rafmacalaba/datause-encoder-data huggingface dataset:
|Table|6: Comparative<br>(|Trends In Produci6 Prices During the 19<br>% Increase or Decrease)|70.|
|---|---|---|---|
|Crop|1969/7<br>Nonminal|0 1973/74<br>1973/<br>Real<br>Nominal|74 - 1979/80<br>Real|
|Export<br>Crops||_<br>_||
|Cashew|0.0|-38.4<br>91.2|-24.0|
|Coffee|7.2|-33.9<br>_<br>104.7|-18.6|
|Cotton|3.8|-36.0<br>157.3<br>|+2.3|
|Tobacco|29.1|-20.5<br>r<br>_58.1_|-37.1|
|Domestic Crops|i|_<br>__|_ _|
|Maize|17.9|-27.4<br>202.9|+20.4|
|Paddy|9.6|-32.5<br>163.2|+4.6|
|Groundnuts|25.0|-23.0<br>247.8|+38.3|
|Sunflower|31.0|-19.3<br>154.5|+1.2|

Source: Eliis (1982) Shortages of raw materials were one of the main factors undermining the viability of the new processing factories. Even though more than half of the total production was locally processed between 1980 and 1982, this level of production was insufficient to operate more than a few of the factories. Two of the completed factories were never operated, these lacking access to reliable sources of water and power. Of the remaining ten factories, only four operated in 1982/83 and only two operated during the subsequent two seasons.

Raw materials availability was not the only problem. With poor grading of raw nuts and with power and water interruptions, the quality of the finished product was uneven and generally below international standards. Tanzanian kernel exports thus brought price discounts of 35 to 50% from the Indian price.2Y Tbis, together with the favorable prices for raw nuts imported by India, resulted in the Tanzanian factories accruing negative value-added in 1981 and 1982 and sustaining continued financial losses.

# Reversing the Downward Slide (1985-1991)

In 1985, CATA was replaced by the Tanzania Cashew Nut Board and the cooperative unions were re-established. Crop procurement arrangements would henceforth resemble those prevailing during the early 1970s, whereby primary cooperative societies would procure the crop from farmers (paying official pan-territorial prices for the two grades), using funds supplied by cooperative unions. The unions would in-turn transport the crop to the TCMB which would then either process the nuts or export them 5 Over the 1981-83 pexiod.

18
FIGURE 1. DISTRIBUTION OF KKM RULE OF LAW SCORE FOR THE WORLD AND POST-COMMUNIST COUNTRIES <!-- Start of picture text --> 1996 2006<br>-2 0 2 -2 0 2<br>rule of law rule of law<br>world post-communist countries world post-communist countries<br>k d e n s ity ru le _ la w k d e n s ity ru le _ la w<br>0 0<br><!-- End of picture text --> The challenge is to try to identify the role that expected political alternation (measured by lagged cumulative alternation) plays in explaining the variation in rule of law scores. One source of difficulty is simultaneity in the relationship between alternation and quality of governance.

Causation may run in both directions. Expected alternation may change firm behavior in ways that foster the emergence of the rule of law, but greater adherence to the rule of law could increase the likelihood of alternation by making elections fairer, or decrease the likelihood of alternation by improving outcomes and thus voter satisfaction with the incumbents. Lagging political alternation, as we do, does not fully resolve this problem because the quality of governance in one period might affect the quality in later periods.

A second difficulty is spurious correlation. For example, it might be that that the opportunity to join the European Union by virtue of proximity to Western Europe causes both high alternation and good governance, and that there is no causal connection between the two. The 5
7.6 The case of a stable or slightly increasing dispersion of returns to material inputs in the mixed regime commands special interest. If prices and plan shares of outputs and material inputs are coordinated across enterprises, as intended by the policy of "gaojin-gaochu", we would expect greater uniformity of average and marginal returns relative to the case in which plan shares of outputs and inputs are uncoordinated. The correlation of 0.23 between output and material plan shares reveals only modest coordination of price regimes for inputs and outputs, within the subset of enterprises operating partly within and partly outside the plan. This condition may help to explain why, even as returns to capital and labor ' -monstrate convergence, we see no tendency for returns to materials to converge within this sample of mixed regime enterprises.

The Contribution of Relative Price Changes: To investigate the relationship between the convergence of enterprise efficiency and changes in enterprise product prices, we calculate three sets of correlation coefficients. These are: 1980-89: corr(ln NTFPS0,PDg **B8)** (4a) 1980-85: corr(ln NTFP.,PD.m85) (4b) 1985-89: corr(ln NTTPg,PDg5,9) (4c) where the log of NTFP is for the years t = 1980, 1985 and 1989 and the price deflators (PD) are ex-factory price deflators for 1989 with a 1980 base price, 1985 with 1980 base and 1989 with 1985 base.<sup>19</sup> 7.7 The estimated correlation coefficients, shown in Table 5, can be interpreted in the following way. Coefficients with a negative sign indicate that enterprises with relatively low levels of NTFP at the beginning of the relevant period experienced higher than average increares in their product prices. In this case, changes in relative prices tended to contnbute to a more equal dispersion of enterprise efficiency.' Conversely, a positive coefficient implies that enterprises with the highest initial levels of measured efficiency experienced relatively large increases in product prices, causing the dispersion of measured NTFP to become more pronounced.

7.8 For the full sample, the results show that for the entire period, 1980-89, as well as for each of the sub-periods, 1980-85 and 1985-89, changes in relative product prices generally

**19** Obtained from JRZ (forthcoming), Table 4.

X Assuming limited instances of overshooting.

_Asuse&ng Gains In Fficnt Producion Among China 's Industrial Enterprises,_ by Gary H. Jefferson and Xu Wenyi 13
# **Media Sentiment and International Asset Prices**

Samuel P. Fraiberger (World Bank & NYU), Do Lee (IMF), Damien Puy (IMF), Romain Ranciere (USC, NBER & CEPR)<sup>1</sup> <u>Keywords: Asset Pricing, Capital Flows, Behavioral Finance, Investor Sentiment, News</u> Media, Natural Language Processing.

<u>JEL Codes : F32; G12; G15; G41</u> 1 We are grateful to the IMF Big data initiative for financial support, as well as participants in the CEPR International Macroeconomic and Finance meeting and the Macro-Financial Research Seminar of the IMF for helpful comments and suggestions. Authors’ email: <u>sfraiberger@worldbank.org, Dlee2@imf.org, Dpuy@imf.org, Ranciere@usc.edu. Samuel Fraiberger is also visiting scholar at Northeastern and Harvard</u> University. The views expressed in this paper are those of the author(s) and do not necessarily represent the views of the IMF, the World Bank, their Executive Boards or their management.
Health services are provided through the National Health Fund (NFZ), which is financed from health contributions. NFZ contracts health service providers and reimburses the cost of services provided to insured clients. The Ministry of Health directly funds a few highly specialized services along with pre-hospital emergency services. Contributions to the public system are mandatory for all classes of workers except those on pay-for-performance civil contracts, so most workers are covered, either directly or through family since anyone with uninsured family members can insure them. Persons receiving contributory benefits like pensions or unemployment benefits are also covered. In addition, an uninsured person without insured family members may have the right to health services financed from public money if they are: (a) below the age of 18, (b) pregnant or in the period of confinement, or (c) have income below the legal poverty threshold. In practice, only non-poor individuals who do not pay voluntary health insurance contributions are uninsured and would have to bear the cost of health services. Private insurance is available and is usually provided as a top-up to public health insurance, with people opting-out from publically provided services for primary or dental care, as well as some in-patient curative services.

# **III. Data sources, method, and assumptions**

## **Data Sources**

Data for 2014 were used to conduct this incidence analysis study in line with the availability of survey data. Specifically, we used the 2014 Poland Household Budget Survey (PHBS) by the Central Statistical Office of Poland (GUS). In contrast to the EU SILC, the PHBS contains both income and expenditure data, along with demographic and household characteristics, thus enabling the identification of direct and indirect taxes and benefits across the distribution. Following standard practice, the collected survey is corrected for nonresponse through sample grossing-up weights. However, these weights take into account and correct only for the original data sample design probabilities and do not reflect the additional bias in survey participation given the characteristics of participating households. For example, the survey over-represents children in the survey and people who live abroad for more than 12 months, so that the age structure does not match that of the census. In order to match the age structure of the population along with critical characteristics of the tax and benefit system, we follow Myck and Najsztub (2015) to correct population weights in the PHBS.<sup>8</sup> Household survey data are combined with data from GUS Statistical Yearbooks, National Income Accounts and public finance accounts from the Ministry of Finance. This information is complemented with administrative data from the Social Insurance Institution (ZUS), National

> 8 In particular, in addition to correcting for the age structure, weights are calibrated to ensure that the number of taxpayers, those paying health insurance, receiving pensions, unemployment benefits, and those benefiting from joint taxation are in line with administrative data.

11
matching. Take-up rate in the ENLACE test is high, 87.3 percent of the population finishing lower secondary in 2010 and 89.4 percent of the population finishing upper secondary in 2013. The difference in graduation or survival rates between the ENLACE panel and administrative data is not trivial (6 or 7 percentage points, depending on the grade), but it is reassuring that most of the attrition observed in the panel is explained by grade repetition and school dropouts, subjects we want to examine in this paper.

Two additional issues should be addressed. First, the states of Oaxaca and Michoacán are excluded from the panel because they had very low test take-up rates in 2007 (65 percent and 49 percent, respectively) and even lower in 2010 (34 percent and 0.7 percent, respectively). Second, the states of Nuevo Leon and to a lesser extent Coahuila and San Luis de Potosi have two-year upper secondary schools (as opposed to three-years), reducing substantially the matching between lower- and upper-secondary students in these states. Despite the significant share of two-year upper secondary schools in these states, they were kept in the sample.

ENLACE included a context questionnaire which was applied to a random sample of students (and parents in the case of primary and lower-secondary students) to gather information on socioeconomic characteristics, schooling aspirations, and studying habits. Although the sample size was relatively large,<sup>3</sup> including controls from the context questionnaire reduces considerably the number of observations in the regression analysis, as we will show in the results section.

We use student identification variables to identify 20,187 twins in the ENLACE panel. We define twins as students with identical first and second last names and birthdays, and who attend the same school in Grade 6.<sup>4</sup> After the matching, the twins identified account for 1.02 percent of the ENLACE panel in 2007, a level close to the prevalence of multiple pregnancies in Mexico.<sup>5</sup> Table 1 presents summary statistics for the ENLACE panel dataset. Columns 1-3

> 3There were 7,557 observations for students and 13,766 for parents.

> 4The CURPs as well as the students’ names and dates of birth are confidential data protected by Mexico’s personal information laws. We were able to identify the sample of twins due to a close collaboration with SEP for the present study.

> 5This corresponds to 10,350 pairs of twins, 93 groups of triplets, and four of quadruplets.

9
# **Appendix 3: Matching program data to administrative data on formalization**

This appendix describes the protocol to match businesses in the administrative database on formalization provided by the GUFE (around 550 businesses every month) with the program data (3,596 informal businesses prior to the program start).

# **Information available:**

We had the following information in both databases:

- Surname of the business owner. It can be written with different spellings in each database.

- Between 1 and 5 first names. In GUFE data we usually have more than one first name. In the program data we only have one first name in most of the cases.

- The business activity as described by the owner (no codes). The business activity is missing for 30% of businesses in the GUFE data.

- Business addresses. In the GUFE data addresses were given by the business owner whereas in the program data, we are using “official” addresses used by the tax administration (there are 144 neighborhoods in Cotonou). In practice only neighborhoods can be matched. In GUFE data, there are few missing variables and some cases for which the neighborhood does not belong to the official list of neighborhoods.

- Gender of the business owner.

- Phone number of the business owner.

# **Definition of a match:**

We consider it to be a match if: (i) the surname, at least one first name, the activity, and the neighborhood match or (ii) if surname and at least one first name match and either the activity, the neighborhood or the phone number also match, and the others are missing (or does not exist for the neighborhood or the phone number).

# **Method of matching:**

We used first the STATA command “reclinck” designed for fuzzy name matching. This command uses record linkage methods to generate matching scores. For this first step, we used tree variables: surname, first name and gender. As a second step, we looked manually (in an 56
once the concentration rate has been assessed. If it turns out that a few companies have a fairly high market share, the approximation is likely to be weak. The second is that it does not lead to the simple decision rule equivalent to the rule adopted by the US Department of Justice.

We suggest the following adaptation which could serve to any analyst interested in assessing supranational concentration rates:

- (i) if both HHmin and HHmax are below 1000, there is no concentration problem;

- (ii) if both HHmin and HHmax are over 1800, there is a clear case of concentration;

- (iii) if HHmax is above 1800 and HHmin between 1000 and 1800, the test is inconclusive but there are good reasons to be concerned with market power from concentration so the rule should read as inclusive but presumption that “yes there is concentration”

- (iv) if HHmax is between 1000 and 1800 and HHmin below 1000, then the test is inconclusive but the presumption is that “no there is no concentration”.

- (v) if HHmax is above 1800 and HHmin below 1000, then the test is really inconclusive.<sup>9</sup>

# **3. The sector specific assumptions and results**

This section briefly discusses the basic data and presents the results of the computation of the various maximum and minimum HHI indices. Most of the data is from 2003, unless otherwise specified. For every sector, we summarize the information. We report the market indicator selected, the minimum and maximum HHI computed, the number of operators covered by the indicator and their aggregate market share, the number of countries as well as the total number of contract or “cases” included in the database. The concentration index is computed for the main regional markets of the world as well as for the global market. Table 1 summarizes the information collected.

> 9 Note that a large discrepancy between the lower and upper bound is not necessarily a signal of concentration.

8
that all countries will grow at that faster rate from now until 2030? Table 4 gives the regional breakdown of where the world will be if this more optimistic scenario prevails over the next 13 years. Unfortunately, even with this rosier projection, global poverty will reach 5.5% and so we will still be far away from our target. Table 4 gives the regional breakdown of where the world will be if this more optimistic scenario prevails over the next 13 years.

# **Table 4: Poverty in 2030 at $1.25 per day PPP**

**(Assuming country-specific National Accounts-based growth rates over the past 10 years)**

|**Region**|**Headcount Poverty**<br>**(%)**|**Number of Poor**<br>**(millions)**|
|---|---|---|
|East Asia and Pacific|0.2|3.5|
|Europe and Central Asia|0.1|0.6|
|Latin America and the Caribbean|3.9|27.3|
|Middle East and North Africa|3.8|16.4|
|South Asia|2.5|52.0|
|Sub-Saharan Africa|26.4|356.1|
|**Total Developing World**|**6.3**|**455.9**|
|**World**|**5.5**|**455.9**|

Source: World Bank Poverty and Inequality Research Group staff estimates.

Certainty about the economic future is the province of the foolish and I have no intention to go down that path. It is possible that growth over the next decade and a half will be much faster than what we have seen or that growth will naturally become more munificent toward the poor. However, if the past is any indicator of what is to come, these scenarios must be discounted as unlikely. In brief, based on the simple projections done above, it appears reasonable to assert that reliance on growth alone is unlikely to get us to the target. We need policy interventions, from well-designed micro-interventions targeted at the poor (Banerjee and Duflo 2011), through reasonable taxation policy, to a stable macroeconomic environment. Further, it is arguable that even if growth alone will do the job, why not step on the other pedals to move faster toward the target of ending chronic poverty and mitigating the great and shameful inequalities that prevail in the world today?

14
# **1. Introduction**

In recent years, a host of developing countries have experienced robust economic growth but to sustain such growth prospects these countries need to significantly increase investment in infrastructure.<sup>1</sup> To bridge the investment gap they currently face, developing countries need both to improve the quality of public spending in infrastructure as well as to attract more private capital. Rapid urbanization and economic growth, demographic trends, and climate change are all but some of the challenges that developing countries have to face and that call for an acceleration of public and private investments to rehabilitate, upgrade, and expand their infrastructures. Moreover, sustaining good quality of infrastructure service delivery requires a better composition of the infrastructure stock, a good level of maintenance, and an appropriate sequencing of institutional reforms across sectors including the financial sector.

Low or non-existent sovereign credit ratings and the absence of proper financial instruments to mitigate risks inherent to infrastructure projects are among the factors that limit private commitments in developing regions’ infrastructure projects.<sup>2</sup> After a sharp decline from relatively high levels in the mid-1990s, annual private investment in infrastructure in developing countries has stabilized in the 11-to-16 billion USD range since 2001 with a debt-equity distribution that varies across regions. For instance, while bonds have become an important tool for financing infrastructure investments in the Latin America and East Asia regions, representing, during the 1996-2004 period, 29% and 14% of infrastructure financing respectively, bond financing is nearly non-existent in the Middle East and North Africa region where about 98% of private investments in infrastructure has been in the form of loans from banks.

Because they mobilize lumpy investment and deliver future payoff in local currency, infrastructure projects financed with hard currency are exposed to currency devaluation and to the volatility of interest rates. Therefore, strengthening the capacity of local financial markets so they can extend debt and equity financing instruments denominated in local currency in competitive terms is crucial to

> 1Empirical work (Yepes, 2008) suggests that developing countries need to invest approximately 5 to 7 percent of their GDP in infrastructures to be able to maintain economic growth in the period 2008-2115 at their current average rate of 5 percent. For a survey on the relationship between infrastructure development and growth, see Straub (2008).

> 2The experiences of Cameroon, Nigeria, and Tanzania have indeed shown how macroeconomic, institutional, and financial reforms can increase longer-term local currency financing for banks, and therefore progressively increase local bank financing for infrastructure projects.

2