rafmacalaba/datause-encoder-data huggingface dataset:
interaction term is mostly insignificant for market diversification indicators (and that the openness variable and the interaction term are always jointly insignificant), we are limited to the regressions involving product diversification indicators for this exercise. Thresholds are then established on the basis of the system GMM estimates (specifications _B2_ – _B5_ ), which represent our preferred model.

The total effect of openness on volatility is the sum of the coefficients on the openness variable and the product of the interaction term and the coefficient on the interaction term. It is straightforward to determine threshold values at which the total effect of openness on growth volatility changes sign. In other words, we can identify a value for each diversification measure which, in theory, a country needs to underscore (a lower value implies a more diversified export basket) in order to benefit from a marginal increase in trade openness in terms of a reduction in growth volatility. The threshold can be identified by setting the total effect of trade openness on growth volatility to zero, that is, by taking _β_ 1 _OPEN_ + _β_ 3 _OPEN × DIV_<sup>_∗_</sup> = 0, and solving for the value of the critical diversification measure _DIV_<sup>_∗_</sup> , for which the relationship holds.

This yields _DIV_<sup>_∗_</sup> = _−β_ 1 _/β_ 3. We apply the Wald test to determine the joint significance of the two variables forming the total effect. Moreover, we compute joint standard errors for _OPEN_ and _OPEN × DIV_ , and use these to determine confidence bands around the thresholds.

Table 4 presents the thresholds calculated for each of the four regressions, along with their corresponding 10 percent confidence intervals. It also reports Wald test results for the joint significance of the openness variable and the interaction term. The Wald test statistics (column 3, Table 4) indicate that the total effect of openness on growth volatility is statistically significant at the 90% level or higher across specifications. There is only one exception for which it is not—for the PC product indicator—and we accordingly refrain from making inferences based on that measure.

Having computed joint standard errors for the two variables in question in order to determine confidence intervals, we can plot confidence bands around the total effect of trade openness on growth volatility. Figure 2 presents the plot for the 5 product index (specification _B2_ ) as an example. We can see that the impact of trade openness on growth volatility is significantly lower than zero with 90 percent confidence, as long as a country scores lower than about 0.24 on the diversification variable (Table 4). The effect gradually increases and changes sign (threshold) at about 0.48. In contrast, above a value of about 0.71, the impact of trade openness on growth volatility is significantly positive. A qualitatively equivalent illustration can be made for the 10 product and the indicators.

18
Table 1 (continued) : Proposed criteria for assessing housing subsidies programs

|_Criterion_|_Questions to be answered_|_Measurement indicators (examples=* - this is_<br>_not clear)_|
|---|---|---|
|Targeting<br>/<br>Vertical<br>equity (redistribution)|- What is the distribution of transfers (explicit or implicit) implied<br>by the subsidy ?<br>- Who are beneficiaries of the subsidy in theory ?<br>- Are the most needy covered by the subsidy ?|- Estimation of distribution of transfers (explicit or<br>implicit) implied by the subsidy. Depending on the case :<br>- Estimation from tax files<br>- Econometric calculation from survey data<br>- Use of administrative files on beneficiaries<br>- comparison of official targets and effective target<br>possible with type of subsidy and implementation chosen.|
|Coverage/horizontal<br>equity|- What share of the targeted population is covered by the program ?<br>- Does the subsidy introduce different treatments for different kinds<br>of households / firms in the target population ?|- share of the targeted population covered by the<br>program.<br>- subsidy reach in different groups of households / firms<br>within target group.|
|Efficiency|- What is the social loss of resources implied by the subsidy ?<br>- Could the same resources be used more efficiently ?<br>- Which economic margins are affected by the subsidy?|Depending on the case considered:<br>- partial equilibrium calculation<br>- integration of subsidies in complete models of the tax<br>and benefits system<br>_Ad hoc_qualitative assessment<br>_Ad hoc_approach, e.g. :<br>- calculation of multiplier effect of public spending on the<br>program,<br>- calculation of effective tax rates on investment in<br>different sectors*<br>- econometric calculation of crowding out effects*|

56
New data from three sources make the need for this revision compelling. The first new data source is the 2005 _International Comparison Program_ (ICP). The price surveys done by the ICP have been the main data source for estimating PPPs. This started in 1968 with PPP estimates for just 10 countries, based on rather crude price surveys.<sup>4</sup> Prior to the present paper, our most recent global poverty measures had been anchored to the 1993 round of the ICP. An independent evaluation (known as the _Ryten Report_ ; see UN, 1998) of the 1993-96 ICP rounds identified a number of methodological and operational concerns, including lack of clear standards in defining internationally comparable commodities. This is a serious concern when comparing the cost of living between poor countries and rich ones, given that there is likely to be an economic gradient in the quality of commodities consumed; without strict standards in defining the products to be priced, there is a risk that one will underestimate the cost of living in poor countries by confusing quality differences with price differences. PPPs will be underestimated in poor countries. This highlights the difficulty of doing price surveys for the purposes of international comparisons. The existence of non-traded goods is (on the one hand) the main reason why we need to use a PPP rather than market exchange rate, but (on the other hand) non-traded goods are harder to compare between countries. The only way to deal with this is through detailed product listings and descriptions, which add significantly to the cost of the data collection. A better funded round of the ICP in 2005, managed by the World Bank, has taken considerable effort to address this problem as well as introducing other improvements in the data and estimation methods for PPPs (World Bank, 2008a,b).<sup>5</sup> A number of methodological and operational improvements were implemented by the 2005 ICP. The new ICP data imply some dramatic revisions to past estimates, consistent with the view that the old ICP data had under-estimated the cost-of-living in poor countries.

The second data source is a new compilation of poverty lines for developing countries provided by Ravallion, Chen and Sangraula (RCS) (2008). Based on these data, we implement

> 4 The ICP started as a joint project of the UN and the University of Pennsylvania, with support from the Ford Foundation and the World Bank. Prior to 2000, the Penn World Tables (PWT; see Summers and Heston, 1991) were the main source of the PPPs for consumption used in the World Bank’s global poverty measures. In 2000 we switched to the PPPs estimated by the Bank’s Development Data Group. There are methodological differences between the PWT and the Bank’s PPPs, as discussed in Ackland et al. (2006) and World Bank (2008, Appendix G).

> 5 While we do not know of any cost comparisons, there can be little doubt that the 2005 ICP entailed a far higher cost than previous rounds; as the Ryten Report had also discussed, fixing the problems with the ICP data would inevitably come at a cost.

3
Middle Eastern and North Africa region would gain the most. The increased oil price would adversely impact the demand for oil at the global level and consequently cause a drop in outputs from the petroleum refinery sector at the global level. Global outputs from oil intensive industries such as transport and chemicals would also drop. On the other hand, the increased oil price would cause a significant boost in the biofuel industry. The natural gas industry would also gain but on a lesser scale compared to biofuels. Oil exporting countries, particularly Middle Eastern countries and Nigeria, would experience a gain in their international trade. Like existing studies analyzing macroeconomic impacts of oil price shocks using a CGE model, our study also concludes that the impacts of oil price shocks to economic output measured as GDP elasticity with respect to oil price change, would vary significantly across countries depending upon their adjustment capacity to the oil price shocks.

# **References**

- Aydın, Levent and Mustafa Acar (2011). Economic impact of oil price shocks on the Turkish economy in the coming decades: A dynamic CGE analysis, Energy Policy, Vol. 39, pp. 1722–1731.

- Balke, N.S, S. P. A. Brown, and M. K. Yücel (2008), An International Perspective on Oil Price Shocks and U.S. Economic Activity.  Working Paper 20, Globalization and Monetary Policy Institute, Federal Reserve Bank of Dallas (September).

- Energy Information Administration (2009), Annual Energy Outlook 2010. United States Department of Energy. http://www.eia.doe.gov/oiaf/forecasting.html

- Hamilton, James.D. (2011), Nonlinearities and the Macroeconomic Effects of Oil Prices, _Macroeconomic Dynamics_ **15** (Supplement 3): 364–378.

- Jimenez-Rodrıguez,  Rebeca and Sanchez Marcelo (2005). Oil price shocks and real GDP growth: empirical evidence for some OECD countries, Applied Economics 37: 201–228.

- Kilian, L. (2008), A Comparison of the Effects of Exogenous Oil Supply Shocks on Output and Inflation in the G7 Countries, _Journal of the European Economic_ Association 6(1):78–121.

Sanchez, M. V. (2011). Welfare Effects of Rising Oil Prices in Oil-Importing Developing Countries, 13
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# 3.4 Gender preference in job postings

Hiring practices based on gender are not uncommon in Pakistan, and gender preferences of employers can be explicitly signaled in job postings in Rozee.pk. Every job posting has the option to indicate gender preference, i.e., male or female. While 80% of jobs do not indicate any gender preference, 14% and 6% of job postings indicate that they prefer, respectively, men and women (Figure 19). This indication of gender preference is effective in attracting preferredgender applicants, as shown in Figure A1.3. The proportion of male applicants is 97% for malepreferred job vacancies, while the proportion of female applicants is 51% for female-preferred vacancies.

**Figure 19. Percentage of genderearmarked vacancies**

_Source_ : Authors’ calculation using the data from Rozee.pk.

## **_Q. What type of jobs prefer male and female workers? Are there salary differences associated with gender preference?_**

**_A. Jobs that require longer professional experience and are at higher occupation levels are more likely to prefer males. Job postings that indicate gender preference, irrespective of which gender is preferred, advertise lower salaries than the job postings that do not indicate a preference._**

We examine what types of job postings indicate gender preferences. We also examine how gender preferences are associated with advertised salaries (Figure 20). Key findings are summarized below.

- _Finding 1: Female workers are more likely to be preferred in jobs requiring higher education._ 7% of the jobs requiring bachelor and graduate-level degrees prefer female workers, whereas only 3% of the jobs requiring the secondary and less education level prefer females.

- _Finding 2: Jobs that require longer years of experience and managerial jobs tend to prefer men._ 30% of the jobs that require 10 or more years of experience prefer males, whereas 13% of the jobs requiring zero to two years of experience do so. 27% of manager jobs prefer men, whereas 6% of intern jobs prefer men.

22
# **Appendix G: Documentation of Input-Output table for Armenia**

# **Jesper Jensen and David G. Tarr**

# **_Summary_**

The core of the model data consists of an input-output table. There exists no recent input-output table for Armenia, so we produced the table based on data provided by the National Statistical Office of the Republic of Armenia. Lacking resources for a full survey, these data were obtained by the National Statistical Office by a pilot sample survey. Our data sources include an unbalanced supply-use table with 16 sectors for the year 2006 and detailed data on GDP for 2007 by types of income, expenditure, and production. The supply-use table contains all the elements we need for the input-output table, but supply deviates significantly from use in most of the sectors. Accounting identities require that supply must equal use for a balanced input-output table and for a table that we can use in our model. The sheer size of the deviations in the supply-use table calls for funding a full survey by the National Statistical Office to obtain additional and more accurate detailed data to reduce the deviations.

Without access to more accurate and detailed data, we develop a balancing procedure to arrive at a balanced input-output table. The procedure involves an optimization problem in which the elements of the table are adjusted such that the sum of the squared deviations from the initial values are minimized and subject to a number of side constraints, including supply-use balance. As part of the procedure, we also use detailed GDP data to update the dataset to the year 2007. Finally, we disaggregate two services sectors to get more details on transport, communication and financials sectors. The final table contains 21 sectors.

# **Introduction**

We begin with a dataset compiled by Light (2010), whose data sources include an unbalanced supply-use table with 16 sectors for the year 2006, and detailed data on GDP for 2007. All data is provided by the National Statistical Office of the Republic of Armenia.

Light (2010) documents a procedure to balance the data, and also shows how the data is used to implement a simple CGE model of Armenia. The balancing procedure is very simple, however, as the majority of adjustments are made to only two elements of the data: household consumption and compensation of employees. The large imbalances in the supply-use table translate into large adjustments in these two data elements. In turn, this implies large changes in GDP.

We use an alternative balancing procedure which extends the procedure by Light (2010) in several ways. First, the procedure allocates adjustments to the entire dataset, and auxiliary data and information is used to guide the adjustments. Second, we explicitly account for GDP in the balancing procedure, so that GDP in the final data set is indeed close to official GDP. This applies to both total GDP and the components of GDP.

The next section shows how we construct the initial estimate of our input-output table and reports the imbalances in the data set. The following section documents the procedure we 103
standard errors clustered at the customer-level and report odds ratios and average marginal effects for customer gender.

We complement this analysis with a dyadic regression model using the sample of transactions drawn from May 2017. The dependent variable is a dummy variable indicating whether a transaction between these potential pairings occurred that day of the month. In particular, the model that we run is as follows: Transactionacn = α+ β1SameGenderac + β2SameMarketac + β3BothFemaleac + β4AgeDifferenceac + εac (2) where Transactionacn is the dummy variable which takes the value of 1 if a transaction took place between agent a and client c on day n of May 2017, SameGenderac is a dummy variable that takes the value of 1 if both agent a and client c are of the same gender, SameMarketac is a dummy variable that takes the value of 1 if both agent a and client c are located in the same market area and finally, AgeDifferenceac is the absolute value of the difference between the agent of the agent and that of the client. This model is estimated with ordinary least squares (OLS) using two-way clustered standard errors both at agent and client level. We also include client and agent location fixed effects.

The empirical specification for the amounts transacted at male and female agents is similar to that in equation (1) and given by: log (TransactAmount)ji = α+ β1GenderClienti + β2GenderAgentji + β3(GenderClienti ∗ GenderAgentji) + β4Currencyji + β5TransactTypeji + β6CustomerAgei + β7AgentAgeji + β8Marketji + β9Monthji + εji (3) where log(TransactAmount) indicates the logged transaction amount of transaction _j_ of customer _i_ . The independent variables are almost identical to those in equation (1). Most notably, we introduce an interaction between GenderAgent and GenderClient to estimate the effect of gender on transaction amounts. We estimate these models using ordinary least squares (OLS) regressions with standard errors clustered at the customer-level. Appendix Table 1 contains more details about the variables.

# **Probability of transacting**

Table 4 reports the determinants of transacting with female agents. Column 1 excludes controls and market and time fixed effects while Column 2 includes market and time fixed effects but still excludes other controls. The odds ratio that a female client transacts with a female agent is 1.531 (p-value is 0.000) in Column 1 and 1.520 (p-value is 0.000) in Column 2. When control variables are added in Column 3 and market and time fixed effects (in addition to controls) in Column 4, the effect declines only slightly (odds ratio = 1.514, p-value = 0.000 in Column 3 and odds ratio = 1.518, p-value = 0.000, in Column 4).

8
-7-

working adults' fiscal burden for public spending on education. (Sources: Sussangkarn 1990, World Bank 1991, 1989) Although problems still exist, such as a low secondary enrollment ratio, large regional disparities, and high unit costs in primary education, etc., Thailand's educational reforms have been designed to tackle the problems. And the policies have remained flexible and responsive, with a willingness to adopt new approaches if the old ones fail to meet objectives. In sum, a steady economic growth and subsequent demand for educated labor, lower population growth, and flexible and responsive sectoral policies provided a favorable environment for Thailand's success in educational development. B. Brazil: Mis-targeting and inefficiency Brazil spends a significant share of its GDP on social services, a total of 18 percent of GDP in 1986 (federal 9 percent; state 9 percent). In 1985, about 1.4 percent of GDP was spent (by central government alone) on education and 1.8 percent on health. However, social welfare indicators are strikingly low: infant mortality in Brazil (59 per thousand in 1989) is well above the average for countries in its income group (45.2), and in the Northeast of Brazil (116 per thousand in 1985), it is higher than in much of Sub-3aharan Africa. Secondary enrollment was only 21 percent in 1984, compared with 90 percent in Korea. Large regional disparities exist in literacy rates, school enrollment, other social indicators, and in per capita income.

Why Brazilian federal expenditure has not achieved more? First, inflation and poor expenditure policies have resulted in stop-and-go financing and inefficient inter-governmental transfers. Transfers are often made with an uncertain time and in an inflationary economy, the real value of the resource can be quickly eroded, forcing agencies into inefficient budget adjustment, "start and stop" construction programs, disrupted procurement and delayed maintenance. Second, excessive transaction costs and wastes were caused by institutional arrangement that led to overly centralized federal programs, and lack of incentives for cost-effectiveness due to the lack of transparency and accountability in the financing of social programs.