rafmacalaba/datause-encoder-data huggingface dataset:
9as the government becomes a riskier borrower). This has obviously not yet happened, and the reason has to do with the particular arrangements of the government's domestic debt: it has systematically pay _negative ex-ante real Interest rates_ (Chart V). In other words, its real value has been diluted by inflation.

Effectively, then, the inflation tax base has been particularly large, as it has not only included money but government domestic debt papers too. Consequently, the marginal revenue from the inflation tax has been big enough to avoid the need for high inflation tax rates. It is important to emphasize that that fortunate large-base-cum-low-rate scenario hias been sustainable only because **er-ante** real interests were negative. That is, for some reason, there have been investors willing (or obliged) to let their real wealth dilute. Otherwise, they would have demanded nominal rates of return big enough to compensate for their inflation expectations over the bonds' maturity. This would have caused higher nomin i deficits aga, so (ceteris paribus), higher monetization and inflation.7 Naturally, from the ex-post real interest rate series (as plotted in Chart V) we cannot univocally infer the behavior of the _er-ante (or expected)_ one; expectations are not dil-ectly observable. Optimally, more sophisticated proxies for expected inflation could be used (ARIMA fittings, instrumental variables estimation of broadly defined equations, inverted money demand models, etc). Lowever, the sheer magnitude and persistence of the negativity of those real rates make it difficult to believe that it is entirely due to forecast errors. (Even rough survey evidence shows that Egyptian agents know before-hand that government debt papers pay negative real returns).

But, if the public sector has indeed been able to collect huge inflation tax revenues through the dilution in the real value of its debt, questions arise as to who holds that debt, and why. Traditionally, domestit public debt has been sold exclusively to local banks and to the Central Bank of Egypt (CBE) (not to individuals). The former have the option to rediscount it at the CBE up to a maximum; they systematically (and immediately) excersice this optic-., giving further indication that the ex-ante real return on that debt is not appealing (to say the least). Nevertheless, and through various enforcement mechanisms, a quarter of the domestic public net debt remains outside the CBE. (Chart VI).

In principle, it is not very surprising that CBE has been able to afford the 7/ Of course, it could be argued that the government may have unexpectedly accelerated inflation after floating its debt and, so, may have raised inflation tax revenues from already-locked-up investors. However, that is not a very appealing explanation. It implies that over (at least) a decade the authorities were able to deceive private agents without serious loss of credibility and, hence, without the need to resort to higher and higher inflation surprises.
2 The second page adds results for every 25 years from 2000 to 2150 (as well as rates for the 1985-2000 period). This page provides fewer indices, including only the population by age and sex, the birth, death, net migration, and growth rates, total fertility, life expectancy, and infant mortality.

Most of these numbers are standard demographic indicators, but the infant mortality rate requires ome qualification. More properly labeled lqo, it is an index extracted .;:om a Coale-Demeny model life table chosen to provide an appropriate life expectancy. It need not, and often will not, correspond to an observed infant mortality rate.

# Methods and Data Sources

The key elements in chese projections are base-year (mid-1985) total population estimates and age-sex structures, base-period (1985-90) mortality, fertility, and migration rates, and assumed trends in the rates. These elements and additional assumptions that are made will all be described.

The basic methodology is identical to that used last year (see Zachariah and Vu 1988). Those familiar with the previous publication may therefore wish to skip to the summary of results. Data and sources have been updated; an Appendix indicates the sources for all base-year or baseperiod estimates.

# Total Donulation and age-sex comDosition

The estimates of total population in mid-1985 come from diverse sources. Where possible, estimates are Bank projections from reasonably complete censuses or official figures from governments with comprehensive population registration. Other official figures are also used, either from the U.N. Population and Vital Statistics Report or from government publications. When these figures do not pertain to 1985, 'hey are projected forward. Other sources of estimates include the U.N. 1988 revision of world population prospects (forthcoming), the U.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 PoRulation and Vital Statistics ReRort or government publications. Other figures are taken from the U.N. 1988 revision or the
Another indicator of average productivity is yield gaps between Pakistan's farmers which are also significant. While interpretation of yield gaps is difficult, careful controlling for other factors still suggests a yield gap (measured by the difference between best and average farners' yield) of 30 percent in the case of wheat, for instance. For rice, measures show that up to half the potential yield remained to be exploited for average farmers<sup>2</sup> . These yield gaps are associated with lack of inputs when needed, insufficient water, and seed impurities.

Another indicator of productivity problern is that output is less than potential. For wheat between 1966 and 1986, Byerlee and Siddiq found that yield growth was less than would be expected from the application of green revolution inputs. They attributed this to a decline in the quality of the resource base. Indicative of resource degradation is the fact that the yield of high yielding varieties in farmers' fields has not grown since 1970, despite the intensification of fertilizer use. As with wheat, growth in yields of high-yielding varieties of rice has been disappointing. HYV rice yields grew at 0.2 percent per year between 1969 and 1981, and did not grow at all between 1981 and 1990 (Mehmood et al).

The best measure of productivity is total factor productivity, which compares an index of all outputs with an index of all inputs. Two recent studies; find that TFP has stagnated or even declined in post-green revolution Pakistan, i.e. the period since the mid 1970s. While nearly 4 percent output growth in agriculture over the past decade might seem impressive, the TFP statistics suggest that agriculture's true contribution to economic growth is less than this. The most recent study is by Ali and Velasco (1993), who calculate total factor productivity by region and cropping system. Growth in TFP has been extremely disappointing in all the major systems (Table 4). The adverse trend is illustrated in Chart 2 for cropping systems in Punjab.

|Table 4: Annual Growth Rates in To<br>Cropping Systems in Pakist<br>|tal Factor Pro<br>an, 1970-79 an<br>J|ductivity in Various<br>d 1980-89.<br>|
|---|---|---|
|System/Region|1970-79|1980-89|
|Wheat-Cotton, Punjab|-0.62|0.01|
|Wheat-Maize, Punjab|0.42|-8.79|
|Wheat-Mixed, Punjab|-1.92|-1.53|
|Wheat-Rice, Punjab|-2.00|-2.90|
|All Punjab|-1.30|-0.80|
|All Sindh|_-0.5_|-1.70|

Source: Ali and Velasco (1993) 2Yield gap data are taken from Byerlee (1994) and Saleemi (' 994). 3 Cited in Byerlee (1994).

7
17 Due to the importance of the issue of the switch-over costs, there have been a number of efforts at estimating these costs based on rather aggregate unit value data without a microeconomic investigation of product quality.1" The most systematic of these is Kenen (1990). He has estimated the terms-of-trade loss as a result of the switch-over based on 1989 quantities and unit value data for Bulgaria, Czechoslovakia, Hungary, Poland and Romania, but calls his estimates illustrative. Konen's calculations involve the valuation of the import and export bundle using domestically applicable cross exchange rates of the TR to the dollar, with adjustments as he deems appropriate. In the case of Hungary, he estimates it will lose approximately $2.08 billion and its bilateral commodity terms-of-trade will deteriorate by 36.7 percent. Based on the data of table 3, we believe these estimates are too high for 1989; they failed to adjust prices of the industrial consumer goods sector and prices of the machinery sector were adjusted in the opposite direction indicated by the data of table 3.19 In unpublished notes, Marrese has estimated the terms-of-trade effect of the switch-over for the six Eastern European CMEA countries. He assumed that the

**1(** ... continued) of all import and export prices in TR will induce the authorities to appreciate the domestic currency against the TR. A proportionate appreciation of the domestic currency will leave everything unchanged. The example, nonetheless emphasizes that it is best to avoid a calculation which is so dependent on a somewhat arbitrary exchange rate.

'<sup>8</sup> See, for example, Institute of International Finance (1990) and Havlik (1990). No explanation of the methodology is available in these studies.

19Data in Marrese and Wittenberg (1990) suggest a quality adjustment similar to that indicated by table 3. They found that the prices of Hungarian machinery goods sold in the Soviet Union were far lower than comparable Hungarian exports sold in the West (about 80 percent lower even after their adjustment for product quality).
5 The organization of the paper is as follows. Section 2 provides a brief history of health system reforms in the OECD countries, and discusses the ways that choice of one system over another might affect health spending, health status, and labor market outcomes. Section 3 presents a brief summary of the methods used, section 4 the data, and section 5 the empirical results. Section 6 presents the paper’s conclusions.

# **2. OECD health systems, and health sector and labor market outcomes**

The OECD countries—with the exception of the United States, which relies largely on private insurance and out-of-pocket payments, and is excluded from the analysis in this paper— rely largely on general revenues or SHI to finance health care. This section provides a brief review of the transitions from and to SHI, and speculates on the possible impacts on the outcomes analyzed in the empirical analysis. These include: per capita national health spending; amenable mortality; employment; and the formal-sector share of total employment. Too few years’ worth of data on measures of health system throughput—inpatient admissions, length of stay, etc.—are available to allow these to be analyzed, or variables building on these (such as cost per inpatient admission).

## _2.1 Transitions to and from SHI_

In terms of health financing, the (now) 29 OECD countries (excluding the United States) fall into four groups (cf. Figure 1).<sup>5</sup> Group (i) comprises countries that have maintained a SHI system since the early 1960s: this group includes Austria, Belgium, France, Germany, Japan, Korea, Luxembourg, Mexico, the Netherlands, Switzerland and Turkey. Except in Mexico and

> 5 This section relies heavily on the Health Systems in Transition (HiT) series, downloadable from <u>http://www.euro.who.int/observatory/Hits/TopPage, and Saltman and Dubois (2004).</u>
The implementation of the RSE policy varies slightly between each of the five kick-start countries according to terms set out in Inter-Agency Understandings (IAU) between the New Zealand Ministry of Labor and the respective Labor Ministry in the Pacific country. For example, in Tonga the minimum age for participation is 18, the same as Kiribati, Tuvalu and Samoa, but different from the minimum age of 21 in Vanuatu. One of the major areas where some differences occur is in how recruitment takes place. In Tonga, the IAU sets out two recruitment options for New Zealand employers wishing to recruit from Tonga.<sup>2</sup> The first option, which is noted in the IAU as preferred by the Tongan Ministry of Labor, Commerce and Industries (hereafter referred to as the Tongan Labor Ministry), is for the employer to recruit from a “work-ready” pool of Tongan nationals pre-screened and selected by the Tongan Labor Ministry. The second option is for the New Zealand employer to recruit directly, after informing the Tongan Labor Ministry.

The work-ready pool is established by pre-selection and screening at the district level.<sup>3</sup> District and town officers, together with church and community leaders, pre-select and screen candidates. The IAU states that the Tongan Labor Ministry will provide a set of criteria to the districts for the purposes of pre-selection and screening, together with an indication of the number of candidates to nominate, specifying that the number of candidates that can be nominated will be fairly distributed in proportion to population size. These candidates are then all entered into a single database with the Tongan Labor Ministry. New Zealand employers can then either select nominees who all come from a

> 2 New Zealand Department of Labour (2007). 3

> There are 17 districts in Tonga, each with an average population of approximately 6,000 people.

- 4 -
<mark>treatment arm. We anticipate monitoring to lead to an increase in inspector effort and improved customs outcomes as it lowers the expected return to colluding with importers to lower the customs value.</mark>

# **3. Data**

## **3.1. Data sources and representativeness**

The analysis combines a number of unique data sets from different providers.

## **Administrative customs data**

From Madagascar’s customs administration, we obtained highly disaggregated administrative data tracking imports at the transaction level spanning the period January 2016-February 2019 (extracted through the customs management system (ASYCUDA)). For each import declaration, ASYCUDA data cover several standard variables: the port of entry, the HS 8-digit products included (designated as items) and for each item the import value (in Madagascar Ariary), taxes paid (tariff and value added tax as well as exemptions, all in Madagascar ariary) and weight (in kilograms), the country of origin (transit). The data also include more unique and novel variables such as the time stamps for date of registration, date of assessment, and other relevant dates in the customs clearance process (day/month/year/hour), the import value initially declared by the broker on the behalf of the importer, the inspection channel initially recommended by: (i) the risk management system; and (ii) by the RMU (documentary control/yellow channel, physical frontline inspection/red channel or no inspection/blue channel), and unique numeric identifiers for the broker, the importing firm and, importantly, the customs inspector assigned to handle the declaration, which allows us to study inspector performance.

## **Internal control and fraud data**

We merge these ASYCUDA data with additional data sets from the customs administration’s internal control systems. First, we combine these data with information on modifications made by the customs inspector during the customs clearance process to the inspection channel (upgrade in the channel the import declaration was initially allocated by the RMU, as discussed in Section 2), the import value and the import weight. Second, we obtained information on fraud records from the Legal Department ( _Service des Affaires Juridiques et du Contentieux_ ), namely which 13
14 Because both substitution possibilities and labor-intensity are higher in irrigated lands, the positive effect of lower rural product wages offsets the negative impact of lower maize prices.

|Table<br>2: L|and Values<br>end Lan<br>Rain-fed<br>Land|d-Holdingsl<br>Irrigated<br>Land|Land-holdings<br>of<br>Subsistence and<br>Rain-fed<br>**farmers**|Land-holdings<br>of<br>Irrigated farmers|
|---|---|---|---|---|
|Base Case|12.065|40.169|12.065|40.169|
|Case 1: 0 year adjustment, no CNA Program|9.231|40.800|9.231|40.800|
|Case Z: 5 year adjustment, no CNA program|9.443|40.725|9.443|40.725|
|Case 3: 0 year adjustment, with CNA program|9.180|40.668|11.499|40.668|
|case4:5 year adjustment, with CNA program|9.390|40.597|11.703|40.597|
|case5:5 year adjustment, with CIA program,<br>acceass to US F&V market|9.608|42.175|12.030|42.175|
|case 6:as 5. but maize price cuts take last 6<br>years & start**one**year after CIA program|9.726|42.137|12.141|42.137|
|a/million pesos of 1989 per hect.are.|||||

Contrasting the fall in land values with the reduction in rural product wages, it is clear that a larger part of the adjustment falls on land. The reason for this is that labor is more mobile than land. Labor can be reallocated within agriculture towards other crops with much more ease than rain-fed land, and in addition some of the impact on labor is shifted to urban workers through rural-to-urban migration.

Figure 2 shows the migration response. Note first that under the reference case migration is substantial. Long-term productivity trends do not favor agriculture, particularly not rain-fed agriculture. This, together with the exhaustion of land on the extensive margin, makes it clear that even with current maize policies future migration will be substantial. The model predicts a cumulative migration of almost 1.2 million workers over the next decade. Such large migration suggests that maize protection as a rural
|Recreation and spare time section was much more complete, it<br>solicited information about: sport and camping equipment; swimming<br>pools, gym, tennis courts expenditures; tickets to sporting shows,<br>concerts, theater, museums, etc.; lottery tickets, photographic<br>equipment, musical instruments, amusement items, etc.|Recreation expenditures were collected using a recall period of 12 months (Section M). It<br>included: “sports and hobby equipment, musical instruments, toys” and “film, film processing,<br>camera”. There were only two questions in this category and the reported values were low<br>compared to the HBS.|
|---|---|
|Detailed questions about the main dwelling expenditures included<br>questions on: electric power; fixed telephones; mobile phones; TV<br>subscriptions; internet subscriptions; water; common expenditures<br>such as lighting cleaning on primary and secondary buildings; gas,<br>charcoal, kerosene, coal, and firewood.|Utility expenses included: matches (7 days recall), electricity, gas, water, charcoal, light bulbs<br>(30 days recall).|
|Miscellaneous non‐food expenditures included around 25 detailed<br>questions about furniture and furnishing, tools and appliances for<br>household maintenance, small electric household appliances, dishes,<br>utensils and domestic workers.|‐<br>Household expenses had a 30‐day recall period and included: milling fees and grain;<br>household cleaning products (dish soap); wages paid to servants; repairs to household and<br>personal items; carpet, rugs, drapes, curtains; linens, towels, sheets, blankets; mats for<br>sleeping or for drying maize flour; mosquito nets, mattresses; repairs to consumer<br>durables.<br>‐<br>Miscellaneous non‐food expenditures had a 30‐day recall period and included: bar soap<br>(body soap or clothes soap); laundry soap (powder); toothpaste, toothbrush; toilet paper;<br>glycerin, Vaseline and skin creams; other personal products (shampoo, razor); insurance<br>(health or auto); other costs not stated anywhere.|
|There is a section on travel, holidays and hotels outside and inside<br>Tanzania and another one on restaurants.|The non‐food consumption did NOT include expenses incurred at restaurants (This is different<br>from HBS). However, the NPS collected detailed information on food consumed outside the<br>home (which included full dinner with a 7‐day recall period) in Section F.|
||Alcohol expenditures included information from the non‐food question as well as from Section<br>F (food outside household), which included beer, wine, or hard drinks consumed outside the<br>household in the past 7 days.|

29
Research also seeks to identify the underlying financial market imperfections that limit entry into entrepreneurship. The literature stresses two main financial constraints limiting entrepreneurship. In Evans and Jovanovic (1989), the financial constraint is due to limited liability, where wealth facilitates the pledging of collateral that limits default. With limited liability, borrowing does not automatically imply entrepreneurs do not have the wealth to selffinance a project. Agents can supplement their personal stake in entrepreneurial activity by borrowing to diversify risk. Hence borrowing can increase with wealth. Another source of financial constraints can be moral hazard, as in the model of occupational choice featured in Aghion and Bolton (1997). In this case, entrepreneurial effort is unobserved and repayment is only possible if a project succeeds. Hence with moral hazard, poor borrowers who have little to lose if a project fails, also have little incentive to be diligent, increasing the likelihood of project failure and default. This requires lenders to charge higher interest rates to low-wealth borrowers. In contrast to the limited liability case, when there is moral hazard and wealth increases, constrained entrepreneurs will increasingly self-finance and borrowing diminishes. In a moral hazard environment, all entrepreneurs who borrow are constrained. Paulson, Townsend and Karaivanov (2006) estimate a general equilibrium model using Thai survey data to see if they can distinguish limited liability from moral hazard. Their results indicate that the dominant source of the financing constraints preventing new entry is moral hazard and that limited liability alone cannot explain differences in entrepreneurship across Thailand.

General equilibrium models illustrate important aspects of the relationship between improved access to finance and the development process. Within the context of a controlled laboratory, one can carefully scrutinize the mechanisms linking finance and inequality. As suggested by Jeong and Townsend (2007), however, general equilibrium models do not capture the full complexity of even the simplest economies.

# **3.1.2 Natural policy experiments**

Natural policy experiments represent an additional way of assessing the impact of financial development on economic opportunity. Instead of building a general equilibrium model and examining the impact of parameter changes within the model, natural policy experiments policies in Thailand might have slowed the growth of financial intermediation below the endogenous growth rate that would have resulted from increasing per capita income, as predicted by Greenwood and Jovanovic (1990).

22