rafmacalaba/datause-encoder-data huggingface dataset:
skill vary with international competition and we expect it to be positive.

We consider two different specifications of equations (1) and (2). In the first, the worker’s level of skill is based on schooling, defined as the number of completed years of education (equation (2.1)). In the second, skill is computed using the International labor Office’s (ILO) correspondence table between major groups of occupations in the 1988 International Standard Classification of Occupations (ISCO-88) and skill level (equation (2.2)). The ISCO-88 “is based on the nature of the skills required to carry out the tasks and duties of the job not the way these skills are acquired” (Hoffmann 2000, pp. 2), considering formal education, training and experience. Table 1 presents the correspondence Table and description of requirements associated with each skill level provided by Elias et al (1999).<sup>11</sup> In the econometric analysis we aggregate skill groups 1 and 2 as low skilled workers, which is then used as the base category in the estimation of returns to skill.

– **Table 1:** Definition of skill groups ISCO 1988

|**Skill level**|**ISCO-88 major groups**|**Description**|
|---|---|---|
|4th|1. Legislators, senior ofcials and managers<br>2. Professionals|Normally requires a degree or an<br>equivalent period of relevant<br>work experience.|
|3rd|3. Technicians and associated professionals|Requires a body pf knowledge<br>associated with a period of<br>post-compulsory education but<br>not to degree level.|
|2nd|4. Clerks<br>5. Service workers and shop and market sales workers<br>6. Skilled agriculture and fshery workers<br>7. Craft and related trades workers<br>8. Plant and machine operators and assemblers|Requires knowledge as for frst<br>skill level, but in addition<br>typically have a longer period of<br>worker-related training or work<br>experience.|
|1st|9. Elementary occupations|Competence associated with<br>general education usually<br>acquired by completion of<br>compulsory education.|

_Sources_ : International labor Office (1990, pp. 2-3) and Elias et al. (1999) as in Upward and Wright (2007).

> 11Using information for each worker’s schooling and tenure we have also checked the consistency of the described educational requirements with those in our data for each skill group and our results are in line with the Elias et al. (1999) description.

8
# Table 2 about here

As compared with _voucher_ households, _cash_ households used their transfer to purchase a more diverse set of food and non-food items (panel A, column 2). Focusing on food items, _cash_ program recipients were significantly more likely to purchase staple grains (a 24 percentage point increase), beans (a 38 percentage point increase), condiments (a 27 percentage point increase), as well as oil, meat and vegetables as compared to the _voucher_ group (panel A). Of these items, only meat and condiments were not available at the voucher fair. For non-food items, _cash_ households were significantly more likely to use the transfer to pay for school fees, buy medicines, reimburse debts and purchase clothing and housing materials as compared with _voucher_ households (panels C and D).<sup>30</sup> These differences in non-food purchasing patterns are not surprising, as these items were not available to _voucher_ households for the last two transfers. Yet this general pattern is similar across all transfers (appendix S4).

While _cash_ households were more likely to purchase a more diverse set of food and non-food items, the voucher modality distorted the purchasing decisions of _voucher_ households for specific food items. _Voucher_ households were 10 percentage points more likely to purchase other grains (namely rice) and 13 percentage points more likely to purchase salt than _cash_ recipients, although only salt is statistically significant at the 5 percent level (table 2, column 2). These patterns are largely similar across all transfers, although the statistical significance varies (appendix S4).

## **Intensive Margin of Overprovision and Food Expenditures**

While table 2 shows the extensive margin of overprovision for each food item, we might be interested in the intensive margin of the uses of the transfer. Given the high degree of measurement error related to the amounts purchased, as well as the limited time for surveys, we only collected expenditure data (related to the transfer) for a subset of food categories. While the data do not represent the entire allocation of the transfer, they do provide some insights into the extent of the intensive margin of

> _measured_ expenditures rather than _actual_ expenditures. This concern is alleviated by the way in which the question was administered: after program recipients listed their initial categories, enumerators were instructed to go through a comprehensive list of potential categories and ask the recipient if they used the transfer on that particular category.

> 30 Two _cash_ households used money from all three transfers to purchase a parcel of land.

17
indicating that the country may have a relatively substantial scope or potential to raise tax revenues.<sup>13</sup> In addition to taxes, total fiscal revenues can be also analyzed in a similar way. As was the case in Le, Moreno-Dodson, and Rojchaichaninthorn (2008), the same estimation techniques are used to calculate the capacity of countries in total fiscal revenue (tax plus non-tax collection) generation, which is named as _fiscal revenue capacity,_ and their effort in revenue generation, named as _fiscal revenue effort_ .

In this section, the estimation results are produced using the regression specifications of Le, Moreno-Dodson, and Rojchaichaninthorn (2008). The main difference is that the new dataset covers a longer time period (instead of 1994-2003, it is now 1994-2009) and more countries. The study includes 110 developing and developed countries. We also focus on sub-periods to understand how the tax effort of countries has changed overtime. The first sub-sample is 19942001 and the second one is 2002-09.

# **_Empirical Specification, Variables, and Methodology_**

The empirical specifications used in the paper consist of possible determinants of tax revenues and total fiscal revenues as a share of GDP:<sup>14</sup> TAX/GDPit = α0 + α1. GDPPCit + α2. DEMOGit + α3. TRADEit + α4. AGRit REV/GDPit = β0 + β1. GDPPCit + β2. DEMOGit + β3. TRADEit + β4. AGRit + β5. GOVERNANCE QUALITYit + regional dummies+ time dummies+ ε, TAX/GDP is total tax revenues in percentage of GDP; REV/GDP is total fiscal revenues in percentage of GDP;

> 13 It should be noted that cross-country tax effort calculations presented in the paper cannot substitute for a comprehensive study of taxation, focusing on a particular country. There are potential problems related to this methodology such as the sensitivity of the calculation of the tax-effort index to the predicted results of a country’s taxable capacity; systematic errors in measurement of independent variables; regression specifications can calculate the tax collection performance of a country in comparison with the average effort exercised by an average country in the selected sample, and this average may not be the actual tax collection performance. Given these potential problems, the results should be used to assess the feasibility of raising additional revenues, given the tax mix policy and collection effort attained at the average level, rather not the measure of actual performance (Ahmad, et al., 1986; Chelliah et al., 1975; Le, Moreno-Dodson, and Rojchaichaninthorn, 2008).

> 14 See Tanzi and Davoodi (1997), Bird, et al. (2004), and Le, et al. (2008) for details. Since tax revenue is in percent of GDP, it controls for fluctuations in the tax base, which can be approximated by GDP.

8
a $1 increase in cash flow is associated with reduction in short-term debt of 4.5 cents, increase in long-term borrowing of 7.6 cents, increase in equity issuances of 13.9 cents and increase in share repurchases of 4.3 cents. In 2008, a $1 increase in cash flow is associated with increase in equity issuances of 9.8 cents, increase in share repurchases of 8.8 cents and decrease in other financing of 1.7 cents for manufacturing firms. For non-manufacturing firms in 2008, $1 increase in cash flow is associated with increase in long-term debt of 6.5 cents, decrease in short-term debt of 6.1 cents, increase in equity issuances of 10.2 cents, decrease in other financing of 1.2 cents and increase in share repurchases of 3.8 cents. In 2009, the non-manufacturing firms reduce their short-term debt by 4.5 cents, increase their equity issuances by 10 cents and reduce share repurchases by 3.2 cents while manufacturing firms reduce short-term debt by 3.7 cents and increase equity issuances by 6 cents. During the recovery period in 2010, manufacturing use their positive cash flow shocks to repay long-term debt (10 cents), repay short-term debt (2.7 cents) and issue new equity (15.6 cents) while non-manufacturing firms use their positive cash flow shocks to repay short-term debt (2.3 cents), reduce other financing (1.7 cents) and issue new equity (6.9 cents) without any significant change in their long-term debt positions.

As explained before, the interpretation of the coefficients in the negative cash flow sample is such that a negative sign indicates an increase and a positive sign indicates a decrease.

Focusing on the firms facing adverse cash flow shocks in B1 and B2, we find that both manufacturing and non-manufacturing firms have increases in long-term debt and equity issuances over the entire period. They also have substantial addition to their cash balances and increases in capital expenditures and other investments suggesting that these firms may not be very financially constrained. Focusing on the differences between manufacturing and nonmanufacturing firms we find that decreases in cash flow are associated with significant increases in dividend payments (although very small and ≤ 2 cents) only in the case of manufacturing firms in the pre-crisis period, 2009 and 2010 but not in the case of non-manufacturing firms. Manufacturing firms also have significant asset sales of 0.2 cents and 0.5 cents in 2008 and 2009 while non-manufacturing firms do not. On the financing side, we find that manufacturing firms reduce short-term debt 2.1 cents in 2010 while non-manufacturing firms borrow 4.9 cents of short-term debt and 2.1 cents of other financing in 2010.

31
potential), whereas other investments such as safety net programs may target poorly endowed areas. Furthermore, local agroclimatic characteristics are also likely to be highly correlated with other potentially unobserved community features that could affect program placement, such as local political influence proxied by the distance of the village from a thana head quarter (Binswanger, Khandker, and Rosenzweig 1993).

Agroclimatic and location factors can be measured (  _j_ ) and unmeasured (  _j_ ). Given the rural context of the samples, (  _j_ ) can be characterized by soil quality, flood potential, temperature, sunshine, and related factors affecting the earnings opportunities of the locality.<sup>35</sup> Other village/thana locational variables, _Rit_ , including variation in annual rainfall, can also affect village-level policy characteristics, _M jt_<sup>. Along with a time-specific</sup> error term,  _jt_ , this relationship can be specified additively as follows:

> Household outcomes, _Cijt_ , according to equation (6.1), such as per capita expenditure are affected by these agroclimate endowments and resulting policies.  Such interactions make it difficult to identify the precise role of infrastructure and other policy initiatives on income, expenditure, and poverty.  Observed and unobserved village/thana endowments as well as income earning opportunities determine jointly both household and policy interventions, and hence, household level income and poverty.  Unobserved heterogeneity at the thana/village level may therefore affect both the outcomes of interest and program placement.

As Binswanger, Khandker and Rosenzweig (1993) study shows, when both public policies and private investments are jointly determined by the same agroclimate and location

> 35 Agroclimatic data were obtained from the Bangladesh Agricultural Research Council’s website, <u>http://www.barc.gov.bd/Data_Stat.htm</u> .

29
numerical example in the next section illustrates that, in this context, “sufficiently close” need not be very close: even when the initial capital stock of the emerging economy is 15% of its steady state level (and _kd,_ 0 is at its autarkic steady state), it is a net lender in every period and made worse off by financial integration.

Interestingly, regardless of initial conditions, developed economies are always made weakly better off by financial integration. In this model, this conclusion holds true even in the presence of enforceability risk. For agents in developed economies, the autarkic consumption sequence is always feasible under financial integration, as they always have enough publicly backed liquid claims to finance the autarkic level of employment. For emerging economies this is not the case, because given equilibrium wages, domestically issued liquidity is insufficient to finance autarkic production.

# **7 Numerical example**

This section presents two quantitative exercises. First, abstracting away from enforceability shocks, I compare the autarkic equilibrium with the integrated equilibrium. Second, I consider the implications of a permanent enforceability shock.

I assume that the intertemporal elasticity of substitution is 1, or _u_ ( _c_ ) = ln( _c_ ). The choice of parameters is summarized in table 1. The first three parameters, _β_ , _δ_ , and _α_ , are standard. The choice of _l_ = 1 and _Ad_ = 1 are convenient normalizations. The productivity of the emerging market is calibrated based on Hsieh and Klenow [2009], who estimate manufacturing productivity in the US to be about 150% higher than in India and China.<sup>10</sup> The value of _θ_<sup>_d_</sup> was chosen to match the value of M3 over GDP in the US. The value of _θ_<sup>_em_</sup> was calibrated to roughly match M3 over GDP in emerging

> 10I chose to calibrate TFP differences based on the differences in manufacturing productivity, which are relatively small compared to current differences in TFP levels. This reflects some expected convergence in TFP in the long run.

24
poorer areas within provinces. About a third of the overall improvement in targeting poor areas came from better targeting of provinces, and the rest from better targeting of poor areas within provinces. However, despite higher provincial allocations to poorer provinces, provinces were initially less able to target their poor areas, possibly because wealthier areas were better able to propose and cofinance good projects. In response to this, project monitoring tools were set up which continuously updated targeting performance at the intra-district level. Ravallion (2000) has shown that this simple but powerful tool, which could be put in place within the context of regular project M&E, was able to substantially improve the intra-provincial targeting of the poor. This project demonstrates how targeting of poor areas within a decentralized system can be improved by providing stronger incentives from the center for pro-poor targeting by local governments.

_Preference targeting._ Since one of the theorized advantages of participation is that it makes development demand driven, participation should improve the match between what a community needs and what it obtains—what can be called preference targeting (see, for example, McLean and others 2001). There is little reliable evidence on preference targeting, which ideally requires panel data with baseline information on major problems faced by the community and post-project follow up in experimental and control communities. Rao and Ibanez (2003) present evidence on the Jamaica Social Investment Fund. Sampled communities were asked to answer retrospective questions on their assessment of major problems in the community before the social fund had been introduced. Rao and Ibanez find that the overall match between the project and the problems identified was poor. Only in two of the five communities studied did the project 23
> Table l a: Probit results of the probability of enrollment into Secondary Education: Albanian Males in comparison with Serbs

||Albanian<br>|**Males: Ag**<br>|**e16-26**<br>|**Albanian**<br>|**Males:Ag**<br>|**e27-35**|SerbMales|& Females:|Age:16-35|
|---|---|---|---|---|---|---|---|---|---|
||Coefficient<br>|Z<br>|Mean<br>|Coefficient|Z|Mean|Coefficient|Z|Mean|
|Ageis16years<br>|0.585<br>|3.381<br>|0.114<br>|||||||
|Ageis24 years<br>|0.362|2.191|0.097||||-<br>|-<br>|-|
|Age is27-35years<br>|-|-|-||||-0.113<br>|-0.558<br>|0.331<br>|
|Females<br>|-<br>|-<br>|-<br>|-<br>|-<br>|-<br>|-0.216<br>|-1.083<br>|0.524|
|Serbsandothers<br>|-0.053<br>|-0.251<br>|0.044<br>|0.125<br>|0.366<br>|0.048<br>|-1.400<br>|-1.998<br>|0.008|
|BorninUrbanareas<br>|0.094|0.888|0.377|0.511|2.692|0.320|0.465|1.777|0.356|
|BornoutsideFormer Yugoslavia<br>|-|-||||||||
|Residence outsideFormer<br>||||||||||
|Yugoslaviabeforeconflict<br>'|-0.133<br>|-0.408<br>|0.019<br>|0.021<br>|0.058<br>|0.050|-|-|-|
|Fathers education(inyears)<br>|0.120|9.098|10.445|0.089|3.803|8.880|0.084|3.179|10.703|
|ValueofHouseholddurables<br>||||||||||
|over5yearsold(log)<br>|0.073|2.034|6.498|-0.039|-0.621|6.951|0.321|2.863|7.236|
|AreaofResponsibility<br>||||||||||
|France<br>|-0.317<br>|-1.967<br>|0.119<br>|-0.419<br>|-1.314<br>|0.093|-0.214|-0.791|0.517|
|Germany<br>|-0.998<br>|-7.171<br>|0.208<br>|-1.165<br>|4.773<br>|0.224<br>|-|-|-|
|Italy<br>|-0.245<br>|-1.605<br>|0.158<br>|-0.949<br>|-3.571<br>|0.147<br>|-|-|-|
|USA<br>|-0.378<br>|-2.693<br>|0.204|-0.703<br>|-2.853<br>|0.212|-0.417<br>|-1.466|0.234|
|Constant|-0.644|-2.421||0.997|2.053||-1.330|-1.458||
|N<br>|1048<br>|||424|||454|||
|Loglikelihood<br>|-474.734<br>|||-171.615<br>|||-98.452|||
|PseudoRsquare|0.178|||0.155|||0.195|||

Note: The variable "Serbs and others" is modified to "Albanians and others" in the Serb model.

25
whether data of this nature are likely to be helpful when a government crafts its response to the cold winds of global recession.

We begin by examining in more detail the timing of the 2008-2009 recession as it affected Thailand, and documenting the effects on output and unemployment. After developing a serviceable measure of welfare, we then use data from the Thailand Socio-Economic Surveys to construct a month-by-month time series for consumption, which we deflate to obtain real per capita consumption levels. This allows us to trace the effects of the recession on different sociodemographic groups, both on average, and controlling for a wide variety of household-level variables.

Our main, and somewhat surprising, conclusion, for which we argue more carefully below, is that the only major socio-demographic group hurt by the 2009 recession was younger wageearners in the greater Bangkok region, who were hit by the sharp, if relatively short, drop in export-oriented manufacturing. On average, the poor in Thailand actually benefitted from the recession, because the effects of lower prices of food and energy outweighed the other influences on their economic wellbeing. We offer some further thoughts, including on the role of government in the crisis, and the importance of rooting policy in actual data, in the concluding section.

# **Timing the Recession**

The annual data on GDP growth rates shown in Figure 1 indicate that economic growth in Thailand, and worldwide, slowed in 2008, and GDP fell in 2009. From the standard macroeconomic identity, we have: It is useful to decompose the growth of GDP into the contributions of each of these components, and this is shown in the bars in Figure 1. Most of the drop in Thai GDP in 2009 was due to a sharp fall in investment spending, which then rebounded in the following year. Real consumer spending rose in every year except 2009, when it dipped slightly (by 1.1%). The rise in net exports was the major contributor to GDP growth in 2006 and 2007; in 2009, Losers and Winners in Recession: Thailand 2008-09 5
- 22 -

For other coefficients, unlike crop production income, the time-specific fixed-effects are estimated to be positive for fertilizer use, meaning that the fertilizer use among farmers systematically increased in comparison with the baseline year, 2012. In addition, the level of education attained by household-heads may also be important. Some education coefficients are significantly positive. This is also a different result from the above crop income regression.

**<u>Table 7. Truncated regression on input use with fixed-effects</u>**

|<br>Dependent var.|<br>ln_F_||<br>ln_F_||ln_P_||ln_P_||
|---|---|---|---|---|---|---|---|---|
||Coef.|Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|
|D<sup>RSDP2km</sup><br>|0.419|(0.205) **|0.695|(0.220) ***|0.226|(0.499)|0.532|(0.569)|
|D<sup>URRAP2km</sup><br>|0.035|(0.151)|0.106|(0.147)|<sup>-0.369</sup>|<sup>(0.284)</sup>|<sup>-0.277</sup>|<sup>(0.281)</sup>|
|D<sup>RSDP2km</sup>•D<sup>URRAP2km</sup>|||-0.804|(0.391) **|||-1.069|(0.942)|
|ln_L_|0.020|(0.055)|<br>0.021|<br>(0.055)|<sup>-0.107</sup>|<sup>(0.168)</sup>|<br> <sup>-0.108</sup>|<br><sup>(0.164)</sup>|
|ln_R_|1.084|(0.079) ***|1.083|(0.080) ***|1.035|(0.165) ***|1.081|(0.163) ***|
|ln_I_|0.019|(0.027)|0.020|(0.027)|0.052|(0.076)|0.051|(0.075)|
|ln_P_|0.050|(0.014) ***|0.050|(0.014) ***|||||
|ln_F_|||||0.058|(0.030) **|0.049|(0.029) *|
|ln_SIZE_|-0.038|(0.204)|<sup>-0.044</sup>|<sup>(0.203)</sup>|<sup>-0.652</sup>|<sup>(0.425)</sup>|<sup>-0.688</sup>|<sup>(0.425) *</sup>|
|ln_MALE_|-0.340|(0.256)|<sup>-0.345</sup>|<sup>(0.258)</sup>|0.058|(0.300)|0.034|(0.307)|
|ln_AGE_|0.245|(0.230)|0.243|(0.229)|<sup>-0.582</sup>|<sup>(0.484)</sup>|<sup>-0.628</sup>|<sup>(0.489)</sup>|
|EDU2|0.393|(0.273)|0.374|(0.270)|<sup>-0.569</sup>|<sup>(0.519)</sup>|<sup>-0.663</sup>|<sup>(0.524)</sup>|
|EDU3|0.491|(0.288) *|0.484|(0.286) *|1.393|(0.627) **|1.384|(0.633) **|
|EDU4|0.031|(0.417)|0.220|(0.560)|<sup>-0.453</sup>|<sup>(0.377)</sup>|<sup>-0.096</sup>|<sup>(0.454)</sup>|
|EDU5|0.115|(0.310)|0.111|(0.327)|<sup>-0.296</sup>|<sup>(0.554)</sup>|<sup>-0.508</sup>|<sup>(0.610)</sup>|
|EDU6|0.936|(0.338) ***|1.099|(0.387) ***|-7.335|(0.832) ***|-7.337|(0.833) ***|
|EDU7|-0.071|(0.217)|<sup>-0.056</sup>|<sup>(0.215)</sup>|0.424|(0.316)|0.450|(0.321)|
|COOK|0.379|(0.426)|0.409|(0.429)|0.367|(0.386)|0.393|(0.356)|
|LIGH|-0.144|(0.248)|<sup>-0.185</sup>|<sup>(0.251)</sup>|0.337|(0.509)|0.270|(0.518)|
|T2013|0.157|(0.141)|0.162|(0.141)|0.085|(0.254)|0.116|(0.255)|
|T2014|1.173|(0.179) ***|1.160|(0.177) ***|-0.366|(0.229) *|-0.378|(0.230) *|
|T2015|0.152|(0.166)|0.139|(0.165)|<sup>-0.626</sup>|<sup>(0.221) ***</sup>|-0.618|(0.221) ***|
|T2016|0.365|(0.170) **|0.343|(0.169) **|-0.376|(0.306)|<sup>-0.352</sup>|<sup>(0.303)</sup>|
|Constant|-1.471|(1.126)|-1.445|(1.125)|4.137|(2.206)*|4.373|(2.254)*|
|Obs.|4639||4639||4639||4639||
|Of which, truncated|3712||3712||4198||4198||
|No. of groups|390||390||287||287||
|Wald chi2|.||.||.||.||

The dependent variable is the log of fertilizer or pesticide used. Cluster standard errors are shown in parentheses. *, ** and *** indicate the statistical significance at the 10, 5 and 1 percent, respectively.