rafmacalaba/datause-encoder-data huggingface dataset:
21 Table 4: MODELLING INFLATION BY OLS (sample size: 89Ql-97Ql)

|**_Explanatory_**<br>**_variables_**|**Reg.**<br>coefficients|**I**<br>st. error|**Reg.**<br>coefficients|**II**<br>st. error|**Reg.**<br>coefficients|**III**<br> st. error|
|---|---|---|---|---|---|---|
|_Constant_|o0.067***23|0.011|-0.055***|0.007|-0.054***|0.009|
|_DC_|0.069*|0.039|||-|-|
|_DC_2_|0.226**|0.091||-|||
|_M2_1_|||0.548***|0.054||-|
|w|0.450***|0.105||-|0.431**|0.104|
|_w_I_||-|||0.217*|0.109|
|_w_2_|||||_--_0.162*|0.083|
|_rer_|||_--_0.193***|0.065||-|
|_rer_I_|-0.643***|0.073|-0.575***|0.038|-0.425***|0.078|
|_rer_2_|-0.240***|0.054|0.114**|0.044|-0.128**|0.059|
|_Tsd_|**0.029*****|**0.007**|**0.046*****|**0.004**|**0.031*****|0.007|
|_Tsd_l_|**0.012****|**0.005**|**0.020*****|**0.003**|**0.013***|**0.007**|
|_Tsd_2_|**0.016*****|**0.003**||**-**|**0.026****|**0.006**|
|_Tsk_|**0.070****|**0.031**|**0.113*****|**0.025**|**0.081****|**0.031**|
|_Tsk_I_|-|-|-|-|**0.058***|**0.031**|
|_CSeason_|0.040|0.026|0.019|0.016|0.004|0.032|
|_CSeason_I_|0.030|0.026|0.056***|0.017|0.056*|0.027|
|_CSeason_2_|0.067**|0.029|0.030|0.018|0.046|0.028|

_source: author's calculations using GUS and IFS data_ In the first version of the model, domestic credit (DC) was used instead of _M2_ to reduce the danger of multicollinearity. However, even though of all monetary aggregates, _DC_ is the one least closely correlated with wages, its correlation coefficient is still relatively high (0.68) and poses serious risk that the estimates of the coefficients' standard errors will be distorted. Therefore, two additional regressions were carried out, each including only one of the two correlated variables. Second and third column in Table 4 present the output of regressing inflation on the set of explanatory variables including money _(M2)_ and wages, respectively.

> **23** Number of stars beside a coefficient or a statistic indicates the level of significance: no stars -insignificant at up to 10% level, * - significant at 10%, ** - significant at 5% and *** - significant at 1%.
- 10 -

and hence affect their prices. Barnhart found that the news contained in the variables not predicted by theory are not generally important in affecting prices. As in his earlier study Barnhart found that surprises in the monetary variables (MI, discount and surcharge rates) cause the majority of the significant comnodity price responses following announcements.

17. Gilbert (1985) derived the theoretical links between commodity price changes and innovations in interest rates, exchange rates and inflation, and between innovations in expected supply and demand for the commodity over the period during which stocks are to be held. The main contribution of Gilbert's work is on the theoretical linkage between exchange rate shocks and commodity prices. His model predicts that an unexpected appreciation in the dollar results in a less-than-proportional fall in the dollar price of commodities (as a weighted average of the exchange rate changes, where the weights depend on production and consumption shares and supply and demand elasticities). The predictions of the other variables of his model are discussed in the next Section.

18. In his 1987 article, Gilbert used quarterly data on metal 1/ prices to verify the predictions of his 1985 theoretical article. He measured news

> 1/ These were prices of aluminum, copper, lead, nickel, silver, tin, and zinc from the London Metal Exchange. The sample covered the period 1978ql to 1985q4.
productivity. Finally, we consider the implications of a free trade arrangement in which one economy (either China, ASEAN4, or the US) is left out, in order to assess quantitatively whether ASEAN4 or China gain from the exclusion of the other, and whether the Asian region is better off proceeding without the US as a partner.

The next section provides an overview of the economic structure, trade linkages, and structure of protection among countries in the APEC region, while also introducing the data used in our model. Section three introduces the conceptual tools that are needed to analyze trade liberalization and regional integration, and presents the main features of our APEC CGE model. Section four presents the alternative regional integration scenarios, and section five presents conclusions. An appendix contains a more complete description of the model.

# 2. **Economic Structure and Trade Patterns in Major APEC Economies**

Our APEC model is constructed around a six-region, ten-sector, four-factor Social Accounting Matrix estimated for the year 1992.5 This section outlines the structure of production, demand, income, taxation and trade patterns in the base year for each economic region included in the model, and briefly describes the patterns of protection among the relevant regions. The purpose of this SAM-based data analysis is to provide an overview of the structure and linkages among the regional economies and lay the groundwork for an understanding of the simulation results reported later in this paper.<sup>6</sup> Table 1 presents data on factor endowments, intensities, and costs for the regions included in the model. It reveals enormous differences in factor endowments and factor cost among these regions.

> 5 The data set is drawn primarily from the GTAP 1992 dataset, version 2, which is described in Hertel (1995). Features of this type of multi-regional SAM and aspects of its construction are described in Wang (1994).

> **6** Note that for model regions that are made up of more than one national economy (ASEAN4, Asian NIEs, China, and Eli), all figures on exports and imports reported in these tables (and used in the model) refer to trade with economies _outside_ that region, and thus exclude trade that occurs among members of the same region. In constructing the regional data sets, this "within region" trade is netted out and treated as another source of domestic demand. Thus care must be taken in comparing trade shares and structure with other published sources on regional trade flows that do not adjust for this intra-regional trade.

_Lewis, Robinson, and Wang_ _5_
Despite the programmatic focus on vulnerable groups at the national level and the existence of good practices across the country, programs and interventions for targeting most at risk groups are still limited—mainly due to the lack of sound evidence of vulnerable populations at the territorial level.

# **Restrictive Limit for Initiating ARV Therapy**

The current clinical HIV/AIDS guidelines are outdated. Most interviewees pointed to the need to review the criteria for commencing lifelong antiretroviral therapy – which is currently on CD-4 counts below 200 cells/μL,<sup>53</sup> despite the fact that the most up to date literature and technical advice recommends establishing the threshold at 350 cells/μL<sup>54</sup> .

The concurrence of the weaknesses discussed in this section – including the failure to make the most of the opportunities available at the primary care level, the barriers to access to services, and the outdated current clinical HIV/AIDS guidelines – account for the fact that, despite all of the great efforts made so far, the mortality levels for HIV in Colombia have not yet been reduced.<sup>55</sup>

# **_(d) Summary of Findings_**

Box 4 below summarizes the key findings of the analysis of the Service Delivery component of the HIV/AIDS response in Colombia, including both the main strengths of the system and the challenges that need to be addressed:

> 53 MPS (November 2006) Guía de práctica clínica VIH / SIDA. Bogotá: MPS.

> 54 World Health Organisation (2009) _Rapid advice: antiretroviral therapy for HIV infection in adults and adolescents._ Geneva: WHO. http://www.who.int/hiv/pub/arv/rapid_advice_art.pdf 55 UNGASS 2010.

51
LAC Region 28.7 7.2 3.602 1.618 66.4 **56** 23.1 6.2 2.762 1.285 **69.6** 42 **16.4** 7.2 2.102 1.005 73.5<sup>25</sup> 1 Brazil 28.0 7.5 3.415 1.531 65.3 63 22.6 6.5 2.730 1.270 68.8 48 16.3 7.6 2.090 1.000 73.1 28 2 Cuntral America 30.4 6.0 3.909 1.7t6 67.6 49 25.0 5.1 2.863 1.343 70.7 35 16.7 6.1 2.093 1.004 74.0 22 3 Caribbesn ard ot tr 30.2 7.5 3.725 1.623 65.3 60 23.4 6.5 2.795 1.275 68.5 42 16.7 7.3 2.149 1.021 72.6 26 4 Temprate and Tropical 26.2 8.2 3.416 1.559 67.7 51 21.1 7.2 2.633 1.231 70.5 35 15.9 7.9 2.090 1.000 73.9 18 rates (TFR) and net reproduction<sup>rates</sup> Note: Crude birth rates<sup>(C#P'</sup> and crude death rates<sup>(CDR) are per 1,000 population; total fertility</sup> rates (TFR) and net reproduction<sup>rates</sup> (NRR) are per wonmn 15-49<sup>years of age; life expectancy at birth, E(O), is given in years; and infant mortality rates (IMR) are per 1,000 live</sup> births. (See definitions in lorld<sup>Bank 1988.) World Bank regions and country departments are defined in Table 14.</sup>
# **Jobless Development***

Franziska Ohnsorge<sup>a,b</sup> , Richard Rogerson<sup>c,d</sup> , and Zoe Leiyu Xie<sup>a</sup> a World Bank, Washington DC, USA b CEPR, London, UK; CAMA, Canberra, Australia c Princeton School of Public and International Affairs

> <sup>d</sup> National Bureau of Economic Research

Originally published in the Policy Research Working Paper Series on _September 2024._ This version is updated on _April 2025._ To obtain the originally published version, please email prwp@worldbank.org.

## _JEL-codes:_ J11; J16; J21; F66; O41; O47

_Key words:_ employment; emerging and developing countries; structural transformation; female labor force participation.

__________________________________________________

* We would like to thank Nina Arnhold; Margaret Arnold; Najy Benhassine; Ximena Del Carpio; Patricia Fernandes; Isis Gaddis; Jon Jellema; Nandini Krishnan; Robin Mearns; Gaurav Nayyar; Anna O’Donnell; Nethra Palaniswamy; Lokendra Phadera; Martin Raiser; and Javier Sanchez-Reaza for their helpful comments. The findings, interpretations and conclusions expressed in this paper are those of the authors and should not be attributed to the World Bank, its Executive Directors, or the countries they represent.
19

**Table 2. Quality growth: summary statistics of data**

|_Variable_|_Mean_|_Standard_<br>_Deviation_|_Minimum_|_Maximum_|
|---|---|---|---|---|
|Growth Rate of Quality|0.001268|0.089368|-5.608547|5.964081|
|Ln Initial Quality|-0.201023|0.280277|-8.454018|1.044727|
|Ln Initial GDP per capita|7.723025|1.585803|3.565800|11.626510|
|Initial Institutional Quality|2.080978|14.636410|-88|7|
|Initial Human Capital|19.153060|13.310290|0.027734|69.751091|
|Initial Trade Lib. Index|0.715041|0.231503|0|1|
|Initial Agricultural Lib. Index|0.471090|0.382185|0|1|
|FDI inflows as % of GDP|2.362129|4.948364|-34.756802|136.193100|
|Initial Domestic Fin. Lib. Index|0.520379|0.298308|0|1|
|Initial Ext. Capital Account Lib.|0.599943|0.373698|0|1|

_Note_ : The annualized growth rate of (product) quality is expressed in annualized natural units. The indexes of liberalization of trade, agriculture, the domestic financial sector, and the external capital account are _de jure_ indicators that range between 0 and 1, with higher values corresponding to greater liberalization (see Prati et al., 2013, and Abiad et al., 2010). Institutional quality is proxied by the “Constraints on the Executive” variable from the Polity IV data set. GDP per capita and human capital, as proxied by the secondary-school completion rate, are drawn from the World Bank’s _World Development Indicators_ . Foreign Direct Investment as a percentage of GDP is drawn from the IMF’s _International Financial Statistics_ .
14 _F;iscal Crisis in Central Europe_ The general government balance deteriorated considerably between these two periods in both Poland and Hungary, and only modestly in Czechoslovakia. This deterioration is the result of parallel trends in both revenues and expenditures. During the period under consideration, government revenues in fact decreased considerably, by as much as 8-12 percent of GDP. In turn, the main cause of this drastic fall is tax revenues from public enterprises. Tax collection from both private enterprises and individuals increased, but far less than necessary to offset the former impact.

On the expenditure side, the increase in transfers to households - by 5-11 percent of GDP - is more than offset by the cut subsidies - by 11-19 percent of GDP -, so that overall total government expenditures _decrease -_ by 4-11 percent of GDP. However, overall the reduction in government revenues was too large to be offset by the decrease in government expenditure, particularly in Poland and Hungary. Hence the emergence of the fiscal crisis. This can be considered the heart of the "traditicnal" approach to fiscal crisis during transition in Poland and Hungary.

In Czechoslovakia, on the other hand, the decrease of government expenditure was larger than in Poland and Huingary, and it almost matched the decrease of government revenues. This is mainly, due to the fact that transfers to households increased much less in Czechoslovakia. Such transfers account for a large fraction of government spending in all these countries, and they almost doubled in size - relatively to GDP - in Poland and Hungary, while they increased by less than 40 percent in Czechoslovakia. We will return to this issue, which, as we argue, is the single most important factor behind the fiscal crisis in transition economies.

# **111.2.** A **Revenue Crisis?**

Ihe data just shown, however, needs to be better interpreted. As mentioned in the previous section, one of the most important features of a socialist economy is the centrally planned determination of prices for both producer and consumer goods. Profits of socialist enterprises
The economic damage ( _D_ ) from each hurricane is the sum of all the losses caused by it. In this analysis, we focus primarily on lost buildings and infrastructure but we also include an analysis of fatalities in the United States. The economic damage of capital losses is the present value of lost future rents. This should be equal to the market value of the building. Note that the market value of capital is often less than the replacement cost.

In order to model hurricanes, it is critical to recognize that they are rare events. An important component of expected hurricane damages is the frequency or probability ( _π_ ) the hurricane will occur in each place. In this case, we are interested in the probability that a hurricane with particular characteristics (X) will strike a particular place. For example, important characteristics of the hurricane include minimum barometric pressure ( _BP_ ), maximum wind speed ( _WS_ ), and where the hurricane strikes ( _i_ ). Atmospheric science can help predict the probability a hurricane ( _j_ ) with particular characteristics will strike each place ( _i_ ) given the climate ( _C_ ): The damages associated with any given hurricane ( _j_ ) also depend on the vulnerability (Z) of each place ( _i_ ).

For example, the vulnerability in each location ( _i_ ) could depend on its population and income: Damages will also depend upon the adaptation policies taken to prevent extreme event damage. For example, building codes could encourage homes to be able to withstand high wind speeds, land use policies could discourage development in flood plains, or restrictions could keep people away from vulnerable coast lines. In contrast, mal-adaptation could make matters worse. Poorly conceived policies could increase damages by encouraging people and capital to be in harm’s way. For example, policies could subsidize flood insurance in risky places or subsidize disaster relief. Unfortunately, 4
[ POLICY RESEARCH WORKING PAPER 1269

# Summary findings

Union-nonunion wage differentials have been extensively studied by labor economists, but for lack of data on the developing world the study has been confined largely to the industrial world. This paper is one of the first attempts to empiricaily examine those differentials in a developing country.

Panagides and Patrinos find that union-nonunion wage differentials in Mexico have many of the same attributes and show many of the same patterns as those in industrial nations. But there are marked differences.

Based on a household survey in 1989, Panagides and Patrinos find that:

* Overall, the union-nonunion wage gap is 10.4 percent.

* Unions have a positive impact on the earnings of employed women and indigenous people.

* Organized labor in Mexico's northern statcs is considerably weaker **in** collective bargaining strength than it is elsewhere in Mexico.

This paper-a joint product of the Human Resources OperationsDivision, L.atin America and the Caribbean, Country Department - 11, and the Education and Social Policy Department is part of a larger effort in the Bank to investigate labor markets and labor market institutions in developing economies. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Ian Conachy, room S 10-022, extension 33669 (35 pages). March 1994.

_The Policy Research Working Paper Series disseminates the findings of uwork in progress to encourage the exchange of ideas about development issues. An objectiveof the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be used and cited accordingly. The findings, interpretations, and conclusions are the authors' own and should not be attributed to the World Bank, its Executive Board of Directors, or any of its member countries._ Produced by the Policy PerParch Dissemination Center
The case of Guyana and Suriname case is similar that of Trinidad and Tobago. The ferry between these two counties costs only $US 0.10/NM, much less than tariffs of the ferry service of the small island states and the Caribbean hubs.

The small island states have the highest ferry tariffs in the Caribbean region, up to three times higher than those of the hubs and continental countries. Grenada, with a tariff of US$3.81 per nautical mile, has the highest rate for the small island states and for the whole region (Figure 31). Antigua and Barbuda with US$1.71/NM and Dominica and St. Lucia with US$1.83/NM have the second and third highest tariffs for the small island states and the whole region. The exception to such high tariffs is St. Vincent and the Grenadines, which has the lowest tariff (US$0.40) of all the small island states and one of the lowest of the region.

**Figure 31: Average Tariff in US Dollars per Nautical Mile**

<!-- Start of picture text --> 4.00<br>3.50<br>3.00<br>2.50<br>2.00<br>1.50<br>1.00<br>0.50<br>0.00<br>Bahamas Dominican Trinidad Guyana Suriname Grenada Dominica St. Lucia Antigua St. Kitts St. Vincent &<br> Republic & Tobago & Barbuda & Nevis the Grenadines<br>US$ per Nautical Mile<br><!-- End of picture text --> _Source: Authors’ calculation based on data collection._

# _v. Main Constraints_

All ferry operators face some challenges to improve their service and even to provide a regional ferry service. Some of the private operators expressed that in order to have an effective regional ferry system, government coordination would be needed for both setting a consistent affordable price for port access and harmonize regulations among countries.

Aside from the high energy and fuel costs, another challenge is that initial investment costs are high and that market penetration is slow. Some of the operators are afraid to make such a high investment since it takes approximately 2 years for passengers to start using the service.

70
# Table 5:  Determinants of Net Female Enrollment Ratios

Dependent Variable: Flog Net Enrollment Rate

||(1)<br>Primary|(2)<br>Secondary|
|---|---|---|
|Total Fertility Rate|-0.349<br>(7.35)**|-0.555<br>(14.87)**|
|Life Expectancy|0.068<br>(6.04)**|0.043<br>(6.51)**|
|Log GDP Per Capita|0.139<br>(1.34)|0.893<br>(10.67)**|
|Constant|-1.774|-5.213|
||(1.39)|(8.14)**|
|Observations|1849|1025|
|R-squared|0.89|0.98|

Absolute value of t statistics in parentheses * significant at 5%; ** significant at 1% 30
# **_a. Treatment Effects Regressions_**

In many Heckman-type selection models, the dependent variable is observable only for those individuals (or households or countries) that received the treatment. In our case, indicators of financial development are observable for borrowers and nonborrowers alike. We therefore opt for treatment effects models in which: _Yi = α + βXi + δZi+ εi_ (2) where _Y_ is an indicator of financial development and _X_ is the vector of macroeconomic, institutional, and policy control variables. _Z_ is the endogenous treatment variable indicating whether or not country _i_ borrowed. As is typical in this literature, the decision to borrow is modeled as the outcome of an unobserved latent variable Z<sup>*</sup> , which is a function of exogenous covariates _W_ and a random component _u_ : _Zi* = γWi + ui_ (3) We observe: Because there is an element of self-selection in borrowing from the World Bank, and because the error term of the model that summarizes this choice (namely, 3) could be correlated with the error term in the regression of interest (namely, 2), we must search for a set of valid instruments. These instruments should be highly correlated with the endogenous regressor (the borrowing dummy), but contemporaneously uncorrelated with the error term in (2) ( _i.e._ , truly exogenous).

It is, however, difficult to find exogenous variables that can capture the nonlinearities in borrowing outcomes described above. In particular, it is highly likely that proxies for borrowing needs as reflected in measures of countries’ fiscal health and indebtedness are themselves endogenous. The governmental checks variable is less susceptible to criticism on these grounds, but those checks tend not to vary over time, and thus are not well suited for use as instruments in panel regressions that incorporate country fixed effects.

We therefore use past information on fiscal health and indebtedness in our selection equation, which is by definition not _contemporaneously_ correlated with the error term in the financial development regressions. Because the hypotheses above 12
both instruments represent easy access to immediate liquidity and both have shortterm maturity. About 18% of firms have an overdraft facility and about 20% have a line of credit, and about 30% of firms in our sample have either overdraft or credit line. Credit availability increases with firm size: only 10% of micro enterprises, between 20% and 30% of small and medium enterprises, and about 40% of large enterprises have a credit line or overdraft. Table 1, Panel B reports distribution of firms with and without access to credit, by size.

The survey also asks firms to rank a number of different obstacles to doing business (rankings range from no obstacle to major obstacle). Access to credit is listed as one of the major obstacles. Interestingly, there is no correlation between the extent to which firms rank access to credit to be an obstacle to business and firm size. While access to a credit line or availability of debt are objective measures of access to credit, the obstacle rankings are very subjective, and are likely to depend on personal characteristics of managers. In fact, there is no significant correlation between obstacles rankings and actual presence of credit lines and debt. Therefore, we prefer to use objective measures of access, such as our variable LINE.

Table 2 reports descriptive statistics for the main variables.

# **3. Estimating productivity**

Firm productivity is an unobservable firm characteristics. However, estimates of productivity can be recovered as the difference between actual output and output 7