rafmacalaba/datause-encoder-data huggingface dataset:
inflation variation of global demand and oil price shocks have increased to 22 and 17 percent, respectively.

Finally, the contribution of global shocks to domestic inflation variation was larger in advanced economies and countries with higher trade and financial openness, fixed exchange rate regimes, and greater reliance on commodity imports. Domestic shocks contributed more to domestic inflation variation in countries that were less open to global trade and finance and had inflation targeting monetary policy regimes with flexible exchange rates.

# PART B. Expectations and Pass-Through

Part B delves deeper into two key challenges that confront EMDE central banks. Burdened by a history of high inflation, many EMDE central banks struggle to build credibility, leaving inflation sensitive to shocks and inflation expectations unanchored. Additionally, EMDE exchange rates can be subject to severe swings, amplifying the impact of exchange rate movements on inflation.

# Chapter 4. Inflation Expectations: Review and Evidence

In Chapter 4, Kose, Matsuoka, Panizza, and Vorisek argue that, since EMDEs tend to experience more pronounced business and financial cycles than advanced economies, they face greater challenges in anchoring expectations. This makes understanding how inflation expectations are affected by different types of shocks especially critical for policy makers in these economies.<sup>7</sup> Since robust measurement is key to evaluating inflation expectations, they first examine the pros and cons of survey-based and market-based measures.<sup>8</sup> Due to the breadth of country coverage and the availability of long time series, they employ survey-based inflation expectations in their empirical work. They then present a survey of the literature on inflation expectations. Theoretical studies have examined how public and private information is used by economic agents in formulating inflation expectations.<sup>9</sup> A large body of empirical work has tested the predictions of theoretical models and assessed how firmly inflation expectations are anchored, by measuring the sensitivity of expectations to various shocks. This literature, while extensive, has mainly focused on advanced economies.

The chapter, therefore, represents the first comprehensive analysis of the evolution and determinants of inflation expectations in EMDEs. Specifically, it addresses three key questions:

- How does the degree of anchoring of inflation expectations differ between advanced economies and EMDEs?

- How sensitive are inflation expectations to global and domestic shocks?

-

- What are the main determinants of the degree of anchoring of inflation expectations?

> 7 Bernanke (2007b) explains the importance of inflation expectations for the design of monetary policy.

> 8 For background on market- and survey-based measures of inflation expectations, see Coibion et al. (2018) and Grothe and Meyler (2018) for the United States and the Euro Area, and Sousa and Yetman (2016) for EMDEs.

> 9 Coibion, Gorodnichenko, and Kamdar (forthcoming) and Mankiw and Reis (2018) survey the literature on the formation of expectations.

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# **3. Theoretical Considerations**

Since Stark and Bloom’s work on the “new economics of migration” (1985), migration has been recognized as part of a household’s strategy for maximizing its welfare. Migration from Kerala for work is appropriately viewed as a household decision rather than an individual choice. The sending household finances migration expenses upfront—particularly for a move overseas—in return for a future stream of remittances from the migrant family member. Migration expenses to the Gulf are costly for low-skilled jobholders from Kerala, averaging US$1,018 as computed from the 1998 KMS, which was 2.7 times Kerala’s per-capita domestic product for that year.<sup>10</sup> An expected utility model would see a household that receives such remittances choosing between consuming the income and investing it in assets during the early period of migration. The availability of remittance income would then be expected to raise the recipient household’s investments in assets in subsequent periods, to the extent that the household were to treat that income as a positive transitory income shock.<sup>11</sup> Remittances from temporary migrants working in the Arabian Gulf can be readily regarded as transitory income for the household back home. There is limited opportunity to settle permanently in these countries or acquire citizenship, and foreign workers earning below a certain threshold are prohibited from bringing their dependents.<sup>12</sup> Existing evidence shows that these “single” migrants transfer the bulk of their earnings back home and for the most part, spend in the host country only on what they require for subsistence.<sup>13</sup> Hence, the origin household’s marginal propensity to invest using international remittances is potentially high, given the impermanent nature of overseas migration to the Gulf. A mitigating factor, however, is that a

> 10 Migrant households must typically cover the costs of an airline ticket, visa, commission to the recruitment agency, and even bribes (KMS, 1998).

> 11 Investing in assets should provide a return to justify giving up immediate consumption. This need not be financial gains—for example, housing improvements would offer a stream of future utility to the intended beneficiaries, as would durable goods, such as a vehicle, refrigerator, etc.

> 12 For example in Qatar, a minimum monthly income of approximately US$2,192 is required to bring dependents into the country.

> 13 Recent data from a 2013 survey of workers residing in dormitory-style accommodations in Qatar show that nearly 60 percent of a worker’s annual income is remitted back home. Most of the low and unskilled migrants in the country live in this sort of dwelling.

7
3,400 PPP projects with at least a six-year history is simple: a project's status (i.e., conclusion after full contract term, early cancellation and ongoing operation) is only reasonably observable and measurable when the project has been (or could have been) in effect for a sufficient amount of time. Contract cancellation rates would undoubtedly be underestimated if one-year operational projects were included, for example, as these projects are likely too early in their development to reveal potential problems that might otherwise lead to cancellation. Upon examining the project data in the PPI Database, cancelled projects exhibit an average duration (between financial closure year and cancellation) of 5.89 years (Marcelo and House, 2016). This average duration is justification for truncating the data set to remove projects whose closure dates are less than six years before the analysis.<sup>3</sup> Of the 3,400 PPPs with at least six-year histories, 94% are active – i.e., operational or under construction– while 6% (191 PPPs) were canceled. Figure 1 describes the subsets of data used for modeling, and Table 1 provides summary statistics for the 2,833 projects used in modeling.

**Figure 1. PPP data selection**

<!-- Start of picture text --> PPI projects<br>(7,192 projects)<br>PPPs<br>5,478 projects (76%)<br>PPPs reaching financial closure before 2011<br>(Excluding 58 distressed and 203 concluded PPPs)<br>3,400 PPPs (62%):<br>Active PPPs (94%)<br>Canceled PPPs (6%)<br><!-- End of picture text --> In order to capture the effect of country-level experience on PPP cancellations, a variable to proxy the degree of familiarity and experience in facilitating public-private partnerships was estimated. The _PPP country experience_ associated with each project is defined as the number of PPPs that reached financial closure within the past decade in the same country. So then, the level of PPP experience associated with any project initiated in country � in year � _,_ is equivalent to the sum of all PPPs that reached financial closure in that country in the previous 10 years.

Following this definition, Figure 2 gives a visual overview of the maximum PPP experience reached by each country over the past decade from 1990 to 2016. African and Central Asian countries have the lowest levels of PPP experience in the developing world, though the region includes three countries (i.e. South Africa, Nigeria and Algeria) with more than 20 PPP deals. In contrast, South American countries have relatively high levels of PPP experience, led by Brazil (387 PPPs). China and India are developing countries with the highest PPP experience, with more than 600 PPP deals each.

> 3 In the Sensitivity Analysis section, this data set constraint is tested. See also Annex 4.

5
Figure 8: Specification Charts for Total Seasonal Rainfall <!-- Start of picture text --> Ethiopia Malawi<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.001 .002<br>.0005 .001<br>0<br>0<br>-.0005<br>* Weather Product * -.001 * Weather Product * -.001<br>TAMSAT TAMSAT<br>ERA5 ERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br>Niger Nigeria<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.005 .0015<br>.001<br>0 .0005<br>0<br>-.005 -.0005<br>* Weather Product TAMSAT* -.01 * Weather Product TAMSAT* -.001<br>ERA5 ERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br>Tanzania Uganda<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.0005 .001<br>.0005<br>0 0<br>-.0005<br>* Weather Product * -.0005 * Weather Product * -.001<br>TAMSAT TAMSAT<br>ERA5 ERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br><!-- End of picture text --> _Note_ : The figure presents specification curves, where each panel represents a different country, with three different models presented within each panel. Each panel includes 36 regressions, where each column represents a single regression. Significant and non-significant coefficients are designated at the top of the figure. For each Earth observation product, we also designate the significance and sign of the coefficient with color: red represents coefficients which are negative and significant; white represents insignificant coefficients, regardless of sign; and blue represents coefficients which are positive and significant.

39
80 do so regularly – but lower than the national average because the evaluation study sample was drawn from the poorest areas of the Philippines. Most children in the 10 to 14 age range are in primary school, although about 20 percent are already in secondary school. A substantial proportion, about 20 percent, worked in the 12 months before the interview and about 16 percent in the 7 days prior. Conditional on any work, children work about 30 days a year and about 12 hours a week. Children are as likely to report working for pay outside the household as working without pay inside the household. Most of the work carried out by children is unskilled, and most children who work (about 4 in 5) are also in school. A sizeable group of children (about 7 percent) neither worked nor attended school in the 12 months prior to the interview. As we show below, the cash transfer program had a particularly strong effect on the schooling and labor supply of this last group of children.

# _Impact of Pantawid on education_

A key goal of _Pantawid_ is to improve children’s school participation. Table 2 presents estimates of the effect of the cash transfer program on the school participation of children aged 10 to 14. Overall attendance increased by 4 percentage points relative to a control mean of 89 percent (column (1)). Regular attendance, defined as attendance of at least 85 percent of school days in the two weeks prior to the interview, increased by 9 percentage points (over a control mean of 80 percent, column (4)). This increase occurred especially in primary school (Columns (2) & (3) and Columns (5) & (6)). The reported number of days children attended school in the two weeks prior to the interview increased by approximately a full day, from 7.5 to 8.5 (column (7)). The increase in the number of days children attend school reflects changes in both the probability of school attendance and the number of days attended in the 2 weeks prior to the interview conditional on having attended 13
randomly sampled fifteen eligible nonelite academic high schools from each province (thirty schools in total).

The next step was to choose which students would be surveyed within the sample schools. In each vocational high school, we randomly sampled two first-year computer major classes (one class if the school only had one computer major class) and surveyed all students in these classes. In each nonelite academic high school, we randomly sampled two first-year classes and surveyed all students in these classes.

# _2.2 Data Collection_

Our data collection started with a _baseline_ (October 2011) survey. The baseline survey collected data from students, students’ homeroom teachers, and school principals. Among vocational high schools, 7,114 first-year students in 184 classes filled out the baseline survey. Among academic high schools, 2,957 students in fifty-nine classes filled out the baseline survey.<sup>5</sup> In total, we surveyed 10,071 students (7114+2957).

We followed up with the sample vocational and academic high school students in May 2012 (hereafter known as the _endline_ survey). The survey forms used in the endline survey were similar to those used in the baseline survey. Most importantly, our data allowed us to create three primary outcome variables: (a) student dropout (whether a student was enrolled in a high school as of May 2012); (b) student gains in computing skills (according to a standardized exam); and (c) student gains in math skills (according to a standardized exam).

> 5 Because of low enrollments, there was one academic high school (out of the 30) that only had one class (instead of 2). This explains why there are fifty-nine classes as opposed to sixty.
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Chami, Ralph, Ekkehard Ernst, Connel Fullenkamp and Anne Oeking (2018), “Are Remittances Good fpr Labor Markets in LICs, MICs and Fragile States? Evidence from CrossCountry Data”, Working Paper WP/18/102.

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Felipe, Jesus; Kumar, Utsav (2011) : Unit labor costs in the eurozone, Working Paper, No. 651, Levy Economics Institute of Bard College, Annandale-on-Hudson, NY Gazi, Hassan M. and Mark J. Holmes (2013), “Remittances and the real effective exchange rate”, _Applied Economics_ , Vol 45, Issue 35, pp4959-4970.

Golub, Stephen S., Janet Ceglowski, Ahmadou aly Mbaye and Varun Prasad (2017), “Can Africa compete with China in manufacturing? The role of relative unit labor costs”, _The World Economy_ , 2017;00:1–21. <u>https://doi.org/10.1111/twec.12603</u> 23
Hoffmann, R., and R. Muttarak. 2017. “Learn from the Past, Prepare for the Future: Impacts of Education and Experience on Disaster Preparedness in the Philippines and Thailand.” _World Development_ 96 (August): 32–51.

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exceptions. Strict non-promotion ages, in combination with designated and lengthy periods of tenure at each level, cause career stagnation a few years prior to the mandated retirement age. The retirement age of mayors and party secretaries at the _prefectural city_ level is set at 60, and this would not change even if they are promoted to the next level of positions, e.g. deputy provincial governors. If the politician has not finished his five-year tenure when they reach the retirement age, they are usually given a maximum of two-year extension to finish their tenure. If the local politician is promoted to the next higher position _after_ age 58, he will not be able to finish his tenure. To avoid this disorder in the tenure system, when a local politician reaches age 58, he is unlikely to be promoted—instead, he is likely to retire or be transferred to a ceremonial position (Xi, Yao and Zhang 2018). As a result, an upper bound of age 57 is implicitly imposed for officials at the prefecture level or below, and this rule is commonly referred to as “Seven-Up, Eight-Down ( _Qishangbaxia_ ).”

# **Privatization in China**

After China started market-oriented reforms in 1978, the profits and taxes per unit of net capital stock and working capital in industrial SOEs had fallen from 24 percent in 1978 to 7 percent in 1996 (Qian 2000), and more than one-third of SOEs had losses in 1996. Starting from the mid-1990s, China started to rejuvenate its ill-performing SOEs by incorporating private and foreign shareholders. Meanwhile, the affiliation and regulatory power of many SOEs was commensurately decentralized to the local governments (Xu, Zhu and Lin 2005; Xu 2011). The rising losses of SOEs and the heavy fiscal burdens on local governments fueled the pace of large-scale privatization in the late 1990s. In 1997, the central government officially announced its policy to restructure the state sector by allowing local governments to experiment with different ways to restructure SOEs. To facilitate economic turnaround, local governments were encouraged to privatize SOEs. In the Annual Survey of Industrial Firms (ASIF) used in this study, which accounts for the vast majority of Chinese industrial firms in terms of value added and employment, roughly 80% of local SOEs had been privatized by 2009.

Given the extraordinary scale of Chinese privatization and its potential implications, the number of studies on the impact of Chinese privatization is surprisingly small (Bai, Lu and Tao ``` 9 ```
fund and these funds have different objectives such as value, growth, or blend strategies. An important benefit of the exercises performed here is that they do not need to identify the exact stocks held by different mutual funds within a mutual fund family at every point in time. The only information needed is mutual fund returns over time and the investment scope. This allows us to extend the time horizon of the data to start in the late 1980s.

The summary statistics of the simulated and actual global funds are shown in two tables. Table 10 reports the results for simulated global funds constructed with the largest number of available specialized funds (the largest cross-section), called the “largest number of funds” simulation. This simulation includes all possible specialized funds for each family and adjusts the time series accordingly to use the sample available for all funds included in the simulation; however, it does not necessarily entail a very long time span due to data availability on mutual fund returns. Table 11 reports results that use instead only specialized funds that allow for estimations of simulated global funds with relatively long time series, called the “longest available sample” simulation. In particular, specialized funds are excluded from the simulations if they reduce the sample size by at least six months. Naturally, this longer time series come at the cost of having fewer specialized funds in the comparisons. On average, three specialized funds are excluded from these simulations if compared to the largest number of funds simulations, which leads to an increase on average of 44 months in the time span of the simulations.

Tables 10 and 11 present the following statistics based on daily and weekly data: the average annualized returns of the actual global funds and the simulated global funds, the average annualized difference in accumulated returns between each simulated global fund and the corresponding global fund, the standard deviation of returns, and the number of comparisons.<sup>33</sup> Annualized differences in accumulated returns are calculated over the entire sample for each simulation. Averages across simulations are then computed.<sup>34</sup> The tables report separately statistics for world funds, foreign funds, and a pool of world or foreign funds. Results are presented using daily and weekly (Wednesday) returns.

> 33 We also compute these tables at the family level to show the heterogeneity across mutual fund families. The results are reported in Appendix Tables 4A and 4B for the largest number of funds simulations, and in Appendix Tables 5A and 5B for the longest available sample simulations.

> 34 Though not reported, for robustness purposes we also calculated these return differences at every point in time and then computed the averages within and across simulations. The results are qualitatively similar to those reported here.

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