rafmacalaba/datause-encoder-data huggingface dataset:
9 of a child depending on whether that child died in the first month of life, died later in the first year cf life or survived 12 months. The median intervals were examined and regressions were used controlling for maternal age and education, husband's occupation, wife's work status and rural residence. (See Cochrane and Zachariah, 1983). These effects were estimated separately for all women and also only those that had never used contrdceptives and thus for whom the biological effect could not be compounded by the behavioral effect of child loss. In 122 of 143 comparisons the median birth intervals were longer, the longer the child lived. In the exceptional cases the sample sizes were often very small. In the regression equations, 88 of the 141 regression coefficients were significantly negative controlling for other factors that might effect birth intervals. (115 of 141 medians were as expected when samples were restricted to women who had never used contraception. 86 of 141 regression coefficients were significant for this group.) The magnitude of the effects are shown by averages in Table 5 taken from Cochrane and Zachariah. The effects range from a low of 2 months to a high of 7.8. The effects for individual countries seem to be consistent with what was found in several Latin American countries in earlier studies. (Rutstein and Medica, 1978). In general the effects estimated by the regression-coefficients are substantially larger than the differences between medians. These birth interval effects can be converted into the number of births averted per death averted by dividing the interval effect by the average length of a birth interval. Table 6 taken from Cochrane and Zachar'ah shows that these effects range from less than a tenth of a child to .35 of a child.(See last two columns of the table.) While it would be expected that these effects would be greater the greater the effect of
# **Introduction**

Thailand is one of the most successful developing countries. After decades of rapid growth, the economy rebounded quickly from the 1997–98 Asian crisis and is set to continue its expansion into the future. Nevertheless, there are doubts about the resilience of the Thai economy. The country appears to be on a lower growth projectory now than before the crisis and growth in 2005 may well fall short of expectations. What growth can Thailand realistically expect? And what can the government do to sustain such growth into the future?

Looking at the evidence up to end 2005, this paper attemps to answer these questions. It is part of a series of country studies which apply a new methodology for identifying binding constraints to growth (Rodrik 2004 and Hausmann et al 2005). In the spirit of their approach, this study is built around five principles. First, this study is about economic growth. It looks at human, physical and social resources and asks whether they contribute to or hinder growth. Of course, gains from, say, improvements in education and health should not be seen only in this light. Income is only one factor in public welfare and social outcomes contribute to human capabilities that are ends in themselves.

Second, the analysis is pitched at the big picture, applying a stylized typology of constraints to growth with the intention of prioritizing qualitatively the main issues over the short to medium term. While international experience suggests that growth ultimately depends on a broad range of reforms supporting sound institutions and effective use of resources, only some of them matter for releasing checks on growth at a given point in a country’s development stage.

Third, since countries differ in history, resources and institutions, country-specificity in the diagnostic is important. One tool for identifying binding constraints is to exploit differences in a country’s recent growth episodes. For Thailand, this suggests contrasting the economic recovery process since the Asian crisis with the growth period prior to the Asian crisis. The idea is to find changes in the current growth episode relative to the previous one and then assess its implications for growth. Another tool is to look at actual or shadow prices. This study analyses trends in, among others, exchange rates, interest rates, and returns to education to evaluate candidate hypotheses for what might be constraining growth.

Fourth, since Thailand’s growth has been satisfactory up to end 2004, these assessments have to be forward-looking in order to anticipate likely future growth constraints. In the words of Indermit Gill, the microscope should be used together with a telescope.

Finally, while certain reforms may be urgent economic priorities, they may not be politically feasible. This study conducts the economic evaluation without venturing into a political cost-benefit analysis.

5
30 .

# Table 6 Private saving: Alternative definitions of the private sector, full sample, system estimator (Dependent variable:_Gross private saving/GPDI)

|Private sector definition|1<br>CU|2<br>CA|3<br>PU|4<br>PA|
|---|---|---|---|---|
|Lagged private saving rate|0.593<br>(11.921)|0.582<br>(13.697)|0.483<br>(6.734)|0.587<br>(9.254)|
|Real per-capita GPDI*|0.046|0.029|0.043|0.049|
||(3.345)|(1.912)|(2.412)|(2.458)|
|Real growth rate of per-capita GPDI”|0.481|0.493|0.472|0.450|
|Real interest rate* ©|(8.943)<br>-0.135|(9.969)<br>-0.108|(6.274)<br>-0.249|(5.828)<br>-0.253|
||(-2.165)|(-1.59)|(-4.949)|(-5.011)|
|M2/GNP|0.085|0.062|-0.026|-0.020|
||(3.374)|(2.783)|(-0.787)|(-0.562)|
|Terms oftrade*|0.078|0.080|0.062|0.078|
||(5.324)|(6.529)|(4.500)|(5.096)|
|Urbanization ratio|-0.197|-0.4704|-0.337|-0,382|
||(-3.207)|(-1.563)|(-3.481)|(-3.538)|
|Old dependency ratio|-0.873|-0.752|-0.578|-0.655|
||(-4.934)|(-4.084).|(-3,273)|(-3.069)|
|Young dependency ratio|-0.148|-0.160|-0.321|-0.299|
||(-2.411)|(-2.732)|(-4.375)|(-4.017)|
|Government saving/GPDI|-0.296|-0.388|-0.238|-0.285|
||(-7.101)|(-10.982)|(-4.437)|(-5.097)|
|Private credit flow/GPDI|-0.297|-0.251|-0.245|-0.318|
|a|(-4.624)|(-4.897)|(-3.392)|(-3.989)|
|inflation rate” °|0.219|0.247|0.164|0.143|
||(3.636)|(4.17)|(2.548)|(2.034)|
|Waldtest ofjointsignificance(p-value)|“0.000|0000|0000|© 0.000<br>||
|Sargan test (p-vaiue)|0.356|0.400|0.442|0,400|
|Testfor 1st-order serial correlation (p-value)|0.000|0.000|0.002|0.001|
|Test for2nd-order serial correlation (p-value)|0.168|0.190|0.159|0.121|
|Test for 3d-order serial correlation (p-value)|0.087|0.099|0.257|0.221|
|Number<br>ofobservations(Numberofcountries)<br>—=|—=—S—«<774(69)~=—s—|<“‘<«é‘HG(GO)|~=—S——«B(GQ)<br>~—|8:72(69)|

Notes: T-statistics (in brackets) cornputed with heteroskedasticity-consistent standard errors.

* Expressed in logs (log of [1+x] for the real interest rate and the inflation rate). ® Measured by the first difference of the log. ° Both the real interest rate and the inflation rate are bounded between -50 percent and 50 percent.
income distribution to reflect differences in family size by income bracket, in an attempt to reflect the distribution of income per child.

4. To determine how many of the _narp_ children in the corresponding ( _a_ , _r_ , _p_ ) combination a given block group fall in each income bracket, we assign a low-income status to the lowest incomes. For instance, consider a block group in which 20 percent of 5-year old white children are poor, and the remaining 80 percent are not. In the corresponding tract, 5 percent of white families have incomes below $20,000; 15 percent have incomes between $20,000 and $40,000, and the remaining 80 percent have incomes between $40,000 and $60,000. Thus, we assign a family income of $10,000 (i.e., the midpoint for the $0-$20,000 income bracket) to a quarter of the 5-year old white children, where 1/4 = 0.05 /0.20, and a family income of $30,000 (midpoint for the $20,000-$40,000 income bracket) to threequarters of those children. To the 80 percent of 5-year old white children who are not poor, we assign an income of $50,000 (midpoint of the $40,000-$60,000 income bracket).

5. Based on step (4), for each block group _l_ we calculate the number _μlam_ of children of each age and demographic type _m_ .

Calculating the number of children aged 18-years old in each demographic type and location is challenging for D.C. because the number of 18-year olds is much higher than the number of 17-year olds. The age bracket 18-24 has different demographics than the age bracket 12-17, most likely because it reflects college-age students, many of whom are not originally from Washington, D.C. Thus, we set the block-group level number of 18-year olds equal to the average number of children by age in the 12-17 year-old bracket. We assign to 18-year olds the average demographics of the 12-17 year-old bracket.

# **A.3.2 Household Types for Years 2003-2007**

Recall our assumption that each grade draws equally from the two most frequent ages in the grade, and only from those ages (for instance, 50 percent of second graders are 6 years old, and 50 percent are 7 years old). We also assume that while the marginal distribution of child age may change over time, the distribution of demographic types conditional on age remains constant. We assume, then, that all demographic types of a given age grow at the same rate _θat_ .

Based on these assumptions, for year _t_ we calculate the number of children of each age, _Nat_ , as follows. Let _θat_ = _Na_ , _t_ / _Na_ ,2000 be the proportional growth for age _a_ between year 2000 and year _t_ , _t_ = 2003,... 2007. The household type measure _μlamt_ for _t_ = 2003,... 2007 is then equal to _μlamt_ = _μlam_ ,2000 _θat_ , where _μlam_ ,2000 is the Census 2000 measure described above.

For each year, these measures imply a breakdown of the children eligible for each grade by race and poverty status. When compared with the observed breakdown of student enrollment by race and poverty status, two discrepancies arise. The first is that the resulting fraction of white children is lower than the fraction of the student body that is 47
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firms. Although the definition of exports is likely to vary across these sectors, the idea of selfselection of more efficient firms into export markets and how labor market frictions impact firms’ exporting decision can be applied to firms in service sectors. A wholesaler or a construction firm needs to incur extra sunk costs to provide services for foreign buyers and these firms also have to consider the effects of EPL on deciding to adjust their workforce.

Enterprise Surveys conducted in 2002 and 2005 include a section regarding firm’s employment level. In one of the questions in this section firms are asked how much they would adjust the number of their full-time workers if there were no restrictions in the labor markets for hiring and firing. The exact question is: _“If you could change the number of regular full-time workers your firm currently employs without any restrictions (i.e. without seeking permission, making severance payments etc.), what would be your optimal level of employment as a percent of your existing workforce? (e.g., 90% implies you would reduce your workforce by 10%, 110% means you want to expand by 10%)”._ The question targets to measure the restrictiveness of labor regulations, more specifically EPL, on firms’ hiring/firing decisions. Out of the firms who responded to this question, 40% want to increase, 20% want to decrease and the rest 40% do not want to change their employment levels. In manufacturing sector the values are 40%, 22%, and 38% in respective order.

Using this information, I compute potential employment growth rate for each firm if it was not constrained by the EPL. In calculating the growth rates I follow Davis and Haltiwanger (1992). I divide the difference between the actual employment level and the potential level once hiring or firing decision is made by the simple average of both employment levels. Let �� be the employment level of firm _i_ and � _′_ i be the potential employment level once hiring or firing decision has taken place. Define _li_  ( _l_ ' _i_  _li_ /) 2 as average employment level. Then the percentage change in employment that would have been achieved by making labor regulations more flexible is formulated as The value of gi shows the deviation between actual and potential employment level that stems from the frictions in the labor market caused by EPL. Its value is bounded by values of -2 and 2 which makes it a robust measure to outliers.

6
|**IV. The Action Matrix**<br>||||
|---|---|---|---|
|**Issue**|**Current Status**|**Recommended Action**|**Responsible**<br>**Institutions**|
|**National Water**Policy|No formal policy|Establish NWP|WARPO, NWC|
|National Water Act|Many scattered<br>and disassociated<br>legislation|Prepare and enact NWA|WARPO, NWC,<br>parliament|
|Water-land use<br>Regulation and Rules|Many scattered<br>and disassociated<br>regulations and<br>rules|Prepare comprehensive<br>water regulation and<br>rules|WARPO|
|Apex organization for<br>water sector planning<br>and coordination|WARPO|Reorganize and<br>strengthen WARPO|WARPO, MW|
|Implementation of<br>policies and regulations|Rudimentary and<br>uncoordinated|Make line ministries<br>responsible for<br>individual actions and<br>WARPO responsible for<br>overseeing enforcement|Line ministries,<br>WARPO|
|Water resource data<br>and information|Scattered,<br>insufficient and<br>uncoordinated|Make WARPO<br>responsible for<br>consolidation of data<br>from all sources|WARPO, MW|
|National Water Plan|Incomplete NWP<br>of 1989,<br>insufficient water<br>shed planning|Prepare comprehensive<br>NWAP with intersectoral<br>inputs and attention to<br>water shed development|WARPO|
|Sub-sector plans|Uncoordinated<br>and conflicting|Coordinate intersectoral<br>planning through<br>WARPO|WARPO|
|Institutional Capacity|Inadequate<br>capacity of<br>BWDB to plan<br>and implement<br>water projects<br>and programs|Rationalize BWDB's<br>organizational structure<br>and strengthen capacity<br>for project planning and<br>implementation|BWDB, MW|

36
the financial crisis at over 100% of GDP; it was over four times the ratio in Ireland. Portugal with 68% was a distant second and Spain was closer to Ireland (Figure 4). These debt ratios are much higher than either the EZ or EU averages. In the 2009 recession, debt ratios rose even faster.

**Figure 5.a: Current account balance Figure 5.b: Real effective exchange rate as a share of GDP (%)                                          (index 2000=100)**

<!-- Start of picture text --> 5.00 140<br>0.00<br>130<br>‐5.00<br>120<br>‐10.00<br>110<br>‐15.00<br>‐20.00 100<br>2000 2002 2004 2006 2008 2010 2012 2014 2000 2002 2004 2006 2008 2010 2012 2014<br>Greece Ireland Portugal Spain Greece Ireland Portugal Spain<br><!-- End of picture text --> Source: Source: European Commission, Annual macro‐economic database Source: IMF, International Financial Statistics.

_<u>Worsening external accounts and rising external debt for all.</u>_ The boom in aggregate demand led to strong import growth particularly in the two years immediately preceding the crisis; net exports were at a huge deficit in GIS (over 12% of GDP in Greece) and Ireland’s huge surplus halved, leading to substantial current account deterioration (Figure 4a).<sup>10</sup> By 2007/8, _the level_ of the overall current account deficit was particularly high in Greece (15% of GDP), followed by Portugal (9.7/12.1%) and Spain (over 9%), but _the deteriorations_ in Ireland and Spain were dramatic, the current account deficit worsening by over 100%. In Ireland, a 2003 surplus changed to a 6% deficit.<sup>11</sup> Increases in domestic demand, accompanied by rising wages and prices and consequent real effective exchange rate appreciation (Figure 5b) was associated with rising external imbalances.

**Figure 6: External debt to GDP (%)**

<!-- Start of picture text --> 400 1200<br>350<br>1000<br>300<br>800<br>250<br>200 600<br>150<br>400<br>100<br>200<br>50<br>0 0<br>2002 2004 2006 2008 2010 2012 2014<br>Greece Portugal Spain Ireland<br><!-- End of picture text --> Values for Ireland are on the right axis.

Source: The World Bank, Quarterly External Debt Statistics and World Development Indicators (for GDP data).

High private or public borrowing led to large increases in external debt (Figure 6); in Greece and Spain the gross external debt/GDP ratios rose by 50%, to reach about 150% of GDP in 2007. Portugal, 10 Transfers dropped from 6.5% of GDP in 1995 to 1.5% in 2008 in Portugal representing a secular trend; as Portugal became richer, remittances and transfers dropped (Felke and Eide, (2014).

> 11 Net interest income/workers remittances and current transfers were important‐ the net outflow was large enough to turn the positive trade balance into a current account deficit.

7
vulnerable neonates – which appears to be the case in the Philippines – is that as more preterm newborns survive past the first 30 days of life, the composition of children at older ages will include a larger share who are at a greater risk of stunting.

It is not clear how important these factors are in the Philippines’ overall stunting story. A major objective of the sections that follow is to more precisely identify which pathways seem to drive stunting, through an empirical assessment of the relationship between stunting and its known risk factors.

# **5 Methods**

# **_5.1 Conceptual framework_**

The underlying environmental conditions and risk factors that influence stunting in the ante-natal and postnatal period can be grouped into five categories : (1) maternal anthropometry, health and nutrition (such as whether a pregnant women is stunted or has anemia) and birth outcomes, (2) the care and feeding practices that a child receives at home (such as breastfeeding and timely transition to complementary foods), (3) the degree of household food security and access to diverse foods, (4) the ability of the household to prevent transmission of infectious disease with adequate water, sanitation and hygiene (WSH) infrastructure and (5) the utilization of child health care services, technologies and interventions to prevent disease and boost nutrition. While building off the original UNICEF conceptual framework, this categorization, and the analyses that follow also draw on two recent reports by the World Bank examining the drivers of stunting in Indonesia<sup>40</sup> and across Sub-Saharan Africa.<sup>41,</sup> 42 The multiplicity and interlinked nature of stunting’s potential causal pathways carries at least three implications. The first is that the specific factors that most heavily influence growth failure in one context may not prevail in another. For example, stunting among children in poor, low-lying coastal neighborhoods affected by floodwater may be in large part caused by increased exposure to infectious disease in a setting without adequate WSH infrastructure. Whereas in neighborhoods without flood risk and good WSH infrastructure stunting may prevail due to inadequate dietary diversity caused by the high prices or limited supply of nutritious foods. The second implication is that presence of one driver (such as exclusive breastfeeding) may not necessarily prevent stunting without the presence of another driver (for example, if the mix of complementary foods introduced at 6 months inadequately meets the caloric needs of the child). In other words, there may be a subset of drivers that are strictly necessary for healthy growth that cannot be substituted for by the presence of other drivers. The third implication is that stunting is cyclical (Figure 17). Several studies have established a link between maternal anthropometry – such as the birthweight, SGA status and height of the mother – with offspring outcomes including stunting. Some frameworks make the cyclical nature of stunting explicit and suggest that making sustained progress against stunting requires interrupting its intergenerational causal pathways.<sup>43</sup> 25
attriting households. In Columns 5 and 6 we restrict the cohort effects to be equal for the two samples, while Columns 7 and 8 allow them to differ. Conditional mobility is found to not differ between the two groups when we restrict the cohort effects to be the same for non-attritors and attritors. However, once we allow the cohort effects to vary, the attritors do show somewhat greater conditional mobility than the non-attritors. A 10 percent difference in income between two households with the same fixed effect would be reduced to a 8.2 percent difference after one quarter in the non-attritors sample, and a 7.5 percent difference in the attritors sample.

Overall these results show that attrition has a rather small impact on measurement of mobility, and therefore that measurement error is the main source of bias in the OLS genuine panel estimation. While those who attrit are exhibit slightly more income mobility, the fact that 35 percent of households attrit over the five quarter panel leads us to speculate that changes in income are only one of a large number of reasons why households attrit. A host of idiosyncratic reasons for non-response, temporary absence, and refusal to answer are likely to mitigate the impact of attrition arising from income changes.

# 5.2 Mobility in Full Income and in Expenditure

The above analysis has been for mobility of household labor earnings in urban Mexico. We can compare mobility in labor earnings with mobility in total household income and in expenditure using Mexico’s national income and expenditure survey, the Encuesta Nacional de Ingreso-Gasto de los Hogares (ENIGH). The ENIGH has been carried out in third quarter of the year on a biannual basis since 1992, and we use the six surveys from 1992 to 2002. Each round surveys a new random sample of approximately 10,000 to 14,000 households, so we do not have a panel of households. We can, however, form cohorts based on the same five year birth intervals and three levels of education of the household head as above, and follow cohorts over time. We consider two subsamples of the data. The first consists of urban households, defined as households in areas of 22