rafmacalaba/datause-encoder-data huggingface dataset:
8

**Table 3:** _Exports and imports of commercial services with and without transport and travel services in 1998-2004 (in million of US dollars)_

||1998|1999|2000|2001|2002|2003|2004|
|---|---|---|---|---|---|---|---|
|Exports of commercial services|23,526|16,590|20,112|15,884|14,722|18,928|23,788|
|Imports ofcommercialservices|9,761|8,813|8,467|6,435|6,271|7,708|10,280|
|Exportsin% of imports|241|188|238|247|235|246|231|
|Exports excluding travel and transportation|13,229|8,487|9,521|4,940|3,448|3,541|4,633|
|Imports excludingtravel and transportation|5,642|5,241|4,291|2,676|2,457|2,888|3,425|
|Exports in % of imports|234|162|222|185|140|123|135|

Source: Balance of payments statistics.

**Figure 2** : _Change in exports and imports of commercial services (1998=100)_

<!-- Start of picture text --> 110<br>100 Exports of<br>commercial services<br>90<br>80<br>70<br>Imports of<br>60 commercial services<br>50<br>40<br>30 Exports of<br>commercial services<br>20<br>excluding travel and<br>10<br>transport<br>0<br>1998 1999 2000 2001 2002 2003 2004<br><!-- End of picture text --> Source: Balance of payments statistics.

Because of the strong performance of tourism, the decline in trade turnover of other services and the collapse in exports of other commercial services (excluding travel and transport) had no impact on the ratio of exports to imports of commercial services. Annual changes in the value of exports varied in the range between –29 percent and +29 percent and in the value of imports between –24 percent and +33 percent. Moreover, they coincided in time—the value of the correlation coefficient for annual changes in 1999-2004 is high at 81 percent. In consequence, exports in terms of imports remained stable—the average ratio for the 1998-2004 period was 2.32, with a low value of the coefficient of variation of 0.09 and standard deviation of 0.20.

While undoubtedly earthquakes, war in Iraq and perceptions of instabilities in the Middle East might have suppressed tourism in the region, tourism (travel services) recorded hefty growth in current US dollars of 15.5 percent per year in 1998-2004. It was the only item that did not experience contraction in exports in terms of value over this period. But its performance was not sufficiently robust to offset the decline in other exports, as total receipts had a negative growth rate in 1998-2004. Among other categories of services, the strongest performer was transport, with the average annual, negative growth rate of (-) 1.5 percent in 1998-2004.

Stagnation in exports of services over 1999-2004 was the result of the collapse of receipts from sales of all services sectors identified in balance of payments statistics except for tourism and transport (Table 4). The aggregate receipts from other sectors stood in 2004 at 55 percent of their level in 1999 and 35 percent of their level in 1998. The inclusion of transport does not significantly alter the overall picture—revenues in 2004 were 37 percent below their 1999 level and 52 percent below their level in 2000.
# **2.3 Multipliers across Infrastructure Sectors**

The literature on fiscal multipliers has mostly focused on the overall effects of public investments and expenditures on growth. However, infrastructure spending is distinct from general public investment, and each infrastructure sector is unique in the way that it interacts with other sectors and in the contribution that it provides to the economy. Hence, the composition of infrastructure investment and expenditure also determines the size of the impact. So far, the literature estimating the effects of infrastructure components such as investments in transportation, energy, and ICT is not only much more limited, but also uses much less sophisticated tools than the ones we examined in the previous sections.

Within this last section, the concept of “multiplier” is different and simply captures the rate of return of infrastructure investment in terms of income, GDP or simply reports a crude estimation of the direct, indirect, and induced job creation of a given investment/spending in infrastructure. Indeed, this section reviews both the evidence related to income as well as job multipliers, as particularly during crises protecting or creating employment is of crucial relevance. Job multipliers capture the ratio of indirect and induced employment to direct employment created in the creation of an infrastructure asset.

**Table 3: Infrastructure Multipliers by Type**

|**Country &**<br>**Region**|**Author(s)**|**Infrastructure Type**|**Multiplier Type**|**Multiplier (direct and indirect**<br>**effects)**|
|---|---|---|---|---|
|Germany|Katz_et al_. (2010)|Broadband|Employment|1.45, 1.92*|
|Switzerland|Katz_et al._ (2008)|Broadband|Employment|1.38|
|United<br>Kingdom|Liebenau_et al._<br>(2009)|Broadband|Employment|2.76*|
|United States|Atkinson_et al._<br>(2009)|Broadband|Employment|3.60*|
|United States|Crandall_et al._<br>(2003)|Broadband|Employment|2.17*|
|United States|Katz and Suter<br>(2009)|Broadband|Employment|1.83, 3.42*|
|United States|Bivens (2003)|Communications|Employment|2.52|
|Malaysia|Bekhet (2011)|Electricity & Gas<br>Water works & supply<br>Building &<br>Construction|Employment|3.5*<br>2.4*<br>1.7*|
|United<br>Kingdom|Kelly_et al._<br>(2016)|(i) Electricity<br>production &<br>distribution<br>(ii) Land transport<br>(iii) Railway<br>(iv)<br>Telecommunications|Income|3.56, 5.56*<br>1.54, 2.40*<br>2.00, 3.10*<br>1.54, 2.41*|
|United States<br>|Pereira (2000)<br>|Transport (Highway)|Income|1.97 (long-run)|
|United States|Chi and Baek<br>(2016)|Transport|Income|0.55|
|United States|Leduc and<br>Wilson (2012)|Transport|Income|2.7 (Short-term)<br>6.2 (Long-term)|
|United States|Perotti (2004)|Transport|Income|1.47 (Short-term)<br>0.37 (Long-term)|
|Egypt, Arab<br>Rep.|Estache_et al._<br>(2012)|(i) Electricity<br>(ii) Construction &<br>Building<br>(iii) Transport &<br>Communications|Employment|1.06, 1.49*<br>1.36, 1.63*<br>1.34, 1.82*|

13
7 To implement such a two-stage procedure, it is necessary to identify variables that are distinct for the receipt of remittances in the first-stage equation, and for the determination of household income in the second-stage equation. The model is identifiable if there is at least one independent variable in the first-stage choice function that is not in the second-stage income function.

The main econometric problem lies in selecting the variables that should go into the equations. Identifying variables that are truly exogenous to the receipt of remittances is difficult. While some variables – such as age of household head – are probably exogenous to household decision-making, other variables – such as those relating to household education – are more problematic. In the literature, the cleanest strategies for identifying exogenous variables affecting migration and/or the receipt of remittances have focused on short-term economic shocks. For example, Yang (2005) uses panel data from the 1997 Asian currency crisis to analyze how short-term changes in currency rates affect the value of international remittances received by Filipino households. Since our Ghana data come from a single, cross-sectional survey, we are not aware of any identifiable exogenous shocks to exploit in our data set.

To address the problem of endogenous variables, we constructed seven instrumental variables and nine control variables using information from the two most recent nationallyrepresentative household surveys in Ghana: the 2005/06 GLSS 5 Survey, which is the focus of our analysis, and the earlier 1998/99 GLSS 4 Survey. We constructed these variables using the following three steps.

First, since past research has found that migration networks are important in migration decisions and the receipt of remittances (e.g. Woodruff and Zenteno, 2007, Munshi, 2003), and since ethnicity and religion represent two important forms of association in Ghana, we assume
The _headcount index_ measures the percentage of the population living with per capita consumption or income below the poverty line, defined as US$1 a day. This is the most popular measure of poverty because, though arbitrary, it provides a quantifiable metric of people living in what a society at one point in time considers unacceptable conditions.

The _poverty gap_ takes into account the distance of the poor from the poverty line. This measure characterizes how far below the poverty line lies the average income of the poor and provides some sense of distribution. Unlike the headcount index, this indicator captures a decrease or increase in the income of the poor even when it does not cross the poverty line.

The _income of the poorest quintile_ is defined as the average per capita income of the poorest 20 percent of the population, and measures relative poverty. Indeed, indicators based on the poverty line tend to describe poverty in absolute terms. Yet studies suggest that an individual’s welfare depends not only on absolute income, but also on how his or her income compares with that of the rest of the population. Whereas the Gini coefficient measures inequality over the entire distribution of income, the income of the poorest quintile only focuses on the bottom of the distribution. This is an important feature since changes in the Gini coefficient may be associated, for instance, with income redistributed from the top to the middle class without affecting the bottom quintile (Deininger and Squire, 1996).

This paper will consider two indicators of financial development in an attempt to highlight the channels through which finance could be associated with poverty. The ratio of private credit to GDP, which excludes credit to the government or state-owned enterprises, captures the actual amount of credit channeled from savers to private firms through financial intermediaries. The ratio of broad money to GDP (M3/GDP) captures the deposit-gathering activity of the financial system and its ability to offer an instrument to save and diversify risk. One would expect money and credit to be closely linked, representing the two sides of the same balance sheet. Deposits enable banks to lend and bank credit ends up in deposits. In an environment characterized by rationing or inappropriately developed institutions to support credit, however, these two indicators may diverge.

The research reviewed above suggests that financial development is associated with progress in strengthening property rights. Following this literature, this paper examines the role of the interaction of property rights and financial development in poverty reduction. The strength of property rights is captured by an index measuring the ability of individuals to accumulate private property, secured by clear laws that the state fully enforces. The indicator also assesses the likelihood that private property may be expropriated and the ability of individuals and businesses to enforce contracts. The index ranges between 0 and 100 (strongest), and is compiled annually by the Heritage Foundation.

7
underpins their robustness. For literacy only the regression for the sub-sample of nonOECD makes sense because firmly all OECD countries have a constant level of literacy of exactly 99 percent in the UNDP data. The results are presented in Tables 1 to 6.

Let us start the discussion of the results with our analysis for life expectancy, both for non-OECD countries and the complete sample. We start with a regression including democracy and a full set of dummies and then step by step include the other explanatory variables (Tables 1 and 3). The impact of democracy on life expectancy is positive and in all specifications highly significant. The HIV/AIDS dummy carries the expected negative sign for the more recent years 1990, 1995 and 2000. The positive sign of the time dummies captures the overall upward trend in the level of life expectancy. As expected, war has a negative and significant impact on life expectancy.

The results for our other main explanatory variables are as expected from the theoretical reasoning. GDP and education in all specifications have a positive and highly significant impact on life expectancy. Ethnic fractionalization in contrast is negatively linked to life expectancy. Although the latter result mainly confirms theoretical expectations it must be taken with caution because of the critical assumption of homogeneity over years. Inequality carries a negative coefficient, however it is not significant in the final specification with all control variables.

In Tables 2 and 4 we proceed with the final specification including all variables and step by step include the different interaction effects of the conditioning variables with democracy. What do the interaction effects tell us? Whereas inequality alone carries a negative sign, its interaction with democracy indicates that increases of life expectancy due to democracy are stronger in more unequal societies compared to more equal ones confirming in part the median voter hypothesis and the need for more redistribution in more unequal societies. The other interaction effects turn out to be statistically insignificant. This leads us to the conclusion that despite the fact that GDP per capita, 21
Ianchovichina, E., A. Nicita, and I. Soloaga, (2001) "Trade Reform and Poverty: The Case of México," _World Economy_ ; 25 _,_ 945-972.

de Janvry, A., M. Fafchamps, and E. Sadoulet. (1991). "Peasant Household Behavior with Missing Markets: Some Paradoxes Explained,” _Economic Journal_ 101:1400-17.

Leamer E. (1996). “Wage inequality from international competition and technological change: theory and country experience” _American Economic Review_ ; 86, 2:309-14.

Lustig, N. y M. Székely. (1997). México: Evolución Económica, Pobreza y Desigualdad. Proyecto Programa de Naciones Unidas Para el Desarrollo (PNUD) / Banco Interamericano de Desarrollo (BID) / Comisión Económica para América Latina y el Caribe (CEPAL). Mimeo.

Krugman, P. (1991). “Increasing Returns and Economic Geography” _Journal of Political Economy_ 99:483-99.

Larson, D. (2002). “Exchange rate pass-through and domestic price transmission: Methodology and application.” OECD Global Forum On Agriculture: Agricultural Trade Reform, Adjustment And Poverty, Paris, 23-24 (May), CCNM/GF/AGR(2002)9 McCulloch, N., A. Winters and X. Cirera .(2002). _Trade liberalization and poverty: a handbook._ CEPR London.

Nicita A. (2004). “Efficiency and Equity of a Marginal Tax Reform: Income, Quality and Price Elasticities for Mexico”, _World Bank Policy Research Working Papers_ , forthcoming.

Taylor E. and I. Adelman (1996) _Village economies : the design, estimation, and use of village wide economic models_ . Cambridge ; New York : Cambridge University Press.

Porto, G. (2003). “Trade Reforms, Market Access and Poverty in Argentina” _World Bank Policy Research Working Papers_ , forthcoming.

Reimer, J. J. (2002). “Estimating the Poverty Impacts of Trade Liberalization” _World Bank Policy Research Working Papers_ No. 2790.

Revenga, A. (1997) "Employment and Wage Effects of Trade Liberalization: The Case of Mexican Manufacturing" _Journal of Labor Economics_ ; 15, S20-S43 Robertson, R. (2000). “Wage shocks and North American labor-market integration” _American Economic Review_ (U.S.); 90, No. 4:742-64 33
<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>< g RE? ES< EY RECESS EX RECES< EX REVES< EY RECES<br>“ Bg25 6 “ BE25 6 ~“ BE25 © ~ BE.25 ~ BE25 ©<br>Water supply Sanitation Hygiene Waste management Environmental cleaning<br>mBasic Service mLimitedService mNoService minsufficient Data<br><!-- End of picture text -->
This approach, used to evaluate the adequacy of social benefits and the living wages, defines a minimum consumption basket encompassing goods and services necessary for minimum living standards. A comprehensive basket accounts for various factors beyond basic sustenance. Although the EU relies on relative poverty measures, the absolute concept of the poverty line based on a minimum cost of living is valuable for establishing appropriate social benefits or minimum wages. A food basket reflecting an adequate diet for various reference households is a valuable tool for benchmarking. Estimates based on a Romanian food basket estimated by the EC under the European Reference budget network indicate that in 2020 euros, the food basket's average monthly per capita cost is around 126 EUR, and approximately 180 EUR when factoring in non-food components and other functions.

**Given these EC estimates of the food basket, our results show that the net monthly statutory minimum wage in Romania (276 EUR) exceeds the living wage required for food expenses (245 EUR) yet falls short of covering both food and non-food components (351 EUR), holding economic implications.** An adequate minimum wage encompassing basic consumption costs could diminish reliance on social assistance, enhance labor market participation, elevate incomes, reduce income inequality, and promote job stability. However, striking a balance between worker income and job market stability is vital, warranting an assessment of its impact on labor dynamics. Periodic adjustments to maintain purchasing power over time is also important.

**Drawing from a rich administrative tax database, we show that the minimum wage seems binding in Romania.** According to administrative tax data, the proportion of individuals earning the minimum wage slightly surpasses 25%. Yet, a related study (Robayo-Abril, Balaban, Wronski, 2024) demonstrates that this estimate is inflated compared to survey data, including survey data supplemented with estimated tax income. Tax evasion and off-the-books wages could account for the overstated prevalence of minimum wage earners in tax records.

**The prevalence of minimum wage recipients exhibits noteworthy variation across several employee categories, economic sectors, geographical regions, and types of firms.** Among genders, women outpace men in being minimum wage earners. This trend is also observable among part-time workers compared to their full-time counterparts. The youngest employees stand out, with a higher proportion being minimum wage earners. Across various sectors of the economy, the proportion of minimum wage earners displays substantial diversity. Specific sectors (such as construction, food and accommodation, transport, and storage) witness over 40% of their workforce fall within this category. Conversely, in other sectors (like mining, education, public administration, and electricity, gas, and steam), the proportion of minimum wage earners is below 10%. Microenterprises exhibit a notably elevated share of minimum wage earners, potentially attributed to unreported payments and tax compliance challenges.

48
large cross-country wage differentials. Borjas and Bratsberg (1996) model return migration within a life cycle framework where immigrants return home having realized some predetermined savings goals. Mesnard (2004) develops a formal model of return migration triggered by achieving a savings target based on imperfect capital markets in the country of origin. Borjas and Bratsberg (1996) also predict return migration because of an error in the expected monetary benefits of migrating to the host country. In addition to these preference models of return migration, some of the recent brain-gain literature focuses on experiences gained abroad having higher returns in the home country (Reinhold and Thom, 2009; Barret and O’Connell, 2000; Barret and Goggin, 2010; Iara, 2006). Similarly, skills acquired abroad may have higher returns in the home country than in the host country, causing return migration (Dustman, 1994, 1995; Borjas and Bratsberg, 1996; Santos and Postel-Vinay, 2003; Mayr and Peri, 2008). Other factors that may affect the return migration process include information costs (Jayet and Gannon, 1991, Stark, 1995; Dustmann, 1998), the role of remittances (Rapoport and Docquier (2003); Dustman and Metres, 2009) and purchasing power differentials (Djajic, 1988; Stark, Helmenstein and Yegorov, 1997; Dustmann, 1997).

# _Empirical Literature Review_

Zimmermann (1995) provides a historical account of migration within Europe in the post-World War II era that highlights four major migration periods, the last being triggered by the collapse of the Soviet Union in the late 1980s and early 1990s. Labor migration within Europe was assumed to be temporary in nature, unlike migration to North America, which was more likely to be permanent. Empirical evidence to support this view is found in Bohning (1981, 1987) and Glytsos (1988). With respect to transitional countries’ migrants, surveys undertaken by the International Organisation for Migration (1998) found that over 80 percent of these migrants wished to return home from Western European countries within a few years of departure.

While the number of migrants is both politically and economically important, much recent academic work has focused on testing the theories of return migration using cross-sectional data. Borjas (1989) finds that less–skilled, poorer paid migrants in America are more likely to return home, while Reagan and Olsen (2000) find that migrants with a US college degree are more likely to return to their country of birth consistent with the returns to a degree being greater in the home (poor) than host (wealthy) country due to relative scarcity. In Europe, Dustmann (1996) analyzes the likelihood of return migration by workers in Germany, finding that years of residence in the host country, competency in speaking German and level of education all reduce 7
<!-- Start of picture text --> Table 4D: Regression results from the full sample of 153 countries and all years 1988-2015<br>() Q) 8 4) 6) 6 0 5<br>HD GP share HD sectors share<br>IBRD country -0.039** = -0.049** -0.055 -0.069* -0.048*** = -0.049** -0.031 -0.035<br>(1=yes) (0.017) (0.023) (0.037) (0.035) (0.013) (0.021) (0.031) (0.030)<br>Log population -0.005 -0.083 -0.003 -0.005<br>(0.005) (0.081) (0.004) (0.074)<br>Log GNI per capita 0.017 0.064** 0.009 0.047**<br>(0.011) (0.027) (0.011) (0.024)<br>Net aid flow -0.001 -0.001 -0.001 -0.002<br>(millions of 2010 dollars) (0.001) (0.002) (0.001) (0.001)<br>Number of observations 2,509 2,365 2,509 2,365 2,509 2,365 2,509 2,365<br><!-- End of picture text -->

|Table 4D:|Regression|results from|the full sam|ple of153 co|untries and all|years 1988-|2015||
|---|---|---|---|---|---|---|---|---|
||()|Q)<br>HD GP|8<br> share|4)|6)|6<br>HD secto|0<br>rs share|5|
|IBRD country|-0.039**|= -0.049**|-0.055|-0.069*|-0.048***|= -0.049**|-0.031|-0.035|
|(1=yes)|(0.017)|(0.023)|(0.037)|(0.035)|(0.013)|(0.021)|(0.031)|(0.030)|
|Logpopulation||-0.005||-0.083||-0.003||-0.005|
|||(0.005)||(0.081)||(0.004)||(0.074)|
|LogGNI per capita||0.017||0.064**||0.009||0.047**|
|||(0.011)||(0.027)||(0.011)||(0.024)|
|Net aid flow||-0.001||-0.001||-0.001||-0.002|
|(millions of2010 dollars)||(0.001)||(0.002)||(0.001)||(0.001)|
|Number ofobservations|2,509|2,365|2,509|2,365|2,509|2,365|2,509|2,365|
|R2|0.027|0.031|0.141|0.142|0.029|0.029|0.127|0.126|
|Country fixed effects|no|no|yes|yes|no|no|yes|yes|
|Fiscal year fixed effects|yes|yes|yes|yes|yes|yes|yes|yes|
|MeanofIDA/Blend|0.226|0.222|0.226|0.222|0.207|0.203|0.207|0.203|

Notes: Each observation is a country year, thus the coefficients can be interpreted as the average differences between IDA/Blend and IBRD country. Standard errors are clustered at the country level. * Significant at 10%, ** Significant at 5%, *** Significant at 95%.