rafmacalaba/datause-encoder-data huggingface dataset:
# References

Abiad, A. and T. Baig. (2005). “Underlying Factors Driving Fiscal Effort in Emerging Market Economies.” _IMF Working Paper_ No. 05/106.

Alesina, A., Ardagna, S., (1998). “Tales of fiscal adjustment.” _Economic Policy_ 27, 489-545.

Alesina, A. and A. Drazen. (1991). “Why Are Stabilizations Delayed?” _American Economic Review_ 81(5): 1170–88.

Alesina, A. and R. Perotti. (1995). “Fiscal Expansions and Fiscal Adjustments in OECD Countries.” _NBER Working Paper No_ . 5214.

Alesina, A., R. Perotti, and J. Tavares. (1998). “The Political Economy of Fiscal Adjustments.” Brookings Papers on Economic Activity 0(1): 197–248.

Alier, Max, (2006), “Measuring Budget Rigidities in Latin America,” IMF Working Paper, forthcoming (Washington: International Monetary Fund).

Ardagna, S. (2009), “Determinants and Consequences of Fiscal Consolidations in OECD Countries” in: European Commission: European Economy 2009, European Commission, Brussels.

Barrios, S., S. Langedijk and L. Pench (2010). “EU Fiscal Consolidation after the Financial Crisis-Lessons from Past Experiences.”, paper presented at the 12<sup>th</sup> Banca d’Italia Public Finance Workshop Fiscal Policy: Lessons from the Crisis held in Perugia on 25-27 March.

Beetsma, R., J. Cimadomo, O. Furtuna, and M. Giuliodori. (2015). “The confidence effects of fiscal consolidations,” ECB Working Paper, No. 1770.

Cetrángolo, O., J.P. Jiménez and R. Ruiz del Castillo (2010) “Rigidities and fiscal space in Latin America: a comparative case study”, Series Macroconomía del Desarrollo 97, Economic Commission for Latin America and the Caribbean (ECLAC), Santiago, Chile.

de Haan, J., Klomp, J., (2013). “Conditional political budget cycles: a review of recent evidence.” _Public Choice_ 157, 387-410.

Dollar, D. and J. Svensson. (2000). “What Explains the Success or Failure of Structural Adjustment Programs?” _Economic Journal_ 110(October): 894–917, Royal Economic Society.

Echeverry, J.C., J.A. Bonilla and A. Moya (2006) “Rigideces institucionales y flexibilidad presupuestaria: los casos de Argentina, Colombia, México y Perú”, Documento CEDE 2006-33, Universidad de los Andes, Bogotá, Colombia.

Echeverry, J.C., L. Fergusson and P. Querubín (2005) “Budget inflexibility”, Documento CEDE 2005-52, Universidad de los Andes, Bogotá, Colombia.

Echeverry, J.C. V. Navas, y A. Clavijo, (2009). Rigideces presupuestales en Colombia y Peru, en Centrangolo y Juan P. Jimenez. CEPAL, 2009.

Edin, P. and E. Ohlsson. (1991). “Political Determinants of Budget Deficits: Coalition Effects versus Minority Effects.” _European Economic Review_ 35: 1597–1603.

Escolano, J., L. Jaramillo, C. Mulas-Granados and G. Terrier (2014). "How Much is A Lot? Historical Evidence on the Size of Fiscal Adjustments," IMF Working Papers 14/179, International Monetary Fund.

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**Table 1: Characteristics of Households by RSE Status**

||Mean<br>Full S|Std dev.<br>ample|Selected<br>RSE Worker<br>Households<br>Mean|Unselected<br>RSE Applicant<br>Households<br>Mean|Non-Applicant<br>Households<br>Mean|
|---|---|---|---|---|---|
|_Household Characteristics_||||||
|_Proportion with:_||||||
|Piped Water|0.29||0.28|0.21|0.34<sup>+</sup>|
|Flush Toilet|0.17||0.22|0.08***|0.17|
|Electric Light|0.26||0.44|0.07***|0.20***,<sup>+++</sup>|
|Telephone|0.24||0.38|0.11***|0.19***,<sup>++</sup>|
|Motor vehicle|0.10||0.14|0.04**|0.10|
|Bank account|0.49||0.62|0.51*|0.37***,<sup>+++</sup>|
|ATM card|0.27||0.42|0.18***|0.18***,<sup>+++</sup>|
|Receive overseas remittances|0.18||0.15|0.29***|0.16|
|Receive some cash income|0.87||0.84|0.89|0.84|
|Have relative in New Zealand|0.07||0.11|0.07|0.02***,<sup>+++</sup>|
|_Quantities_||||||
|Household Size|4.73|2.01|4.55|5.13**|4.69|
|Asset index|0.00|2.41|0.87|-0.81***|-0.34***,<sup>+++</sup>|
|Number of pigs|3.54|5.65|3.07|3.80|3.82|
|Number of chickens|11.72|15.27|8.48|14.85***|13.02***|
|Number of cattle|1.64|4.33|0.69|2.48***|2.06***,<sup>+</sup>|
|Household Weekly cash income (Vatu)|8724|15408|9434|7957|8497|
|Household Weekly own production (Vatu)|4309|7554|5235|4245|3571**,<sup>+</sup>|
|Weekly total income per head (Vatu)|3020|3756|3694|2458**|2729**|
|Household Weekly Food expenditure (Vatu)|2455|2998|3383|1665***|2059***,<sup>++</sup>|
|Monthly Total Expenditure per head (Vatu)|5921|8678|8852|3295***|4732***,<sup>+++</sup>|
|_Median weekly total income per head (Vatu)_|1529||2382|1337***|1343***,<sup>+++</sup>|
|_Median weekly food expenditure (Vatu)_|1000||2000|1000***|1000**|
|_Median monthly total expenditure per head (Vatu)_|2445||5030|1714***|2250***|
|**Sample Size**|**472**||**170**|**97**|**205**|

# Notes:

*, **, and *** and +, ++, and +++ denote t-test shows significantly different from the RSE Worker household sample (*'s) and the RSE applicants (+'s) at the 10%, 5% and 1% levels respectively. Difference in medians carried out using a non-parametric two-sample test for equality of medians.

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# **4. Analysis**

The approach used for the validation exercise is to estimate a first-stage model using a “pseudo-survey” drawn from the PROGRESA households, using a two-stage sampling procedure. Welfare measures are then predicted with target populations composed of groups of PROGRESA households. The PROGRESA communities themselves have too few households to produce meaningful confidence intervals for the estimates using the methodology. Previous experience, e.g. ELL (2002), has shown that standard errors are very large for target populations with less than a few hundred households. In order to generate a group of more suitably sized target populations, the communities were grouped at random into 20 target populations. Both the pseudo-survey and the target populations were drawn repeatedly, in order to generate estimates for a large number of target populations.

Specifically, the steps in the analysis were as follows:

- 1) A random sample of 50 localities was drawn from the 496 localities, with probability of selection proportional to the size of the locality. From each of the 50 localities, 10 households were selected at random. The data from these households (a total of 500) serve as a pseudo-survey.

- 2) The first-stage methodology described above was applied using the pseudo-survey. A set of explanatory variables for log per capita expenditure was selected from a candidate list.  An additional set of explanatory variables which best explained estimated location effects were selected from a set of community-level averages.<sup>12</sup>

- 3) The 496 localities were grouped into 4 groups of 24 communities and 16 groups of 25 communities. These serve as the 20 target populations for the poverty mapping

> 12 From equation (2) and (3) it is clear that the variance of the location effect η _c_ must be small if acceptable standard errors on welfare predictions are to be obtained. We have found that the inclusion of means of 2 explanatory variables, calculated from the census for the relevant enumerationa areas, reduces ση considerably. See ELL(2002) for details and see also below.

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# **Table 1. Migration stocks 25+ in 1990 and 2000 (in millions)**

||Total<br>(million)|To OECD<sup>_a_</sup><br>(million)|To non-O<br>(million)|ECD<sup>_a_</sup><br>(%)<sup>_b_</sup>|Including im<br>(million)|puted stocks<br>(%)<sup>_b_</sup>|
|---|---|---|---|---|---|---|
|Year 2000|||||||
|Total|111.9|59.3|52.6|47.0|16.7|14.9|
|College graduates|28.8|20.9|7.9|27.4|2.5|8.7|
|Less educated|83.1|38.3|44.7|53.9|14.2|17.1|
|Males|57.4|29.0|28.4|49.4|8.7|15.1|
|College graduates|15.1|10.6|4.5|30.0|1.4|9.0|
|Less educated|42.3|18.4|23.8|56.4|7.3|17.3|
|Females|54.5|30.2|24.3|44.5|8.0|14.7|
|College graduates|13.7|10.3|3.3|24.4|1.1|8.3|
|Less educated|40.8|19.9|20.9|51.3|6.9|16.8|
|Year 1990|||||||
|Total|85.3|42.5|42.7|50.1|30.5|35.7|
|College graduates|16.3|12.6|3.7|22.8|2.4|14.9|
|Less educated|69.0|30.0|39.0|56.6|28.1|40.7|
|Males|44.4|21.0|23.4|52.7|15.6|35.2|
|College graduates|9.0|6.7|2.3|25.2|1.4|15.1|
|Less educated|35.4|14.3|21.2|59.7|14.3|40.3|
|Females|40.9|21.6|19.3|47.3|14.8|36.3|
|College graduates|7.3|5.9|1.4|19.8|1.1|14.6|
|Less educated|33.6|15.7|17.9|53.3|13.8|41.0|

Notes.<sup>_a_</sup> 34 OECD destination countries;<sup>_b_</sup> Share of migrants to non-OECD countries, and imputed migration stock, in total migration.
which tend to supply lower quality goods than the US and the EU. A full liberalization would lead to a shift in imports of digital goods towards China, and a reduction in the share of digital goods sourced from the EU, and therefore to reductions in the quality of imports, on average. Notice, however, that the decline in the quality of imports of digital goods is relatively small compared to the increase in its value. Since the quality of imported goods tends to be higher than that of goods produced domestically, the quality of digital inputs that is available to firms in the region would likely increase on average.

Taken together, these findings suggest that the AfCFTA countries should consider including tariff concessions on digital goods imports from non-members, potentially lowering costs significantly, and thus expanding access to imported digital goods, especially in the poorest countries with the highest tariffs on digital goods.

# **7 Conclusion**

A growing body of evidence reveals that digital goods are critical for productivity growth in low-income countries. Since digital goods are mainly produced in advanced economies, the quality and prices at which firms and consumers in developing countries can acquire them in international markets are especially important. In this paper, we used detailed data on international trade flows and tariffs to study Africa’s imports of digital goods. We first provided evidence on the relative quality and quality-adjusted prices of Africa’s digital goods imports, as well as the current degree of tariff protection they face. We then estimated the likely impacts of AfCFTA on digital goods imports, which we compared with a scenario of full unilateral tariff liberalization towards all countries.

Despite this heavy reliance on imports, Africa imposes relatively high tariffs on digital goods globally, with an average import-weighted tariff of approximately 4 percent, compared to an average of 0.5 percent in other regions. These tariffs have remained relatively constant in Africa, in contrast to significant declines observed in non-African countries over the past decade. Moreover, the level of tariff protection on digital goods correlates negatively with income per capita across African countries.

Applying the methodology proposed by Feenstra and Romalis (2014), we estimate the quality and quality-adjusted prices of Africa’s imports of digital goods, using detailed data on international trade flows and tariffs. We report evidence that African nations tend to import relatively low quality, low price digital goods, as would be expected given their relatively low income per capita. Once quality differences are accounted for, digital goods in Africa are sourced at rela14
**Table 6: Impact of PBF on coverage of incentivized health services**

|**Variable**|**Baseline**<br>**Mean in**<br>**PBF**<br>**districts**|**Trend**|**PBF impact**|**p-**<br>**value**|**N**|
|---|---|---|---|---|---|
|**Panel A: Child health services**<sup>**a**</sup>||||||
|Under-5: Growth monitoring in 6 months|0.188|0.130|-0.042|0.424|13165|
|12-23 months: all basic vaccinations|0.853|-0.081|0.074|0.431|4277|
|12-23 months: any basic vaccination|0.894|-0.004|0.039|0.781|4277|
|24-35 months: All basic vaccination|0.803|-0.045|0.099|0.554|2550|
|24-35 months: Measles, Mumps and<br>Rubella vaccine|0.805|-0.059|0.115|0.617|2403|
|24-35 months: any vaccination|0.892|0.013|0.062|0.546|2550|
|**Panel B: Maternal health services**<sup>**b**</sup>||||||
|At least 4 antenatal consultations|0.526|0.058|0.007|0.648|6068|
|Timely initiation of antenatal care<sup>e</sup>|0.726|0.127|-0.098|0.940|5682|
|Postnatal consultation within 3 days after<br>discharge from maternity<sup>f</sup>|||0.18***|0.001|3565|
|**Panel C: Family planning**<sup>**c**</sup>||||||
|All women: any method|0.633|-0.006|-0.046|0.930|9343|
|All women: Modern FP method<sup>g</sup>|0.290|0.025|-0.047|0.956|9343|
|Want to delay or stop: any method|0.698|0.006|-0.063|0.487|5958|
|Want to delay or stop: Modern method<sup>g</sup>|0.375|0.018|-0.058|0.862|5958|
|**Panel D: Blood pressure measurement fo**|**r adults ag**|**ed 40 an**|**d above**<sup>**d**</sup>|||
|Blood pressure measurement in past year|0.497|0.122|0.032**|0.034|9885|

Note: data from household surveys. Unless otherwise noted, regressions are of the difference-in-differences specification with facility fixed effects. Standard errors are clustered at the district and survey round level with wild bootstrapping. All regressions include controls for catchment areas’ exposure to the collaborative quality improvement and citizen scorecards interventions, region, household wealth quintile, and age.

a Sample of children under-5. Regressions include a control for the gender of the child. b Sample of women with a live birth in the preceding two years. The indicators relate to care received during the most recent pregnancy. Regressions include controls for employment status, and education level.

c Sample of women age 15-49 who were pregnant in the preceding two years.

d Sample of adults 40 years and above. Regressions include controls for employment status, education level, gender, and marital status.

e First antenatal consultation within the first three months of the pregnancy.

f The PBF impact is estimated using a propensity score weighting specification as the baseline survey did not distinguish between postnatal care provided before and after women were discharged from the facilities where they gave birth. Standard errors are clustered at the district level.

g Modern methods include: sterilization, IUD, injectables, implants, pills, condoms, and diaphragms. Level of statistical significance: *p<0.10; **p<0.05; ***p<0.01 28
**Figure 4. Direct, indirect, and foreign services value added in gross exports, percent, 2011**

<!-- Start of picture text --> IRL<br>GRC<br>CHE<br>LTU<br>ESP<br>IND<br>CRI<br>FIN<br>POL<br>JPN<br>PHL<br>AUS<br>THA<br>MYS<br>NOR<br>CHL<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>Domestic Direct Domestic Indirect Foreign<br><!-- End of picture text --> Source: Miroudot (2016) based on OECD-WTO TiVA 2015.

# **_The share of services value added in exports also varies across industries_**

The share of services value added in exports ranges from 11% in mining to 38% in chemicals and motor vehicles (figure 5). Caution in interpreting these results is advised, since value-added measures are based on input-output tables defined by arm’s length transactions and thus exclude services provided in-house. Mining exports are in many cases driven by large state-owned enterprises that are likely to provide most services in-house (Miroudot 2016). In Australia, for example, where that is not the case, the share of services value added in exports is 24%.

11
Refinitiv’s Security Data Corporation (SDC) Platinum, which provides transactions of new issuances of common and preferred equity, as well as publicly and privately placed bonds.<sup>3</sup> Because the analysis focuses on corporate financing, we exclude all public sector issuances, comprising issuances by national, local, and regional governments, government agencies, regional agencies, and multilateral organizations. We also exclude mortgage-backed securities and other asset-backed securities.

We classify bond issuances as domestic or international using the residence-based approach followed by the Bank of International Settlements (BIS). We compare the location of the issuance with the residence of the issuing firm (Gruić and Wooldridge, 2012). Domestic securities are those issued by residents in their local markets. International issuances are those issued by residents abroad.<sup>4</sup> Using this methodology, the data set includes 71,489 bond issuances in domestic markets and 9,703 bond issuances in international markets (Table 1, Panel A). Besides the residence-based approach, the literature uses two alternative criteria to classify international and domestic corporate bond issuances: the nationality-based and currency-based approaches (Gruić and Wooldridge, 2012; Avdjiev et al., 2014; Shin, 2014; McCauley et al., 2015). The nationality-based approach considers the nationality of a firm instead of its residence. Therefore, issuances by a subsidiary of a foreign-owned firm in the domestic market are considered as international, as the parent company resides outside the domestic market. Under the currency-based approach, debt issuances denominated in foreign currency are considered as international, and those in local currency as domestic. Because our paper focuses on the role of domestic markets for firms located in East Asia, we use the classification of issuances by the residence approach as our main results. But for robustness we also use the nationality-based and

> 3 Refinitiv’s SDC Platinum is one of the most widely used databases on transaction-level research (Henderson et al., 2006; Kim and Weisbach, 2008; Bruno and Shin, 2017). Dealogic, an alternative database, yields similar estimates of issuance activity.

> 4 In the case of China, domestic issuances are those performed by firms residing in Mainland China and issuing in Mainland China’s bond markets (Shanghai, Shenzhen, and over-the-counter markets). Therefore, we consider issuances in Hong Kong SAR by firms residing in Mainland China as international issuances.

9
structures and debt profiles. In addition, countries have different frequencies and bases (average value, end point or mixed) across metrics (OECD, 2010).

In Turkey’s case, the strategic benchmarks, and thus the debt metrics, aimed at the composition of financing before 2007. Between 2003 and 2007, the one-to-one correspondence between issuances and the debt portfolio, due to relatively shorter maturity of borrowing, necessitated the management of cash flows rather than debt portfolio figures. In parallel to the extension of maturities, Turkey’s strategic benchmarks now focus on the composition of the debt portfolio. The current method allows for a holistic approach to cover outright sales, buy-backs, debt exchanges and possible use of derivative instruments.<sup>6</sup> Therefore, the front offices should also consider the effects of debt exchange and buyback auctions on targets for key portfolio indicators when designing their auction schedules.

The evolution of debt structure metrics are summarized in the following section.

# **3.3.1 Debt Roll-over Metrics**

The main metric used to track re-financing risk was the ―average maturity of borrowing‖ until 2007. Although the strategies implemented in line with this metric proved to be useful in improving the re-financing risk profile of the Treasury, this metric does not take into account the redemption profile in each period and may thus lead to high redemptions in specific periods. The adjusted debt rollover metric, ―portion of debt maturing within 12 months,‖ takes into consideration the rollover risk in the short run. Furthermore, since this metric is employed from a multi-year perspective (for each year in the decision horizon) it also helps smooth the redemption profile. Several ―maturity‖ related indicators (i.e., issuance maturities, remaining time to maturity of outstanding debt) are also monitored as complementary measures. Second metric for re-financing risk management is the ―level of liquidity buffer.‖<sup>7</sup> Maintaining a certain level of liquidity buffer enables Treasury to reduce roll-over risk and surpass adverse effects of short-term interest rate volatility. In addition, it provides flexibility during auctions and improves market confidence.<sup>8</sup>

> 6 Turkish Treasury resorts to debt exchange and buy-back operations, occasionally.

> 7 TDSM is not used to determine the desired level of liquidity buffer. Instead, a comprehensive analysis of debt redemption profile, demand for Treasury auctions and availability of non-financing sources is used to set strategic benchmarks regarding the level of liquidity buffer. Then, these targets are used as inputs for the implementation of TDSM.

> 8 It should be also noted that excessive or idle cash balances may be costly due to the cost of carry. Given the benefits associated with maintaining a certain level of liquidity buffer, this cost of carry can be considered as an equivalent of the insurance premium.

15
to consume. Indeed there is some evidence that supports this potential bias. Using only the October 1998 cross-section collected six months after the intervention, Albarran and Attanasio (2002) find that both the likelihood of receiving a private transfer and the amount received -conditional on receiving private transfers, are significantly and negatively affected by the program.

To assess the extent of the potential bias, we have replicated Albarran and Attanasio’s results for the 3 periods for which data on private transfers are available (October 1998, November 1999 and November 2000). First, the amount of private transfers is very small as only 7% of households receive a private transfer in any given period. The estimation results are presented in Table 12. Panel AI reports the effect of OPORTUNDADES on receiving any private transfer and Panel AII reports the estimated effects on the amount of the private transfer received. We estimate four specifications of the OPORTUNIDADES treatment: in the first row we report the results with a OPORTUNIDADES treatment dummy, in the second row we replace the treatment dummy with the potential OPORTUNIDADES transfer amount, in the third row we add potential transfers squared, and in the fourth row we instrument actual OPORTUNIDADES transfers and actual transfers squared with potential transfers and potential transfers squared. We find that the transferred squared models have significant effects, with the marginal effect size falling with the transfer amount.

The same argument could be made for other public transfers. To test this hypothesis, we estimated the effects of OPORTUNIDADES transfers on other public transfers and report the results in Panels BI and BII in Table 12. We find results similar to the private transfer effects.

To test the robustness of our main results to the effect of OPORTUNIDADES on private and other public transfers, we estimate a variant of the consumption equation (9) by adding private and other public transfers to OPORTUNIDADES transfers. If the coefficients on current transfers and lagged cumulative transfers do not change by adding private and other transfers, then we will conclude that there is no bias. The results are presented in Panels A and B of Table 13, for private and other public transfers respectively. The first column in Panel A, estimates the original 25