rafmacalaba/datause-encoder-data huggingface dataset:
The outcome of local discussion is not entirely conclusive even though most writers agree on several important points, as we shall see below. We shall start with estimates based on the disequilibrium theory.

## 2.2 Disequilibrium Theory and InMlation

The disequilibrium approach, originally pioneered by R. Barro and H. Grossman in the early 1970s (Barro-Grossman [1971, 1974, 1976]), was first applied in modelling CPEs by R. Portes, see e.g. Portes and Winter [1980], and later developed by many others. In Czechoslovakia the most important followers were Dlouhy or Dlouhy and Dyba (1985) and V. Rudlovcak (1987, 1988).

All applications, either related to the consumer market only (Dlouhy [1989], Rudlovcak (1988]) or covering the linkage between consumer and labor ;..arkets"4 (Dlouhy (1984), Dlouhy-Dyba (1985)), are open to the standard criticism of disequilibrium modelling (see, for instance, Komai (1980) or Winiecki (1985). Not even later developments incorporating rational expectations and plan data"' changed the fundamentals of the approach. The major concern of critics is with the "min-condition" which states that there cannot be excess supply and excess demand in the market at the same time. The simplest disequilibrium model looks like this:

# (2) _Qt5= b Y, + de_

## (3) _Qt =_ mm(n **_d,_** _O)_

where **Qd,** Q' represent demand and supply, respectively; a, b are row vectors of parameters; X and Y are column vectors of explanatory variables to be explained further in the text below; e, d are random terms; and Q is an actual size of transactions. Subscript t refers to time period. It is obvious that this is a highly simplified model of macroeconomic disequilibrium. The mincondition equation does not capture simultaneous disequilibria in individual markets. Nor does it capture the spillovers from a particular market to others. These issues have been amply discussed in the literature - for example in the analysis of the 'aggregation problem'. In addition, the model refers to the goods market only, with no attempt to integrate it with labor 1"This linkage is ideologically based on the supply multiplier developed by Barro Grossman (1974). An alternative approach has recently been proposed by Feltenstein and Ha (1992). Their model tests for suppressed inflation by estimating a "true" rate of inflation that explains behavior of observed money demand.

"In Czechoslovak literature, the plan data were used by Rudlovcak (1987).

11
Relationship”, Quarterly Journal of Economics, CX, 407-43.

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- (66) Stiglitz, J. E., "Credit Markets and the Control of Capital”, _Journal of Money, Credit and Banking_ , 1985, 17: 133-152.

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- (69) Tybout, James R.,“Manufacturing Firms in Developing Countries: How Well Do They Do, and Why?” _Journal of Economic Literatur_ e, 2000,38 :11-4.

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29
16 average, to about 40 percent of GDP in 1997 and then fell somewhat, to about 37 percent of GDP in 2000.<sup>36</sup>

**Figure 5.  Credit to the Private Sector 1990 - 2000**

<!-- Start of picture text --> 140 140<br>Thailan<br>120 120<br>100 100<br>Malaysia<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>1989 1991 1993 1995 1997 1999 2001<br>Credit Pvt. Sect. Ave. Credit Pvt. Sect.<br>Percent of GDP<br><!-- End of picture text --> This analysis suggests that crowding-out during the 1990s is not straightforward, as discussed in Section II.2, and that the productivity of private sector credit and the crises are major issues for financial intermediation. The non-performance of numerous loans exposed by the crisis implies that they were less productive than might have been expected.<sup>37</sup> In East Asia, for example, aggregate capital output ratios began to rise before the crisis, at the same time as private sector credits and off-shore borrowings were rising rapidly (World Bank 2000a). And, the overhang of the recapitalization bonds after the crisis seems likely to crowd out the growth of new private credit.

Another related issue is the credit that privatizations made available. Public enterprises are notorious in their excess use of capital and credit. Hence, one might expect that credit demands of these enterprises should fall once they are privatized, making more credit available for other private enterprises.<sup>38</sup> 36 Thailand, despite its major banking crisis, reported a rise in bank credit to its private sector over the decade as a whole, though it fell sharply after the crisis, as shown in Figure 6. The bank restructuring in Thailand was somewhat different than in Brazil, Indonesia, and Mexico (footnote 25) and deposits and credit from non-banks in Thailand fell substantially after the crisis, as discussed in Section IV.1. A shift in these deposits to banks may account for the continued growth of Thai bank deposits after the crisis. Malaysia’s private sector bank credit suffered a similar but less intense fall after the crisis.

37 See World Bank, 2000a, for a discussion of some possible explanations of why the credits in East Asia were unproductive, including corruption and massive over-leveraging by the borrowers. Nonetheless, Annex 1 indicates that the initial stock of private credit in 1990 was positively related to growth during the 1990s, in spite of the large volume of non-performing loans that developed over the decade.

38 The potential increase in credit may have been damped to the extent that better cash management by the privatized enterprises reduced the enterprises’ bank deposits.
raring the periods of high inflation rates, the exchange rate was fixed and grossly overvalued. The purpose of this policy was to tax maintain a high tax rate on exporters (cf. Pinto, 1986). Imports were restricted by licenses. Because the ..o-.nal exchange rate was completely out of line with the scarcity of foreign exchange, an active black market developed. The parallel market premium over the official rate reached very hig-z values.

Policy in the Adjustment Period Following the policy shift in 1983, inflation fell sharply, though it remained relatively high reaching almost 40 percent in 1987. The experience of other countries shows that stopping inflation effectively requires a firm commitment to avoid monetary financing of the fiscal deficit. No such commitment is evident in the Ghanaian data. From the third quarter of 1984 to the same period in 1988, the monetary base increased at an average annual rate of 40 percent. This growth was obviously induced by the expansion of the credit to the government as witr.ess the fact that total bank credit to the government grew at an annual rate of 48 percent during the same period. Thus policy did not convev a very strong signal that inflation was over. The best guarantee against the renewal of inflation would be to let the expansion of financial assets be determined by the process of financial intermediation in which banks match the savings in deposits and real investment. However, credit to the private sector increased in the adjustment period at an annual rate of only 10 percent, and this was from a very small base, because credit to the private sector had been almost eliminated before the adjustment began: it has represented less than one percent of GDP since 1984.

The Demand for Money in Ghana (1972-1988) The first period of high inflation in Ghana occurred in 1977 when the rate exceeded 100 percont per annum. Between 1977 and 1984, the inflation rate fluctuated a great ieal, with high values in 1979, 1981 and 19834. In 1984 a change of policy regime took place with the implementation of the adjustment program. A glance at monetary data indicate that there was a response to the sustained inflation For example, the Ml to GDP ratio decreased from 21 per cent in the fourth quarter of 1976 to less than 12 percent in the first quarter of 1979.

Looking more closely, money demand equations were estimated to check for a shift in response to the policy change. The model estimated was of the error correction type, following the two equations: _YL_ - alpt-l + a2 X1 + **U,** (1) Yt Yt-i - bl(p, - p,1) + b2ut + b3zt + et (2) where the terms xt and zt represent vectors of exogenous variables, pt is the rate of inflation, ut and et are disturbances, and y, represents the endogenous variable the real money stock.

4 Price controls were implemen:ed at various times since 1975. Although these controls were not fully effective, nevertheless, they are likely to ha-e affected the reporting of price indices and the measurement of the inflation rate.

67
the stock series is sufficiently comprehensive and reliable to be useful.

54. Estimation of the Gilbert-Palaskas price adjustment equations (46, 49) is complicated, but not notably more complicated than estimation of the Pesaran-Trivedi adjustment equation. It is necessary to generate for each sample period t a sequence of expected market imbalances rt, rt+11t, tt+21t etc. In principle, these sequences should be infinite, but in practice it is probably satisfactory to curtail them at a relatively short horizon - Gilbert and Palaskas looked 10 periods ahead and Ghosh _et at._ (1987) used an even shorter horizon. The expected imbalances may be obtained by taking the estimated model with the price equation deleted, and running it forward say 10 periods from each starting date In the sample. In the rurn for period t every price dated t+l or later is replaced by the sample mean price F. TIhe estimated supply-demand imbalances tt+qt are then estimated as forecast production in period t+i less forecast consumption in period tIi in the run starting at date t. These estimated imbalances are then treated as data in the commodity price adjustment equation which can be estimated by NLLS. It is apparent that estimation of md forecasting with the Gilbert-Palaskas model requires estimated equations for the exogenous variables in the production and consumption equations in the same way as does the Pesaran-Trivedi approach.

55. The same procedure is followed in forecasting out of sample. An initial set of forecasts is made for the expected market imbalances with the price set at its sample mean value, and the price in the current and each succeeding period is then forecast using these expected imbalances as data. Note however that there is no need in this case to calculate a new set of expected imbalances for each successive period since under the REH Etxt+ijt+j = xt+it.

56. A difficulty with estimating equations in which some of the variables are calculated from preliminary regressions is that the sampling error associated with the estimated 34
Table 1: Correlation between gross and value‐added trade in good

||Parts and<br>components|DVA|DVA<br>final|DVA<br>interm.|DVA interm.<br>re‐exported|<br>FVA|FVA<br>final|FVA<br>interm.|
|---|---|---|---|---|---|---|---|---|
|Parts and components|1.00||||||||
|DVA|0.87|1.00|||||||
|DVA final|0.80|0.97|1.00||||||
|DVA intermediate|0.88|0.97|0.88|1.00|||||
|DVA int. re‐exported|0.82|0.85|0.72|0.91|1.00||||
|FVA|0.79|0.89|0.87|0.85|0.67|1.00|||
|FVA final|0.77|0.88|0.90|0.81|0.63|0.98|1.00||
|FVA intermediate|0.75|0.84|0.77|0.85|0.68|0.96|0.88|1.00|

Table 2: Deep trade agreements and GVC integration, PPML

||(1)<br>|(2)<br>DVA of exports|(3)|(4)<br>|(5)<br>FVA of exports|(6)<br>|
|---|---|---|---|---|---|---|
|No. of provisions|0.00400***|||0.00258*|||
||(0.00125)|||(0.00133)|||
|Core provisions||0.00777***|||0.00520*||
|||(0.00266)|||(0.00273)||
|PCA depth (1st)|||0.0292***|||0.0203*|
||||(0.0101)|||(0.0105)|
|BIT|0.121***|0.118***|0.119***|0.0924**|0.0910**|0.0914**|
||(0.0440)|(0.0441)|(0.0441)|(0.0379)|(0.0379)|(0.0379)|
|PTA (in force or|||||||
|inactive)|‐0.0296|‐0.0611|‐0.041|‐0.00848|‐0.0302|‐0.0189|
||(0.0433)|(0.0492)|(0.0461)|(0.0410)|(0.0481)|(0.0440)|
|Observations|26,520|26,520|26,520|26,520|26,520|26,520|
|R‐squared|0.994|0.994|0.994|0.996|0.996|0.996|
|Country‐pair FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Country‐time FE|Yes|Yes|Yes|Yes|Yes|Yes|

Clustered standard errors at the country pair level in parentheses. *** p<0.01, ** p<0.05, * p<0.1 22
then explores a set of independent variables which mostly relate to the household head. For instance, information on gender, birth cohort and educational attainment but also the region of residence and the location (urban and rural) are assumed to be time-invariant. Furthermore, the model only includes households with household heads who were between 25 and 55 years of age in the first period; this way behavioral changes due to life events such as retirement do not complicate the construction of a consumption measure for the second period. Notwithstanding these limitations, synthetic panels allow the use of existing data in a novel way to better understand patterns of economic mobility and its determinants.

**In response to methodological challenges, Dang and Lanjouw (2013) introduce a new approach to determine patterns of economic mobility based on point estimates and confidence intervals.** Compared to the lower and upper bound estimates, point estimates have the potential to generate a more precise measure of mobility which accounts explicitly for sampling and modeling errors. For the lower bound approach, the degree of bias depends on how far the true correlation between the error terms in both periods is from 0. If the true correlation term is close to 0, we have better estimates, and vice versa. Here, the point estimates approach incorporates the true cohort level correlation rho between error terms from both periods (look at the pseudo panel literature). The approach developed by Dang and Lanjouw (2013) requires a bivariate normal distribution between error terms in both periods. If this assumption holds, the probability of escaping poverty is given by .

# **Assumptions**

**Patterns of economic mobility based on the synthetic panel methodology discussed decompose the net change in poverty into transitions in and out of poverty (see presentation of results above) and show plausible estimates for the size of each of the four groups.** For instance, the correlation between the observed level of income in the first period (either 2004 or 2010) and the predicted household specific probability of belonging to the group “poorpoor/non-poor” is negative, and the corresponding correlation for the group “non-poor – poor/non-poor” is positive. However, the methodology builds on strong assumptions which are discussed in more detail below:

**Assumption 1:** The sample (with household heads between 25 and 55 years of age) is sufficiently close to the true population in the country. _Summary statistics in Table A4 suggest that for a set of time-invariant characteristics, differences between the sample and the population (as defined by the household survey) are small._

**Assumption 2:** Characteristics – as proxied by time-invariant variables – of the sample for the first period and the second period, do not change between the two time periods. _Summary statistics in Table A4 suggest that differences between characteristics for the two different samples in the two periods are small._

45
2 of European countrles has opened up new opportunities for detecting short-run effects. In a recent review, Lee (1990) finds similarities in demographic responses to economic fluctuations between contemporary developing countries and historical observat.ons for developed countries. With grain prices as the index of economic fluctuation, historical, detrended European series show a positive relation between mortality and prices, with the cumulative elasticities for noninfant mortality (over a five-year period) ranging from +0.05 to +0.6, concentrated on lags from zero to two years. Galloway (1988) shows that the mortality response is generally larger in poorer, more agricultural populations, and disappears in England in the nineteenth century. Hill (1989) finds a small positive mortality response to economic reverse in Chile, both in the 1970s and 1980s and in the 1930s, though with a lag of one to two years.

The historical record clearly shows that mortality rates do respond to shortterm economic fluctuations, at least in poor and largely agricultural settings, and that mortality rates are strongly associated with the level of development. It would therefore not be surprising to find that the economic crisis of the 1980s in Sub-Saharan Africa has had the effect of increasing mortality, or at least of reducing its rate of decline. It is this question that this paper addresses.

# ECONOMIC CRISIS AND STRUCTURAL ADJUSTMENT

Many Sub-Saharan African countries experienced major economic crises in the late 1970s and early 1980s, brought about by rising real interest rates, a rapid deterioration in the terms of trade, the continuation of over ambitious public investment programs often supported by external borrowing and distorted incentive
# **Table 11 Performance benchmarks**

This table reports the loadings of the portfolios sorted by Active Share an investor type on various factors. Panel A reports loadings for domestic institutional investors and Panel B for foreign investors. MARKET is the return of the Colombian market index in excess of the one-month Colombian T-bill rate. SMB is the return on a portfolio of small stocks minus large stocks. HML is the return on a portfolio long on high book-to-market stocks and short low book-to-market stocks. WML is the return on a portfolio long on stocks that are past-12-month winners and short on those that are <u>past-12-month</u> losers.

||Market-adj<br>|C<br>|APM<br>||Carhart<br>|4-Fact<br>|or<br>||
|---|---|---|---|---|---|---|---|---|
|Active Share|(average)|alpha|MARKET|alpha|MARKET|SMB|HML|WML|
|||Pane|l A. Foreign|Investors|||||
|Group 1 (low)|0.23%|0.23%|0.95|0.21%|0.92|-0.07|-0.10|0.02|
|Group 2|0.30%|0.40%|0.92|0.38%|0.88|-0.13|-0.05|-0.01|
|Group 3|0.33%|0.69%|0.85|0.61%|0.76|-0.34|0.03|0.01|
|Group 4 (high)|0.64%|0.80%|0.66|0.69%|0.52|-0.50|0.00|0.00|
||Pa|nel B. Do|mestic Instit|utional I|nvestors||||
|Group 1 (low)|0.38%|0.50%|0.95|0.45%|0.90|-0.18|0.01|0.02|
|Group 2|0.33%|0.32%|0.88|0.26%|0.82|-0.20|-0.05|0.05|
|Group 3|0.31%|0.90%|0.87|0.85%|0.79|-0.26|0.02|-0.04|
|Group 4 (high)|0.34%|0.53%|0.45|0.49%|0.46|0.03|-0.07|0.17|

36
- <u>In-demand. Occupations had to have an employment level above the first quartile and have</u> non-negative 1- and 3-year employment growth.<sup>11</sup> These criteria were relaxed in some cases for occupations considered to be strategic to Vietnam’s economic development.

In the VSCO, 111 4-digit occupations met these requirements. To narrow this to the 30 occupations that could be surveyed given resource constraints, several additional criteria were used.

- <u>Digital transformation. Vietnam has prioritized digital transformation as one of the pillars of</u> its development strategy for the next 10 years. This digital transformation may impact jobs either by increasing the demand for digital skills within existing jobs or by creating (destroying) jobs as the country adopts new technologies (World Bank 2022). Drawing on research on digital skills in Vietnam, jobs likely to be affected by digital transformation are selected (World Bank 2021; Cunningham, et al. 2022). Education-related occupations that may influence the transfer of digital skills to workers were also selected.

- <u>Megatrends. Occupations that may be affected by megatrends influencing Vietnam’s growth</u> trajectory were also included. These megatrends include green growth, the fourth industrial revolution and automation (), and aging (Cunningham and Pimhidzai 2018; World Bank 2021b; Frey and Osborne 2017).

- <u>Strategic sectors. Occupations relevant to strategic sectors for economic growth, particularly</u> based on exports, were included. These sectors include electronics, textiles, and, to a lesser extent, machinery. For example, the electronics sector represented 38 percent of Vietnam’s 2019 export basket.<sup>12</sup> The sector’s exports grew 2,363 percent in the last decade (from $4.75B to $117.5B). The Textile sector represented 22 percent of the 2019 export basket, and its exports grew 283 percent in the last decade (from $17.8B to $68.2B). The machinery sector represented 8 percent of the export basket, and its exports grew 529 percent (from $3.85B to $24.2B).

**Table 1** lists the selected occupations.

> 11 Given data availability, employment indicators were constructed using 2018 as the base year.

> 12 See Harvard University Growth Lab’s Atlas of Economic Complexity available at https://atlas.cid.harvard.edu/.

8