rafmacalaba/datause-encoder-data huggingface dataset:
42 The total increase of 40-120 percent (depending on the index) was far less than the increase in the peso price of the dollar, which had peaked at 260 percent by the end of June 2002 (with the exchange rate at 3.75 per dollar) and eased slightly to 240 percent by the end of December (3.4 per dollar). This sharp delinking of domestic prices from the exchange rate, contrary to widespread expectations of a strait-jacket tying the two together based on past Argentine experience (as discussed above), was one of the few rays of light in the otherwise dark economic picture. Importantly, in contrast to past experience in Argentina, there was no surge in wages in response to the exchange rate depreciation, reflecting the weakness of the economy, a government incomes policy for public sector workers, and perhaps the lesser degree of unionization than in the past. Nonetheless, there were grounds for concern that the muted inflationary impact of devaluation could prove transitory, because it had reflected the severe depression of demand as well as the artificial collapse in liquidity from the freeze on bank deposits.

Figure 8 Consumer and wholesale price inflation (percent) (monthly, left; cumulative, right) <!-- Start of picture text --> 25.0 140.0<br>120.0<br>20.0<br>100.0<br>15.0<br>80.0<br>10.0<br>60.0<br>5.0<br>40.0<br>0.0<br>20.0 CPm<br> WPm<br>-5.0 0.0<br> CPcm<br> WPcm<br>Jan02Feb MarAprMayJun JulAugSepOct NovDecJan03Feb MarApr<br><!-- End of picture text --> The course of the economy in 2002 can be understood in the following framework relating policy to outcome. The severe decline in output was driven by the initial seizing up in liquidity with the freeze in bank deposits and the debilitation of the banks by asymmetric treatment of devaluation effects, and by the shock to balance sheets from borrowers’ devaluation losses on dollar-denominated external debt. The muted response of prices to the steep devaluation reflected not only depressed demand and constrained liquidity but also partial but influential price freezes, most notably on utility prices, as well as a de facto freeze in government salaries. For its part, the mild recovery that began at mid-year can be seen as a consequence of a large runup by then in the money supply, especially in currency held by the public (including the provincial quasi-moneys), on the one hand, and the devaluation-induced stimulus to production of import substitutes (such as textiles and machinery and equipment), on the other.
15

# Rererences

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- Banker, R., A. Charles and W. Cooper (1984). "Models for Estimating Technical and Scale Efficiency in Data Envelopment Analysis", _Management Science,_ 30, 1078-1092.

- Bessent, A., W. Bessent, J. Elam and D. Long (1984). 'Educational Productivity Council Employees Management Science Methods to Improve Educational Quality", _Interfaces, 14(6)_ November-December, 1-8.

- Bessant, A., W. Bessant, J. Kennington and B. Reagan (1982). "An Application of Mathematical Programmning to Assess Productivity in the Houston Independent School District", _Management Science,_ 28, 1355-1367.

- Butler, R.]- and David H. Monk (1985). "The Cost of Public Schooling in New York State: The Role of Scale and Efficiency in 1978-79", _The Journal of Hunan Resources,_ Summer 1985, 361-81.

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- Kumar, Ramesh C. (1983). "Economies of Scale in School Operation: Evidence from Canada", _Applied Economics,_ 15, 232-340.

- Lockheed, Marlaine E. and Eric Hanushek (1994). "Concepts of Educational Efficiency and Effectiveness", _World Bank Human Resources Development and Operations Policy Working Paper No.24,_ March 1994.

- National Statistical Institute, (1994). _General and Professional Education 1992-1993 School Year_ (in Bulgarian), Sofia.

National Statistical Institute (1992), _Statistical Abstract, Sofia._

- Ray, Subhash C. (1991). "Resource-Use Efficiency in Public Schools: A Study of Connecticut Data", _Management Science,_ 37, No.12, December, 1620-1628.

- Schmidt, Peter (1985). "Frontier Production Functions", _Econometric Review,_ 289-328.

- Scott, Callan 1. and Rexford E. Santerre (1990). "The Production Characteristics of Local Public Education: A Multiple Product and Input Analysis", _Southern Economic Jourmal, 57,_ No.2, October, 468480.
offered business training. In contrast, households that are not self-employed only increase business knowledge by 4.7 and 4.8 percent of a standard deviation, and these increases are not statistically significant. The same pattern arises with the expenditure and assets aggregate. Self-employed households offered business training experience increases of 17.3 and 16.3 percent of a standard deviation (p-value of 0.00 in both cases) while households that are not self-employed only gain 4.3 and 1.6 percent of a standard deviation, neither significant at conventional levels.

We now turn to Panel B of Table 5 to examine the impacts on individual and household outcomes by gender. By and large, the impacts on expenditure and assets (column 1) and CO Cohesion (column 2) are concentrated among male CO members. Assets and expenditures increase by 11 percent of a standard deviation among male members offered BT only and by 14.3 among male CO members offered BT and assigned to be lottery winners. In contrast, women’s outlook on life improves substantially, which is surprising given the lack of improvements in the other aggregates.

To sum up, female CO members improve business knowledge but do not seem to put it into practice in their existing or new businesses. As a result, we see no improvements in expenditures and assets or CO cohesion. In contrast, and more consistent with the model, business training leads to lower business failure and likely improvements in business practices, operations and sales for men in addition to improvements in expenditure and assets and CO cohesion.

<u>5.3 Exploring Gender Differences</u> Given the results just described, a question of interest is why women fail to capitalize on the training offered. There are several potential answers. First, given the substantial differences between male and female CO members and the process by which women select into CO membership and into self-employment, gender differences may simply reflect differences in other characteristics. These male-female differences may stem from biological factors or from “learned” social behavior, that is, may be the result of culture and the environment (Gneezy, Leonard, and List 2009, for example and World Bank, 2012). Similarly to del Mel et al (2009), we address this possibility by including a range of controls and their interaction with the treatment dummies in the specifications of 22
- 38 -

transition when demand is constrained is more difficult to manage than one in which the fruits of institutional change and productivity growth find ready outlets. Moreover, although price movements are not yet clear, it appears that removal of subsidies on feed credit, fertilizer, machinery, and energy will move terms of trade against agriculture, particularly against the large livestock sector. The need for productivity increase will thus be even greater than in the past. Productivity growth will be difficult to achieve if demand is constrained. Any progress on the demand side, through expansion of domestic demand or improved performance in export markets, will therefore give a major impetus to the institutional changes needed on the supply side.

# **BIBLIOGRAPHY**

- Blanchard, Oliver, Rudiger Dornbusch, Paul Krugman, Richard Layard and Lawrence Summers. _"Reform in Eastern Europe."_ 1990 Report of the WIDER World Economy Group.

- Brooks, Karen. _M. "Lease Contracting in Soviet Agriculture in 1989,"_ **Comparative Economic** Studies, Vol. XXXII, No. 2, Summer 1990.

- Brooks, Karen M., J. Luis Guasch, Avishay Braverman and Csaba Csaki. _"Agriculture and the 7ransition to the Market,"_ **oumrnal** of Economic Perspectives, forthcoming Fall 1991.

- Braverman, A. and J.L. Guasch. _"The Theory of Rural Credit Markets,"_ in Braverman, Hoff and Stglitz (eds), Agricultural DeveloIment Policies and the Theory of **Rural** Or:ganization, Oxford University press, 1991.

- Chhibber, A., _"The Aggregate Supply Response: A Survey,"_ in StIructural Adjustment **and** Agriculture, S. Commander (ed), London: Overseas Development Institute, 1989.

- Ruttan, Vernon W. _"What Happened to Political Development?"_ Economic Development and Cultural Change, Vol. 39, No. 2, January, 1991.

Vickers, John and George Yarrow. _"Economic Perspectives on Privatization."_ **_J.ournal_** d Economic Perspectives, Vol. 5, No. 2, Spring 1991.
# **Big Dragon, Little Dragons:**

**China’s Challenge to the Machinery Exports of Southeast Asia**

Sjamsu Rahardja<sup>∗</sup> JEL Code: F1, F2 This is a revised version of a background paper for trade chapter in “East Asia Project” of The World Bank. Previously this work was titled “Big dragon, little dragons: the role of China in Export of Machinery from Developing Southeast Asia The author would like to thank Mona E. Haddad, Hal Hill, Homi Kharas, and Silja Baller for various comments, discussions and helpful suggestions.

- ∗ The World Bank Office in Jakarta
Figure 2 Dominican Republic and Haiti: Bilateral Exchange Rate, 1979–2007 (Dominican pesos per gourde) <!-- Start of picture text --> 2.50<br>2.00<br>1.50<br>1.00<br>0.50<br>0.00<br>1990M1 1991M5 1992M9 1994M1 1995M5 1996M9 1998M1 1999M5 2000M9 2002M1 2003M5 2004M9 2006M1 2007M5 2008M9<br><!-- End of picture text --> Source: International Monetary Fund.

Figure 3 shows the behavior of annual inflation—measured in terms of the consumer price index—in the Dominican Republic and Haiti during the period 1963-2007, measured as deviations from US inflation. The figure shows that inflation has displayed significant spikes and considerable volatility at times in both countries—for the Dominican Republic for instance, most recently following the financial crisis and sharp exchange rate depreciation in 2003-04. The correlation between the two series is also quite high at times. These results suggest that although both countries could gain from a CCA in terms of price stability, their medium- and long-term performance in that regard does not appear to have been sufficiently effective and comparable to envisage a rapid transition toward such an arrangement.

At the same time, however, it is important to note that convergence in inflation does not need to be complete (in the sense of very narrow differentials among members) before adopting a CCA; as discussed later, the very process of union formation may bring about greater similarity in inflation between member countries—in part through greater integration in goods markets.

12
-Teacws-Non-teediers Pay DIfferences 2-

of teachers being overpaid in Brazil. For Cote d'Ivoir-, a study by Bourguignon (1986) using a job rationing approach concluded that reducing teacher salaries would result in a shortage of teachers.

# II. THE MODEL.

This paper uses a monthly wage rate function, which explicitly recognizes that differences in salaries can be due to differences in both human capital and other personal characteristics of workers. The model is as follows: LnY1<sup>.F(S</sup> 1<sup>, El.</sup> E<sup>2</sup> , Og; LnH1; ZI, Tj) Wher Y- Is the *othly earnings of Individual 1.

The first group of explanatory variables are the stan"-. Auman capital variables: S - ruee of years of shoolIng, E * dmber of years of current and previous Job experlence, v . a dumy variable equml to 1 If a diplom has beon obtained, zero othwnise.

In the model, expeoience is often interpreted as on-the-jib training, as opposed to formal training. Its square is also introduced to capture decreasing marginal returns to experience. The diploma variable will capture the possibility of remuneration being based to a certain extent on the formal credentials of the worker.

The equation also controls for the number of hours (H) actually worked during the month, because there can be important differences between occupations, especially between teachers and others.(l) The last group of explanatory variables consist of a set of personal attributes (Z) and a dummy variable (T) to indicate if the individual is a teacher. A positive significant coefficient of T would indicate the presence of a salary premium, going to teachers, relative to
causal effects in this context is that the employment of migrants in specific geographic regions or in specific occupations is likely to be endogenous, driven by market conditions, expectations, social networks, and historical precedents. This section discusses the empirical strategy used to address such selection biases.

We first construct measures of different types of risks (at the NUTS2 level) to which native workers are exposed, based on their occupations. Since the analysis is at the regional level, we first must control for differences in terms of the distribution of workers by their education, gender, and age. This step is necessary because those personal characteristics affect the supply of skills required for different types of occupations, and, at the same time, they may be correlated with the size of immigration in the region. To control for this spurious correlation, we first run individuallevel regressions to predict how occupation- and labor-market-related characteristics for each individual _i_ depend on individual observable characteristics. We perform these regressions separately for each of the years of study (2011 to 2018) and by immigration status (native vs. migrant): _Status_ stands for each of the main six variables capturing natives’ and migrants’ status in the labor market. The first four are occupation-level measures defined in the previous section: essential jobs (Measure # 1); amenability to telework (Measure #2); degree of income-safety (Measure #3) or health-safety (Measure #4). The last two are dummy variables based on the employment status (employed or unemployed) or the income level (in the top three income deciles) of the individual. _Xi_ is a vector of individual-level characteristics such as gender, education, and dummies for age groups. Using this measure, we calculate the gap between the actual measure and the predicted � one (S S S ) for each individual based on the estimated parameters: ii � S S = S S S −S S S (5) � � ii ii ii gg_S where S S S S S . This gap is a measure of each of main six labormarket outcomes of interest “cleaned” of differences in individual level characteristics. We then ii ıı ıı ii = EE[S |XX ] = SS� + bb<sup>�</sup> XX calculate the _weighted mean_ of the variable _g_Statusi_ across individuals in each NUTS2 region using their personal weights in the sample, and the hours worked.

20
_PACI_ <mark>Pr_</mark> _<mark>zero</mark>_ <mark>_</mark> _<mark>cases</mark> z_ provides a useful indication of the precision of the PACI also for countries with at least one case of corruption on record. For smaller countries, the signal that the PACI provides is rather noisy because of the rarity of observed corruption events. For example, the probability of observing zero cases in Tunisia would be equal to 0.31 in a situation where the level of corruption is the same everywhere in the world. Notably, two cases were actually observed, close to twice as many as the expected figure if cases were distributed according to trade shares, with a resulting PACI of about 170 and a ranking measure for Tunisia similar to that indicated by the _WB-CCI_ and the _TI-CPI_ (see the last two columns of Table 4).

# **III. The PACI and perception-based measures: A comparison**

The differences in the rankings yielded by the PACI and the WB-CCI and TI-CPI are also explored (see last columns of Table 4), where the perception-based measures are for the year 2005, the middle of the time interval of reference the PACI. A positive value indicates that a given country is considered to be relatively more corrupt based on the PACI. In most cases, differences are modest. However, results diverge considerably for a few countries.

These information are summarized by showing the Spearman rank correlations between the different versions of the PACI and the perception-based alternatives (Table 5). The rank correlations of _PACI1_ , the version which includes all cases (presented in Table 4), with perceptionbased measures are rather high, typically pegged at above 0.7.<sup>9</sup>

> 9 Note that more corruption corresponds to lower values of the _WB-CCI_ and the _TI-CPI_ indices, but to higher values for the _PACI_ and the _TI-GCB_ .

17
Family size/rooms in the house .01578*** .01334*** .02437*** (.00451) (.00322) (.00471) a Observations: 4,323 (enrollment), 4,953 (application) and 8,515 (learning).

_Source:_ ENCELURB. Regression includes neighborhood fixed effects and control for missing information on distance. Standard errors are shown in parentheses. *Significant at 10%; **Significant at 5%; ***Significant at 1%.

33
sector, which drive the growth of the economy. However, overall the restructuring process has been slow and there are signs of a “reverse development”, that is a move toward an obsolete employment structure. First, the agriculture’s employment share has gone up, indicating that the agricultural sector has acted as the “shock absorber” of the economic transition. That by itself is not necessarily a bad thing, and similar process has occurred in other transition economies, such as Poland and Romania.

Second, employment in the services sector has fallen faster than in other sectors, lowering the services’ share in employment. This is a more worrisome trend, which runs counter to developments in other transition economies, where the service sector has expanded during the transition and has been the main provider of new jobs. The employment structure biased toward agriculture and manufacturing is unsustainable in the longer run, implying that industrial restructuring – accompanied by the reallocation of labor and structural unemployment – is still ahead of Moldova. In order to facilitate this process and minimize its social costs, existing barriers to firm creation and growth need to be removed. So far, Moldova has experienced substantial job destruction without much restructuring. The challenge now is to turn the process into “creative destruction” whereby the destruction of old jobs in the declining segments of the economy is offset by the creation of new jobs in the expanding segments.

# **_Region_**

The analysis of job turnover by region sheds light on the regional labor market conditions, helps to identify expanding and declining regions, and finally shows the extent of regional restructuring.

The regional picture of job turnover in Moldova is dim. The regional distribution of job opportunities is very uneven. The only region where employment is growing is the capital region. In all other regions job creation falls short of job destruction (Figure 4). In many regions job creation is negligible, while job destruction is substantial, implying large employment losses. In fact, most of the regions in Moldova exhibit such a negative pattern of low job creation coupled with high job destruction.

This low job creation and associated employment decline in all regions except the capital one implies that there is little reallocation of jobs across regions. Regional job 27
# **Tables and Figures**

**Table 1. Descriptive Statistics of Household and Individual Characteristics**

|Variable|(1)<br>Mean|(2)<br>SD|(3)<br>Min.|(4)<br>Max.|
|---|---|---|---|---|
|**_PANEL A. Household Characteristics_**|||||
|Household size (N)|3.71|1.44|1|14|
|Dependency ratio (%)|0.27|0.20|0|0.8|
|Households in rural area (%)|0.50|0.50|0|1|
|Households with assets index above the median (%)|0.47|0.50|0|1|
|Moderate to severe food insecurity (%)|0.36|0.48|0|1|
|Remittances received (%)|0.35|0.48|0|1|
|Households has a non-farm enterprise (%)|0.33|0.47|0|1|
|Households engages in agricultural activities (%)|0.54|0.50|0|1|
|Observations|1008||||
|**_PANEL B. Individual Characteristics_**|||||
|Female (%)|0.55|0.50|0|1|
|Age (years)|35.49|13.52|15|64|
|High school or higher education (%)|0.28|0.45|0|1|
|Read and write (%)|0.87|0.33|0|1|
|Never married (%)|0.34|0.47|0|1|
|Access to mobile phone (%)|0.88|0.32|0|1|
|Access to Internet (Wi-Fi) (%)|0.19|0.39|0|1|
|Observations|2480||||

_Notes:_ This table shows the average summary statistics of the household- and individual-level variables— Panels A and B, respectively—used in our analysis. Columns (1) and (2) present the mean and standard deviation (SD) for each characteristic of the participants in our sample. Columns (3) and (4) show the minimum and maximum values of the characteristics, respectively. These variables were collected using the following modules: sociodemographic characteristics, education, food security, household characteristics, and assets. Appendix C includes definitions for each of the variables. All indices are estimated using Anderson’s (2008) approach of inverse covariance weighting and take a value between 0 and 1. All variables are dummies except when the unit of measurement is indicated in parentheses.

28
30

**Table 9. Total population and its urban share (in millions)**

|Region||1970|||1985|||2000|
|---|---|---|---|---|---|---|---|---|
||Total||Urban|Total||Urban|Total|Urban|
|World|3693|1371|(37%)|4837|1983|(41%)|6122|2854 (47%)|
|Africa|361|81|(23%)|555|165|(30%)|872|340 (39%)|
|Asia|2102|503|(24%)|2818|791|(28%)|3549|1242 (35%)|
|Latin America|283|163|(57%)|405|279|(69%)|546|420 (77%)|

Source: [United Nations 1987]

**Table 10. Average annual growth rate of population and urbanization** (percent)

||Total|Populati|on|Urban|ization||Urban|Popu|lation|
|---|---|---|---|---|---|---|---|---|---|
||1970|1980|1990|1970|1980|1990|1970|1980|1990|
|Region|to<br>1980|to<br>1990|to<br>2000|to<br>1980|to<br>1990|to<br>2000|to<br>1980|to<br>1990|to<br>2000|
|World|1.9|1.65|_1.55_|0.65|0.75|0.9|2.5|2.4|<br>2.4|
|Africa|2.8|3.00|3.00|1.80|1.80|1.8|4.5|4.8|<br>4.7|
|Asia|2.1|1.70|1.50|1.10|1.10|1.6|3.2|2.9|<br>3.0|
|Latin America|2.5|2.20|1.80|1.30|1.00|0.7|3.8|3.3|<br>2.6|

Source: [United Nations 1987]