup> The sudden-stop episodes end when capital flows recover at least to the level of the prior mean minus one standard deviation.

Similarly, we identify surges in capital flows when total capital inflows as percent of trend GDP exceed the average of the previous 20 quarters by at least one standard deviation, when the increase lasts for more than one quarter, and when flows are two standard deviations above their prior average in at least one quarter. The surge episode then ends when flows decline below their prior mean minus one standard deviation. Altogether we identify 25 episodes of sudden-stops, with an average duration of 2.6 quarters; and 55 episodes of surges with an average duration of 3.9 quarters.

We regress the use of various policy instruments on dummies for sudden stops or surges that take a value 1 for the country-quarter observations when a country experienced a sudden stop or surge, respectively, and 0 for all other observations. Since the regression of CFMs on episodes related to capital flows may be prone to endogeneity, we lag sudden stops and surges by one quarter.<sup>23</sup> Our results suggest that CFMs are not used in response to the sudden stops of capital inflows. The coefficients for all four types of restrictions are insignificant in Table 9. However, policy makers use CFMs to modulate the impact of sudden surges in capital inflows. Table 10 shows that restrictions on capital outflows by residents are significantly liberalized when countries are confronted with a surge in capital flows, possibly as a way to mitigate the macroeconomic impacts of large inflows. Capital flow measures on other capital flows are not changed in response to surges.

> 22 Data for capital flows are from the IMF’s Balance of Payments Statistics. We identified sudden stops for total capital flows. One could identify sudden stops only for non-FDI inflows as in Eichengreen and Gupta (2016). Results are unaffected if we use this criterion instead.

> 23 Some may argue that the endogeneity of capital flows should not be a concern. Rey (2015) argues that capital flows to emerging markets are driven primarily by global liquidity and financial conditions, and not by their domestic conditions. Eichengreen and Gupta (2016) confirm this in the period since 2002.

20
institutionalized care<sup>12</sup> into the model, net income of a single parent increases by only 16.4 percent of MW when working at an MW job compared to inactivity. This estimation does not yet account for other costs of full-time work such as transportation costs. All in all, the incentive to start working is noticeably low and does not encourage joining the labor force.

**Figure 4: Change in income accounting for childcare costs**

<!-- Start of picture text --> 120<br>110<br>Total income<br>100<br>+16.4?%<br>90<br>80<br>70<br>60<br>50<br>40 Transfers<br>30 income<br>-52.2?%<br>20<br>10<br>0<br>Earnings Composition before Composition at MW Composition of Earnings Lost<br>ITpl FBpl HBpl SCpl SApl 500+ Net Labor Income Child Care Costs<br>% of Gross Minimum Wage<br><!-- End of picture text --> _Source: Source:_ Authors elaboration based on TMB for Poland 2016. _Note:_ The figure represents earnings’ composition before a single parent is working (left) and after taking a job at MW (center). Net labor income represents total labor income minus childcare costs, income tax, and social contributions.

> 12 Numbers taken from OECD calculations on the average cost for one child in public nursery. Because many have to resort to private childcare, the actual average price in Poland for childcare is likely to be higher. After talks with local think tanks and academics, the assumed cost for 1 child in urban areas is about PLN 1,000 per month. If these costs are included in the analysis rather than the estimates used by the OECD, the incentive to join the workforce would be completely eliminated. See Box 1 for a detailed explanation.

11
that of individuals with only secondary schooling. As an example, 2.6 percent of Cambodians aged 25 and above with primary education live abroad, compared to 5.9 percent of those with secondary education and 18.3 percent of those with tertiary education.

Table 1 uses data from the 2008 American Community Survey to show the top 10 occupations for tertiary-educated developing country migrants in the United States according to their level of tertiary education.<sup>1</sup> In order to focus only on migrants who received at least their undergraduate tertiary education in their home country, we consider only individuals who migrated at age 25 or older, with the sample size in the table showing the number of developing country migrants in this survey who meet these criteria. The majority of skilled migrants come from a variety of professions, with computer specialists, accountants, managers, and among higher education levels, scientists and academics, the most common.

Much of the popular discussion and debate about brain drain concerns the migration of doctors and nurses, but as Table 1 shows, while medical professionals are indeed among the most common occupations, they account for only 12-15 percent of educated immigrants. Health professionals are also the minority of tertiary-educated immigrants in other countries. In the 2001 Canadian Census, health professionals constitute 5.7 percent of working individuals who arrived in Canada at age 25 or older and have bachelor‘s degrees or higher, 3 percent of those with a masters degree or higher, and 3.9 percent of those with Ph.D.s. In Canada, scientists, teachers and professors are the most common occupations, accounting for 45 percent of those with masters degrees or above, and 65 percent of those with Ph.D.s. Similarly, according to the 2001 United Kingdom Census, health professionals are also only 6.9 percent of the tertiary-educated migrants from developing countries who came to the United Kingdom after age 24, with this number increasing to 10 percent if one also includes non-professional positions in healthcare occupations. In South Africa, the 2001 Census shows health professionals to be 8.2 percent of tertiary-educated immigrants from developing countries who arrived after age 24, and 14.1 percent of those with masters degrees or higher.

> 1 We take ―developing country‖ to refer to all countries which are not classified by the World Bank as ―high income‖ -- that is, which have 2009 Gross National Income per capita of $12,195 or lower.

4
**Table 3. Current Smoking and Smoking Cessation for Individuals Born between 1937 and 1956, Ordinary Least** **<u>Square Results</u>**

|Dependent<br>variable|1<br>smoking<br>now|2<br>stopped<br>smoking|3<br>smoking<br>now|4<br>stopped<br>smoking|5<br>smoking<br>now|6<br>stopped<br>smoking|7<br>smoking<br>now|8<br>stopped<br>smoking|
|---|---|---|---|---|---|---|---|---|
|Years of<br>education|–0.04***|0.041***|–0.047***|0.05***|–0.04***|0.041***|_|_|
|above|||||||||
|High school|[–10.53]|[19.35]|[–14.12]|[19.87]|[–11.08]|[20.11]|_|_|
|College and|||||||–0.173***|0.178***|
|above|**_**|**_**|**_**|**_**|**_**|**_**|||
||**_**|**_**|**_**|**_**|**_**|**_**|[–11.68]|[18.56]|
|Male|<br>0.102***|<br>0.048***|<br>0.073***|<br>0.063***|<br>0.079***|<br>0.051***|0.077***|0.052***|
||[14.56]|[10.56]|[12.08]|[14.16]|[11.16]|[9.79]|[10.89]|[9.59]|
|Trendmale|–0.001**|–0.003***|–0.0002|–0.003***|–0.0003|–0.003***|–0.0003|–0.003***|
||[–2.14]|[–7.44]|[–0.53]|[–9.41]|[–0.84]|[–7.62]|[–0.69]|[–7.68]|
|Log family<br>income in|–0.076***|0.084***|_|_|–0.077***|0.084***|–0.084***|0.091***|
|1983$|||||||||
||[–27.94]|[25.10]|_|_|[–26.59]|[25.37]|[–22.52]|[28.31]|
|Vietnam veteran|_|_|0.061***|–0.004|0.063***|–0.005|0.059***|–0.001|
||_|_|[5.83]|[–0.60]|[5.94]|[–0.73]|[5.59]|[–0.24]|
|Black|–0.002|–0.051***|0.036***|–0.095***|–0.0009|–0.051***|–0.0005|–0.052***|
||[–0.32]|[–8.66]|[5.65]|[–16.69]|[–0.13]|[–8.75]|[–0.06]|[–9.05]|
|Other race|–0.07**|–0.014|–0.051|–0.039**|–0.066**|–0.015|–0.068**|–0.01|
||[–2.63]|[–1.10]|[–1.60]|[–2.71]|[–2.34]|[–1.12]|[–2.54]|[–0.78]|
|R-square|0.068|0.09|0.055|0.074|0.069|0.09|0.065|0.08|
|Observations|74,810|40,753|83,069|45,108|73,952|40,319|73,957|40,323|

***, **, * denote statistical significance at the 1%, 5% and 10% confidence levels, respectively.

_ indicates that the variable was not included in the regression.

_Note_ : Analysis from smoking histories from the 1983, 1985, 1987, 1988, 1990, 1991, 1992, 1994, and 1995 National Health Interview Surveys for individuals age 25 and above at the time of the survey. Individuals born between 1937 and 1956. Linear regression model, where the dependent variable is current smoking behavior (odd columns) or smoking cessation for individuals who ever smoked (even columns). Robust t-statistics, clustered by birth cohort and gender, in square brackets. Birth cohort, age at interview and survey dummies included in all specifications. Trendmale, a dummy for male multiplied by a yearly trend where born in 1937=0, accounts for the trend in the difference in smoking behavior between males and females. See note 20 for details on the family income variable. Sample sizes vary across specifications as the Vietnam veteran status and the income variable might have been missing.

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7 hydraulics, port, airports, and modal integration. Moreover, they assume that investments are used efficiently. Ignoring the important fact that in the telecom sector progress continues to cut costs dramatically, these estimates are thus probably lower bounds of the needs. Moreover, the assumption that investments are used efficiently is a strong assumption for most countries, industrial or developing.<sup>6</sup>

**Table 1: Expected annual investment and operations and maintenance needs, 2005-2010** (percentage of GDP)

|**Country group**|**Investment needs**|**O&M needs**|**Total**|
|---|---|---|---|
|**Low income**|3.5 - 4.5|4.0 - 4.5|7.5 - 9.0|
|**Middle income**|2.8 - 3.8|2.7- 3.2|5.5 – 7.0|
|**Developing**<br>**county average**|3.2 – 4.2|3.3 - 3.5|6.5 – 7.7|

Note : These are preliminary estimates, currently being updated. Source: Briceno and others 2004 The most interesting fact emerging from these rough orders of magnitude is that there are large differences across country income groups, with needs ranging from as much as 9 percent of GDP for low-income countries to 5.5 percent for middle-income countries, with an average of about 7.1 percent for all developing countries.<sup>7</sup> Combining this fact with the lessons from the growth convergence literature, it is not difficult to see why some academics, some policymakers, and many NGOs make a case for an extra co-financing effort by the international community in favor of the lowest-income countries to ease their catching up and accelerate the reduction in poverty.<sup>8</sup>

# **Public versus private financing revisited**

During much of the 1990s infrastructure policies in developing countries were based on the assumption that financing was going to be rebalanced from its two traditional sources—the public sector (government or public utilities) and official development assistance—toward a third source: the private sector. Although there was a rebalancing, it did not go as far as many had hoped, particularly in the lowest-income countries and in the poorest regions of middle-income countries. Indeed, during the 1990s the public sector financed 70 percent, the private sector 20–25 percent, and official development assistance 5–10 percent of infrastructure spending. These estimates were

> 6 There is a quickly growing literature on the need to cut costs and improve efficiency in the delivery of public services in general and in infrastructure in particular. For a recent overview, see Coelli and others (2004) or Kessides (2004).

> 7 These are rough estimates

> 8 Although the lack of good data on investment needs reflects a lack of accountability of international and local communities in terms of the effectiveness of the resources allocated to these sectors and some observers question the size of the request for new funding, raising the concern for whit elephants.  In that sense, infrastructure significantly lag behind health and education in terms of possibilities of measuring the effectiveness of aid in improving the fate of the poorest.
24

**30. The impact of deleveraging could be particularly significant for those countries that lack well-developed capital markets and alternative sources of stable nonbank financing to substitute for bank credit.** The CEE region, in particular, has been experiencing a tightening of credit standards and a sharp slowdown in loan growth, given strong ownership links with Euro Area banks, an overreliance on nondeposit funding to finance credit expansion (figure 17f), and relatively shallow capital markets. Constrained credit could particularly affect SMEs because only the largest companies would likely be able to deflect some of the impact by relying on bond market financing.

**Figure 18. Foreign Bank Ownership, 2010**

<!-- Start of picture text --> AS CEE MENA SSA WH AE OA<br>Source: BCL Survey<br>KORSGPPAK IDNMYSPHL IND THA BTN ESTSVK BIH HRVROM HUNBGRLTU YUGLVA ARM POLUKR MDA SVNBLR RUSTUR EGY LSOMOZ NAMUGASYC MUSSLEKEN ZAF SLV MEX PANURY PER PRYCHL CRIARGCOL BRAGTMDOM LUX MLT FIN IRL BEL CYPNORGRC DNKGBRITA AUT FRACHE DEUESPISR ISL AUS<br>100<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text --> Source: World Bank. AS: Asia; CEE: Central and Eastern Europe; MENA: Middle East and North Africa; WH: Western hemisphere; AE: Advanced economies; OA: Other advanced economies.

# **V. IMPACT ON EMERGING MARKET CREDIT CONDITIONS**

**31. Euro Area problems are already producing ripple effects around the globe.** Recent lending surveys by the Institute of International Finance (IIF) suggest that emerging markets have also been facing tighter credit conditions since the third quarter of 2011. Moreover, survey respondents explicitly stated that they had been adversely affected by the fallout of the euro debt crisis. Although less drastically than in the last quarter of 2011, the deterioration in lending conditions has continued for the third consecutive quarter in 2012:Q1 (figures 19a and 19b). While tighter underwriting standards, reduced loan demand, and rising nonperforming loans (NPLs) have all contributed to the deterioration of credit conditions in emerging markets, the most significant factor appeared to be an erosion of local and global funding conditions (most markedly in CEE, Africa, and Middle East).<sup>17</sup>

> 17 Of the banks surveyed, 63 percent of the banks in emerging Europe reportedly acknowledged a tightening of credit standards due to the financial strains in the euro area in the last quarter of 2011 (IIF 2012).
level.

We next investigate whether distance to the closest population center has an effect on rural labor productivity. We use again data from Uganda and take different distances of the household enterprise to the closest population center. We compare the density of labor productivity by various distance categories from these population centers. We expect, following Christiaensen et al. (2013), that proximity to a population center is associated with higher productivity.

Figure 2b confirms again our expectations. Enterprises of households located up to 10 km from a population center are the most productive ones, followed by enterprises in households residing up to 25 km and 50 km away, respectively. If households are located more than 50 km away, the results show a significant decline in labor productivity. These findings support the idea that rural secondary towns and cities, by providing links between rural areas and major cities, may play an important role in the structural transformation and poverty reduction of agrarian societies like Uganda.

**Figure 2:** Productivity Dispersal - by Region and Distance

<!-- Start of picture text --> (a) Uganda - by geographical region (b) Uganda - by distance (in km)<br><!-- End of picture text --> <!-- Start of picture text --> Kernel density estimate Kernel density estimate<br>0 5 10 15 20<br>log of enterprise productivity 0 5 10 15 20<br>Kampala Central Eastern log of enterprise productivity<br>Northern Western <10 km 10-25 km 25-50 km >50 km<br>kernel = epanechnikov, bandwidth = 0.5556 kernel = epanechnikov, bandwidth = 0.4566<br>.3 .3<br>.2 .2<br>Density<br>Density<br>.1<br>.1<br>0<br>0<br><!-- End of picture text -->

# **4.2.3 Determinants of Non-Farm Enterprise Productivity**

We use a Heckman selection model to estimate the determinants of labor productivity. We implement this model using data of the whole sample, and add a binary variable to account for the household’s location in a rural or urban area. Table 6 reports the regression results, including the selection and outcome stage.

The first stage, the selection into non-farm entrepreneurship, is reported in the lower part of the table. For the selection stage we make the assumption that the number of adults in a household selects households into this sector, as surplus labor is allocated into these enterprises. The results of the second stage, the outcome stage, reported in the upper part of the table, can be interpreted as though we observed data for all households in the sample.

The individual characteristics in the selection stage are taken from the household head, 21
# **6 Mechanisms**

In this section, we explore potential mechanisms driving the effects of the survey methods presented in Section 5. We test if the LOA and ESR can improve the reporting of women’s and young men’s labor indicators based on prevalence of peers’ informal employment or by social norms related to domestic obligations. Although our findings are suggestive, we believe they warrant further exploration, since they can serve as a starting point for new avenues of methodological and policy-relevant research.

## **6.1 Prevalence of Local Informality**

Women and young men are more likely to engage in informal work in developing countries (ILO 2018). For example, in low- and middle-income countries, 92% of women are engaged in informal employment compared to 87.5% of men. In El Salvador, the gap is wider: 72% of women are engaged in informal work relative to only 56% of men (UN Women, 2023). Standard labor surveys fail to accurately measure informal work or employment because when respondents are asked about income generating activities, they automatically think only about formal employment or work. In contexts where informal employment is high, respondents’ reference of labor market indicators may be even more biased. For example, a proxy respondent in a community with high levels of informality may be more likely to report that a woman or youth living in his household are not working because the respondent observes activities that he does not categorize as work or employment.

To test this hypothesis, we first estimate a measure of prevalence of peers’ informal employment at the community level to which each respondent is exposed. This measure is defined as the share of employment in the informal sector<sup>16</sup> in each EA by sex, excluding the respondent’s employment status.<sup>17</sup> Within our sample, the average informal employment to which the respondents are exposed at the EA-level is 76%, with an average of 70% for men and 84% for women (Table A4). To ease the interpretation of these results, we standardize this

> 16 In El Salvador, employment in the informal sector is defined using the characteristics of the entity, enterprise, or employer of the employed population. Thus, a person is employed in the informal sector if any of the following conditions are true: a) the employer does not keep accounts or only keeps accounts for personal use; b) the employer is not registered at the national registry; c) the employer is unincorporated (business and owner are not separate legal entities); d) the place of work is without fixed premises; and e) the employer has fewer than five employees.

> 17 By using the average at the EA-level and excluding the respondent, we aim to account for potential endogeneity.

16