rafmacalaba/datause-encoder-data huggingface dataset:
Figure 3 shows the effective tariff an as a function of a. When b Is uero, the effective tariff starts at sero and rises linearly with *, with slope 11 - (1 - sp(i))Is a (0,1). When b > 0, no underinvolcing occurs for a S i, **0o** an - a up to that point. At a - *, doWds - 1, but any further rise In a causes an to fall below s. As a gets large, the cost parmtor b becomes less end less important, and an approaches the value it would have had if b Vero z ro.40 Tbs Doesstic Pric of Uportables A siailar analysis applios on tho export side, where the incontive is to overinoice export good4 in order to collect a higher subsidy per unit. The equilibrium price of the exportable satisfies (26) P_ - (I + ay) **X** - Esxl(l + *)4ty,(1 + (l+(l-%p(A))s + p(A)-p1U **)** zpx UT~~~~~~~~~ Without loss of generality, have assumd in (26) that a and b **are** the **am as** - - they w*re n the import case (this implies that Ax(s) AM(s) and * 4) Figure 3 shows the effectiv **e** xport subsidy, whLch equals a for s S ; and then rises monotonically towards an asymptote of [1 **+** (1 - sp(A))Ia.

# Iquilibrium

In thhe smugling case, illegal trade did not affect the domestic relatiLv pricos of the traded goods as long as there was **some** legal trade in 40 It is interesting to notice that 8UWOA - 0 in equilibriuo. Increases in A do not get passed on to the domestic prlce because importers wN4t be compensated for the increasod expected costs associated with the higher degree of underinvoicing.

35
The correlation _ρ_ is unknown. Therefore, the authors use the age-cohort correlation of residuals obtained from cross-sectional data to produce an estimate of _ρ_ .

In this paper, I propose an adaptation using the lower and upper bound estimates. Since the correlation term is likely to be non-negative, residuals in equations 2 and 3 are likely to “sandwich” the actual error term. Therefore, this paper suggests taking a weighted average of residuals to get point estimates of income mobility. First round predicted incomes under this method are where superscript _NP_ refers to the non-parametric estimate and 0 ≤γ ≤1. Non-parametric incomes are equal to lower bound estimates (ŷi12NP = ŷi12L) when γ = 1 and to upper bound ones (ŷi12NP = ŷi12U) when γ = 0. The proportion of poor households who escape poverty is given by the joint probability Pr(ŷi12NP < z and yi22 > z). Once again, since we are randomly drawing from the empirical distribution of residuals from the first round, the procedure needs to be repeated _R_ times.

This method requires an estimate of the unknown underlying weight γ. As noted, γ = 0 and γ = 1 correspond to the upper and lower bound mobility estimates, respectively. Dang et al. (2014) argue that the estimates would fall between these two extreme values. I can narrow down the gap between the lower and upper bound estimates by selecting 0 < γ < 1. Therefore, I take “unweighted” averages of residuals (i.e., γ = 0.5) to validate estimates. By doing so, the paper is replacing the arbitrary assumption of normality and the need to estimate an unknown correlation term _ρ_ by another arbitrary assumption of weighting lower and upper bound estimates equally (instead of, for example, setting γ equal to 0.4 or to 0.6). We could further refine this method by “weighting” residuals in equations 2 and 3 differently (i.e., γ ≠0.5). As such, I also perform a sensitivity test in Section 4 to analyze the robustness of results to changes in γ.

It is important to note that, by introducing an arbitrary assumption of balanced weights, the method also presents at least two advantages when compared to existing approaches. First, the adaptation introduced in this paper does not assume any given distribution for the disturbances. Second, unlike the parametric approach, this adaptation predicts the unobserved income in the first round of data rather than estimating probabilities of mobility in and out of poverty. This is also 6
Specifically, we estimate the following regression: where yh,t is the natural logarithm of annual spatially adjusted household consumption expressed in local currency units for household _h_ in time _t_ . Xi,h,t is a vector of household head’s characteristics, Zonet are geographical zone-areas dummy variables. The error term is drawn from a normal distribution. The results of this estimation are available in Table _5_ .

In the third step, using the parameters estimated from this consumption model, we can impute consumption into the target survey and calculate the relevant poverty rates. The imputed consumption vector is estimated using 100 imputations. Table _6_ shows the distribution of the imputed consumption vector and compares it to the distribution of the NLSS-based consumption aggregate.<sup>16</sup> The imputed consumption vector is then converted to US$ 2011 PPP to estimate poverty rates at different international poverty lines. We conduct the same exercise separately using data on the same non-monetary indicators from each wave of the GHS to impute consumption in 2010/11, 2012/13, 2015/16, and 2018/19.

# 5. Main results

The results of the backcasting and survey-to-survey imputation yield very similar results. Both show a decline in poverty in the first half of the decade followed by a period of stagnation – and even a slight increase – between the 2016 economic recession and 2019. The backcasted poverty series shows a decrease of at most 7 percentage points between 2009 and 2018/19, when using a pass-through rate of one. The survey-to-survey imputations show that poverty fell by around 3 percentage points between 2010/11 and 2015/16, but that progress towards poverty reduction has halted since then.

# _<u>5.1. Backcasting</u>_

Figure 1 shows the backcasted trend in poverty headcount rates (Panel a) and number of poor (Panel b) at the US$1.90 poverty line for the period 2009-2019, assuming a pass-through rate of one, that is, the growth rate in sectoral GDP is assumed to be the same as the growth rate in household consumption. The backcasts suggest that poverty rates were considerably lower in 2009, 2010, and 2011 than the estimates obtained using the 2009/10 HNLSS directly. Specifically, the backcasted trend estimates a poverty rate of 46.3 percent in 2009, 42.8 percent in 2010, and 41.8 percent in 2011, corresponding to 71.4, 67.8 and 68.1 million poor respectively (see Table _7_ in the Appendix, for the full backcasted series). The 2009/10

> 16 This confirms that all values of the consumption vector are positive.

11
cooperatives and (ii) sectors that are open with certain conditions – i.e., max foreign ownership or certain recommendations<sup>1</sup> The reform eliminated foreign equity limits across a wide range of sectors and turned many sectors where FDI was not allowed or restricted by minority shareholding into sectors fully open to FDI. The reforms were intended to boost FDI inflows and attract/encourage the entrance of multinational corporations (MNCs). In this paper, the term “fully liberalized” refers to the removal of all these restrictions in all the business activities or sub-sector within a broader sector, i.e the number of business activities within a sector that has been liberalized (more below).

This study assesses the impact of this reform, specifically making the following contributions: First, we examine the early effects of the regulatory reform on both planned and realized FDI as well as realized direct domestic investments (DDI), by comparing the pre-and post-policy periods and comparing across sectors in response to the most recent version of the regulation (Perpres 49/2021); by analyzing DDI, we test whether foreign firms substitute or crowd-in domestic investments (Barrios et al., 2018). We also assess whether there was growth among fully liberalized sectors in sustainable investment (i.e. investments linked to Sustainable Development Goals (SDGs) as defined by UNCTAD (2022)).

The analysis is done using a difference in difference in difference (DDD) event study model and controlling for the sector and time-fixed effects. As the data is at a more aggregated level than the listed business activities in the reform regulation, the term “fully liberalized” refers to all the sub-sectors or business activities within the broad sector that have had all the restrictions discussed removed (more in the data section). We focus on liberalized non-commodity sectors to highlight investments less likely to have pressure on the natural environment from the expected increase. Therefore, in line with the World Bank’s support to the reform.<sup>2</sup> As robustness, we further account for two key events that were taking place around the reform time: the recovery from Covid-19 lockdowns around the globe and related trade reforms, especially on non-tariff measures the government undertook in November 2021 (Montfaucon et al., 2023). The trade policies also

> 1Perpres 49/2021 deleted the specific appendix containing the closed business activities. The closed sectors were listed in Job Creation Omnibus Law and the main text of the new investment regulation.

> 2This is in line with the World Bank support to the reform as detailed https://projects.worldbank. org/en/projects-operations/project-detail/P172439

2
# **5. REFORMING THE SYSTEM**

## **NATIONAL INNOVATION SYSTEM**

An essential tool to moving beyond the present approach to private sector development is the National Innovation System (NIS. A National Innovation System is the set of government and private sector policies and incentives that generate much of a country’s research and technological innovation. A successful NIS would seek to improve private sector investment in research and development, labor skills and education, and encourage collaboration between businesses and research institutions.<sup>55</sup> Chile has succeeded in introducing new technologies, engaging in technology transfers, improving the business environment, and improving company strategy and operation. However, it remains behind similar-income countries in East Asia and Eastern Europea in investment climate and innovation frameworks. In addition to attracting more foreign technology, these countries have invested in public and private research and developmemt, and encouraged collaboration between scientific institutions and local firms.<sup>56</sup> In contrast, Chile does not have a formally declared and accepted technology innovation policy. Nor does it have a mechanism to coordinate activities in science, innovation, and technology. The lack of a central authority to coordinate the innovation and technology efforts and provide a strategic framework with unifying principles leads to a loose _ad hoc_ coordination. It would be far more effective if there were a forum for exchange, a shared diagnostic and a strategic framework to insure the efficient use of resources, minimize mixed signals to potential product users, and maximize new business applications.

In addition, Chile is a leader in some areas of scientific research, but not in business applications that may result from the research. The Chilean scientific community garners well-deserved international respect, and is moving toward a leadership position in research among industrializing countries. However, this substantial progress has not yet matched the country's aspirations. The table below shows the strengths and weaknesses of the Chilean NIS.

> 55 Chile produces fewer than 100 doctorates per year and would need to produce around 3,000 per year to reach a level comparable to the knowledge-based developed economies. Similarly, Chile has only one scientist for every thousand economically active inhabitants, while the developed countries have an average of five scientists per thousand. Lauritz Holm-Nielsen and Alex Norsworthy, Chile's Millennium Science Initiative: Building Human Capital for the Global Knowledge Economy.  En Breve Series, World Bank, December 2002, Issue No. 15.

> 56 World Economic Forum (2004); Chile: New Economy Study, Volume 1, Executive Summary and Policy Recommendations, World Bank

35
# Carbon price by fuel type (2011 USD)

<!-- Start of picture text --> 40<br>0<br>—— Carbon price for coal (2011 USD)<br>-40 —— Carbon price for oil (2011 USD)<br>—— Carbon price for gas (2011 USD)<br>-80<br>-120<br>-160<br>80 90 00 10 20 30 640 50 60 70 = 80 90 00<br><!-- End of picture text --> Carbon price by fuel type (2011 PKR) <!-- Start of picture text --> 4,000<br><!-- End of picture text --> <!-- Start of picture text --> 0<br>—— Carbon price for coal (2011 PKR)<br>-4,000 —— Carbon price for oil (2011 PKR)<br>—— Carbon price for gas (2011 PKR)<br>-8,000<br>-12,000<br>-16,000<br>-20,000<br>80 90 00 10 20 30 §640 50 60 70 = 80 90 00<br><!-- End of picture text -->
been widely employed to correct bias in survey estimates arising from noncoverage problems (Duncan and Stasny 2001), late response (Czajka et al. 1992), and nonresponse (Smith et al. 2000; Little and Vartivarian 2003).

This paper examines two PSW approaches. The first approach follows the concept of stratification or blocking, where a combined dataset of a reference survey and a biased survey is split into multiple (usually five) groups based on the estimated propensity scores (Schafer and Kang 2008 and Austin 2011). Samples of both surveys that fall into the same group are considered comparable. Lee (2006) adjusts the weights of the phone/web survey in a way that the sum of weights in the phone/web survey sample in each group is equal to the counterpart of the reference survey. The second PSW approach uses inverse-propensity scores to adjust bias (for example, Morgan and Todd 2008, Schafer and Kang 2008, and Austin 2011). This approach multiplies the original sampling weight of a phone survey with the odds of a sample being selected into the reference survey from the combined data set. Both of the abovementioned PSW approaches aim to eliminate sampling bias to make summary statistics of the phone/web survey comparable to those of the reference survey and thereby make the adjusted phone/web survey representative of the population of interest. Below we present each PSW method in more detail.

# <u>PSW following Lee (2006)</u>

6
However, these sources of consumption such as own consumption of livestock has decreased following the drought (see Annex 1).

# **Results**

Vulnerability is decomposed into poverty and risk components (see Annex 2 and Figure 1). About 23.6 percent of the rural Serbians are vulnerable. It is interesting to compare the relative contribution of poverty and risk to overall vulnerability. With headcount poverty rate of 16.2 percent in rural areas according to the 2003 household survey data, poverty is thus the major contributor of rural vulnerability in Serbia. Poverty accounts for over 70 percent of rural vulnerability. The findings contrast to that of Ligon and Schechter (2003) for Bulgaria that show poverty only accounting for about 53 percent of national vulnerability.

Close to 30 percent of the vulnerability is attributable to risk. Columns 3, 4 and 5 on Annex 2 show the various components of risk and their determinants. Since risk explains a non-trivial 30 percent of the rural vulnerability to poverty, the fact that rural poverty has increased in 2003 suggests a disproportionate exposure of rural areas to risk. This arises from several sources. First, most rural households in Serbia depend predominantly on agriculture for their livelihood. The shock to agriculture from the drought of 2003 led to a decline in agricultural output and incomes, and an increase in poverty. Second, land and rural credit markets are imperfect, with general lack of access to credit, high risk, and lack of marketable titles to land. Third, the current policy environment may have had an adverse and countercyclical effect on rural incomes. And finally, the recent enterprise restructuring may have also affected the employment opportunities in the manufacturing sectors, with direct and indirect negative impact on rural poverty. Public policies that help create better access to markets, diversification of income to non-agricultural sources, and greater ownership of assets will help reduce rural poverty and vulnerability.

# **Correlates of Vulnerability and Poverty**

The regression results presented in Annex 2 show the various non-stochastic determinants of poverty and vulnerability such as household demographic and educational, occupational and health, physical and financial assets ownership and regional characteristics variables. One of the most striking results of this exercise is that factors determining vulnerability and poverty are quite similar. For rural Serbians, poverty and vulnerability are thus inextricably linked. The characteristics of those who are observed to be poor are strikingly similar to the characteristics of those who are estimated to be vulnerable, whether they are currently poor or not. This finding is in accord with those of Kamanou and Morduck (2002) in Cote d’Ivoire and others such as Banerjee and Newman (1994) and Morduck (1994). Therefore, interventions that aim to reduce the level of poverty in rural Serbia would also contribute to reduction in vulnerability.

8
**28**

what the Bank's Regional staff sometimes advocate as rates to replace official rates in those cases where **IEC** and the Region agree that official rates differ egregiously from transaction rates.

Table **6:** Frequency Distribution of Deviation of Parallel/Black Market Rates from Official Rates (percent)

|Deviation|**1975**|**1976**|**1977**|**1978**|**1979**|**1980**|**1981**|**1982**|**1983**|1984|**1985**|**1986**|**1987**|**L9**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**0-10**|49.4|443|**53.8**|**57.7**|**48.7**|49.4|48.1|40.5|34.2|**29.5**|**33.9**|**37.1**|**28.6**|**28.**|
|11-20|**10.1**|**13.9**|**9.0**|6.4|**12.8**|**12.7**|**10.1**|**10.1**|11.4|14.8|**13.6**|**9.7**|**163**|**17.**|
|**21-30**|**63**|**_5.1_**|**7.7**|**_5.1_**|6.4|**7.6**|**63**|**3.8**|**63**|**9.8**|3.4|14.5<br>|**6.1**|**_7.5_**|
|31-40|2.5|**_5.1_**|**2.6**|**5.1**|**2.6**|**3.8**|**2.5**|**1.3**|**5.1**|**6.6**|**6.8**|**1.6**|4.1|**5.7**|
|41-40|**8.9**|**5.1**|**1.3**|**1.3**|**2.6**|**_13_**|**5.1**|**8.9**|**3.8**|**1.6**|**1.7**|**1.6**|10.2|**5.7**|
|**51-60**|**0.0**|**1.3**|**1.3**|**1.3**|5.1|**0.0**|**3.8**|**8.9**|**5.1**|**1.6**|**1.7**|4.8|**0.0**|**3.8**|
|**61-70**|**2.5**|**1.3**|**3.8**|**1.3**|**3.8**|**5.1**|**3.8**|**3.8**|**_13_**|**1.6**|**6.8**|**0.0**|4.1|**3.8**|
|**71-80**|**2.5**|**2.5**|**2.6**|**3.8**|**2.6**|**2.5**|**2.5**|**5.1**|2.5|4.9|**1.7**|**1.6**|**0.0**|**0.0**|
|**81-90**|**1.3**|2.5|**1.3**|**3.8**|**3.8**|**0.0**|**1.3**|**1.3**|**1.3**|**1.6**|**1.7**|**0.0**|**0.0**|**0.0**|
|**91-100**|**5.1**|**1.3**|**1.3**|**1.3**|**0.0**|**3.8**|**0.0**|**1.3**|**1.3**|**3.3**|3.4|**0.0**|2.0|**1.9**|
|**>100**|11.4|**17.7**|15.4|**12.8**|**11.5**|**13.9**|**16.5**|**15.2**|**27.8**|24.6|25.4|**29.0**|**28.6**|**26.**|
|No.of|||||||||||||||
|Countries|**79**|**79**|**78**|**78**|**78**|**79**|**79**|**79**|**79**|**61**|**59**|**62**|49|**53**|
|Deviations<br>**>30%**|**27**|**29**|**23**|24|**25**|24|**28**|**36**|**38**|**28**|**29**|24|24|25|

Sources: **1975-1983:** Pick's World **Currency Yearbook** (various issues);

**1984-1988:** Financial Times International Reports: Statistical Market Letter (weekly issues)

# Countries for Review and Adjustment for Parallel Rates

_45._ Table **7** below shows countries with egregious (say, **30** percent or greater) deviations between the black market and official rates. They represent that set of countries for which the appropriateness of the official rate as a general conversion factor merit further review and adjustments, if deemed necessary. Also, a closer look at the deviations of the black or
12 Figure 7 Net Resource Flow to Mutual Funds (as a fraction of net worth) <!-- Start of picture text --> .06<br>.04<br>.02<br>.00<br>-.02<br>-.04<br>-.06<br>-.08<br>1998 1999 2000 2001 2002 2003<br><!-- End of picture text --> The sell-off of government securities caused a fall in their price (rising spreads), and pressured the exchange rate to depreciate (Figure 8). The rising spreads and the exchange rate depreciation were also associated with capital outflows from Brazil. As Figure 9 shows, in September and October of 2002, capital outflows reached a peak of almost 20 percent of international reserves of the central bank.<sup>4</sup> During these months the exchange rate also reached a peak of 4 Reais per dollar. Consequently, the debt level rose due to its indexing to the exchange rate. This fact aggravated concerns on debt sustainability which exerted further downward pressure on the demand for Brazilian sovereign bonds and pushed their prices even lower in a vicious circle.

<!-- Start of picture text --> Figure 8 Figure 9<br>Brazilian Spreads and Exchange Rate Capital Flows to Brazil<br>Jan 2000 – April 2003 (ratio to international reserves<br>4.0 2800<br>.3<br>$R/US Exchange rate bps<br>3.6 (left scale) 2400<br>.2<br>3.2 2000 .1<br>2.8 1600 .0<br>2.4 1200 -.1<br>2.0 800 -.2<br>Sovereing spread<br>1.6 400 -.3<br>2000 2001 2002 2003 96 97 98 99 00 01 02 03<br><!-- End of picture text --> Source: Bacen and JP Morgan 4 Capital flows exclude foreign direct investment and IMF resources.
**Figure 3:** In the data from MACTool, many emission reduction scenarios (+) may be approximated by a piecewise-linear function (red curve). The slope of the first piece provides the diffusion speed for that measure. The second part is interpreted as the maximum potential, that grows over time.

We use the model with data collected at the World Bank to build a MAC curve (using MACTool) during a case study on Brazil (de Gouvello, 2010). The MAC curve provides a list of emission-reduction measures, their marginal abatement cost, and the potential achievable by 2030. While the list of measures and their cost can be used directly in our spreadsheet program (see the first two columns of Tab. 1), our program requires the full-abatement potential and diffusion speed.

Since this data was not collected when the MAC curve was prepared, we have to reconstruct it with indirect methods, using the emission-reduction scenarios that were provided to MACTool. For each measure, the shape of the emissionreduction scenarios can be in one out of three cases.

In the first case, emission-reduction scenarios may be approximated by a two-phases piecewise-linear function as in figure 3. In this case, the diffusion speed is given by the slope of the first piece, and the second phase is interpreted as the growth in full potential. About half the measures fall in this category.

Other emission-reduction scenarios may be approximated by a single linear diffusion (Fig. 4a). In this case, the full potential is not binding before 2030. We calibrate the diffusion speed from the slope of the penetration pathway, and denote the lack of data on the full potential with a dot ( _·_ ) in the two last columns of Tab. 1.

In some cases the emission-reduction scenario lacks the first phase; abatement immediately “jumps” to a growing full-potential (Fig. 4b). We denoted them with a dot in the diffusion speed column in Tab. 1. There is usually a handful of such cases in MAC curves exercises. One example from the Brazilian study is solid residues management. In the emission-reduction scenario, solid residues management is able to reduce emissions by more than 40 MtCO2 in one year, and then grow at less than 1 MtCO2/yr. From the perspective of the user of a MAC curve, it may be unclear whether this should be considered as a shortcoming in the data (if the investigation could not identify the constraints that limit the diffusion of solid residues management), or a realistic emissionreduction scenario (if solid residues management can actually save lots of GHG 7