rafmacalaba/datause-encoder-data huggingface dataset:
# **4. Lao – Vietnam trade and harvest price dynamics**

In this section we use data on official exports and harvests to analyze how they affect Lao rice prices. Increases in exports should raise prices, while good harvests should reduce them. The focus is on Vietnam because it is the destination for practically all official exports and because of the close links with the Lao market revealed by the cointegration analysis. There are informal exports of paddy to Thailand across the Mekong River, but mostly for milling. The rice is then presumably re-imported to Lao PDR, possibly as Thai rice (FAO, 2011).

We have data on monthly official exports of rice for October 2005 - November 2012, and annual harvest data for the entire 2001 – 2012 period (see appendix.). The harvest data were first de-trended (by subtracting a linear trend) to obtain a measure of unusually good or bad harvests. We then converted the data to monthly observations by applying prior knowledge about the distribution of the harvest between the two harvest periods, the wet and the dry seasons (GIEWS, 2014a).<sup>11</sup> Fig. 8, upper panel, shows the monthly rate of change of Lao prices and exports to Vietnam. The increase in exports at the end of 2009 and the rapid price increase about six months later in 2010 are highly visible. A similar pattern exists for 2005 and the price increase in 2006. Fig. 9 depicts the rate of change of prices and de-trended harvests. Prices tend to decline soon after the large harvest in the wet season (October to December). However, about six months after the bumper harvest in 2009, well before the 2010 harvest,

> 11 In the case of Lao PDR, we assign the following weights to each of the 12 months: Jan-Mar (0), Apr-May (0.085 each), Jun-Aug (0), Sept (0.083), Oct (0.249), Nov (0.332), and Dec (0.166). For Vietnam, the weights were set to: Jan-Feb (0.05 each), Mar-May (0), June (0.05), Jul-Sept (0), Oct (0.15), Nov (0.40), and Dec (0.30).

20
The SFC also reports quarterly equity ownership information, i.e. names and holdings of the largest twenty shareholders, total institutional ownership, and total ownership by foreign investors in each stock. We classify each stock-PFA pair as part of the same business group when they share the same principal shareholder. Thirty stocks are matched with a particular PFA (only one PFA has no match). These listed firms with group-affiliation represent 41% of the total market capitalization and 49% of trading activity. Table 2 reports assets under management as of December 2010, average monthly net flows as a percent of assets under management, number of stocks in the portfolio, and stocks with group affiliation for each PFA. We end up with 10,704 monthly observations of PFAs’ stock holdings.

Table 3 presents summary statistics sorted by affiliation type. The last column includes t-statistic for a difference in means test for each of the variables in the table (group-affiliated minus non-affiliated). Affiliated firms have on average more assets, greater institutional ownership, and are more leveraged than non-affiliated firms.

# **4 Information flows in business groups**

In this section we test hypothesis H1 by studying the trading patterns of pension funds in affiliated and non-affiliated firms. Our strategy is to test whether trading activity of a pension fund in group-affiliated firms are more correlated with future stock returns than those trades of the same fund in non-affiliated stocks. We also test whether the trades in an affiliated firm are more correlated to future performance relative to those trades of other PFAs in the same stock.

Our main dependent variable is a measure of fund trading activity. We define the percentage of stock _s_ held by each fund at the end of month t as where _Af,s,t_ is the number of shares in the portfolio of pension fund _f_ and _SOs,t_ is the 11
# value.

Further findings include significant effects of the NHIS on non-food consumption and remittances, which suggests that in the absence of the insurance, households first resorted to risk coping strategies that arguably have less detrimental long term consequences. Yet, in monetary terms, these effects are only a fraction of the estimated savings in OOP. In particular, non-food consumption increased by a fifth and remittances fell by another fifth of the estimated drop in medical expenditures. Thus, households were not able to fully cope with the expenditure shock on the basis of these measures alone in the absence of insurance and had to use additional measures, which is consistent with our findings of child labor effects of the NHIS. We also find a significant negative effect of the NHIS on employment participation and hours worked of healthy adult household members.

This paper adds to a small evidence base on the deployment of informal and self-insurance strategies to cope with health shocks and the extent to which formal health insurance substitutes for welfare-decreasing coping mechanisms such as child labor. Chakrabarty (2012) uses data from Bangladesh and finds that micro health insurance in combination with credit can reduce child labor for poor households. Landmann & Fr ̈olich (2015) use a randomized control trial that paired micro-credit with a micro health and accident insurance in Pakistan. Their findings suggest a lower incidence of child labor and reduced child labor earnings. To the best of our knowledge, Liu (2016) is the sole other study that examines a wider range of coping mechanisms in the context of a rural health insurance program in China. The author reports that households insure income and consumption against health shocks through increased levels of labor supply in the absence of access to health insurance. The health insurance reduced the use of these smoothing mechanisms. Also, rural health insurance increased investments in school attendance and reduced the employment of children aged between 7 and 18 years. A limitation of the study is that it does not show to what extent the employment of children constitutes child labor according to the international definition of child labor (ILO convention). The specific measure that the author uses mostly captures whether a child is reported as working or not (excluding household work) without taking into account the number of hours worked per week even though this is an important criterion for child labor. For instance, according to the ILO convention, children between 14 and 17 years old are only child laborers if they work more than 43 hours per week. Thus, the number of employed children is likely to exceed the number of child laborers in the study sample.<sup>4</sup> It is well possible that the effects of the health insurance are much smaller on child labor than

> 4He (2016) argues that child labor rates in China are generally very low due to compulsory education consisting of six years of primary school and three years of junior middle school. Official statistics are not available.

3
|Table 3Dynamic effects of<br>Dependent: Change in grad<br>|Bangladesh fl<br>es from 1998<br>|ood onschool<br>to 2004<br>|ing progression<br>|
|---|---|---|---|
|Flood variable:|Depth|Days|Repair cost Outhome|
|Flood<br>|-0.1352<br>|-0.0005<br>|-0.00007 -0.0164<br>|
||(1.510)|(0.110)|(1.210) (2.250)|
|Flood * female|0.0777|0.0074|0.00006 0.0131|
||(1.140)|(1.530)|(1.410) (1.250)|
|Union FE|yes|yes|yes yes|
|Number of observations|489|489|489 489|
|Number of unions|21|21|21 21|
|R squared (within)|0.0488|0.0474|0.0457 0.0591|

Numbers in parentheses are absolute t values using robust standard errors with union clusters. Age and female dummies are included in all specifications.

25
- **Intermediaries/agents** are expanding their roles to include an array of services to buyers, including design, product development, and quality control in addition to providing a network of suppliers and logistics.

- **Increase in Private-label Brands:** There is a sharp increase in the volume and diversity of retailer private labels.  Retailers that develop proprietary brands use in-house design teams and outsourced manufacturing capacity, often by direct foreign product sourcing. By eliminating the middleman associated with national brands, retailers can shave costs and widen profit margins. Today, retailers are expanding the range of private-label products offered and developing higher-margin private-label goods (Euromonitor, 2009).

- **Brand Marketers Creating Exclusive Product Lines with Mass Merchant Retailers:** Exclusive product lines are a new way for mass merchants to offer unique merchandise. Retailers are striking agreements with brand marketers to develop and distribute brands that are sold exclusively through the one retailer’s stores instead of the traditional brand marketer model in which goods are sold via multiple retail outlets. (Asaeda, 2008; Euromonitor International, 2009).

- **Importance of Social and Environmental Standards:** This began with corporate social responsibility (CSR) campaigns and social advocacy groups. Now environmental compliance requirements and green initiatives are moving to the forefront (Asaeda, 2008; Barrie & Ayling, 2009; Driscoll & Wang, 2009; International News Services, 2009; Tucker, 2009). Consumers are demanding that lead firms become more responsible and transparent about their practices. Success of ethical clothing brands (e.g., Patagonia) is a testament to the power of consumer demand and green credentials.

- **Dual Sourcing Strategies: Quick Response and Fast Fashion:** Buyers tend to source fashion-sensitive products from suppliers that can deliver in a flexible and speedy manner, while basic products are sourced from the lowest-cost countries (Technopak, 2007). This leads to the distinction between fast fashion and quick response. Fast fashion emerged from quick response, but the two are different. Quick response is associated with replenishment purchases for basic products (Jassin-O'Rourke, 2008). Fast fashion is quick response in new merchandise (with little or no replenishment), involving shipping fewer pieces, in a great variety of styles, and more often. Predictions thought fast fashion would lead to local sourcing, but this has not been the case. Asian suppliers have quickly adapted the capabilities to serve fast-fashion buyers, including reducing minimum-run requirements. These suppliers have also lowered the cost of goods, thus putting intense pressure on regional manufacturers ( _The clothing industry,_ 2009).

# **F. Trends in Lead Firm Sourcing Strategies Accelerated by the Crises**

The activities and strategies of lead firms have a profound effect on supply chain relationships and the capabilities expected from suppliers. Key trends affecting lead firms in the apparel value chain that have been accentuated by the MFA phase-out and economic recession include: 18
**Figure 1: The Evolution of Trilemma Indices, 1970-2009**<sup>32</sup> (a) Industrialized Countries

<!-- Start of picture text --> Mon. Indep., Exch. R. Stability, and KA Open., IDC<br>1970 1980 1990 2000 2010<br>Year<br>Mon. Indep., IDC Exchr. Stab., IDC<br>KAOPEN, IDC<br>1<br>.9<br>.8<br>.7<br>.6<br>.5<br>.4<br>.3<br>.2<br>.1<br>0<br><!-- End of picture text -->

- (b) Emerging market economies  (c) Non-Emerging Market Developing Countries

<!-- Start of picture text --> Mon. Indep., Exch. R. Stability, and KA Open., EMG<br>1970 1980 1990 2000 2010<br>Year<br>Mon. Indep., EMG Exchr. Stab., EMG<br>KAOPEN, EMG<br>1<br>.9<br>.8<br>.7<br>.6<br>.5<br>.4<br>.3<br>.2<br>.1<br>0<br><!-- End of picture text --> <!-- Start of picture text --> Mon. Indep., Exch. R. Stability, and KA Open., non-EMG LDC<br>1970 1980 1990 2000 2010<br>Year<br>Mon. Indep., non-EMG LDC Exchr. Stab., non-EMG LDC<br>KAOPEN, non-EMG LDC<br>1<br>.9<br>.8<br>.7<br>.6<br>.5<br>.4<br>.3<br>.2<br>.1<br>0<br><!-- End of picture text -->

# **Definitions: The index for the extent of monetary independence** (MI); _MI_ =

1  0.5[ _corr_ ( _ii_ , _i j_ )  ( 1)] , where _i_ refers to home countries and _j_ to the base country (the base country is defined as the country that a home country‘s monetary policy is most closely linked with, as defined in Shambaugh (2004).<sup>33</sup> By construction, higher values of the index mean higher monetary policy independence. For the countries and years for which Shambaugh‘s data are available, the base countries from his work are used, and for the others, the base countries are assigned based on IMF‘s _Annual Report on Exchange Arrangements and Exchange Restrictions (AREAER)_ and _CIA Factbook_ . **Exchange rate stability (ERS)** , ERS = Annual standard deviations of monthly exchange rate series between the home country and the base country are calculated and included in the following formula to normalize the index between zero and one: _ERS_  0.01/[0.01  _stdev_ (  (log( _exch_ rate_ ))] .

**Financial openness (KAOPEN): KAOPEN** = A _de jure_ index of capital account openness constructed by Chinn and Ito (ref), normalized between zero and one. Higher values of this index indicate that a country is more open to cross-border capital transactions.

> 32 Source: Aizenman, Chinn and Ito (2011, forthcoming, JJIE)

> 33 The base countries group identified by Shambaugh include Australia, Belgium, France, Germany, India, Malaysia, South Africa, the U.K., and the U.S., Shambaugh fitted the base country j to country i, reflecting county i‘s policy choices.

31
**Table** 26: UK **Pension** fundst Long **term** returns on **equity** relative to benchark **indices** 1981/89

||1981|1982 1983|1984 1985|1986|1987 1988 198sv|erage|
|---|---|---|---|---|---|---|
|USA|-3.5|-4.2 -4.1|-8.3 -2.7|-3.0|-3.7 -3.4 -0.5|<br>-3.7|
|Japan|8.5|<br>6.9<br>9.3|-15.4 -8.7|-1.1|-13.6 -8.9<br>5.3|-2.0|
|Cont<br>Europe|-6.8|<br>5.7<br>0.8|-5.3 -4.6|-4.0|-3.0 -0.4<br>1.7|-1.8|
|World|-3.8|-3.6<br>1.9|-8.5 -1.9|-2.6|-10.1 -5.8<br>6.7|-3.1|
|UK|0.5|<br>1.2 -0.5|-1.6 -0.5|-1.2|-0.8 -1.0 -0.1|-0.4|

(1) Prior to 1987, local indices for US and Japan, MSCI for Europe. After 1987, FT indices.

Source: WM Table 27: UK Pension funds: Performance and Turnover by Management Method, 1986-90

||Nominal Annual<br>returns**(')**|Activity Cl)|
|---|---|---|
|Internal|11.1|64%|
|Part internal/external|10.9|118%|
|2 or more manager.|10.6|119%|
|Financial conglomerates|10.8|106%|
|Life company managed|11.2|96%|
|Life company segregated|10.4|117%|
|Independent managers|10.6|118%|

(1) Activity is the element of turnover in excess of net investment of new money, as percent of assets.

34a
enrollment decision. Specifically, students in the control group may be better motivated, as they decided to study despite the lower financial support from SOFES. To the extent that this motivation effect increases student performance, the treatment effect is biased downwards. Our estimated impact from loans on student performance from the RD approach should therefore be considered as a lower bound of the unbiased impact.

Table 4 shows the results using the survey data. We now can use two proxies for student performance, namely grade point average and fraction of exams that the student had to repeat. The first column shows the results for the student’s grade point average in university. SOFES-recipients have a 0.175 point higher GPA than students without a SOFES loan. This effect is statistically significant at the 1%-level. This is a 2% improvement in academic performance. Also, female students, older students, and students with a higher grade point average at upper secondary school show higher GPAs at university.

|**Table 4: Impact of the SOFE**|**S loan on student performa**<br>GPA|**nce, survey data.**<br>REPEAT|
|---|---|---|
|_Treatment effect_|||
|**DCREDIT**|**0.175**<sup>*******</sup>|**-0.005**|
||**(0.037)**|**(0.004)**|
|_Socio-economic controls_|||
|TUITION (×1000 pesos)|0.006<sup>***</sup>|0.000|
||(0.002)|(0.000)|
|INCOME (×1000 pesos)|0.001|-0.000|
||(0.001)|(0.000)|
|DFATHERTECH|0.110|-0.001|
||(0.073)|(0.008)|
|DFATHERUNIV|-0.016|0.000|
||(0.040)|(0.004)|
|DMOTHERTECH|-0.065|-0.001|
||(0.052)|(0.006)|
|DMOTHERUNIV|0.012|0.005|
||(0.044)|(0.005)|
|DFEMALE|<br>0.112<sup>***</sup>|<br>-0.005|
||(0.037)|(0.004)|
|AGE|0.018<sup>***</sup>|-0.003<sup>***</sup>|
||(0.005)|(0.001)|
|DWORK|-0.057|-0.001|
||(0.040)|(0.004)|
|_Educational background_|||
|GPA_high school|0.152<sup>***</sup>|-0.004|
||(0.027)|(0.003)|
|DPRIVATE_ high school|0.002|0.006|
||(0.036)|(0.004)|
|DAREA_II|0.041|0.005|
||(0.051)|(0.005)|
|DAREA_III|<br>0.029|<br>0.012<sup>**</sup>|
||(0.054)|(0.006)|
|_Intercept_|<br>6.255<sup>***</sup>|<br>0.121<sup>***</sup>|
||(0.243)|(0.026)|
|N|1,412|1,424|
|R<sup>2</sup>|0.064|0.026|

Note: The regression method is OLS. Standard errors are between brackets. * indicates significance at the 10%-level, ** indicates <u>significance at the 5%-level, and *** indicates significance at the 1%-level.</u> 22
**<u>Table 6: Impact of reform on new enterprise startups</u>**

||**Any startu**|**p in village**|**agriculture**|**... if yes, share of**<br>**industry**|<br>**services**|
|---|---|---|---|---|---|
|**Panel A**||||||
|Chengdu*After|0.027*|0.031**|0.580***|-0.220|-0.360*|
||(0.016)|(0.013)|(0.166)|(0.149)|(0.206)|
|Observations|4,186|4,186|223|223|223|
|R-squared|0.004|0.262|0.822|0.809|0.803|
|**Panel B**||||||
|Chengdu*After|0.006|0.008|-0.100|0.313|-0.213|
||(0.013)|(0.019)|(0.323)|(0.603)|(0.584)|
|Observations|2,095|2,095|87|87|87|
|R-squared|0.004|0.419|0.914|0.910|0.951|
|**Panel C**||||||
|Chengdu*After|0.025|0.031**|0.742***|-0.250|-0.492**|
||(0.019)|(0.013)|(0.203)|(0.190)|(0.208)|
|Chengdu*After*Time|0.011|0.003|-0.189|-0.170|0.359*|
||(0.013)|(0.011)|(0.167)|(0.177)|(0.182)|
|Observations|4,186|4,186|223|223|223|
|R-squared|0.004|0.262|0.827|0.810|0.811|
|SFE|Yes|No|No|No|No|

_Notes:_ Village fixed effects are included. Village time-variant characteristics are controlled for throughout but not reported. These include total area and population, share of the households with access to pension and health schemes as well as the amount of agricultural subsidy, distance to the nearest county road, surface of the village road, and indicator variables for primary schools and medical stations within the village. Time is z score of google travelling time from a village to the center of Chengdu ( _μ_ =1.43h, _σ_ =0.40h). Standard errors adjusted for spatial correlation and serial correlation are in parentheses. If spatial fixed effects are included, otherwise they are adjusted for serial correlation within the village. *** p<0.01, ** p<0.05, * p<0.1.

22
were proxy surveys, and 7% were log surveys. Excluding the log surveys, we have a 91% response rate for our full set of job search and migration outcome variables.

We provide full details on attrition rates in Table A2. In column 1, the dependent variable is an indicator for the endline either being completely missing or administered only via the log survey, in which case we are missing the pre-migration outcome measures we examine in columns 1-7 of Tables 2 and 3. We do not find evidence that either type of attrition is substantially related to treatment assignment. Coefficients on all treatments are small in magnitude, and although the coefficient on treatment [T2] + [T3] is individually significant, we cannot reject the null hypothesis that the treatment assignments are jointly unrelated to attrition.

In column 2, the dependent variable is an indicator for the respondent not being included in any of our endline surveys (respondent, proxy, or log surveys). Similar to column 2, we find some evidence of differential attrition for those assigned to treatments [T2] + [T3], significant at the 5% level. However, the difference in response rates is small in magnitude (only 1.7 percentage points). We use the sample that does not include the log surveys for our main analysis, and only use this log survey data as a robustness check.

# **A2. Pilijobs.org**

We developed Pilijobs.org in partnership with several Manila-based overseas recruitment agencies and our local microfinance partner (PALFSI). Pilijobs.org provided applicants with the opportunity to easily contact and apply for overseas jobs with reputable recruitment agencies, and allowed those agencies to directly post job opportunities that could be accessed by Sorsogon residents. We launched Pilijobs.org in early April 2010, 34
36 Estache, A. and D. Martimort (2000), “Transaction costs, politics, regulatory institutions and regulatory outcomes”, in Manzetti, L. ed. (2000), **Regulatory Policy in Latin America: PostPrivatization Realities** , North-South Press Center at the University of Miami pp 49-82 Faguet, J.P (2003), “Does decentralization increase government responsiveness to local needs: Evidence from Bolivia”, _Journal of Public Economics_ Ghazala, M. (2004), “Community-based and –driven development: a critical review _””, World Bank Research Observer,_ 19 (1), 1-39 Isham, J., D. Narayan and L. Pritchett (1995), Does Participation Improve Performance? Establishing Causality with Subjective Data”, _World Bank Economic Review_ , Vol. 9(2), pp175-200 Kleemeier, E. (2000), “The impact of participation on sustainability: an analysis of the Malawi Rural Piped Scheme Program _”, World Development_ ,29(5), 929-944 Laffont, J.J. (2000), **Incentives and Political Economy** , Oxford University Press Laffont, J.J., A. Faure-Grimaud and D. Martimort (2003), Collusion, Supervision with Soft Information", _Review of Economic Studies,_ vol. 70, n. 2, 2003, p. 253-280 Mody, S. (2004), Achieving Accountability through Decentralization: Lessons for Integrated River Basin Management, World Bank, PR Working Paper series, Number 3346 Poitevin, M. (2000), “Can the theory of incentives explain decentralization?” _Canadian Journal of Eocnomics,_ Vol.33 (4), pp 878-906 Serageldin, M., S. Kim and S. Whaba (2000), “Decentralization and Urban Infrastructure Management Capacity”, Harvard University, Center for Urban Development Studies, available at: <u>http://www.gsd.harvard.edu/research/research_centers/cuds/decentralization_paper/decentralizatio n.pdf</u> Shah, A. T. Thompson and H.F. Zhou (2004), “The Impact of Decentralization on Service Delivery, Corruption, Fiscal Management and Growth in Developing and Emerging Market Economies: A Synthesis of Empirical Evidence”, CESifo Dice Report 2: 10-14 Turk, C. (2001), "Linking Participatory Poverty Assessments to Policy and Policymaking: Experience from Vietnam”, World Bank., Policy Research Working Paper, No.2526 Walker, I et al. (1999), Regional analysis of decentralization of water supply and sanitation services in Central America and the Dominican Republic, EHP activity report N. 65, available at <u>http://www.dec.org/pdf_docs/PNACF340.pdf</u> World Development Report (2004), **Making Services Work for the Poor** , World Bank

# **Bibliography for targeting, affordability and regulation**

Ajwad, M.I.. and Q. Wodon (forthcoming) “Marginal Benefit Incidence Analysis Using a Single Cross-section of Data”, _Applied Economic Letters_ Ajwad, M.I.. and Q. Wodon (2002), “Who benefits from increased access to public services at the local level? A marginal benefit incidence for education and basic infrastructure:, in S. Devaradjan and F.H. Rogers, editors, World Bank Economists’ Forum, Volume 2, World Bank, Washington DC, 155-175 Andersen, L.E. and R. Faris (2002), “Natural Gas and Income Distribution in Bolivia”, February, Andean Competitiveness Project Working Paper