rafmacalaba/datause-encoder-data huggingface dataset:
deflators are used to adjust the value of national lines from various years to the ICP benchmark year, before being converted to PPP-adjusted dollars.

The choice of inter-temporal price deflators can have considerable impact on estimates of global poverty because it affects both the PPP-adjusted values of income and consumption, as well as the value of the international poverty line. Jolliffe and Prydz (2015) provide an example where moving from using only official CPI data on inflation to the inter-temporal price indices used in PovcalNet leads to an increase in the global poverty line of $0.12, which implies an increase in the global poverty count of more than 100 million people. While PovcalNet primarily uses CPI data, it does also use different indices for a few countries, and this choice alone has a substantial impact on the count of poor people. Given that the poverty estimate is sensitive to the choice of price deflators, the decisions made about these indices are important.

Table 3 shows that for more than 75 percent of the countries, the World Bank poverty estimates use the annual official national consumer price index (CPI) reported in WDI to deflate current LCU values from the survey year to constant prices of the ICP base year.<sup>28</sup> However, in cases where surveys take place in specific months, or where inflation is high, PovcalNet uses monthly CPIs from official sources instead. These series are reported to PovcalNet by World Bank country and regional teams, citing official national sources. For example, from the LAC region, CPIs are primarily monthly series since survey fieldwork is undertaken during specific months of the year. For China and India, PovcalNet uses separate consumption distributions from rural and urban areas and therefore also use these countries’ urban and rural CPIs as deflators. Indonesia also reports separate rural and urban distributions, but no disaggregated deflator is available, so the national CPI is used.

Last, for a small number of countries (seven), PovcalNet uses alternative inter-temporal deflator estimated from price data in household surveys or other alternative sources. These are all countries where there is either no CPI data or there are concerns about the accuracy of the data for measuring changing price levels as experienced by poor people. Using Tanzania as an example, Sandefur (2013) illustrates that there are large differences between CPI data and household-survey-based estimates of price changes. Gimenez and Jolliffe (2014) similarly show

> 28 The CPI series reported in World Development Indicators originates from the International Financial Statistics data files from the International Monetary Fund (IMF). This series is also used in WDI to extrapolate PPP conversion factors to years other than the ICP base-year.

17
Tables A.11 to A.15, remain qualitatively similar with lower significance for Model 9 in betweenyear imputations. Further meta-analysis that controls for various modeling characteristics such as the imputation methods, the poverty lines, the treatment arms, and the source of deflator data confirms that employing the poverty line that is consistent with the base survey offers better results for between-year imputation (Appendix A, Figure A.2).<sup>17</sup>

# _Alternative modeling options_

We consider two alternative modelling options, which are multiple imputation (MI) and machine learning (ML). It is useful to note that the poverty imputation literature is broadly related to a larger literature on missing data (or multiple imputation in statistics; see, e.g., Little and Rubin, 2019; Carpenter _et al_ ., 2023). Certain differences, however, exist between the two literatures. One difference is that MI studies tend to employ Bayesian techniques for their estimation, which can require more computational time for drawing from posterior distributions. Another difference is that economists tend to use economic theory rather than statistical theory for model selection.<sup>18</sup> We employ two common MI techniques, linear model and predictive mean matching (PMM).

On the other hand, ML methods have been useful additions to the economists’ toolbox <mark>(Mullainathan and Spiess, 2017; Athey and Imbens, 2019). However, unlike our proposed imputation approach that builds imputation models based on economic theory (and perhaps closer</mark>

> non-food components differ. The 2019/20 poverty line includes information from earlier NPS rounds, while the 2020/21 poverty line includes data from later rounds, notably incorporating imputed rents, the value of durable goods, and clothing. To ensure consistency, we employ the appropriate consumption aggregate definition aligned with the respective poverty line.

> 17 This result is consistent with earlier observations for various contexts in Dang _et al._ (2019) and Dang and Lanjouw (2023). The key intuition is that the poverty line that is typically produced using prices (and to some extent, consumption patterns) specific to one time period would be more consistent with the consumption (poverty) levels in that period than those in another period. The full marginal effects from the logit regressions underlying Figure A.2 are shown in Appendix A, Table A.17.

> 18 Recent studies that apply MI techniques to economic issues include Jenkins _et al._ (2011), Douidich _et al._ (2016), Dang _et al._ (2017), Yoshida _et al._ (2022), and Dang _et al._ (forthcoming).

23
# _3.2 SAMPLE CHARACTERISTICS_

The documentation for this study was obtained through interviews with different functions (asset managers, risk managers, back-office) within selected institutions from the emerging market investor universe as described above. Over 30 interviews were conducted in the U.K. and the U.S. The sample included pension funds, asset management firms, hedge funds as well as commercial banks, including custodian banks or affiliates.

The dedicated emerging market assets under management by the different asset managers at the times of the interviews was approximately USD $15 billion with a maximum but very volatile additional USD $5 billion provided in form of cross-over funds. Most of the portfolio managers interviewed indicated that their assets under management had shrunk significantly from a pre1998 (Russia crisis) high, and that since the crisis, the proportion of dedicated emerging market mandates had fallen while cross-over assets had been growing.

While both equity and debt managers were interviewed, covering both local and hard currency investors, the dominant characteristic in the sample is one of a large asset manager managing third party funds with a fixed income mandate in hard currency. Most had long-only positions and strategic rather than “flipper” investment strategies; but some highly leveraged institutions (more often referred to as hedge funds) with short-term strategies were included in the sample nonetheless.<sup>4</sup> No specific method was used to pre-select or pre-screen the institutions or persons interviewed.

A standard set of questions were used for the interviews with some adaptation according to the specific type of function or institution covered. In general all interviews extracted information about the size, mandates and types of funds under management, governance arrangements, type of investment model employed, as well as the existence of an implicit or explicit non-investable list of countries.

To supplement the information obtained from the sample, research of publicly available investment methodologies was conducted including information from the California Public Employees’ Retirement System (CalPERS) and socially responsible investments from such sources as the Social Investment Forum in Europe, the U.S. and the U.K.

> 4 The asset management/investment banking industry is over laden with buzzwords, some of which have squeezed their way into this paper (an example being “flipper” investment strategies).  This term was originally used to describe short-term investors or day traders participating in IPO’s with the aim of swiftly selling their holdings into the market days or even hours after first acquiring the stock. Here it is more generally used to describe investors with a very short term view to rapidly buy and sell an asset – or “flip” an investment – often in connection with a new issue or major announcements.  One dictionary of acronyms and buzzwords can be found at: www.investopedia.com/dictionary/.

6
combinations where less than 30 percent of output is exported (see Table 4). The exportorientation of a given industry in a particular province is calculated by summing the value of exports of all fi rms operating in a given industry, province and year combination and dividing it by the sum of the total production in the same cell. If an observation for a particular year is missing it is substituted with an observation for the closest year available.<sup>12</sup> The results con fi rm our earlier fi ndings. The effects of the MA and SA variables are of similar magnitudes to the previous results. Although the coefficients are a bit smaller than in the full sample, they range between 0.6 and 0.7, as compared to 1.1 in Table 2, they correspond to a similar sized effect when evaluated at the mean number of fi rms: doubling SA is associated with a 51% increase in entry when evaluated at the mean number of foreign fi rms which is equal to one in this sub-sample, and doubling MA is associated with a 44% increase.

The signs and signi fi cance levels of other variables remain unchanged. To ensure that our results are not driven by choosing the cutoff at 30 percent, we also estimate the model for industry-province-year combinations where the share of exported output is less than 50 percent. The conclusions with respect to our key variables remain unchanged.

# 4.2. Extensions

In Table 5, we explore the effect of additional controls for distance to port<sup>13</sup> and the investment climate in the province, proxied by the total number of foreign fi rms in the province

> lagged one period. The results suggest that provinces close to ports appear to be more at-

> 12This data series has been provided by Sourafel Girma. See Girma and Gong (2004) for detailed information on the data source.

> 13This is measured as the shortest distance to one of the three major ports: Shanghai, Hong Kong and Qinhuangdao (Hebei).

20
producing in the same NAICS 6-digits sector of the parent firm. Vertical FDI instead arises when the production of the subsidiary is an input for the production done by the parent firm. In Alfaro and Charlton's words, vertical FDI are defined "as the activity of the foreign-owned subsidiaries in industries upstream from the parent industry (according to the US input-output matrix)”. If the activity of the subsidiary satisfies both these criteria, then the FDI is defined as complex. The remaining case in which the subsidiary produces in a different sector of the parent which is not an input is classified as non-identified investment.<sup>19</sup> More formally, the definition of FDI is based on the intersection of the sets of primary sectors of the parent firm and its subsidiary. Let � be the set of 6-digits NAICS codes of the subsidiary and � be the set of 6- digits NAICS code of the parent. An element � of � is an input of an element � of � (�→�) if the total requirements coefficient of the US Input-Output (IO) table is bigger than 0.03.<sup>20</sup> Given these definitions, we can formally identify the 4 types of connections between the parent and the subsidiary:

- i. Horizontal FDI: if � and � share any element (i.e. if �∩�� Ø);

- ii. Vertical FDI: if any element of � is an input of any element of � (i.e. if ∃ �, � �. �. �→� where �∈ � and �∈ �);

- iii. Complex FDI: if � and � share any element and any element of � is an input of any element of �

- (i.e. if �∩�� Ø and ∃ �, � �. �. �→� where �∈ � and �∈ �);

- iv. Non-identified: if none of the above is satisfied.

For each subsidiary and parent we know the unique core industry at 4-digit NAICS 2007 level and a set of 6-digits NAICS primary codes.<sup>21</sup> To identify the link between two firms, we use the sets of primary codes of a subsidiary and its parent. If the two sets intersect and all the sectors of the subsidiary are not inputs of any sector of the parent, then these firms are linked by a horizontal relationship. Instead, if the subsidiary

> 19 Non-identified links can also be thought as conglomerates. Indeed, Herger and McCorriston (2013) define relationships between firms that neither share the same industry nor are they linked through the supply chain as conglomerate cross border acquisitions.

> 20 The threshold has been chosen following Alfaro and Charlton (2009).

> 21 The cardinality of the set of primary codes is not fixed ex-ante. Some firms report only one primary 6-digits code, some subsidiaries provide up to 36 primary codes.

16
- Ito, K., 2013, ‘Do Consumers Respond to Marginal or Average Price? Evidence from Nonlinear Electricity Pricing,’ _American Economic Review_ , forthcoming.

- Jessoe, K. and Rapson, D., 2011, ‘Commercial and Industrial Demand Response Under Mandatory Time-of-Use Electricity Pricing,’ UC-Berkeley, UCE3 WP‒023.

- Lee, D., 2008, ‘Randomized Experiments from Non-random Selection in U.S. House Elections,’ _Journal of Econometrics_ , 142, pp. 675‒697.

- Lee, D. and Card, D., 2008, ‘Regression Discontinuity Inference with Specification Error,’ _Journal of Econometrics_ , 142, pp. 655‒674.

- Lee, D. and Lemieux, T., 2010, ‘Regression Discontinuity Designs in Economics,’ _Journal of Economic Literature_ , 48, pp. 281‒355.

- Liu, L. and Lin, M., 1991, ‘Forecasting Residential Consumption of Natural Gas using Monthly and Quarterly Time Series,’ _International Journal of Forecasting_ , 7, pp. 3‒16.

- Loewenstein, G. and Ubel, P., 2010, ‘Economics Behaving Badly,’ The New York Times, 14 July. Available at: http://www.nytimes.com/2010/07/15/opinion/15loewenstein.html

- McCrary, J., 2008, ‘Manipulation of the Running Variable in the Regression Discontinuity Design: A Density Test,’ _Journal of Econometrics_ , 142, pp. 698‒714.

- National Institute of Statistics and Censuses, 2003, ‘¿Qué es el Gran Buenos Aires?,’ Press Release.

- Parks, R. and Weitzel, D., 1984, ‘Measuring Consumer Welfare Effects of Time-Differentiated Prices,’ _Journal of Econometrics_ , 26, pp. 25‒65.

- Parti, M. and Parti, C., 1980, ‘The Total and Appliance-Specific Conditional Demand for Electricity in the Household Sector,’ _Bell Journal of Economics_ , 11, pp. 309‒321.

- Pew Research Center, 2012, ‘Assessing the Representativeness of Public Opinion Surveys,’ Washington DC.

- Reiss, P. and White, M., 2005, ‘Household Electricity Demand, Revisited,’ _Review of Economic Studies_ , 72, pp. 853‒883.

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17
**49.** The incidence of receiving credit is higher among women-operated HEs (11 percent versus 5 percent among male-operated HEs). A larger share received credit from business associations, NGOs, or donor projects, i.e., 17 percent of women-operated HEs, compared to 11 percent of male-run HEs (Table A1.15), which likely reflects the focus of microfinance institutions on lending to women. The share of those with access to loans from savings and credit cooperatives is also larger among women (Figure 4.4).

**Figure 4.4: Gender Distribution of Credit Sources Among HEs and Microenterprises, 2006**

<!-- Start of picture text --> HEs<br>0 .2 .4 .6 .8 1<br>Relative or friend<br>Rotating saving & credit group (UPATU)<br>Saving and credit co-operative (SACCO)<br>Business association, NGO, donor project<br>Bank or financial institution<br>Other sources<br>Male Female<br>Micro enterprises<br>0 .2 .4 .6 .8 1<br>Relative or friend<br>Rotating saving & credit group (UPATU)<br>Saving and credit co-operative (SACCO)<br>Business association, NGO, donor project<br>Bank or financial institution<br>Other sources<br>Male Female<br><!-- End of picture text --> _Source: Calculations based on the ILFS 2006 data_ 50. Lack of access to credit for HEs is often caused by lack of a successful business strategy or investment, which would yield returns high enough to pay back the loan. In FGDs, HE operators reported that they did not apply for credit in part because they were not confident about their ability to repay the loan. This finding was corroborated by the results of the FinScope 2009 survey, which show that among the majority of HEs who have never applied for a loan (87 percent), more than a third did not do so out of fear of not having enough money to repay the loan, while 17 percent said they actually did not have enough money. Taken together, these two groups constitute a little more than half of all HEs who did not apply for a loan, implying that their underlying problem is poor cash flow. Another 20 percent did not seek credit because of tough loan conditions (Table A1.16). HEs are therefore subjected to a vicious circle. Their high credit risk associated with their weak business performance makes it difficult for them to access bank credit to grow their business.

1. Even when microcredit companies (such as PRIDE or FINCA) and NGOs (such as VICOBA/DUNDULIZA) manage risk by lending to groups of HEs, the interest rate they charge is high. This is due not only to the high unit costs of providing credit to small borrowers, but also to high risk of default. Some lending companies charge as high as 40 to 100 percent interest per year along with short repayment periods. Weaknesses in the business and regulatory environment of the microfinance sector have constrained their effectiveness in addressing this

25
19

# The Change in Reserves

35. The assumption made by the RMSM authors was that policy makers should try to maintain a level of reserves adequate to finance two months of imports. Each incremental change to the level of imports will therefore -- require an additional one sixth that amount to be set aside as reserves assuming the stock of reserves is already adequate at the beginning of the projection period.15 CHGRES - (IMPt - IMPt 1) * (1,) 33) If the starting level of reserves is insufficient, the modeler may want to modify the formula to calculate how much more should be added to the stock of reserves over the first several years of the projection period. Keep in mind also that governments with fixed exchange rate systems are likely to be more concerned with the level of reserves than those with market systems.

# External Borrowing

36. The need for *additional unidentified borrowing (GAPFIL) is a residual. It is determined in part by the size of the resource balance. This pressure is further amplified by the change in reserves (CHGRES), which is directly linked to imports, and by the need to repay previous debts (AMTGAP) ?lus interest (INTGAP) on the stock of outstanding debt (DODGAP). Exogenous forces are represented by other factor service income (OTHRSY), net current transfers (NETCTR) and net pipeline borrowing (NET).

GAPFIL - - RESBAL - INTGAP - OTHFSY - NETCTR +

- - CURBAL - AMTGAP - LTCAPF - OTHCAP - CHGRES 34)

It is assumed that borrowing in one period will generate a repayment stream in the future: there is no provision for building up arrears as so often happens in real life.16 For the purpose of this discussion it is assumed that all 15. The RKSM also assumes that there will be interest earned on foreign exchange reserve holdings abroad. These earnings are included as part of OTHFSY. They were not made explicit because their inclusion does not significantly change the dynamics of the model.
# Ex-Ante Methods to Assess the Impact of Social Insurance Policies on Labor Supply with an Application to Brazil

David A. Robalino<sup>a</sup> Eduardo Zylberstajn<sup>b</sup> Helio Zylberstajn<sup>c</sup> Luis Eduardo Afonso<sup>c</sup> a World Bank b Getúlio Vargas Foundation/São Paulo c University of São Paulo.

_JEL: J21, J26, J65, H55, C61, C11._ Keywords: Social Security, Unemployment Insurance, Public Pensions Systems, Retirement, Dynamic Optimization, Gibbs sampler.
earned large amounts, with some already seen to reach around 25 markets. Products that expanded their reach of markets include those in which Pakistan has a comparative advantage. Textiles and apparel are obvious, but a product that stands out for rapid expansion is manicure/pedicure sets belonging to the surgical industry, or products like eye and beauty make up, shampoos, medicaments or wedding and gaze with pharmaceutical substances. There are new agricultural products growing, such us rice, or agro industry like chewing gum, chocolates, biscuits, fruit juice and sauces.

**Figure 8**

<!-- Start of picture text --> Pakistan's Export Reach<br>1998 2008<br>15 15<br>10 10<br>5 5<br>0 0<br>0 30 60 90 120 0 30 60 90 120<br>Number of Markets Number of Markets<br>Value of Exports (HS6, in log)<br><!-- End of picture text --> Source: Authors’ calculations

# **2.3. Quality and sophistication**

The technological content of exports in Pakistan is low. High-tech exports constituted less than 2 percent in 2008, a share broadly unchanged in the past 25 years. This is extremely low for a country which has good educational and research institutions, and a large population. For example, Vietnam’s share of high-tech exports went from 0.7 percent to 3.8 percent of total in the same period whereas India’s increased from 2.8 percent to 6.2 percent. Moreover, while the majority of the country’s exports (around 54 percent) was classified as low-tech already in 1985,<sup>12</sup> this share further increased over time, to reach 65.4 percent in 2008.<sup>13</sup> (Figure 9) shows

> 12 Lall, S. (2000). “The technological structure and performance of developing country manufactured exports, 198598”, Oxford Development Studies, 28(3), pp. 337–369.

> 13 World Bank calculations from Comtrade data

12
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