rafmacalaba/datause-encoder-data huggingface dataset:
# **5.3 Wage determination in the manufacturing sector**

In this section we focus on the manufacturing sector. We explain manufacturing sector wages by prices, the unemployment rate, GDP and policy. The analysis shows that also the pattern of wages in the manufacturing sector is out of line with the development in the economy in the beginning of the 1980s-they are too high. By including a policy variable, no obvious misspecification problems remain in the model. The charts presented in Figures 10 and 11 substantiate findings in Table 6 of no misspecification of the model and no indications of parameter non-constancy.

**Figure 11**

**Residual plots, ECM for Manufacturing wages**

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50

# 98:pp. $103-S125.

. 1991 (forthcoming). "Economic Growth in a Cross Section of Countries." Quarterly Journal of Economics.

- , and H.C. Wolf. 1989. ‘Data Appendix for Economic Growth in a Cross Section of Countries." mimeo., Harvard University and MIT.

Baumol, William J. 1989. Productivity and American Leadership: The Long View. Cambridge: MIT Press.

Becker, Gary S. 1964. Human Capital. New York: NBER, Columbia University Press. (2nd ed. 1975.)

- , Kevin Murphy, and Robert Tamura. 1990. ‘Human Capital, Fertility, and Economic Growth." Journal of Political Economy 98(6).

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- and . 1988, "Health and Nutrition." in H. Chenery and T.N. Srinivasan, eds. Handbook of Development Economi¢es.Vol |. New York: North-Holland.

- Bell, Clive. 1988. "Credit markets and interlinked transactions. In Chenery, H. and T.N. Srinivasan, eds. Handbook of Development Economics: Vol !. New York: North-Holland.

- Ben-Porath, Y. 1967, "The production of human capital and the life cycle of earnings," Journal of Political Economy, 75:4.

- Benhabib, Jess and Boyan Jovanovic. 1989. "Externalities and Growth Accounting.* NBER Working Paper No. 3190. Cambridge, MA: NBER.

- Bhagwati, J. 1978. Anatomy and Consequences of Exchange Control Regimes. Cambridge MA: NBER. and R. A. Brecher. 1980. “National welfare in an open economy in the presence of foreign owned factors of production," Journal of Intemational Economics 19: pp. 103-115.

- Bhalla, Surjt. 1991. “Chapter 2: Development Paths, Strategies, and Performance." Draft of background paper prepared for the WOR 1991 draft.

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- . 1989. “Economic Analyses of Rapid Population Growth." World Bank Research Observer 4(1): pp. 23-50.

- Binswanger, H. 1974. “A microeconomic approach to induced innovation," Economic Journal 84 (Dec): pp. 940-958.

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- and N. Stem. 1978. "Productivity, wages and nutrition; Parts | and Il: The theory," Journal of Development Economies 5: 331-398.

- Braverman, A. and T.N. Srinivasan. 1981. “Credit and sharecropping in agrarian societies," Journal of Development Economics, 9:289-312.

- , and J. Stiglitz. 1982. “Sharecropping and the interlinking of agrarian markets," American Economic Review, 72:695-715.

- Brecher,InternationalR. A. and C. EconomicsF. Diaz-Alejandro.7: pp. 317-322.1977. ‘Tariffs, foreign capital and immiserizing growth," Journalof Caballero, Ricardo, and Richard Lyons. 1989. “The Role of Externai Economies in U.S. Manufacturing." NBER
rather than for outliers in the education or skill distribution.<sup>20</sup>

# **3.1 Other estimation approaches**

In the case of binary outcome variables, such as whether a respondent is pregnant by age 18, a nonlinear instrumental variables approach may be appropriate. In particular, I consider the IV probit, with the same first stage given in equation 2a, but with second stage: The IV probit estimation procedure is only correctly specified when the first stage residuals are asymptotically normally distributed, and when the first stage is linear.<sup>21</sup> An alternative, when the first stage outcome is binary, is the bivariate probit (Maddala 1983):<sup>22</sup> This approach uses _Seci_ rather than _Sec_<sup>�</sup> _i_ in the second stage, because it explicitly models endogeneity through the correlation, _ρ_ , between _τi_ and _ωi_ .

> 20By contrast, many US studies relying on date-of-birth identification strategies are focused on relatively low-achieving students; studies such as the work of Saavedra (2008) in Colombia estimate the returns only to the highest-quality universities. Neither class of coefficient is necessarily relevant for the bulk of the population.

> 21A binary endogenous regressor would typically not yield asymptotically normal residuals.

> 22Maddala (1983) presents the model on pp. 122-3; Greene (2007) discusses the model further on pp.823-6; Wooldridge (2002) also discusses it on p.478.

15
**Figure 8. Tariffs**

<!-- Start of picture text --> 5.0<br>5.0<br>4.3<br>4.5 4.0<br>4.0<br>3.5 3.0<br>3.0<br>2.5 2.0<br>2.0<br>1.4<br>1.5 1.0 1.0 1.0<br>1.0<br>0.5<br>0.0<br>Slovak EU15 Czech OECD Bulgaria Hungary Poland MICs Romania<br>Republic Rep.<br><!-- End of picture text --> _Source:_ see Figure 3. _Note_ : Other MICs are Brazil, Mexico, Turkey. Values refer to 2006 for Bulgaria and Romania, 2004 for Brazil, and 2003 for all other countries. For full data set see appendix I.

# **_1.2 Barriers to foreign direct investment_**

Barriers to foreign direct investment include foreign ownership barriers, discriminatory procedures against foreign firms, and other barriers to trade and investment, all of which are presented in detail below. One such barrier concerns legal restrictions on foreign participation in the equity of domestic firms and in telecommunications and airline sectors. In this regard, Bulgaria does not compare well with high income EU countries, or with pre-accession CEE countries and other MICs ( Figure 9).

Foreign ownership barriers take the form of statutory or other legal limits to the proportion of shares that can be acquired by foreign investor or of special voting rights that can be exercised in case of acquisition of equity by foreign investors. Such restriction may apply in general or be limited to specific sectors that are considered ‘strategic’ such as air transport, telecommunications, and electricity generation.

Bulgaria fares relatively poorly in this respect mostly because of general ownership barriers – which in Bulgaria actually apply to domestic and not just foreign investors. (Barriers in specific sectors are in line with those in the EU - for example the 49% foreign ownership ceiling in the airlines sector is standard across EU countries.) 15
low, and convenience of withdrawal, user-friendly outlets, and the complementary package of services are more important that the deposit rate.

Standard Bank does not face much competition for low income clients with low balance accounts. The main competition among banks is for corporate accounts, which requires providing a complete line of corporate banking services, including managing direct deposit of wages for workers. Few banks are interested in low income deposit services per se. However Nedbank also provides low income depository services through a separate affiliate bank, "Peoples Bank," that uses a relatively standard product set and delivery system of traditional branches to serve the market. There is also some competition from building societies, etc., but the E Plan now has a _57_ percent market share for new low balance accounts with fees somewhat higher than others in the market.

In sum, market research and experience supports the design of E Plan. The E Plan outlets don't market other products heavily. The E Plan is essentially a single savings/transmission account, and the marketing efforts are spent in acquiring accounts, migrating customers from the full service branches to the Auto Bank E facilities, and encouraging savers to hold higher balances and undertake more transactions. Not all the features of E Plan are fully utilized by the account holders. For example, only four percent of clients currently use their savings purse; however, they hold roughly 10 percent of all balances in the savings purse. The savings purse is clearly an important feature of the plan; whether it is fully understood by the market is not known. There are relatively low rates of insurance pay out as well; again, it is not known whether account holders are aware of the conditions to qualify for insurance.

Market penetration has been fairly high. Standard Bank estimates that there are approximately 10 million bank customers in South Africa, and roughly 2.5 million fall in the low income consumer market segment. With 1.4 million accounts, E Plan has over 50 percent of those bank clients. There are also at least 10 million adults who do not have bank accounts. There are a variety of reasons for not having accounts, ranging from distance from a bank facility, or extreme poverty, or informal traders operating in 18
**Table 4: Indices of Inequality for Consumption, by Urban and Rural Areas (2005-2008)**

||**Lower H**<br>**Distri**|**alf of the**<br>**bution**|**Upper H**<br>**Distri**|**alf of the**<br>**bution**|**Tails**||
|---|---|---|---|---|---|---|
||**p25/p10**|**p50/p25**|**p75/p50**|**p90/p50**|**p90/p10**|**Gini**|
|**Total**|||||||
|2005|1.26|1.33|1.36|1.95|3.27|28.67|
|2008|1.32|1.33|1.38|1.98|3.48|30.46|
|**Urban**|||||||
|2005|1.33|1.41|1.38|2.04|3.84|30.23|
|2008|1.33|1.37|1.42|2.13|3.87|32.34|
|**Rural**|||||||
|2005|1.23|1.27|1.26|1.56|2.42|20.43|
|2008|1.29|1.31|1.28|1.59|2.68|21.99|

_Source: own estimates based on panel HIECPS survey 2005-2008._ The widening of the distribution is well illustrated by growth rates for different percentiles. Figure 2 shows growth-incidence curves (GICs) for Egypt overall, and for urban and rural areas, over 2005-2008. Such curves show the annual growth rate for household welfare at each percentile from the poorest (1<sup>st</sup> percentile, on the left) to the richest (100<sup>th</sup> percentile, on the right).

The positive slope of the growth incidence curves suggests that the rich gained more than the poor, especially in rural areas, which show even a fall in real welfare for the poorest percentiles in contrast to overall positive growth. In urban areas the growth incidence curve has a characteristic U-shape, suggesting that the richest and the poorest had the highest growth rate, while the middle, especially those between the 20<sup>th</sup> and 60<sup>th</sup> percentiles, experienced the slowest growth, bordering just 1 percent per year.<sup>16</sup> Taking bottom 20 percent as poor in the country, one can see that in absolute sense the poor as a group had a small gain (with the extreme poor in rural areas experiencing a fall in welfare). Taking into consideration confidence intervals, one can say that for Egypt and for rural areas, the poor have experienced growth rates below the mean. In fact, the poor in Egypt had the lowest growth rate, hence the growth was not pro-poor in a relative sense.

> 16 It is important to remember that the period of high growth during 2005-2008 was preceded by five years of losses. For the lower-middle class, 1 percent per annum gain does not even compensate for losses incurred during the preceding five years of negative growth (during 2000-2005). Growth incidence curves for this period were presented in World Bank 2007.

12
official loans to states with balance-of-payments problems, in particular through the US Export-Import Bank and the US Treasury’s Exchange Stabilization Fund, which was established in 1934.

The lessons learned during these operations turned out to have great effect on the design of the post-World War II Bretton Woods System (Bordo and Schwartz, 2001). In 1944, the IMF was founded with the aim of providing short-term official funds to countries with temporary balance-ofpayments problems, alongside with the World Bank that was intended to provide long-term development and reconstruction funds. However, official lending during the post-war decades continued to be dominated by bilateral creditors. The US, in particular, engaged in large scale lending, e.g. via the Marshall Plan and EXIM Bank funds. Also the Soviet Union became an active official lender. Furthermore, outside the convertible dollar and sterling areas, sovereigns financed their current account deficits with the reciprocal extension of clearing credits.

Starting from the late 1950s, the practice of official lending began to gradually shift from bilateral to multilateral creditor institutions, driven by a remarkable increase in the number and variety of multilateral lending institutions. Regional development banks first emerged in the late 1950s with the establishment of the European Development Fund and the Inter-American Development Bank and have spread to all regions of the globe since. In parallel, new Regional Financial Arrangements (RFAs) were set up, typically focused on providing emergency funds during balance-of-payments crisis of member states. Early examples include the European Monetary Agreement founded in 1958 and the Central American Monetary Stabilization Fund of 1970. More recently, the European Stability Mechanism (ESM) founded in 2012, is just one manifestations of this long-run trend towards regional rescue facilities. Beyond regional and multilateral arrangements, central bank swap lines have become an additional main source of official emergency finance. Starting with the defense of the Bretton Woods system in the 1960s, a network of bilateral swap lines has been in place that connects the major central banks of the world. More recently, these networks have been expanded in size and scope. They now include the central banks of main emerging markets and, driven by China’s central bank, also a growing number of developing countries.

Taken together, these institutional developments have transformed the practice of official sovereign lending from occasional instances of ad-hoc cooperation between two states into a multilayered, global financial safety net composed of a broad range of specialized institutions.

# **2.2 Main sources and scope of the database**

Our new database compiles loans, grants and guarantees from the majority of these official creditors and several hundred primary and secondary sources that are described in detail in Appendix III. The resulting database spans official lending from 1790 to 2015 and covers more than 230,000 loans, 8
Fink et al. (2002), they regressed (log) transport price _p_ on the (log) marginal transport cost _mc_ and (log) markup _μ_ with _mc_ being a function of the average country-specific port efficiency and _μ_ being determined by the product fixed effects and liner connectivity index:<sup>38</sup> where subscripts _j_ , _k_ , and _t_ index country-importer, HS2 product, and year, respectively.

Their estimation results indicate that a 0.1 increase in port efficiency reduces the (unit) maritime transport cost of exporting to the US by 2.3 percent. Put differently, moving from the 25th to the 75th percentile reduces transport costs by 3.5%. With similar data sources and empirical strategies, Herrera Dappe et al. (2017) extended this analysis by focusing on the impacts of port efficiency on bilateral trade flows with the US: The same 25th to the 75th percentile efficiency increases the value of exports to the US by roughly 0.56 percent.

Furthermore, Herrera Dappe et al. (2021) find that increasing port efficiency (based on Stochastic Frontier Analysis) from the 25th-75th percentile lowers transport costs by approximately 4 percent. Their point estimate of the unit transportation cost elasticity with respect to efficiency, namely _−_ 0.23, is consistent with the Data Envelopment Analysis efficiency measures of Indian/Western Pa- _− −_ cific Ocean ports which are between 0.22 and 0.24 (Herrera Dappe and Suárez-Alemán, 2016; Herrera Dappe et al., 2017).

# **4.4.2 Surveys of Port Efficiencies and Linking Them to Transportation Costs**

Clark et al. (2004) used the US Import Waterborne Databank alongside the World Economic Forum’s 1999 _Global Competitiveness Report_ (WEF GCR hereafter) to show that moving from the 25th to the 75th percentile of port efficiency reduces transport costs by approximately 12 percent. Their main variables were: (i) liner transportation charges per weight at the HS6 product level and (ii) Country38 Marginal cost is also a function of (i) containerization, (ii) transportation distance multiplied by fuel costs, (iii) economies of scale, (iv) trade imbalances, (v) value per weight, (vi) national income (GDP), (vii) _average_ country-specific port efficiency, and(viii-x) country, commodity, and time fixed effects.

38
**Table 2: Average event-study coefficients for Indonesia’s exports and imports of by domestic lockdown indicators**

|||**Exports**|||
|---|---|---|---|---|
|**Period**|**Workplace**|**Stay-at-home**|**Internal movement**|**International travel**|
|Overall|84.930|143.418|x|11.173|
|1-4 months|x|189.100|x|x|
|5-8 months|x|145.008|x|x|
|9-12 months|x|96.147|x|x|
|13-16 months|84.930|x|x|11.173|
|17th month|x|x|x|x|
|||**Imports**|||
|Overall|-0.577|-15.350|-0.494|-0.480|
|1-4 months|-21.042|-31.525|-20.965|-20.965|
|5-8 months|-10.889|-18.486|-10.751|-10.750|
|9-12 months|4.524|-8.269|4.561|4.643|
|13-16 months|13.407|-3.121|13.501|13.484|
|17th month|11.112|x|11.184|11.189|

Note: All coefficients are significant at 10% level. x indicates that all coefficients are not significant in a specific period. Source: Authors’ compilation significantly affected import trade except for intermediate goods imports subject to NTMs, this may signal that lower demand for imports may have led to lower prices. The external lockdown led to an increase in import prices, specifically between the third and tenth months when prices significantly increased by a monthly average of 6.2%. This suggests that lower supply from the origin countries due to lockdowns may have led to higher prices. We find no evidence of external lockdowns affecting import prices after the tenth month.

As displayed in Table A2, the third column shows that export prices contracted immediately after Indonesia imposed lockdown measures and this effect consistently lasted for the first 12 months of the lockdown. The effect started with a 392% decline in the first month and consistently remained negative until the twelfth month (the average monthly contraction in export prices between the first and twelfth months was 249%). Export prices also shrunk in the fourteenth month by 61%. The second column shows that export prices significantly declined by an average of 2.3% in the fourth and fifth months following external lockdowns.

30
**Table 5. The** _SupLRT_ **Test for a VAR with Linear Restrictions with X=C**

||Statistic|Critical value<br>at 5% level|Signifcant break|
|---|---|---|---|
|_SupLRT_ test|( 1_|_0 ):<br>23.05|21.37|1984q3 (1981q1-1988q1)|
|_SupLRT_ test|( 2_|_0 ):<br>24.24|28.17|-|
|_SupLRT_ test|( 3_|_0 ):<br>24.40|33.70|-|

# **4. Multivariate Time Series Analysis within Subsamples**

In Section 3 we have shown that the impact of house prices on the macroeconomy changed in the mid-1980s. In this section we look for time series evidence to find in which direction the house prices macro-link has moved. More specifically, we seek evidence that the effect of house prices on economic activity and on private consumption has been reinforced after the break. In this aim, we split the sample into two segments using the break-date estimated in the mid-1980s. Impulseresponses and Granger-causality tests are performed for both subsamples. The VAR specification used to identify the breaks in the previous section is also considered in each sub-period.<sup>10</sup>

# _4.1 Generalized Impulse-Response Functions_

In order to analyze the dynamics of a VAR system, most of the empirical papers in the literature use the “orthogonalised”impulse-responses where the underlying shocks are orthogonalised using the Cholesky decomposition methodology. The main problem with this method is that in practice, when the order of the variables in the VAR is modified, the responses are often significantly different. To avoid the issue of potentially different results, we apply the generalized impulse responses (GIRs) proposed by Pesaran and Shin (1998), which is invariant to the ordering of the variables in the VAR. For each of the VAR specifications, either including GDP or consumption, we perform GIRs and cumulative GIRs for each of the two sub-periods (Figure 2). Shocks represent an exogenous increase in one percent in the house prices variable.<sup>11</sup> change in the house prices effect on consumption, but it would be mainly driven by changes in the impact of house prices on investment. The increase in the competition on the secondary market due to the creation of “Freddie Mac” (1970) could have played a role in the modification of the link between housing investment and house prices, however more work would be needed to clarify this issue.

> 10In the previous section we have found that a VAR with one lag is a valid and parsimonious restriction of a VAR with two lags. This result allows us to estimate for this section a VAR with either one or two lags. We choose a VAR with one lag for the sake of preserving an adequate number of degrees of freedom for the estimation.

> 11Because the housing prices series has been log-tranformed and first-differenciated, an increase of one percent of this variable represents an increase in one percent in the growth rate of
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