rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 2006m1 2008m1 2010m1 2012m1 2014m1 2016m1<br>Foreign Investor Domestic Individual<br>Domestic Institutions<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br><!-- End of picture text -->

**Figure 3.** Proportion (%) of assets under management by passive investors in each type. Passive investors are defined as those with Active Share below 60%

<!-- Start of picture text --> 45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>9:30-10:30 10:30-11:30 11:30-12:30 12:30-13:30 13:30-14:30 14:30-15:30 15:30-15:55 Closing batch<br>Foreign Pension Funds Mutual Funds<br><!-- End of picture text -->

**Figure 4.** Proportion (%) of traded value in the interval relative to the trading value of the day. Investors are foreign, domestic pension funds and mutual funds with Active Share below 60% (passive).

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Table 2: Practical Example of Provisions aggregation (raw matrix)

|||Cluster # 1|Cluster # 2|Cluster # 3||
|---|---|---|---|---|---|
|Policy Area|Provision|RCA-1|RCA-2<br>RCA-3|RCA-4<br>RCA-5|Average|
|||(1)|(2)<br>(3)|(4)<br>(5)|(6)|
|Area xx|a|3|1<br>0|0<br>0|0.8|
|Area xx|b|2|1<br>0|1<br>0|0.8|
|Area xx|c|1|1<br>1|1<br>1|1|
|**Coverage by PTA**||1|1<br>0.33|0.66<br>0.33||
|**Coverage by Cluster**||**1.00**|**0.67**|**0.5**||

Table 3: Practical Example of Provisions aggregation (normalized matrix)

|||Cluster # 1|Cluste|r # 2|Cluste|r # 3||
|---|---|---|---|---|---|---|---|
|Policy Area|Provision|RCA-1|RCA-2|RCA-3|RCA-4|RCA-5|Average|
|||(1)|(2)|(3)|(4)|(5)|(6)|
|Area xx|a|3.75|1.25|0.00|0.00|0.00||
|Area xx|b|2.50|1.25|0.00|1.25|0.00||
|Area xx|c|1.00|1.00|1.00|1.00|1.00||
|**Score by PTA**||2.42|1.17|0.33|0.75|0.33||
|**Score by cluster**||**2.42**|**0.**|**75**|**0.**|**54**||

Table 4: Actual values of the raw and normalized matrix for the area “Services”

|||Cluster # 1|Cl|uster # 2|C|luster # 3||
|---|---|---|---|---|---|---|---|
|Policy Area|Provision|EFTA<br>...|PAN-SGP|CHL-JPN<br>...|APTA|TUR-GEO<br>...|Average|
|_Raw Matrix_||||||||
|Services|836|0<br>...|1|1<br>...|0|0<br>...|0.29|
|Services|837|1<br>...|1|0<br>...|0|0<br>...|0.27|
|Services|850|5<br>...|1|2<br>...|1|0<br>...|1.68|
|Services|...|...<br>...|...|...<br>...|...|...<br>...|...|
|**Provisions by PTA**|62|22<br>...|50|48<br>...|17|0<br>...||
|**Coverage by cluster**||**0.289**||**0.460**||**0.267**||
|_Normalized Matrix_||||||||
|Services|836|0.00<br>...|3.43|3.43<br>...|0.00|0.00<br>...||
|Services|837|3.71<br>...|3.71|0.00<br>...|0.00|0.00<br>...||
|Services|850|2.97<br>...|0.59|1.19<br>...|0.59|0.00<br>...||
|Services|...|...<br>...|...|...<br>...|...|...<br>...|...|
|**Score by PTA**||0.952<br>...|2.483|2.901<br>...|1.084|0.000<br>...||
|**Score by cluster**||**0.840**||**1.36**||**0.846**||

_Notes_ : APTA Asia Pacific Trade Agreement, EFTA European Free Trade Association. Provisions: # 836, Are there sectorspecific chapters (e.g. financial services, telecommunications)? Code: _Services − strucchapt_ . # 837, Are there sector-specific provisions in an annex to a chapter on investment or CBTS (such as express delivery as an annex to CBTS chapter)? Code: _Services − strucchapt_ 1. # 850, To be considered a service supplier of a party to the agreement, in the case of the supply of services through commercial presences, does a juridical person have to: “being incorporated under the domestic law of the party and have substantive business operations in the territory of a member” (coded with value 1, most restrictive), ... , “being owned or controlled by natural persons of the other party” (coded with value 6, most liberal). Code: _Services − room_ 3.

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between 5 and 6.7. In SSA, the impact multiplier is 1.5, slightly lower than in the overall sample but not statistically significantly different. The cumulative multiplier rises to 3.1 after three years suggesting a strong complementarity between public and private investment in this region. The effective F-statistics remain above 10 across all horizons suggesting a relevant instrument.

**Table 3:** The response of private investment to government investment

||Cu|mulative Mu|ltipliers (hori|zon h in yea|rs)|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|||Low-i|ncome coun|tries||
||_h=0_|_h=1_|_h=2_|_h=3_|_h=4_|
|Change in government investment|1.511***|1.664**|2.540**|2.341**|1.283|
||(0.532)|(0.776)|(1.019)|(1.062)|(1.106)|
|Observations|722|699|677|654|631|
|F-statistic|12.38|11.04|11.26|12.75|12.23|
|KP Underid stat|0.00324|0.00511|0.00481|0.00343|0.00384|
|||I|DA countries|||
|Change in government investment|2.120*|3.441*|3.067**|3.478**|2.583|
||(1.113)|(1.762)|(1.516)|(1.650)|(1.894)|
|Observations|1,479|1,432|1,387|1,341|1,296|
|F-statistic|5.912|4.999|5.932|6.679|6.041|
|KP Underid stat|0.0165|0.0263|0.0157|0.0108|0.0148|
|||Sub|-Saharan Afr|ica||
|Change in government investment|1.446**|2.191**|2.997**|3.054**|1.543|
||(0.625)|(0.937)|(1.222)|(1.191)|(1.291)|
|Observations|1,363|1,321|1,280|1,239|1,197|
|F-statistic|12.26|10.84|10.83|12.46|10.76|
|KP Underid stat|0.00147|0.00251|0.00242|0.00131|0.00238|

_Notes_ : *** p <0.01, ** p <0.05, * p <0.1. Dependent variable is change in private investment to lagged GDP. Robust standard errors in parentheses are Driscroll-Kraay standard errors to control for the presence of error cross-sectional dependence a ́ la Boehm (2020). All estimations include country and year effects and one year lagged dependent variable. “KP” is Kleibergen and Paap underidentification test.

# 4.2 Crowding-in and the role of corruption control

It may be the case that the effectiveness of public investment and the ability to crowd-in private investment is affected by the level of corruption. There exists a well-established literature that provides evidence of the negative impact of corruption on the business environment, investment efficiency, infrastructure, and public service delivery (for example, Mauro 1995; Davis 2004; Barr et al. 1995, Beekman et al. 2014; and Kaufmann et al. 2002).

14
# **Contents**

- I.    Introduction

- II.    Methodology

1.  Case of Exogenous Aid

2. Public Investment Programs and Aid Requirements 3. Link with a Medium-Term Budget

- III.    Applications

1.  An Illustrative Public Investment Program

2. Aid Requirements and the Medium-Term Budget Framework 3. Tax Reform and Aid Requirements

- VI.    Concluding Remarks

References Appendix —SPAHD Macro Models: Structure and Link with the MDGs Tables Figures 2
20 health shocks that necessitate out-of-pocket spending on medical care. The mix of strategies varies from country to country. Countries where asset disposals feature prominently are likely be those where households find it difficult to get credit.

Figure 7: How households finance their health spending, selected countries <!-- Start of picture text --> 200%<br>other<br>180%<br>160%<br>current income<br>140%<br>120%<br>health<br>insurance<br>100%<br>80% borrowed from<br>others<br>60%<br>borrowed from<br>40%<br>relatives<br>20%<br>sold items<br>0%<br>savings<br>Cumulative % answering "yes"<br>gladesh Brazil ina Faso China ech Rep. Ethiopia India Kenya Lao Malaysia Mexico Myanmar Nepal Pakistan ilippines S Africa Sri Lanka UAE Uruguay Vietnam<br>Ban Burk Cz Ph<br><!-- End of picture text --> Source: World Health Surveys: http://www.who.int/healthinfo/survey/whsresults/en/index.html .

Irrespective of whether it is asset sales, dissaving or loans that are being used to protect living standards in the face of health shocks, it is important to allow for such strategies when estimating the degree of financial protection people enjoy vis-à-vis health expenditures. Failure to do so will result in an overestimate of the extent to which health expenditures are catastrophic and impoverishing, and an underestimate of the degree to which—through one method or another—people enjoy financial protection vis-à-vis health expenditures. As far as catastrophic spending is concerned, the numerator
of market access for low-qualified workers can be explained by the fact that Brazil is exporting mainly goods that are intensive in unskilled labor. The industries in which Brazil has a high comparative advantage on the world market exhibit a higher share of low-skilled workers.<sup>32</sup> Consequently, an increase in the demand for exported goods signifies a higher demand and more jobs for low-educated workers. Also when controlling for wage differentials, β _L_ remains highly significant. This indicates that new employment opportunities created by a stronger local export activity are indeed important for the location choice of this group.

For the highly educated workers, market access remains insignificant in all specifications, even if we exclude wages in order to estimate the joint impact of foreign market access via both channels. The interpretation of this result is less straightforward since it might be driven by various forces, as explained above. One possible explanation is that these individuals have, in general, easier access to “high quality” jobs with good working conditions and career prospects. The alternative explanation of a predominant role of amenities with respect to economic considerations is, however, at odds with the fact that highly educated workers are also responsive to wage differentials.

Before we conclude, we use the estimated coefficients of column 2 of table 3 to simulate the implied change in each observed migration rate in response to a positive shock in the foreign demand for Brazilian goods. This provides more intuition for our results and allows to identify the regions that are particularly affected by a specific demand shock.

# Table 4: Effects of Changes in MA

State Immigrants Positive demand shock in Decrease

> 32 In the PNAD of 1992, the share of high-skilled workers is highest in the two industries with comparative disadvantage. The share of skilled workers was lowest in agriculture and wood, which are in the highest comparative advantage group.

27
We are hence able to assess if the outcomes differ systematically with the diverse initial conditions and subsequent paths.

Turning to the aforementioned hypotheses, which follow formally from equation (4) below, hypothesis H1 implies that high unemployment is caused by inefficient matching. This hypothesis contains two subhypotheses because matching efficiency may be low in terms of either low or negative trend in disembodied efficiency (H1a) or low returns to scale in matching (H1b). This inefficiency may be brought about for example by inadequate labor market institutions leading to decreasing search effort, skills depreciation, rising reservation wage of the unemployed, or geographical or skill mismatch (see also Jurajda and Terrell, 2006). If this hypothesis is correct, one would observe both _U_ and _V_ being simultaneously high, but not necessarily in the same districts or skill groups. In fact, spatial mismatch measured by a standard mismatch index indicates that in general the extent of mismatch has not increased over time.<sup>3</sup> If one finds support for H1, the policy should focus on labor market institutions and measures to foster residential labor mobility, create appropriate skills, and stimulate job search effort.

Hypothesis H2 states that high unemployment is caused by low demand for labor (e.g., due to restrictive macroeconomic policies, overvalued exchange rate, or globalization shocks). The manifestation of this would be low _V_ relative to _S_ causing high _U_ , and the policy implication would be that macroeconomic policies are key.

Hypothesis H3, namely that high unemployment implies that restructuring is at work, is consistent with the observation that inflow _S_ (presumably from old jobs) is high. The manifestation of this situation would be high _U_ brought about by high _S_ and the policy implication would be that restructuring needs to be completed. In the case of West Germany, the phenomenon would not represent the transition from

> countries. While HST (1998,1999) examine the outflow issue using individual unemployment duration data, in the present paper we analyze the process of matching using long monthly panels of district-level data.

> 3 In West Germany, for instance, the spatial mismatch index declined during the 1990s by about one-third and remained stable afterwards. After the early 1990s,  mismatch remained stable in all the transition countries that we study except

− 4 −
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# **Impacts of an Early Stage Education Intervention on Students’ Learning**

# **Achievement: Evidence from the Philippines**

# **Futoshi Yamauchi**<sup>**1**</sup>

# **Yanyan Liu**<sup>**2**</sup>

**Key Words: Human capital production, Education intervention, Philippines, Test Score JEL Classification: I21, O15**

> 1 Correspondence: The World Bank, 1818 H Street, NW, Washington DC 20433; Email: fyamauchi@worldbank.org; Phone: 202-458-4262

> 2 International Food Policy Research Institute, 2033 K Street, NW, Washington D.C
to cover the start-up expenses of self-employment and entrepreneurial activities. Second, prior to their move abroad, migrants have self-employment rates that are very similar to those of non-migrants across all ages (blue and red lines in Figure 6). These two groups of workers may differ in various dimensions, yet their propensity to become self-employed is almost identical until after the migration experience. Third, and most importantly for this paper, the rate of self-employment of return migrants is considerably higher than the rate among non-migrants, and the rate for the same migrants prior to their migration experience. This difference emerges at any age (green line versus the red and blue lines). The gap in selfemployment rates between return migrants and non-migrants is largest at younger ages.<sup>17</sup> For instance, 65 percent of 30-year-old return migrants in our sample are self-employed, compared to 28 percent of workers of the same age who have not migrated. While this difference has been pointed out in different contexts, our data facilitate a comparison not only with nonmigrants, but also with migrants’ own activity prior to migration. This within-individual comparison strengthens the case that migration is instrumental in business creation in the origin country. Across all ages, the rates of self-employment for return migrants are high, reaching levels of between 60 and 70 percent. The gap in self-employment between those who did and did not migrate narrows with age, but it never closes. Data from the BRMS support the pattern. They show that over 90 percent of the enterprises owned by former migrants were established after their return from overseas work. In other words, self-employment patterns are not driven by other forces, such as returning migrants taking over the family businesses. This descriptive evidence highlights the role played by temporary migration in accelerating transitions of workers into self-employment. Faster asset accumulation is the main channel behind this observation (see Section 4 for detail).

While self-employment can be a last resort for workers in certain contexts (Gindling and Newhouse 2014), evidence from Bangladesh indicates that the vast majority of the selfemployed choose this type of work for other reasons. Gutierrez, Kumar, Mahmud, Munshi, and Nataraj (2019) report that 82 percent of the self-employed state that the ability to work independently and the possibility of earning higher incomes are the main reasons behind their decisions. The same study also finds that the self-employed stay in the same activity much longer than casual laborers and wage employees in the private sector. Self-employment thus appears to be an “absorbing state” for many workers in Bangladesh.

> 17Figure 6 includes both dependent and self-employed workers in agriculture. Excluding that sector slightly reduces the self-employment rate but otherwise the pattern carries over.

18
during the Republic of Yemen’s conflict (e.g., Tandon and Vishwanath 2020; Tandon and Vishwanath 2021; Favari et al. 2021; etc.). These estimates suggest that well-being was likely not trending leading up to displacement, and the estimates are not picking up some of these changes as opposed to the immediate impacts of forced displacement.

And second, our results are consistent with displacement in fact being forced, as opposed to households being pulled towards better economic opportunities. Average violence was surging leading up to displacement in our data, and a wide variety of other evidence further suggests that violence was a pivotal factor in displacement during this time period (e.g., TFPM 2016; OCHA 2018; D’Souza et al. 2022; etc.); and among households that would be most likely to migrate due to economic conditions (e.g., those with the worst pre-displacement food access), the results show an identical and dramatic initial drop in food access as opposed to an improvement in food access that might have occurred had these households migrated towards better economic opportunities.

Our results contribute to the literature describing the consequences of forced displacement. First, this is the first analysis we are aware of that is able to arguably identify the impact of forced displacement on food access using data immediately before and after households become displaced.<sup>4</sup> Having pre-displacement data collected from households that would later become forcibly displaced allows us to demonstrate that households are not likely migrating due to sharp changes in economic factors, and further allows us to more precisely estimate the immediate impacts of displacement than comparing post-displaced outcomes of the forcibly displaced to host populations.

Second, these results demonstrate that households are surprisingly resilient to displacement in the Republic of Yemen in a critical welfare dimension. In part, this is likely due to a well-targeted increase in humanitarian assistance, which further offers support to a growing body of literature demonstrating that social protection schemes that reach world. These indicators are used for these purposes in part because they have been shown to be very sensitive to shocks in well-being in many settings across the world (e.g., Headey and Ecker 2012; Vaitla et al. 2015; etc.).

> 4Bohra-Mishra and Massey (2011) use panel data in Nepal on the location of residence to track displacement over time, but do not have panel information on the well-being of households (e.g., food access metrics, expenditure, etc.). Additionally, Hoogeveen et al. (2019) reach forcibly displaced populations primarily after their first displacement and analyze the decision to return.

4
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24