rafmacalaba/datause-encoder-data huggingface dataset:
- 24 -

GDP ratio was the depreciation of the real exchange rate. As noted earlier, although real exchange rate contributed to improving external creditworthiness by increasing exports, it nevertheless. raised the debt-GDP ratio through its impact on capital loss on the stock of external debt. The less rapid increase in the total debt-GDP ratio was partly due to the revenues from seignorage, which Mnded to offset almost a tird of the impact of the primary deficits. Moreover, the debt dynamics component continued to dampen the adverse effects of high deficits because of Pakistan's strong growth performance, even though interest rates turned positive in real terms.

Table 13: Pakistan: Trends in Debt. Interest Payments, Primary Balance and Seignorage as percentage of GDP

|Year|TDGDP|GINTGDP|PBGDP|SENGDP|Treasury use of<br>SENGDP|
|---|---|---|---|---|---|
|1972|72.5|1.8|- 4.1|3.6|2.6|
|1973|79.3|1.7|- 6.5|2.2|0.3|
|1974|67.4|1.7|- 7.0|4.8|-2.3|
|1975|59.6|1.5|-11.4|2.0|0.7|
|1976|60.9|1.9|-11.0|2.3|1.6|
|1977|61.6|_1.9_|- 9-0|2.8|3.0|
|1978|595|_1.9_|- 8.1|2.2|2.1|
|1979|_582_|2.0|- 9.6|3.6|2.6|
|1980|_55.2_|2.2|- 8.2|2.4|0.7|
|1981|49.8|2.0|- 6.5|1.2'|-0.6|
|1982|_55.4_|2.2|- _6.5_|2.3|2.3|
|1983|_.59.7_|2.9|-8.2|1.9|0.9|
|1984|57.6|3.4|- 6.5|2.2|2.2|
|1985|63.9|3.5|- 5.2|1.2|0.4|
|1986|76.0|3.8|- 4.3|2.8|1.7|
|1987|84.4|4.2|- 4.0|3.0|1.3|
|1988|76.0|4.9|- 3.6|1.6|1.0|
|1989|80.4<br>.|_5.0_|- 2.7|2.5|1.8|
|1990|83.9|_5.5_|- 1.0|2.5|2.0|
|1991|84.3|_5.0_|- 3.8|4.1|3.3|
|1992|80.6|5.3|- 2.2|1.4|0.7|

Source: Estimatedfromdataavailable inEconomicSurvey, Ministry of Fmance(various issues); International FmancialStatstics (IFS) of DIF and the Monthly Bulletin of the State Bank of Pakistan (SBP). See also Annex Table A3.
# **3 Conceptual Issues**

What is different about trade in services compared with trade in goods?<sup>7</sup> Services are very heterogeneous, and span a wide range of economic activities. Conceptually, this diversity masks a fundamental economic function that many services perform: they are inputs into the transaction side of production. One dimension of this ’input function’ is that services facilitate transactions through space (transport, telecommunications) or time (financial services) (Melvin, 1989). This transaction aspect is additional to the role of services as inputs in production. Services are frequently direct inputs into economic activities that determine the productivity of the ’fundamental’ factors of production - labor and capital - and that generate knowledge, goods and other services. Education, R&D and health services are examples of inputs into the production of human capital. In this section, we focus conceptually on channels or mechanisms by which services may be important in an economy wide context, and on the mechanics of international services trade. The following section builds on this discussion when mapping from the issues spelled out here to discuss practical implementation in numerical models.

## **3.1 Downstream linkages and direct cost channels**

For applied general equilibrium modeling, the relevance of a focus on services largely hinges on the strength of economy-wide linkages. To illustrate the strength of such linkages, in this sub-section we discuss recent evidence, focusing on how value added structures, and services linkages to trade have evolved over time. Thanks to repeated updating of the GTAP dataset, we have data for both cross border linkages in recent years, and how these have changed since the early 1990s.

Table 1 provides a broad overview of services exports (excluding transport and distribution) on a gross and value added basis for Brazil, China, Korea, and the United States (Francois, Manchin, and Tomberger 2012). There are three sets of values in the table. The first, ”Gross Values” refers to exports by sector from standard balance of payments statistics. The second, ”Direct VA” refers to the direct value added contained in exports. These values reflect an adjustment to gross export values to include only the value of primary factors employed directly within a given sector in production for exports (or domestic absorption). The last set of values ”Total VA”reflect adjustments to gross export values to include both direct value added, and also value added in a particular sector exported indirectly through downstream linkages. This includes, for example, business services value added embodied in the production of steel for export.

> 7What follows draws in part on Francois and Hoekman (2010) and Francois, Manchin and Tomberger (2012).

6
earthquake mortality prevention than rich countries such that they respond less to an elevated earthquake propensity than rich countries.<sup>12</sup> Model 3 interacts the quake propensity variable with dummy variables for democracies and autocracies. Consistent with the argument that the prospect of elections should make democratic rulers more sensitive to earthquake propensity, the estimated coefficient for quake propensity in democracies is 2/3 larger than the respective estimated coefficient in autocracies (-.0044 versus -.0029), although the difference between the two estimated coefficients is not statistically significant. A one standard deviation increase in quake propensity is estimated to decrease expected mortality by 49.4 per cent in democracies and by 42.2 per cent in autocracies, if other variables are held at mean values. The statistically significant coefficient of the linear democracy term indicates that democratic governments suffer significantly fewer earthquake deaths at all levels of quake risk.

Model 4 further distinguishes among both democratic and non-democratic regimes. The specification distinguishes among autocracies according to whether the age of the ruling party at the time the current leader came to power – ruling-party institutionalization – is above or below the median for that variable (see Gehlbach and Keefer 2009). Democracies are distinguished according to whether they are above or below the median years of continuous democratic experience.<sup>13</sup> Since the Database of Political Institutions starts in 1975, and is 12 Given the strong correlation of high income with good governance, it is difficult to exclude an alternative explanation for this finding, namely that poor countries are also generally governed by rulers who are less sensitive to the broad public interest and, therefore, less responsive to increases in earthquake risk. However, this interpretation of the risk-income interaction is also consistent with our theoretical arguments.

> 13 Since we use two different operationalizations for democracies on the one hand and autocracies on the other, there is no single variable which could be added to the estimation model to capture the direct effects of within-regime type heterogeneity on mortality. Results, though, are substantively similar, with all variables statistically significant as before, if we re-estimate the model with two additional variables, namely the number

27
9 betw..en 9,506 individual tariff lines) and that 25 of these items faced a zero MFN tariff. However, on 106 line items Angola received a zero preferenc- rate (second column from the left) with the result that 131 or 98 percent of Angola's exports enter the EEC free of duty.<sup>8</sup> In only two tariff lines (involving $10,000 in total exports of fiesh grapes and strawberries) did Angola _not_ receive preferences and, as a result, paid the full MFN tariff. In short, Angola could experience trade gains from MFN tariff cuts on these two product. and experience export losses on 131 tariff line items.

The data for other African exporters reveal a situation similar to diat for Angola. Preferences result in at least 97 percent of each African country's exports entering the Community free of duty, and in two cases (Congo and Guinea) no duties are paid on any line item. Table 3 also shows how markedly the profile of protection against African countries' exports differs from that of ReDublic of Korea and Taiwan (China). Omy four percent of the latter's eyoorts (less than 200 tariff line items out of a total of over 4,000) have EEC duty free access -- not due to preferences but because of a zero MFN rate. These comparisons underscore the differences in strategies regarding tariffs in the Uruguay Round. The Asian NICs would clearly have a major interest in broad-based MFN tariff cuts, while African countries would want to limit MFN duty reductions to preserve their preferences.

8The picture is complicated somewhat by quotas or ceilings on some products receiving preferences. After these predetermined ceilings are exceeded further imports are taxed at the prevailing MFN rate. Comprehensive tariff line level information on how often these ceilings have been hit is not available, but aggregate statistics of preference utilization ratios provided by the EEC sugges **t** hat African countries' exports generally do not exceed ceilings.
# **4. POVERTY AND VULNERABILITY IN RURAL HAITI**<sup>**27**</sup>

In order to fully characterize the determinants of poverty and vulnerability in rural Haiti, a unique survey can be used to assess the impact of idiosyncratic and covariate shocks on economic well-being (such as household consumption or income). This household survey on Haitian food security and vulnerability has been conducted in 2007 in rural areas. The number of households is around 3,000 distributed in 228 communities. It contains quantitative information on household consumption, production, income and assets as well as a good deal of qualitative information on perceived shocks, coping strategies, response capacity and other risks.

# **4.1.Methodology**

# **_Vulnerability to Shocks_**

In this section, we explore the relationships between consumption or income, on the one hand, and various idiosyncratic and aggregate covariates on the other hand. We suppose that households are imperfectly insured against shocks and have limited access to credit. So, assuming uninsured exposure to risk, we can write: where _yij_ is the consumption of household _i_ in community _j_ , _X ij_ is a vector of household characteristics, _Sij_ is a vector of observable shocks,  _j_ is a community specific effect and  _ij_ is the error term.

In the above equation, two parameters are of particular interest. First, we should assess whether  is significantly different from zero that is whether observable shocks have significant impact on economic well-being. Second, in order to ascertain whether observable shocks have different impacts depending on household and community characteristics, we should also assess whether  is significantly different from zero.

Community specific effect  _j_ can be modelled either as a fixed effect or a random effect. In what follows, we will see how to model this unobservable component within a two-level linear random coefficient model. Finally, we should take into account the possibility that the error term  _ij_ can be correlated with observable household characteristics and shocks so that parameters estimates might be biased.

Following Datt and Hoogeveen (2003), equation (1) parameters estimates are used to measure the impact of the observable shocks on poverty. First, the counterfactual welfare index ( _yij_  ) is derived from the difference between actual consumption ( _yij_ ) and the estimated impact of observable shocks that is:

> 27 See Echevin, D. (2010b) for a complete version of this section.

23
FIELD, Erica & TORERO, Maximo. Do Property Titles Increase Credit Access among the Urban Poor? Evidence from Peru. _Research Program in Development Studies_ , Working Paper No. 223, Princeton University, 2002.

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GROOTAERT, Christiaan. Child Labor in Côte d'Ivoire. In: GROOTAERT, C.; PATRINOS, H. (Ed.). _The Policy Analysis of Child Labor: A Comparative Study_ . World Bank, Washington, DC, 1998.

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HECKMAN, James & HOTZ, Joseph. Choosing Among Alternative NX Methods for Estimating the Impact of Social Programs: The Case of Manpower Training. _Journal of the American Statistical Association_ , vol. 84, pp. 862-874, 1989.

The International Labour Organization. _International Labor Review_ , vol. 141, No. 1-2, 2002.

JACOBY, Hanan G. LI, Guo & ROZELLE, Scott. Hazards of Expropriation: Tenure Insecurity and Investment in Rural China. _The American Economic Review_ , vol. 92, issue 5, pp. 1420-1447, 2002.

JIMENEZ, Emmanuel. Tenure Security and Urban Squatting. _Review of Economics and Statistics_ , vol. 66, issue 4, pp. 556-567, November 1984.

JIMENEZ, Emmanuel. Urban Squatting and Community Organization in Developing Countries. _Journal of Public Economics_ , vol. 27, pp. 69-92, 1985.

KASSOUF, Ana L. _Aspectos Sócio-Econômicos do Trabalho Infantil no Brasil_ . 123p. Ministério da Justiça, Secretaria de Estado dos Direitos Humanos. Brasília, 2002.

LANJOUW, Jean O. & LEVY, Philip. Untitled: A Study of Formal and Informal Property Rights in Urban Ecuador. _The Economic Journal_ , vol. 112 (482), pp. 986-1019, 2002.

MEYER, Bruce. Natural and Quasi-Natural Experiments in Economics. _Journal of Business and Economic Statistics_ , vol. XII, pp. 151-162, 1995.

MICELI, Thomas J.; SIRMANS, C. F. & KIEYAH, Joseph. The Demand for Land Title Registration: Theory with Evidence from Kenya. _American Law and Economics Review_ , vol. 3, No. 2, pp. 275-287, 2001.

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19
47 Table 8: 2SLS Instrumental Variables Results for the Augmented Mankiw, Romer and Weil Model with Pooled Time-Series and Cross-Section Data for 1971-1991.

||Unrcstrcted Model|Rcstricted Model<br>Unrcstricted Modcl|Restricted Model|
|---|---|---|---|
|Indepcudcnt Vaibles|Depcndent Variabic R|aLe of Growth of Real CDP per Capita||
|InY0|0.0004|0.00003<br>-0.0006|0.00007|
||(0.560)|(0.957)<br>(-0.659)|(0.088)|
|In (U/GDP)<br>1|0.024|0.024<br>0.026|0.016|
||(3.729)*|(3.621)*<br>(2.885)*|(2.1298)|
|In (Lifecxp.),|0.015|0.010<br>0.028|0.007|
||(3.061)*|.295)"<br>(2.569)*|(1.062)|
||-0.88|-0.034<br>-1.75|-0.024|
|(n+g+6).|(-2.088)*|(-3.428)*<br>(-2.744)*|(-1.766)#|
|Aln[(SP)/Po1]||0.047|0.016|
|||(0.468)|(0.162)|
|-.ln[(SPY)/P,.||-0.035|-0.027|
|||(-2.315)*|(-2.541)*|
|i2|.0.0342|0.0329<br>0.0583|0.Q402|
|F|4.508|4.454<br>3.805|3.097|
|P-value|0.0014|0.0043<br>0.0011|0.0094|
|N|396|396<br>375|375|

An Asterisk denotes statistically significant at the 5 pernent levcl. The symbol V denotes statistically significalit at the ten percmt level. Numbers in parentheses are t-ratios. Instrumental variables are lagged values and all othcr exogenous variables in the model.
Table 10: Alternative estimations: Electricity provision and tax compliance

||(1)|(2)|
|---|---|---|
|Electricity Access in Town|1.659**||
||(0.810)||
|Reliable Electricity in Town||0.809***<br>(0.307)|
|F Stat|8.708|63.836|
|Observations|33423|33419|

Notes: Dependent variable is tax compliance index derived from a principal component analysis. Robust standard errors in parentheses are clustered at PSU level. F-stat is the IV first-stage F test of instrument strength. All regressions in the table control for individual/household-, community-, and country-level variables. Individual/household-level variables include the respondent’s gender, age, education, quality of housing, employment status, size of the household, and ownership of a TV or mobile phone. Community-level variables include the presence of piped-water systems, paved roads, schools, and hospitals. Country-level variables include total population, GDP PPP, share of the urban population, population density, value added of agriculture and service as a share of GDP, total natural-resources rents (% of GDP), IIAG score, and ethno-linguistic fragmentation score.

> _∗_ Significant at 10 percent level

- _∗∗_ Significant at 5 percent level

> _∗∗∗_ Significant at 1 percent level

32
# **References**

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29
# _Approach in this study_

None of the depreciation rates in the above studies have firm empirical basis. As a result, the depreciation rates used here have been taken from figures published by the Bureau of Economic Affairs in the United States (Table 5), with the exception of housing where official Chinese depreciation rates are available. The US data distinguish three types of assets at the aggregate level:

- Structures

- Equipment

- Intellectual property

In addition, it distinguishes between the capital stock of general government and the rest of the economy.

**Table 5 Non-residential depreciation rates in the United States** Average 1990 to 2015, % per year

||Private sector|Government|
|---|---|---|
|Non-residential|8.7|4.6|
|Equipment|13.9|13.2|
|Structures|3.0|1.9|
|Intellectual property|25.1|16.7|
|Equipment and structures|6.8|3.2|

_Source_ : Bureau of Economic Affairs, https://www.bea.gov//national/FA2004/DownSS2.asp In this study it has been assumed that the depreciation rates for the Chinese business sector are the same as those for the private sector in the United States. The rates for the US government sector have been used for the government and infrastructure sectors in China, with the same depreciation rate being used for both sectors as the split of investment between structures and equipment is very similar in the two sectors. The US data, however, show intellectual property assets as a separate category, while for China such assets are included in equipment.

# _Accounting for software and intellectual property in the capital stock_

The official Chinese investment data split investment into two assets: structures and equipment. A growing share of investment in most countries has been in items such as computer software and other items of intellectual property. This type of investment has a very high deprecation rate so, if the growing importance of this type of investment is not allowed for, the depreciation rate for equipment will be understated and the capital stock over-stated.

No official estimates of investment in software and intellectual property are available for China. The authors of the Penn World Tables have estimated the investment flow in such assets by using the commodity flow methodology. Using this approach, for a given product, investment is determined as the sum of production plus net exports less consumption. The authors have also estimated a price index for this type of asset and assumed an average depreciation rate of just over 31% for these assets. On the basis of these assumptions, they find that though the share of this type of asset in the overall investment has grown rapidly, its share of the capital stock of equipment and software is nonetheless quite small (Figure 2). It is, moreover, at 4% much lower than the equivalent share in the United States, which was 34% in 2015. Thus, it would be incorrect to adopt the overall US non-residential depreciation rate for China, but it would be inappropriate to completely ignore the depreciation of the software component of the capital stock in China.

9
|**Table 2. Descriptive**|**Statistics b**<br>**M**|**y Gender and**<br>**en**|**Year, ages**|**15 - 59**|**Wo**|**men**||
|---|---|---|---|---|---|---|---|
|**Variable**<br>**2015**|**2019**|**2020**|**2021**|**2015**|**2019**|**2020**|**2021**|
|**Continuous (mean)**||||||||
|Age<br>36.1|36.2|36.2|36.1|36.3|36.3|36.3|<br>36.4|
|# Children 0-3 years old at household<br>0.30|0.30|0.30|0.20|0.30|0.30|0.30|<br>0.30|
|# Children +3-6 years old at household<br>0.20|0.20|0.20|0.20|0.20|0.20|0.20|<br>0.20|
|# Children +6-12 years old at household<br>0.40|0.40|0.40|0.40|0.50|0.50|0.40|<br>0.40|
|Years of education<br>9.2|9.6|9.7|9.9|9.6|10.2|10.2|<br>10.4|
|Years of experience<br>20.2|20.0|19.9|19.8|20.1|19.8|19.6|<br>19.7|
|Real monthly labor income - imputed<br>1,239,760|1,225,853|1,088,381|1,137,485|986,352|1,029,838|989,716|<br>1,031,265|
|Real hourly labor income - imputed<br>6,671|6,615|5,914|6,119|6,313|6,539|6,032|<br>6,272|
|Hours worked per week<br>43.0|41.0|36.6|38.7|24.9|23.5|20.0|<br>21.4|
|**Categoricals (%, total)**||||||||
|Rural<br>22.3|21.9|32.8|22.1|19.1|19.0|29.0|<br>19.3|
|Union or married<br>56.9|56.5|56.9|55.2|58.5|58.6|59.5|<br>57.2|
|Poor<br>20.6|28.4|32.5|32.1|24.5|32.8|37.3|<br>37.4|
|Age category||||||||
|_18-24 years old_<br>21.6|20.8|20.8|20.4|21.3|21.0|21.5|<br>20.7|
|_+25-44 years old_<br>41.5|42.6|42.6|43.4|41.1|42.1|41.9|<br>42.0|
|_+45-59 years old_<br>36.9|36.6|36.5|36.2|37.6|37.0|36.6|<br>37.3|
|Children at household (Yes=1)||||||||
|_Children 0-3 years_<br>22.1|21.7|22.1|19.8|26.9|26.2|26.5|<br>24.2|
|_Children +3-6 years_<br>17.5|17.3|18.0|16.7|21.2|21.3|22.1|<br>20.9|
|_Children +6-12 years_<br>27.0|25.5|25.3|24.5|31.2|30.4|30.3|<br>29.7|
|Education category||||||||
|_No education_<br>15.5|12.8|13.7|11.1|13.5|10.2|10.6|<br>8.9|
|_Elementary_<br>24.5|22.5|23.2|21.1|22.0|19.5|20.0|<br>18.1|
|_Secondary_<br>6.6|6.4|6.2|6.3|6.6|5.9|6.1|<br>5.7|
|_High School_<br>35.4|38.7|38.7|40.9|35.2|39.5|39.6|<br>41.5|
|_Technical and technological_<br>9.4|9.6|8.8|9.8|12.6|12.7|12.0|<br>13.0|
|_University_<br>6.1|7.1|6.7|7.9|7.2|8.9|8.5|<br>9.6|