rafmacalaba/datause-encoder-data huggingface dataset:
Gender is also an important dimension of inequality in Croatia. Women have a lower participation rate, a lower employment rate/ a higher unemployment rate, and lower monthly earnings than men (table 4).

**Table 4: Employment rate, unemployment rate and monthly earnings by gender in Croatia (2002-2004)**

||Labor force|||Monthly earnings|
|---|---|---|---|---|
||participation rate|Employmentrate|Unemploymentrate|(kuna)|
|Men|71.91%|61.35%|12.96%|3514|
|Women|58.63%|47.80%|16.36%|2978|

# **1.3 Disparities in labor market performance at the regional and county levels**

The variation of employment rate, unemployment rate, and earnings level across age groups, education groups, and gender suggests that the opportunities in labor market that an individual faces is associated with his or her individual characteristics. However, the disparities in employment and earning, as those in GDP per capita, are also large across counties / regions in Croatia.

Following the regional divides in the 1998 Living Standards Assessment (World Bank, 2000), the 21 counties in Croatia is grouped into 5 regions for analytical purposes as follows<sup>14</sup> : Region 1 – Central Region: Krapina-Zagorje, Sisak-Moslavina, Karlovac, Varazdin, Koprivnica-Krizevci, Bjelovar-Bilogora and Medimurje.

Region 2 – Eastern Region: Virovitica-Podravina, Pozega-Slavonia, Slav. Brod-Posavina, Osijek-Baranja, and Vukovar-Sirmium.

Region 3 – Zagreb Region: Zagreb County and Zagreb City. Region 4 – Adriatic North Region: Primorje-Gorski kotar, Lika-Senj, and Istria. Region 5 – Adriatic South Region: Zadar, Sibenik-Knin, Split-Dalmatia and DubrovnikNeretva.

The human capital endowment, measured by years of school or highest attained level of schooling<sup>15</sup> , varies across regions. The graph 3 shows that the Central Region and Eastern Region have a higher percentage (about 40%) of working-age population with “no school or uncompleted basic education” or “basic education” compared with the other three regions (about 25%). Zagreb region has a disproportionately high percentage of workingage population with university education and above. Both indicators of education, “level of schooling” and “years of school”, indicate that the Central Region and Eastern region are the least well endowed in human capital.

> 14 See Appendix.

> 15 In the following sections, we simply call the “highest attained level of schooling” as the “level of schooling”.

7
for the middle-income countries but only to 30 percent for low-income countries. Thus, a “normal” middle-income country is much more likely to be net food exporter when compared to a “normal” low-income country.

Of the 42 low-income countries that are net food importers, 5 are oil exporters and 7 are conflict countries. Excluding these, 30 low-income countries are net food importers.

Table 2 shows the net exports of raw food, defined as meats and dairy products, grains and cereals, and fruits and vegetables, and the ratio of net export balances to their total imports for the selected 4 two-year periods for the groups defined in Table 1. Annex tables 1 and 2 show imports, exports, food trade balances, for individual countries, and for more detailed product groups.

**Table 2: Trade Balance for Raw Food by Income Group Country**

||Net<br>|Exports/Im<br>|ports($mil<br>|lion)<br>|Net Expo<br>|rts as % of<br>|All Goods<br>|Imports<br>|
|---|---|---|---|---|---|---|---|---|
|CountryGroup (No. of Country)|1980/81|1990/91|2000/01|2004/05|1980/81|1990/91|2000/01|2004/05|
|World (196)|-8,821|1,828|8,042|3,975|-0.6|0.1|0.1|<br>0.0|
|Industrial Countries (33)|8,838|7,624|11,809|3,752|0.8|0.3|0.3|<br>0.1|
|Middle-income, All (105)|-14,052|-4,802|-983|4,205|-3.4|-0.6|-0.1|<br>0.1|
|Mid Income--Oil Exporters (20)|-4,686|-4,320|-7,163|-11,234|-4.4|-4.5|-3.6|<br>-2.8|
|Mid Income--Civil Conflict (4)|-51|-123|-686|-793|-0.4|-0.7|-4.2|<br>-3.0|
|Small Island Economies (30)|-179|-147|-274|-462|-2.9|-1.6|-1.5|<br>-1.8|
|Other Middle-income (51)|-9,135|-213|7,140|16,693|-3.1|0.0|0.5|<br>0.7|
|Low-income, All (58)|-1,122|-393|13|-582|-2.0|-0.6|0.0|<br>-0.2|
|Low-income--Oil Exporters (7)|-679|-320|-898|-1,634|-3.4|-2.8|-4.8|<br>-4.6|
|Low-income--Civil Conflict (8)|-115|-162|-298|-437|-2.2|-2.5|-4.0|<br>-4.0|
|Other Low-income(43)|-329|88|1,209|1,489|-1.1|0.2|1.1|<br>0.7|

Notes: Food is defined as raw food in SITC Revision 2, excluding all cash crops, processed food and seafood (see detailed classifications in Appendix Table 1).

The classification of country groups is provided in Appendix Tables 2 and 3.

Source: Based on partner data from UN COMTRADE Statistics.

Few points are stand out in table 2. First, overall, industrial, and middle-income countries, as a group, are net food exporters. Their surpluses, however, as a percentage of their imports are small. For middle-income countries, the group average masks large net imports by oil exporters, and large net exports of the other middle-income country group. Countries in conflict have a deficit in food account but this is only about $800 million. Similarly, small island economies group contain a lot of countries that have food deficits, but their deficit is only about $500 million and constitutes only 1.8 percent of their imports.

Other middle-income countries, that are excluding the oil exporters, countries in conflict, and small island economies, have a large export surplus. This surplus is caused primarily by net exports of fruits and vegetables with deficits in meats and dairy, and grains. This group is highly heterogonous within itself. The largest five net importers in this group 7
production with market work in nonagricultural occupations; these effects are held to be particularly strong for married women.

In later stages of development, women’s education starts to catch up to men’s, their earnings capacity increases and they gain access to socially acceptable types of work, especially if demand for white-collar workers increases with the expansion of the services sector. This will result in higher FLFP,<sup>3</sup> but country-specific labor demand conditions clearly play a role in this process. The increase in FLFP could depend on growth in employment opportunities of the kind deemed appropriate for educated women, relative to growth in the educated working age population. Boserup (1970) describes how the feminization of clerical jobs proceeds very slowly when the number of educated men is in excess of demand for clerical workers. In those cases, there is likely to be considerable resistance against women’s employment in white-collar jobs, as this would reduce the opportunities for men (Boserup 1970: chapter 7). How the education-labor force participation link evolves over time will thus depend on the structure of labor demand growth in the economy and the status associated with different types of work.<sup>4</sup> If female labor mobility is limited, as is the case in India, the growth in desirable jobs relative to the educated population can generate local mismatches with impacts on female labor force participation rates.

One might further hypothesize that similar factors produce a U-shaped relationship between economic or educational status and women’s labor force participation at a given point in time within a country—as is indeed observed in India. Among the poorest with no or very little education, women are forced to work to survive, while among the very highly educated, high wages

> 3 Over the course of development, changes in circumstances may also be accompanied by changes in women’s behavior, i.e., the degree to which wages, income, and social restrictions affect FLFP. As Goldin (1990) describes the history of women’s work in the United States, economic development is reflected in an increasing own wage effect while women’s responsiveness to other family income declines. Blau and Kahn (2007) and Heim (2007) find similar evidence for women in the United States.

> 4 A gender assessment for Pakistan (World Bank 2005) shows that the increase in urban female labor force participation in the 1990s was driven by an increased demand for teachers. But overall, still a much higher proportion of urban men than women are engaged in white-collar jobs.

6
Joseph-Raji,Gloria Aitalohi; Saldarriaga Noel,Miguel Angel; Hernandez Ore, Marco Antonio; Timmis, Emilija; Ogebe, Joseph Orinya; Rostom,Ahmed Mohamed Tawfick; Cortes, Mariano; Kojima, Masami; Lee, Yue Man; Okou, Cedric Iltis Finafa; Jenq, Christina; Lain, Jonathan William; Vishwanath, Tara; Kimani, Elijah Gatuanjau; Awasthi, Rajul; Semikolenova, Yadviga Viktorivna; Sharma, Arsh; Rana, Anshul; Engel, Jakob; Mohammed, Muderis Abdulahi; Okunmadewa, Foluso; Kamphuis, Bertine; Belay, Tekabe Ayalew; Lothrop, Sean Craig; Agu,Chuka J. 2021. Resilience through Reforms (English). “Nigeria Development Update” Washington,D.C.: World Bank Group. <u>http://documents.worldbank.org/curated/en/389281623682704986/Resilience-through-Reforms</u> Lain, Jonathan and Vishwanath, Tara, 2021 “Tackling poverty in multiple dimensions: a proving ground in Nigeria?” World Bank Data Blog, January 2021. https://blogs.worldbank.org/opendata/tackling-povertymultiple-dimensions-proving-ground-nigeria Lakner, Christoph, Daniel Gerszon Mahler, Minh C. Nguyen, Joao Pedro Azevedo, Shaohua Chen, Dean M. Jolliffe, Espen Beer Prydz and Prem Sangraula., "Consumer Price Indices used in Global Poverty Measurement ", World Bank Group Global Poverty Monitoring Technical Note, no. 4., September 2018.

NBS (National Bureau of Statistics, Nigeria). 2020. “2019 Poverty and Inequality in Nigeria: Executive Summary.” May, NBS, Abuja, Nigeria.

Newhouse, D., S. Shivakumaran, S. Takamatsu, and N. Yoshida. 2014. “How Survey-to-Survey Imputation Can Fail.” Policy Research Working Paper No. 6961, World Bank, Washington, DC.

Prydz, Espen Beer, Dean Jolliffe, Christoph Lakner, Daniel Gerszon Mahler and Prem Sangraula., "National Accounts Data used in Global Poverty Measurement", World Bank Group Global Poverty Monitoring Technical Note, no. 8., March 2019. Prydz, Espen Beer, Dean M. Jolliffe, Christoph Lakner, Daniel Gerszon Mahler, and Prem Sangraula. “National Accounts Data used in Global Poverty Measurement.” Global Poverty Monitoring Technical Note 8. Washington, DC: World Bank. 2019. <u>https://ideas.repec.org/p/wbk/wbgpmt/8.html.</u> Ravallion, M., 2003. “Measuring aggregate welfare in developing countries: How well do national accounts and surveys agree.” The Review of Economics and Statistics, 85(3), pp.645- 652.

Takamatsu, Shinya; Yoshida, Nobuo; Ramasubbaiah, Rakesh; Fatima, Freeha. 2021. “Rapid Consumption Method and Poverty and Inequality Estimation in South Sudan Revisited.” Global Poverty Monitoring Technical Note;No. 18. World Bank, Washington, DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/36540 License: CC BY 3.0 IGO.” 27
9 000 4 sufficient efficiency to ensure that worthwhile investment projects are financed out of domestic savings, the economy must look to foreign sources of financing.

- In a world of perfectly functioning international capital markets such sources would be readily forthcoming. But our analysis suggests that the foreign sector does not smoothly provide needed financing. A more realistic description would be to say that foreign sector financing is determined by factors other than domestic net financing needs.

Furthermore, a shortfall in foreign finance, or an increase in government borrowing, typically forces the business sector to reduce its investment. On average, households and the government do little to absorb external financing shocks by adjusting their net lending.

= The vulnerability of the business sector to the availability of external funds points to the need for improvements in the effectiveness with which the financial sector in developing countries can attract domestic savings in order to finance worthwhile investment projects. This will require that savers are assured of a fair share of the returns to investment projects. This in turn requires realistic interest rates for savers as well as the developmenat of instruments which can allow savers to share in high-risk high-yield projects.

The paper is organized as follows. Section 2 clarifies some terms and in particular explains what a complete set of flow of funds accounts looks like. Some of the serious data pitfalls which exist are noted.

Section 3 provides an analytical framework for the consideration of flow of funds data. (The reader who is mainly interested in the quantitative findings may proceed directly to Section 4.) Some theoretical propositions concerning the interaction between the savings and surpluses of different 2
jb(e<sup>i!</sup> )j<sup>2</sup> = 0 otherwise. The contemporaneous correlation, denoted �ij; between variables i and j can be estimated by It is widely acknowledged in the economics profession that government spending as a fraction of GDP ought to remain constant over the cycle (Alesina, Campante and Tabellini, 2008). Following such a rule would lead to an implicit adoption of a counter-cyclical ...scal policy rule. Figure 3 reports the rolling correlation between government spending as a fraction of GDP and real output growth, along with the one-standard deviation credible sets. The estimates show that ...scal policy in Malaysia has become increasingly pro-cyclical over the last 25 years. This fact appears consistent at all three business cycle frequencies. The estimates show that during the 1980s and early 1990s ...scal policy became progressively more procyclical, although not to any signi...cant degree. Following the Asian ...nancial crisis, however, the median estimate shows the procyclicality of government spending rose. By 2007 the correlation coe¢ cient had reached 0:5. The estimates imply that government spending has grown in excess of real output growth since the Asian ...nancial crisis in 1997/98.

# 5 State-dependent ...scal multipliers

The response functions are calculated using Koop, Pesaran and Potter (1996) nonlinear impulse response functions are de...ned as: where h is the forecast horizon, yt+h contains the forecasts of the endogenous variables at horizon h, t represents the current information set and "t is the current disturbance term. In the linear case, the impulse response functions are time invariant, so that t = 0. In the non-linear case the impulse response functions are conditioned on a particularly history. In the linear case, the expectation of the path of output following the government spending shock, conditional on future shocks, is equal to the path of output when future shocks are set to their expected values. Therefore, future shocks can be set equal to zero for convenience. This is 12
Second, even if there was only one candidate dimension of the business environment, for its effect on firm-level productivity to be estimated requires that it vary at the level of the firm. This is clearly not the case for a number of elements of the business environment such as macroeconomic stability and the court system.

Third, in cases where there is variation at the level of the firm, its effect on performance can be estimated only if there is a way of isolating the quality of such a firm-level micro-business environment from the firm’s characteristics.

A simple example illustrates the problems. It is plausible that a higher productivity firm will attract more attention from rent-seeking bureaucrats; hence, a naïve regression of firm performance on the number of visits would produce a positive estimate of the effect of bureaucratic attention on performance. The major research strategy adopted to get around this problem and uncover the effect of inspections on firm performance separate from the effect of firm performance in attracting inspections has been to use the so-called “cell averages” approach. Instead of using the firm’s own report on the number of visits, the average number reported by firms with similar characteristics (such as firm size, industry and location) is used. The cell averages approach is one of the strategies for addressing the endogeneity bias problem recommended by Dethier et al. (2008, 2010) in their survey paper.

However, the cell averages approach runs into an immediate problem. Unobservable characteristics that cause or are correlated with higher productivity of the firm in question will also tend to raise the productivity levels of the other firms in the cell (e.g., a local demand or industry-specific shock will boost capacity utilization and performance). This will tend to raise the prevalence of inspections, expenditure on abatement such as bribes and the seriousness of this element of the business environment reported by the firm. This is an example of Manski’s (1993) “reflection problem”.

The econometric challenge in trying to tease apart differences in the institutional environment faced by firms in a _single_ country while avoiding the problem of endogeneity, is too much for the data to bear, which is why a recent careful study of 5
<!-- Start of picture text --> 2006 2007 2008 2009 2010 2011 2012 2013 2014<br>year<br>2006 2007 2008 2009 2010 2011 2012 2013 2014<br>year<br>Sample channels (N = 448) Non-sample channels (N = 1381)<br>.08<br>Head - Tail DPR .06.04<br>.02<br>.12<br>.1<br>.08<br>Tail / Head DPR < 0.5 .06<br>.04<br><!-- End of picture text --> Figure 5: Tail shortage by year in Punjab: 2006-14 _Notes:_ Top panel shows average tail shortage, _DPRit_<sup>_H−DPR_</sup> _it_<sup>_T_andbottompanelaverageextremetail</sup> shortage, indicated by whether _DPR_<sup>_T_</sup> _/DPR_<sup>_H_</sup> _<_ 0 _._ 5 _.,_ for _kharif_ 2006-2014. Only data from 2011 (dashed vertical line) onward are used in the empirical analysis.

most important _kharif_ (summer) season, which runs from mid-April to mid-October. During _rabi_ season, from November to March, 42% of channels in Punjab are dry. Letting _d_ index days and _t_ index year, define for _j_ = _H_ ( _ead_ ) _, T_ ( _ail_ ), where _Q_<sup>_j_</sup> _id_<sup>isdailydischargeatposition</sup><sup>_j_ofchannel</sup><sup>_i_and</sup><sup>_Q_ ̄</sup><sup>_j_</sup> _id_<sup>isthe</sup> corresponding authorized daily discharge.

Tail shortage, _DPRit_<sup>_H−DPR_</sup> _it_<sup>_T_,is a measure of water theft,albeit a noisy one.As noted</sup> earlier, the extent to which tail outlets are deprived of water relative to their endowment (given discharge at the head) may also depend on exogenous factors such as flow _variability_ into the channel. At any rate, tail shortage averages 0.053 (or 8.2% of mean tail DPR) across the 448 sample channels and across all years of discharge data (2006-14); the corresponding 22
people per fiber optic line kilometer and each of the industry intensity of internet use measures used in Table 2, in addition to the interaction term using the number of internet users per capita _._ The results are shown in Table 7.<sup>28</sup> The estimates show that firms’ export participation, export intensity, and real exports are significantly higher in provinces with more people per fiber optic line kilometer. Interestingly, the number of internet users per capita is still significant after controlling for the effect of more fiber optic line kilometers per capita. The results thus suggest that while broadband internet connection availability enhances firm export performance, the positive and significant pure effect of internet penetration is maintained.

# **6. Conclusion**

We take advantage of the scale and speed of the internet rollout in China to analyze how the surge in internet penetration, including in less developed inland provinces, improved firms’ export performance. We combine firm-level production data with provincial information on internet penetration in China between 1999 and 2007. We focus on the impact on firm exports since trade is a major channel through which the internet can raise aggregate economic growth. Our econometric strategy allows us to identify a causal impact of the internet on firm performance.

We find that growth in internet penetration has raised Chinese firms’ manufacturing exports. We take a closer look at why, and reach three broad conclusions. We find, first, that the internet did not just enhance trade but improved overall firm performance; higher exports go along with an increase in firms’ real output as provincial internet penetration increases. Thus, firms in China are not simply re-orienting their output to export markets but also increasing the scale of their production. The growth in firms’ real output is due to an increase in productivity as well as in employment. Second, we show that the gains from increased internet penetration on firm export performance are likely to have arisen from a reduction in communications costs with buyers and input suppliers. Third, the benefits from increased internet penetration on firm export performance were also linked to the establishment of a visible virtual presence and were enhanced by but not contingent on access to broadband.

> 28 The sample sizes in Table 7 differ from those in Table 2 due to the availability of fewer years of data for the number of people per fiber optic line.

19
and HIV infection among youth in particular has led to interventions such as the Reproductive Health Program in the state of Bahia in 1997 and its evaluation (see Magnani et al. 2001), which pointed to limited results. Teenage fertility rates appear to be responding to cultural and social changes, and the increase is usually seen as reflecting the inability of health programs to reach their target.

This study analyzes the impact of forces such as exposure to mass media, the existence of support networks, self-esteem, or the occurrence of violence at home and in the neighborhood, on risk-taking behavior, thus going beyond factors more traditionally explored. A bivariate probit model is used to jointly model drug use and teen pregnancy; it relies on an extensive survey by Verner and Alda (2004) that specifically addressed risk factors faced by the population in these neighborhoods, with a particular focus on youth. The major advantages of the survey are the wide coverage of the population of youngsters, both males and females, in and out of school, and the wide set of issues addressed.

Section 2 presents an overview of recent literature on the link between youth drug use and teen pregnancy. Section 3 describes the data and section 4 presents descriptive evidence on the topic. The econometric model and its results are discussed in section 5, before concluding comments are presented.

# **2. Previous literature on the interaction between youth drug and sexual risktaking behaviors**

Early literature pointed to the existence of a link between substance use and risky sexual behavior. One line of reasoning would state that substance use impairs judgment and decision making, rendering individuals more prone to taking risks. From a statistical point of view, however, such an association could hardly be considered a causal link, since factors associated with both substance use and risky sexual behavior were not controlled for (unobservables). More recent work has therefore aimed at taking into account factors unobservable by the econometrician.

Grossman and Markowitz (2005) aim at identifying the causal impact of alcohol and drug consumption on several types of sexual behavior. They use an instrumental variable procedure in which prices are used as an instrument for alcohol and drug 2