rafmacalaba/data-use-mentions-extended huggingface dataset:
- _High-quality accreditation._ A program has a high-quality accreditation if it has satisfactorily passed through a special accreditation process (beyond the usual licensing and authorization requirements) established by the Ministry of Education, and commonly regarded as a signal of quality.

_Local market conditions._ We define three conditions within each local market (recall that a market is a city-field combination).

- _Institution market power_ consists of the number of programs taught by the institution in each local market divided by the total number of programs in the local market. The index ranges between 0 and 1; a higher index indicates that the institution wields more power in the local market.

- _Market concentration._ We estimate a Herfindahl-Hirschman index (HHI) at the local marketlevel. It is calculated as the sum of squared enrollment shares for all programs taught in each local market. This index ranges between 0, indicating a perfectly competitive local market, and 1, signifying a monopolistic or concentrated local market.

- _City size._ We use data on population from Duranton (2016) to create a measure of city size, which we calculate as the number of inhabitants (in log) in each city. This measure varies across cities but, within a city, it is the same across fields.

_Local SCP supply measures._ To characterize students’ program choice and mitigate horizontal selection concerns (Hoxby, 2020), we construct a rich set of supply-side variables to characterize the local supply facing the student in her high school city. We focus on local SCP supply because more than 90 percent of SCP students in our data enroll in a program offered in the same city as their high school. Specifically, we create four measures of SCP supply in the student’s high school city (see below); we calculate them for the city overall and separately by field, institution governance (public non-SENA, private, or SENA), and program length:

- _Program supply_ is the number of available SCPs in the student’s high school city
and non-poor people. Dotto et al. (2017) use the longitudinal component of the EU-Statistics survey on income and living conditions and develop a dynamic latent class model for evaluating material deprivation (without resorting to arbitrarily specified thresholds) over the period 20102013 in Greece, Italy, and the UK. While these studies contribute to understanding aspects of chronic poverty and material deprivations within Europe, none of these studies have looked at the extent of chronic poverty or material deprivations among the Roma population. Furthermore, they either focus on specific dimensions such as material deprivation or analyze persistence primarily from an income perspective, with few directly addressing chronic multidimensional poverty.

**The paper is organized as follows.** Section II introduces the conceptual framework. Section III presents the data and empirical strategy. Section IV presents empirical results, including an analysis of the EU trends in absolute multidimensional poverty, the extent, composition, and mechanisms behind multidimensional poverty in four EU countries, and an analysis of multidimensional poverty among the Roma in Bulgaria, which also includes an assessment of chronic income poverty and material deprivations among this group. Section V concludes and provides policy implications.

# II. Conceptual Framework

**Conventional poverty measures have typically been centered around income or expenditure, which in most countries is based on a minimal threshold necessary to afford a basic basket of essential goods and services.** However, these monetary measures may only partially encompass the various dimensions of well-being. Additionally, while monetary poverty strongly correlates with deprivations in other domains, this correlation is far from perfect ( <mark>World Bank (2022); Evans et al., 2024)</mark> . Multidimensional poverty metrics have emerged to bridge this gap and have gained official recognition within the United Nations' 2030 Agenda and its Sustainable Development Goals (SDGs). Specifically, they are embodied in "SDG 1.2.2" which assesses the proportion of people of all ages, both men and women, living in poverty across all dimensions as defined by
discrepancy between the aggregate import reported in the balance of payments and our data.

8 Seven observations were dropped from the sample as obvious outliers, based on highly implausible unit prices such as a 40 dollars per car, HS code 8703239027, imported from Germany in 2007. 9 We looked at the following editions of decree 718 to track the changes in tariff lines over time: dated by 12.30.2006 for 2006; dated by 12.29.2007 for 2007; dated by 12.19.2008 for 2008; dated by 12.18.2009 for 2009, dated by 04.01.2010 for 2010. For 2011, we used January 1, 2012 for reasons explained in the text. For 2001 to 2005, we took data from editions of decree 830: first, dated 11.30.2001 for 2001; the second, dated 02.06.2003 for 2002 rates; for 2003, 2004 and 2005, we took December 31 of the respective year. Within a year, changes can occur at the beginning as well as in the end of the year, introducing some measurement error. However, this approach is consistent over time and should not introduce biases because the changes are distributed quite uniformly over the course of the year.

10 For a minority of products, the units of measurement of the specific tariffs and the import statistics are not the same in our two data sources. Most prominently, tariff lines within the 4-digit Harmonized System categories 8701-8705 (tractors and motor vehicles) have a specific tariff component defined in euros per cubic meters of the volume of the engine (cm3). The import statistics, however, only report the number of vehicles imported, not the volume of the engine. Taking tariff code 8703 as an example, we resolve this problem as follows.

There are multiple 6 digit tariff lines beginning with 8703, namely 870321, 870322, 870323, 870324. Together they describe the full range of the cylinder capacities. We have that 870321 is for
.N. 1984 assessment (published in 1986), Eurostat (1987), the U.S. Bureau of the Census (1985), and Celade (1987). The specific source for each country is listed in the Appendix.

The 1985 population of each country was distributed by age and sex using a percentage distribution from either the latest census, adjusted for errors in age reporting, or the U.N. 1984 assessment (the 1988 revision was not yet available for this purpose).

# Mortality level and trend

The base-period mortality levels in the projections are represented by life expectancies obtained, sometimes indirectly, from a variety of sources. Most commonly, figures are based on official or other estimates appearing in Population and Vital Statistics ReRort or government publications. Other figures are taken from the U.N. 1988 revision or the
use a research design that involves many transfer policy changes with dierent sizes and timings and over a long period. Through this design, any omitted variable would need to take an unlikely time-varying pattern in high- versus low-transfer states, which is a much higher hurdle for potential confounding variables than in a standard dierence-in-dierence study with a one-o permanent policy change. For example, Social Security stipends increased by 10% in June 1971 and 20%

> 9Voting records reveal that political support for one-o transfers was either mostly partisan (2001) or bipartisan (2008), with no evidence that legislators from slow-growing states were more likely to support the legislation (see Appendix 3.2).

> 10After removing trends and aggregate variation, state growth rates have little quarterly persistence, which removes the possibility of ex-ante targeting states in recession.

10
.6%|
|Senegal|509|0.4%|1.6%|1.9%|‐55|‐187|‐140|3.8%|
|Nigeria|472|0.2%|3.6%|4.0%|‐50|‐168|‐126|3.4%|
|Ghana|307|1.0%|3.1%|4.2%|‐18|‐62|‐46|1.2%|
|Mauritius|4700|4.1%|3.2%|3.9%|219|736|550|14.9%|
|LICs|415|1.2%|3.2%|4.3%|‐40|‐136|‐102|2.8%|
|Sub‐Saharan Africa|598|0.0%|2.4%|3.7%|‐67|‐227|‐169|4.6%|
|MICs|1997|2.6%|4.9%|6.9%|156|525|392|10.6%|

_Source:_ Authors’ calculations. DDP Data, World Bank.

### **Box A 1: Expected Transmission Channels of the Global Crisis to Togo**<sup>**.1**</sup>

|**_The global crisis is having or may have the following spillover effects on Togo:_**|
|---|

- _Lower demand and prices for exports:_ Lower global growth may adversely impact demand for traditional commodity exports, such as cotton and phosphate. Their share in total exports declined from 50% at the end of the 1990s to just 13% in 2008 and could fall further with the crisis. Cement exports, which represent about 20% of total exports, are expected to continue
**Figure 57: Discoveries and disappearing products in Togo, 1985‐2006**

<!-- Start of picture text --> 25<br>20<br>15<br>10<br>5<br>0<br>‐5<br>‐10<br>‐15<br>‐20<br>Total discoveries Total disappearances Total net discoveries<br><!-- End of picture text --> _Note_ : Based on SITC 3 digit data _Source_ : Authors calculations based on COMTRADE data.

**An alternative definition of “emerging products”, confirming Togo’s innovation activities, can be derived from the idea of RCA, already referred to.** About 20% of Togo’s exports comprising 31 product groups from SITC at 3‐ digit level have been identified only in 2000‐2006 (see Table A 2 in Appendix I). Exports of this group have increased 14.5% between the periods 1992‐96 to 2002‐06. The implied income value of this export basket was 30% above the EXPY for the traditional or “classic” products group.

**Despite these positive developments, we cannot go as far as to say that Togo is steadily moving to close the gap with the technology frontier.** The contribution of discoveries to exports remain relatively low and Togo’s growth prospects are highly dependent on its traditional products as judged by the strong correlation observed between the growth rate of real GDP and the evolution of its Terms of Trade explained above. And, as Figure 12 above shows, the share of Togo on world exports is on a declining trend. On the other hand, we cannot argue that self‐ discovery is the current binding constraint to growth in Togo.

# **_6.9.2 Coordination Failures_**

**Turning to the second type of market failures, one would easily suspect coordination activities in Togo to be a constraint as the very
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# CHAPTER VII

# LABOUR PRODUCTIVITY

One of the major purposes of our approach to measurement of real product and purchasing power is to provide information on comparative levels of labour productivity. The estimates of real value added for manufacturing branches in chapter III and IV make it possible to construct reasonable estimates of labour productivity levels.

Labour productivity is here expressed as output per person engaged in production, as the definition of the denominator is the same in the industrial censuses of all three countries. Figures on working hours are generally not available for Brazil, and there are only rough figures for Mexico. In 1975, average working hours in Mexico were 44.05 per week compared with 39.50 for production and non-supervisory workers in US manufacturing'. Reliable comparative information on time off for holidays and sickness is not available, so output per man hour cannot be calculated with any accuraicy, but it seems probable that aggregate hours per person engaged were longer in Brazil and Mexico than in the USA.

The labour productivity ratios presented here do not account for activities in head offices and auxiliaries in any of the three countries. We do not believe that the ratios would change very much by including head office and auxiliary employment. The head office share of total manufacturing employment was 4 per cent in both Brazil and Mexico and 6 per cent in the USA (see table 7.5).

Table 7.1 presents ratios of value added per person engaged in manufacturing branches for the Brazil/USA and the Mexico/USA comparison. These are derived from our estimates of value added levels (former national accounts concept) which are presented in tables 3.15 and 3.16 and the employment figures by branch which are shown in table 7.5. The productivity ratios show a very clear US productivity advantage over both the other countries.

In the Brazil/USA comparison, the geometric ("Fisher") index of labour productivity varied between 33 per cent of the USA for wood and paper products to 76 per cent
1. Introduction

Finding cost-effective ways to improve and expand access to formal financial services is a priority for many policymakers around the world.<sup>1</sup> In economics and finance, researchers typically model demand for financial services using variables such as interest rates, income, investment opportunities, risk, etc., but non-economic factors may also play an important role. One such non-economic factor is religious beliefs and practices which could limit utilization of financial services in certain contexts.

Indeed, about 25 percent of adults reported religious reasons as a barrier to having a bank account (DemirgüçKunt et al. 2015). This is common in Muslim-majority countries, which constitute over 1.4 billion people, due to the Islamic prohibition of dealing with interest<sup>2</sup> . Financial inclusion data support these survey results: in a crude crosscountry analysis, after controlling flexibly for GDP, Muslim-majority countries have a 24 percent lower rate of participation in active borrowing from banks (10.5% versus 7.9%) and a 29 percent lower rate of having a bank account (40.2% versus 28.6%)<sup>3</sup> .

However, surveys that merely ask individuals why they do not borrow or cross-country comparisons of financial sector participation may over- or under-attribute lack of borrowing to religion. There is a lack of non-survey

> 1 See Cull, Ehrbeck, and Holle (2014) for an overview of why financial inclusion is a priority for many policymakers and international organizations. This is often motivated both by micro-level evidence about the impact on households and communities from improved access and usage of financial services (Burgess and Pande 2005; Bruhn and Love 2014) as well as macro-level evidence about the role of a healthy banking sector in the development of the country (Jayaratne and Strahan 1996; Black and Strahan 2002; Levine 2005; Beck, Demirgüç-Kunt, and Levine 2007).

> 2 Many interpret Islamic law to prohibit dealing with _riba,_ often
of which from govt. sources|156.58|151.79||
|of which from own sources|17.84|17.47||
|Centrally sponsored schemes|268.73|201.67||
|No. of observation|180|459||

_Source:_ Own calculation based on REDS/NCAER 2007 Survey. * significant at 10%; ** significant at 5%; *** significant at 1% _Note:_ Number of candidates contested is only for the 15 states where the pradhan is directly elected by popular vote i.e., Kerala and Maharashtra are excluded.

22
capacity of national (“Public”) generation and expenditures on maintenance (and energy consumption) of inverters and generators represent a large share of the income of an average household with these appliances.

**Figure 3: Percentage of households that have an electrical inverter or a generator at home, by socioeconomic class**

<!-- Start of picture text --> 5.9%<br>Inverter Generator<br>54.8%<br>2.1%<br>2.0%<br>18.9%<br>1.3%<br>12.9%<br>1.1%<br>1.0% 5.7%<br>.1% 1.9%<br>Poorest Poorer Middle Upper Middle Richer Total<br>Source: ENIGH survey, 2007<br><!-- End of picture text --> Which strata of the population are opting for installing auto generation devices? Private solutions to counter inefficiencies in public service provision are not always cheap, and, as shown in the graph, there is a large gap in terms of percentage of people that counts with these devices in the lower classes<sup>5</sup> (averaging less than 3% coverage) and those belonging to the upper middle (28.9%) and high (60.8%) classes.

> 5 “Lowest” and “low” groups according to the ONE “socioeconomic group” variable in ENIGH 2007.

[8]
23 Jelliffe, D.B. The Assessment of the Nutritional Status of the Commuitv with Special rteference to Field Surveys in Developing Regions of the World. World Health Organization, Monograph No. 53, Geneva, 1966.

Johns Hopkins University, Population Information Program. "Oral Contraceptives." Pooulation Reports, (A-1), April, 1974.

Johns Hopkins University, Population Information Program. "Family Planning Programs." Population ReRorts, (J-27), May-June 1984.

Johns Hopkins University, Population Information Pro-ram. "Fertility and Family Planning Surveys: An Update." Population Rep,.rts, (M-8), Sept-Oct 1985.

Nortman, D. L. "Population and Family Planning Programs." A Population Council Fact Book, 12th ed, New York, 1985.

Nortman, D. L., Halvas, J., and Rabago, A. "A cost-benefit Analysis of the Mexican Soc,.! Security Administration's Family Planning program." Studies in Family Pl1 ning, 17 (1): 100-106, 1986.

Ory, H.W., Rubin, G.L., Jones, V., Wingo, P., DeStefano, F., Peterson, H., Guidotti, R., Layde, P.M., Levenson, A.G., Michelson, M., and Hatcher, R. "Mortality among Young Black Women Using Contraceptives.' Journal of the American Medical Association, 251 (8): 1044-1048, Februaiy 24, 1984.

Ory, H.W., Forrest, J.D., and Lincoln, R. Making choices: Evaluating the Health Risks and Benefits of Birth Control Methods. Alan Guttmacher Institute, New York, 1982.

Puqui, X. "Systematic Maternal Health Care in Shanghai." In Primary Maternal and Neonatal Health: A Global Concern, Fe Del Mundo (ed.), New York: Plenum Press, 1983.

Rochat, R. 'The Magnitude of
the unit opportunity cost of time (discussed further below). Because the amount of time spent cooking may differ across technologies, with fast-cooking fuel-technology packages offering time savings benefits compared with other cooking methods, we denote the opportunity cost of cooking time as _CTi w_ , where _CTi_ is the amount of cooking time. In addition, some stoves may cause more disamenities than others, with amount of household smoke being a primary example. That cost is denoted by _Di_ . Finally, we denote by _dij_ the annual value of natural resource damages of type _j_ (e.g., deforestation) from the use of stove-and-fuel combination _i_ ; _pij_ is the annual emission of pollutant type _j_ from stove-and-fuel combination _i;_ and the unit health damage cost of pollutant _j_ is _hj_ . Then the total opportunity cost of natural resource and health damages for combination _i_ is given by ∑��� ���������.

One way to apply (1) to different stove-fuel combinations, given a comparable level of cooking activity, is through controlled cooking tests, in which fuel consumption, cooking time, and (to the extent possible) air quality in the vicinity of the cooking location are measured while cooking the same meals with different methods. Satisfaction surveys can be used in connection with the controlled cooking tests to gauge advantages and disamenities (Beyene et al., 2015), and natural resource damage estimates can be made. This is obviously a time consuming and expensive approach. A simpler but less precise method is to apply (1) to different cooking methods, holding constant the total cooking heat produced as a way of approximating comparable levels of cooking activity.<sup>6</sup> Information about fuel costs and fuel gathering and cooking times also can be collected. This can provide information about the more direct costs borne by the household, but it will not shed light on natural resource damages, health effects, or amenity and disamenity values.

# **4.  Challenges in Assessing
animals, meat & edible meat, fish, dairy products, products of animal origin, to mention a few (Table 5). This is an interesting fact that highlights the benefits of diversifying the production to more sophisticated and technologically intensive goods, which are less exposed to the effects of trade protection. To analyze the implications of protection for Eurasian countries, we incorporate the export structure of these economies into the analysis. In particular, we calculate the average tariff that exports from the region will have to pay.<sup>5</sup> The tariff is a weighted average of the import duties applied to each 6-digit Harmonized System product category. The weights represent the share of a country’s total exports to the world of a product on total exports to the world of a group of products that includes the good. To conduct the analysis, we classify products into three groups: i) live animal, meat & edible meat, fish, dairy products, products of animal origin, live tree & other plants, and edible vegetables & fruits; ii) petroleum; and iii) the remaining products.<sup>6</sup> Table 5 presents the results of the average tariffs applied by the EU and EAP countries on exports of Eurasian countries. Data on tariffs is available at 6 digits.<sup>7</sup>

> 5 To conduct the analysis, we employ MFN tariffs in the case when the exporter is a WTO member and non-MFN tariffs otherwise.

> 6 The Harmonized System tariff lines corresponding to group i) are HS 01-08 and to group ii) are HS 2709-2719.

> 7 The ideal situation would be to have information on MFN and non-MFN tariffs for all the trading partners. However, WITS presents information on non-MFN tariffs only for China and Japan. Therefore, to conduct the analysis, we employ, when possible, information on MFN and non-MFN tariffs. Otherwise, we assume that countries impose the MFN tariffs to all the nations independently of their affiliation
_Note:_ Figures in parentheses are robust t-statistics. *, **, ***: significant at 10, 5, and 1 percent respectively. Annual data for 1996–2013 for low- and middle-income countries. RMSE = root mean square error, or sample standard deviation.

Any device that is effective in reducing inflation, and therefore seigniorage revenue, might have the effect of improving the fiscal balance in the same way as hard pegs. Table 4 shows the effect of adding a dummy for an inflation-targeting regime to the table 1 regression. This variable is never significant, however.

Do countries behave differently when they first become commodity exporters? To examine this issue, we construct a dummy variable that is equal to one in the five years after a country first exceeds the 25 percent threshold for fuel exports, or the 10 percent threshold for ores and metals (even if the other threshold is already exceeded). Thus this dummy is zero for countries that do not specialize in natural resource exports and also for those that have been exporting resources of the specified type for more than five years. In table 5 this dummy is added to the regressions in table 1, together with an interaction term between this dummy and political stability, which is designed to test whether political stability affects the behavior of new commodity exporters. Neither of these new variables is significant, even at the 10 percent level, for total revenues or total expenditures, but they are each significant at 10 percent level for the fiscal balance. The interaction term has a positive coefficient, and the new commodity exporter dummy has a negative one. This suggests that with low political stability (below 11.91/0.176 = 68) new commodity exporters have somewhat worse fiscal balances, whereas those with high political stability have slightly better ones.

These results imply that new fuel exporters shift to the higher revenue and expenditure patterns of fuel exporters as soon as they become significant fuel exporters. We have also investigated whether there is an anticipation effect whereby