rafmacalaba/data-use-mentions-extended huggingface dataset:
9 One striking facts concerning the maturity structure of financing is that there is a large number of firms that appear to be cut-off altogether from any access to long term debt. In the SC 1 sample, which is the one to be used to draw inferences about access given the more detailed figures on debt, 214 firms (29.3% of the total) never received long term financial credit, 311 firms (42.5% of the total) had long term debt during some years, while only 211 firms (28.2% of the total) always had long term debt (see Table 6, Part I). In the SC2 sample, which, because of the more aggregate nature of the debt variables is bound to present a rosier picture, 25 firms (2.9% of the total) never had long term liabilities, 538 firms (63.3%) had long term liabilities during some years, and 287 (33.8% of the total) had it during the whole period.

It is interesting to note that according to the SC1 sample, access to long term financing improves over time (see Table 6, Part II). In 1984, only 37% of firms had long term debt. This number increases to 59.2% in 1987 and decreases slightly to 58.9% in 1988. Unfortunately, we cannot track the question of access in the more recent years because the SC2 sample is less informative in this sense; If we split the SC1 sample by size, we can see that access to long term credit varies positively with size (see Table 7, Part I). Among the largest group of companies, 58% had long term debt in every year of the period. Conversely, only 11% of the micro firms and 17% of the small firms had similar access to long term financing. Half of the micro firms and 44% of the small firms never had long term debt, while only 1.
190|
|3|Russia|2.260|3|Russia|3.358|
|4|India|1.806|4|India|1.808|
|5|United Kingdom|0.761|5|Japan|1.560|
|6|Germany|0.646|6|Germany|1.505|
|7|South Korea|0.630|7|United Kingdom|1.126|
|8|Japan|0.560|8|France|0.986|
|9|Canada|0.499|9|Spain|0.850|
|10|Saudi Arabia|0.493|10|Saudi Arabia|0.795|

* Authors’ calculations, using data from the World Development Indicators. Index generated from first principal component of (1)–(5), variously excluding and including (4). Real GDP and PPP-adjusted indicate growth rates calculated from GDP data in real 2000 U.S. dollars and constant 2005 international purchasing power parity-adjusted dollars. _†_ Migration flows are calculated for 2005, the only year where data are available.

parallel in economic geography, where a small fraction of physical space typically accounts for a disproportionate share of economic activity. Thus, like would be the case for geographicallyconcentrated growth poles, global growth poles appear to follow a power law relationship (for cities, this relationship has been termed Zipf’s Law).<sup>13</sup>

> 13Annex Figure A.2 illustrates this power law relationship. This non-normality in the key dependent variable also poses a potential problem for the regressions to follow; a linear logarithmic transform was thus applied to mitigate this concern, and residual diagnostics are discussed.

15
observed today. Given the lack of information about historical inequality indicators, we include the Gini index of years of schooling among the population aged 6 and above and the average number of years of schooling of the population aged 6 and above, where education aims at capturing individuals' earnings potential.

As argued by McKenzie and Rapoport (2011), historical district level migration network can have a direct impact on current consumption levels, for example due to spill-over effects that persists over time. It is likely that such migration effects on consumption are larger if a household is a migrant household than if someone in the district migrated in the past. In other words, we expect the direct impact of the district network to be second order on the consumption level.

# 3.2 Estimation Techniques

> 12 The Akan, Mole- Dagbani, Ewe and Ga-Dangme are the four main ethnicities that together account for almost 90 percent of the population. Albeit all regions have a considerable number of “ _people considered to be ‘strangers’ in their region, there is a rough coincidence of ethnicity and administrative regions in Ghana”_ (Langer, 2009). For example, the Akans, the largest ethnic group, are the majority of the population in five regions (Western, Central, Eastern, Ashanti, and Brong-Ahafo. The Mole-Dagbanis, the second largest ethnic group, are mainly found in the northern regions.

> 13 We consider the impact of migration on household welfare by defining three stocks of migrants. The first includes all household heads who migrated between 1990 and 2011 (the year before the survey), between 2000 and 2011, and between 2005 and 2011.

12
. The major difference is poor households spend a much greater proportion of their expenditure on housing and utilities than is indicated by the CPI basket. Even m non-poor Abidjan households the share of expenditure on housing and utilities as revealed by the survey exceeds the share in the CPI basket. Moreover, the difference is actually understated, since remittances are part of the survey consumption basket, but not part of the CPI. If remittances were eliminated from the survey consumption basket, the survey share of expenditure on housing would be even greater.

Since lodging and utilities had the lowest rates of inflation of any category in the CPI between December 1993 and March 1995 (table 30), a higher share of expenditure allocated to housing and utilities would result in a lower inflation rate, and thus in higher real per capita expenditures on 1995. As a rough indication of how the inflation rate would change if one were to use the survey consumption weights base, mean expenditure per capita on average would be about 2 percent higher using the survey expenditure shares. However, this calculation abstracts from the fact that the composition within the **26** various groups of expenditure may differ significantly between the CPI and the household surveys, as the discussion of the composition of the food subcomponent, below, illustrates.

A second shortcoming of the INS CPI series is that there is no CPI series for the rural areas. To deflate expenditures in the household surveys, INS constructed a deflator for each region for 1985 based on price differentials calculated from the International Comparison of Prices survey data (Grootaert and Kanbur, 1994). The price deflator for Abidjan was used to construct the price series

**26** We assume that the inflation rates for the categories in the remittances, non-food in kind and education in the survey is equal to the weighted inflation rate of the other categories. This calculation is based on the December 1993 and March 1994 CPI inflation rates.

33
the only-relatively income poor in richer countries are, on average, proportionally closer to the relative line than to the absolute line than the incomes of the only-relatively income poor in low-income countries.

The results summarized in Table 1 help us to understand why, when we compute global poverty (see Section 6.4), _HHS_ is about halfway between _HS_ and _HA_ . The reason is that income poverty is concentrated in highly populated developing countries such as India and Indonesia, where the endogenous weight is close to 0.5.

**Table 1:** Endogenous weight _ω_ by country income group

|Country income group|Median|SD|
|---|---|---|
|Low income|0.49|0.06|
|Lower-middle income|0.48|0.05|
|Upper-middle income|0.42|0.08|
|High income|0.31|0.06|

_Note:_ Country income groups as defined by the World Bank.

# **6.3 Country-Level Poverty**

We begin by comparing poverty at the country level between the societal headcount ratio ( _HS_ ), the absolute headcount ratio ( _HA_ ), and our proposed index ( _HHS_ ).

Our first example illustrates how the change of index that we propose can affect the poverty diagnoses of individual countries. Table 2 displays the level of poverty as well as the rankings by those three poverty measures for Botswana and Egypt in 2008. According to _HS_ , Egypt has more poverty than Botswana (the fraction of income poor individuals is 33% and 30%, respectively). However, this comparison masks the very different composition of their income poor populations. The vast majority of income poor individuals in Egypt are only-relatively income poor, whereas about half of the income poor individuals in Botswana are absolutely income poor. Specifically, the fractions of absolutely and only-relatively income poor individuals are, respectively, 5% and 28% in Egypt, and 16% and 14
_The Cultural Trade Index_ Page | **12** of **14**

# **V. Conclusion**

“There is no indicator that can capture something as complex as our society,” as Joseph Stiglitz, Amartya Sen and Jean-Paul Fitoussi argue in Mismeasuring Our Lives: Why GDP Doesn't Add <u>Up. “Even if we succeeded in constructing the perfect measure for today, changes in our economy</u> and our society would necessitate” constant revisiting.<sup>33</sup> The Cultural Trade Index (the focus of this discussion) is a modest beginning, one that intends to consider one sliver of trade in our society. Moreover, the data feeding into this index will need to be improved. That said, the index could stimulate debate on how creative trade could better be harvested for development.<sup>34</sup> The complimentary index on cultural exchanges (even if pursued with great care) will also have limitations, and in both cases these indices must be interpreted with caution. Yet we cannot move forward by just focusing on limitations. We need to focus on possibilities.

In the intellectual space, moreover, the debates can be endless. For instance, it is not easy to tell where all the parts and even services that go into making a piano in China (or elsewhere) originate. Also, in distinguishing between goods and services, while it is plausible, there is no need to know how to play the piano to make or assemble the piano. Whereas to make a movie (as an actor) you need to act. Another issue is the measuring of the quality of goods and services, a task difficult to execute,<sup>35</sup> yet it can be a marker of progress.

In our constantly changing world, as Coyle accentuates, “we now have an economy dominated by services and intangibles, with _much_ greater variety of products and closely linked across national boundaries
Policy Research Working Paper 8843

# **Abstract**

The Sustainable Development Goals recently adopted by the United Nations represent an important step to identify shared global goals for development over the next two decades. Yet, the stated goals are not as straightforward and easy to interpret as they appear on the surface. Review of the Sustainable Development Goals indicators suggests that some further refinements to their wordings and clarifications to their underlying objectives would be useful. This paper brings attention to potential pitfalls with interpretation, where different evaluation methods can lead to different conclusions about country performance. The review of the United Nations’ Sustainable Development Goals database highlights the overwhelming challenge with missing data: data are available for just over 50 percent of all the indicators and for just 19 percent of what is needed for comprehensively tracking progress across countries and over time. The paper offers further reflections and proposes some simple but cost-effective solutions to these challenges.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted athdang@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
more experienced. To measure the learning effects, we introduce an additional regressor that measures the number of years a FiT policy had been put in place in a given country. We find introducing the measurement of policy implementation horizon has very little impact on the results.

Second, although labor costs are only a small component of the overall production costs, it may still affect FiT compensation scale and investment decisions. If labor costs are correlated with both FiT support and wind installation, the elasticity of FiT price will be underestimated. To explore how important is such an effect, we use the average employee cost in electricity generation sector (NACE 3511) reported in AMADEUS database maintained by Bureau Van Dijk as an approximation for labor cost of wind generation. Introducing the additional variable further reduces the sample size, but the basic results do not change.

The preceding results are also robust to alternative specifications assuming FiT remuneration as strictly exogenous, or using instruments for wind installation lagged three periods and earlier to account for possible MA(1) measurement errors.

# **6 Conclusion**

FiTs are rapidly emerging as the most widely used policy instrument to promote renewable energy deployment in the world. Meanwhile, the cost impacts of FiT policies have become a primary concern for policy makers, and particularly so in developing countries. Whether the costs are recovered from ratepayers or taxpayers, rising costs can create both political and economic pressures. Households in developing countries are particularly vulnerable to increase in energy prices because energy comprises a higher share of their income than those in developed countries.

While there exists a large literature on the review of FiT design, empirical studies on the cost-effectiveness of FiT policies are scarce. This paper adds to the literature by estimating the sensitivity of renewable investment to FiT remuneration levels based on wind data of 35 European countries between 1991 and 2010. Understanding the relation between tariff 23
worldbank.org/index.php/catalog/76` on 6 September 2019.

- Tanzania National Bureau of Statistics (TNBS) (2012). Tanzania National Panel Survey 20102011, Wave 2. Public Use Dataset. Ref: TZA ~~2~~ 010 ~~N~~ PS-R2 ~~v~~ 03 ~~M~~ . Downloaded from `https: //microdata.worldbank.org/index.php/catalog/1050` on 6 September 2019.

- Tanzania National Bureau of Statistics (TNBS) (2015). Tanzania National Panel Survey 20122013, Wave 3. Public Use Dataset. Ref: TZA ~~2~~ 012 ~~N~~ PS-R3 ~~v~~ 01 ~~M~~ . Downloaded from `https: //microdata.worldbank.org/index.php/catalog/2252` on 6 September 2019.

28
Policy Research Working Paper 9608

# **Abstract**

The majority of services trade is currently transacted under the terms of preferential trade agreements (PTAs) with increasingly ambitious provisions on crossborder trade and investment in services. This paper exploits novel and detailed information on the architecture and depth of services PTAs (the World Bank Deep Trade Agreements 2.0 database) to study which provisions, or policy configurations, characterise an effective agreement. The richness of policy information is crucial for being able to identify those aspects that matter most, namely an agreement’s structure, its rules of origin for firms and natural persons, and provisions that ensure accountability. Ambitious provisions in these areas are associated with 15–65 percent higher bilateral trade, driven by regulation-intensive services. Services PTAs also lead to an increase in services value added sourced from PTA partners, through provisions that facilitate the exchange of capital and people. This finding sheds light on how services PTAs can affect the configuration of value chain trade.

This paper is a product of the Development Research Group, Development Economics and the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at I.Borchert@sussex.ac.uk and M.Di-Ubaldo@sussex.ac.uk.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/
level. Second, according to our results, the largest barriers to trade that are not captured by the standard gravity covariates are in Agriculture and in Services. We find this variation to be intuitive and it is consistent with localized consumption in the case of services, and more pronounced ‘home bias’ effects in the case of agricultural products.

Third, turning to the estimates on _BRDR_ EU_ CEFTA_ and _BRDR_ EU_ , we see that, without any exception, the additional barriers to trade between CEFTA and EU are larger as compared to the barriers to trade within the EU. This is reflected in the larger (in absolute value) estimates on _BRDR_ EU_ CEFTA_ as compared to the corresponding values for _BRDR_ EU_ , and is consistent with our expectations. Finally, we find that the differential in the borders between CEFTA and EU as compared to the borders within the EU varies across the four main sectors in our sample. In order to see this clearly (and also to be able to gauge whether the differences are statistically significant) in Panel B of Table 1 we report the difference between the estimates on _BRDR_ EU_ CEFTA_ and _BRDR_ EU_ . In addition, following (3), in Panel C of Table 1, we calculate tariff equivalents of the border differentials.<sup>20</sup> Standard errors in panels B and C are constructed with the Delta method.

The estimates from panels B and C reveal several interesting results. First, the border differences for each sector are statistically significant at any conventional level. Second, the tariff equivalents of the border difference are larger as compared to the existing tariff differences for the goods sectors. This supports our assumption that, in addition to tariffs, there are other barriers to trade between CEFTA and EU. For example, in the case of Agriculture, the trade weighted average tariff for the CEFTA countries is 11.58 percent, which is significantly lower as compared to the 19.65 percent border differential tariff equivalent that we obtain from the structural gravity regressions.
# **1. Introduction**

The number of international migrants has been increasing over time. In parallel with that, the participation of women in international labor migration has also been rising. In 2005, the share of female migrants in the world’s migrant stock was close to half (World Bank 2008a). Following an upward, global trend in international migration, Indonesia is now one of the largest migrant labor exporting countries in the world. International migration from Indonesia has been strongly dominated by women. According to official records, the number of documented female migrant workers accounted for approximately 80 percent of Indonesia’s migrant workers in 2007 (Figure 1).

The rise in migration has also contributed to the increase in global remittance flows. Recorded remittances sent home to developing countries were nearly US$ 222 billion in 2006. About 17 percent of global remittances flow back to the East Asia and Pacific region, including Indonesia. According to the IMF Balance of Payments data, remittances to Indonesia totaled approximately US$5.7 billion in 2006 (World Bank, 2008b). Although the amount of remittances appears small relative to total GDP (US$364.5 billion), these inflows may be quite significant in the specific regions of the migrants’ origin.

While international migrant work has become an increasingly important part of the Indonesian economy, not much research has been done to understand to what extent migration and remittances may affect the livelihoods of the migrant’s origin household. The impacts on adult and child labor supply as well as human capital accumulation can manifest through the income effects of remittances, the impacts on work incentives, exposure to new information, and consequences of family disconnects. Previous literature has shown evidence of impacts on the above outcomes in various developing countries (Adams 2010), though the gender dimension of the impacts has been less explored. The existing research on international migration from Indonesia mostly focuses on issues related to the financial literacy and vulnerability of migrant workers. Such research to date is
- 27 -

that would occur if, through development of new technology or lnvestment ln agriculture, government were able to relax the supply constralnt limltlng output of major agrlcultural tradables. Given a one-unit lncrease ln value added from the region's major tradable agrlcultural output, the model estimates the resultlng total increase ln regiona' value added as a function of three parameters: consumers' marglnal budget share spent on nontradables, producers' demand for nontradable intermediate inputs as a ratlo of gross regional output, and the ratlo of value added to gross reglonal output.

Three studies have used Hazell's model to estimate the magnltude of a!;rLcultural growth linkages. Hazell (1984) has applied the model to estimate Asian growth multipllers, Rogers (1986), using data from Mauritania, has computed multipliers for Sahelian West Africa, and Haggblade, Hazell and Brown (1987) have used the model to compare growth multipllers in Africa and Asla as well as to contrast growth linkages resultlng from alternative agricultural growth strategies.

Several important concluslons emerge from these modellng efforts. The flrst concern the accuracy of the model and the magnltude of Asian growth linkages. Hazell (1984) ln his initial formulation, used the model to estimate regional agrLcultural growth sultipliers for the Muda River Region of Malaysia, a moderately prosperous rlce growing region that had been studied ln great detall by Bell, Hazell and Slade (1982). The small, three-parameter model generated a regional agricultural growth multlplier of 1.82, indicatlng that a $1 increase in value added from tradable agrlcultural output would result in an additlonal $0.82 lncrease ln reglonal income. Thls result lles very close to the 1.83 value addod multiplier reported by Bell, Hazell and Slade (1982) after a much more careful analysls, lending credibility to the smaller model's projectLons. A second detalled study, undertaken by Hazell, Ramasamy and Rajagopalan
*Table 4.** Bayes Factors of Different Priors vs Benchmark with _c_ = 0 _._ 01 (Prior 3)

(Random _θ_ , _m_ = 7)

|_i_|Prior on _g_|FLS|SDM|Tobias-Li|
|---|---|---|---|---|
|2|Benchmark _c_= 0_._1|0.3|–|0.4|
|4|Maruyama-George|16.3|10.1|–|
|5|Bottolo-Richardson|23.6|57.2|24.4|
|6|Hyper-_g_, _a_= 3|2.3|8.7|3.0|
|7|Hyper-_g_, _a_= 4|0.7|3.0|1.0|
|8|F-Z, _a_= 2 + 2_/k_<sup>2</sup>|0.1|0.1|0.3|
|9|F-Z, _a_= 2 + 2_/n_|2.4|5.6|0.1|
|10|Horseshoe|11.2|34.6|11.9|
|11|Forte _et al._|53.2|123.2|n.a.|
|12|Hyper-_g/n_|21.8|50.8|1.7|
|13|Horseshoe/_n_|25.9|68.5|7.6|
|14|Zellner-Siow|29.8|60.1|–|
||_g_0|25.5|19.5|19.5|
|15|_g_ =_n_|19.7|37.1|–|
|16|_g_ =_k_<sup>2</sup>|–|0.1|–|

# The SDM data

Another popular growth dataset was introduced by Sala-i-Martin _et al._ (