rafmacalaba/datause-encoder-data huggingface dataset:
Table 2: Balance for Ethnicity

||(1)<br>Chinese|(2)<br>Malay|(3)<br>Indian|(4)<br>Dife|(5)<br>rences|
|---|---|---|---|---|---|
|||||(1)-(2)|(1)-(3)|
|Greater Kuala Lumpur|0.65|0.62|0.62|0.02|0.03|
||(0.48)|(0.48)|(0.48)|(0.02)|(0.02)|
|Small Firms|0.44|0.44|0.45|-0.00|-0.01|
||(0.50)|(0.50)|(0.50)|(0.02)|(0.02)|
|Salary (USD)|744.54|756.94|765.15|-12.40|-20.61|
||(253.32)|(315.34)|(300.69)|(16.15)|(15.76)|
|Days to Process|16.73|16.64|16.85|0.09|-0.12|
||(9.34)|(9.45)|(9.44)|(0.46)|(0.46)|
|Pre-scan Questionnaire|0.53|0.56|0.56|-0.02|-0.03|
||(0.50)|(0.50)|(0.50)|(0.02)|(0.02)|
|Number of questions|1.83|1.86|1.89|-0.04|-0.07|
||(2.20)|(2.24)|(2.23)|(0.10)|(0.10)|
|Business degree|0.50|0.49|0.50|0.00|-0.00|
||(0.50)|(0.50)|(0.50)|(0.02)|(0.02)|
|Engineering degree|0.25|0.26|0.27|-0.01|-0.01|
||(0.44)|(0.44)|(0.44)|(0.02)|(0.02)|
|Number of job applicants|150.82|141.21|147.22|9.60|3.60|
||(284.81)|(235.54)|(211.37)|(11.71)|(11.29)|
|Applicants with more education|7.36|6.56|6.96|0.80|0.40|
||(15.57)|(12.06)|(11.01)|(0.62)|(0.61)|
|Applicants with higher exp. salary|67.53|67.73|69.62|-0.19|-2.08|
||(127.44)|(131.83)|(124.44)|(5.81)|(5.68)|
|Number of foreign job applicants|6.02|5.57|6.52|0.46|-0.50|
||(11.00)|(13.47)|(18.21)|(0.65)|(0.78)|
|Observations|1006|1008|981|||

_<u>Note</u>_ <u>: This table reports descriptive statistics for the sample by ethnicity and balance tests. Columns 1</u> to 3 report the mean and standard deviation for each ethnicity across different set of job and company characteristics. Columns 4 and 5 present the difference between column 1 and 2, and 1 and 3 respectively.

16
19 between 1959 and 1965. This implies that, as shown before, all cohorts were exposed to a significant, although different, extent to the exemption. Moreover, because in 1981 individuals belonging to these cohorts were between 16 and 22 years old they were still cohabiting with their parents. This allows us to include controls for a number of parental characteristics at the town-cohort level<sup>22</sup> . Even in this limited sample and with parental controls, we still detect a rather significant increase in high school completion rates of about 2 percentage points and a decline in death rates of about 0.3 percentage points among younger exempt cohorts.

In column (6) we perform a complementary exercise and estimate equation (2) on the sample of males born in 1963-65 and in 1956-58. With respect to the previous exercise we thus exclude from the sample youths born in 1959-61 a quarter of whom was likely affected by the exemption. On the contrary, in the sample underlying column (6) older cohorts are completely unaffected by the exemption as all of them were at least 23 when exempt. However, this does not allow us to control for parental characteristics since as of 1981 many of the older cohorts were no longer living with their parents. Results confirm the increase in high school completion rates in young exempt cohorts and the decline in mortality between 1991 and 2001.

In column (7) we restrict the sample to youths from the 33 exempt towns that either did not record any damage or were at the lowest of the official damage scale and to their 41 direct neighboring towns. Because this subset of treated towns was only exposed to the exemption, the exercise allows us to rule out the possibility that previous estimates reflect the presence of cohort-specific effects of the quake unrelated to the exemption, for example due to the fact that only younger cohorts responded to the limited income transfers assigned to sampled treated towns. The increase in the high school

> 22The 1991 census does not provide information on family background. We thus recover the information from the 1981 population census as follows. For each town and cohort, we select all households living in the town in 1981 with a male belonging to the relevant cohort and compute the employment rates and educational achievement of parents. In 1981 the males in the relevant sample were 16 to 23 years old, thus largely still living in their parents’ household (see Manacorda and Moretti (2006) for a discussion of the motivations of Italian youths to live in the parental household). Unfortunately, the 1981 census does not collect information on income or wealth.
However, simply creating local agencies might not ensure that people are informed and aware about them, which is a pre-condition for their participation. That is, there might be a lack of basic information about the existence of these agencies, what they can or cannot do, which would need to be addressed along with their creation. An information gap might be particularly salient if members of new local agencies are not required to be formally elected, and the agency is constituted by existing public officials that choose its members more informally, as is the common experience. A broader issue is that people might not be inclined to participate in a manner required to make a difference, despite the existence of new institutions that facilitate their participation, because they don’t rank particular services high on their list of priorities, or because they are uncertain of the potential difference that can be made through their participation. Likely motivated by such lines of thought, there have been recent instances of grassroots information, advocacy and awareness campaigns in communities, to urge people to participate in improving public services (Jenkins and Goetz, 1999; Goetz and Jenkins, 2001; Paul, 2002)<sup>2</sup> .

|People being<br>informed about local<br>agency, and its<br>potential role in<br>improving outcomes<br>Creation of local<br>agency|People’s<br>participation<br>in service<br>delivery|Improvement<br>in service<br>delivery|Improvement<br>in human<br>development|
|---|---|---|---|

How much do people know about local agencies, that is, the provisions and processes at local levels for managing services? What do they know of actual outcomes of service delivery? How inclined are they to local public action to improve these outcomes? In this paper we report findings from a survey of parents, teachers, and members of village education committees, on what they know about provisions and processes for local

> 2 Jenkins, R., and A. M. Goetz. 1999. “Accounts and Accountability: Theoretical Implications of the Right-to-Information Movement in India.” _Third World Quarterly_ 20 (3). Goetz, Anne Marie, and Rob Jenkins. 2001. “Hybrid Forms of Accountability: Citizen Engagement in Institutions of Public-Sector Oversight in India.” _Public Management_ 3 (3): 363-84. Paul, Samuel. 2002. _Holding the State to Account: Citizen Monitoring in Action_ . Bangalore, Books for Change.

4
Tumurchudur, and Shirotori (2009) therefore compute revealed factor intensities, using methodology introduced by Hausmann, Hwang, and Rodrik (2007). Building upon the concept of revealed comparative advantage developed by Balassa (1965), the idea is that a product exported mostly by countries well-endowed with a certain production factor is “revealed” to be intensive in that factor. Online Appendix A3 provides more detail.

# **3.3 Other variables**

We use export data from the UN Comtrade database. The data is reported at the level of 4 or 5 digits of the SITC product classification and comprises 1,299 different product categories. We construct a survival database focusing on the information about the starting and ending year of a trade relationship. To correct for left-censored observations (i.e., export spells that are already in place at the beginning of the sample) and to match the survival database with the period covering the stock market liberalization events, we drop all export spells that started before 1980 and after 1997. We also construct several spell characteristics using the original export data: the number of suppliers (i.e., exporting countries) of a given product to the world market (measured at the producttime level), initial export value of a given product (country-product-time level), total value of country’s exports to the rest of the world (country-time level), and number of export spells during the sample period for a given country-product pair (country-product level).

The physical and human capital intensities at the level of ISIC 3-digits industrial sectors are taken from Braun (2003). The data identifying products as homogeneous, differentiated or price-referenced in trade publications (an intermediate category) is from Rauch (1999). The annual data for GDP per capita in the purchasing power parity terms (GDP pc in PPP terms) is taken from the World Development Indicator Report 2006, and is reported in constant 2000 US dollars. The data for real exchange rate is also taken from the World Development Indicators database. The trade openness variable (the sum of a country’s exports and imports divided by its GDP) is from the Penn World Tables. The IMF dummy capturing whether a given country was a subject to the IMF program (i.e., received an official IMF loan) in a given year is constructed based on data from the 10
_56 Political Econony of Policy Reform in Turkey_

# Appendix Table 1. Advance Deposits on Imports (percent)

||Before 1/24/80|After 1/24/80|1/81 to 12/83|
|---|---|---|---|
|Liberalized List I|||l|
|Importers|40|30|20|
|Industrialists|25|15|10|
|Liberalized List 11||||
|Importers|40|20|20|
|Industrialists|25|10|10|
|Quota List|||abolished|
|Importers|10|20||
|Industrialists|2.5|10||
|Public Sector Imports|--|0||
|Additional Deposit Requirements||.||
|Acceptance Credits|_1|||
|Imports against documents and<br>suppliers credits|50|20||

_Source: OECD Economic Surveys, Turkey, March 1981, p. 48._
**Figure 7: Participation in formal or non-formal training, by age**

<!-- Start of picture text --> 60<br>Participation in formal or non-formal training in the past 12 months<br>(% of adults), by age<br>50<br>40<br>EU 27<br>30<br>20<br>10<br>Serbia<br>Turkey<br>N Macedonia<br>Albania<br>0 Bosnia-Herzegovina<br>25-34 35-44 45-54 55-64<br><!-- End of picture text --> Source: Estimates based on Eurostat data. Refers to 2016 data.

## **Poor health is also reducing both participation and productivity among mature workers** . Good health

is a critical asset for productive work and a central component of human capital. Healthy older people are more likely to participate in the labor market, although again, in poorer countries, those in poor health may still be obliged to work to the extent they can. Health is not the focus of this paper and is not reviewed in detail here. It is worth noting, however, that health interventions may be included among low-hanging fruit for addressing productivity constraints in low- and middle-income settings. One area is nutritional deficiencies occurring as seniors need fewer calories but more nutrient-rich meals with age; other areas are broader lifestyle choices that influence later development of noncommunicable diseases (NCDs). The compression of morbidity and the extension of good health into later years permits longer returns to continuous investment in education.

# Demand-side constraints – Incentives and ability to hire and invest in mature workers

This section looks at employers’ incentives to retain or hire mature workers and adapt working situations to increase their productivity. Factors that constrain relevant opportunities include informal and formal institutions governing employment and wages, market failures that restrict the quantity and quality of training, and psychosocial and normative barriers to employers’ recognizing the potential of older workers.

**Regulations and informal institutions around wage setting may raise labor costs for mature workers above productivity and hence lower their attractiveness to employers** . There is significant evidence that job tenure or seniority leads to higher wages in ways that may be disconnected from productivity increases. In OECD countries, 10 additional years spent in a job with the same employer increase wages by nearly 6 percent on average, all else equal.<sup>90</sup> Wages appear to rise even more sharply with tenure in at least some L/MICs, compared to HICs.<sup>91</sup> A study of seniority wage setting and firm-level

> 91 Donovan et al. 2020.

90 OECD 2019b.

18
Jamasb, T., Nepal, R., & Timilsina, G. R. (2015). _A Quarter Century Effort Yet to Come of_ _Age: A Survey of Power Sector Reforms in Developing Countries._ World Bank Policy Research Working Paper No. 7330.

Lamb, W. F., & Minx, J. C. (2020). The political economy of national climate policy: Architectures of constraint and a typology of countries. _Energy Research and Social Science_ , _64_ .

Macquarie, R., Naran, B., Rosane, P., Solomon, M., Wetherbee, C., Buchner, B., Choi, J., Davila, E., Dreyer, C., & Falconer, A. (2019). _Updated view on the Global Landscape of Climate Finance 2019_ .

Mallawaarachchi, T., Nepal, R., & Ross, H. (2021). Meeting environmental objectives through energy sector reforms in Asia and the Pacific.

_https://doi.org/10.1080/14486563.2021.1989009_ , _28_ (4), 305–313.

Moner-Girona, M., Bender, A., Becker, W., Bódis, K., Szabó, S., Kararach, A. G., & Anadón, L. D. (2021). A multidimensional high-resolution assessment approach to boost decentralised energy investments in Sub-Saharan Africa. _Renewable and Sustainable Energy Reviews_ , _148_ , 111282.

Murray, M. P. (2006). Avoiding Invalid Instruments and Coping with Weak Instruments. _The Journal of Economic Perspectives_ , _20_ (4), 111–132.

Nagayama, H. (2009). Electric power sector reform liberalization models and electric power prices in developing countries: An empirical analysis using international panel data. _Energy Economics_ , _31_ (3), 463–472.

Peñasco, C., Anadón, L. D., & Verdolini, E. (2021). Systematic review of the outcomes and trade-offs of ten types of decarbonization policy instruments. In _Nature Climate_ _Change_ (Vol. 11, Issue 3, pp. 257–265). Nature Research.

39
21

|VARIABLES|(1)<br>Semi-traditional<br>package|(2)<br>Semi-modern<br>package|(3)<br>Modern package|
|---|---|---|---|
|Availability of draught animal service|2.39***|1.86**|2.16**|
|(1=Yes)|(0.88)|(0.95)|(1.06)|
|Number of agricultural cooperative|0.23|0.63*|1.04**|
||(0.34)|(0.35)|(0.43)|
|Proportion of households using chemical|5.68***|6.99***|37.64***|
|inputs (expressed as a fraction 0-1)|(1.77)|(2.00)|(6.42)|
|ln(Rainfall average)|-2.18|4.76*|-22.88**|
||(2.34)|(2.77)|(11.59)|
|Constant|-7.73|13.44|-109.72***|
||(7.48)|(9.20)|(41.25)|
|Observations|1,039|1,039|1,039|

Clustered robust standard errors at village level in parentheses *** p<0.01, ** p<0.05, * p<0.1 At times, the different correlates also affect the intensification practices differently. Plots located far away from the dwelling, for example, are less likely to be ploughed with a draught animal, while distance to the plot does not matter for the adoption of the modern package (with mechanized ploughing). Similarly, while the use of draught animal service seems appropriate for largely humid areas, the use of tractor services is more suitable for less humid areas. In areas with limited mechanized services, farmers tend to rely on manual work.

Concerning the instruments, the results show that the availability of draught animals, tractor services and chemical fertilizers and pesticides are also driving factors of the adoption of more intensified production packages. Consistent with Kirui (2019), the results show that the existence of a rental market is a key driver for the adoption of machinery. Moreover, households located in a village with a high number of agricultural cooperatives are more likely to adopt the technological packages with mechanization. Integrating cooperatives effects in the adoption and diffusion of new technologies has already been documented as important in the empirical literature (Sellare et al., 2020).
<!-- Start of picture text --> 2659-2772.<br>Market Integration, Reallocation, and Productivity Effects of Jamuna Bridge in Bangladesh.<br>Brinkhoff,<br>30(1):17-34.<br>94(1):260-272.<br>, 99(1):265-<br>firms.<br>99(1):46-57.<br>Shariff,<br><!-- End of picture text -->
**Table 8. Employment outcomes (ITT), 2010, 2011 and 2012 cohorts**

|||Pooled 2010‐2<br>|012 Cohorts||
|---|---|---|---|---|
||Baseline mean<br>[Std Dev]|<br>OLS<br>(1)|IPSW<br>(2)|NN<br>(3)|
|Any IGA (1=Yes)|0.612|0.071***|0.093***|0.070***|
||[0.487]|(0.022)|(0.022)|(0.020)|
|Any non‐farm IGA (1=Yes)|0.296|0.149***|0.160***|0.150***|
||[0.457]|(0.023)|(0.024)|(0.021)|
|Trade‐specific IGA (1=Yes)|0.18|0.182***|0.187***|0.184***|
||[0.384]|(0.023)|(0.025)|(0.020)|
|Hours worked in past month|69.261|18.740***|21.130***|19.014***|
||[87.273]|(3.890)|(4.148)|(3.940)|
|Earnings|1271.542<br>[2197.669]|856.087***<br>(152.941)|921.323***<br>(159.517)|850.880***<br>(135.139)|
|Logged earnings|3.291|0.957***|1.209***|0.975***|
||[3.817]|(0.191)|(0.203)|(0.173)|
|Earnings > 3000 NRs. (1=Yes)|0.19|0.130***|0.140***|0.131***|
||[0.393]|(0.021)|(0.022)|(0.020)|
|Self‐Employed (1=Yes), if any IGA|0.317|0.057*|0.063**|0.060**|
||[0.465]|(0.029)|(0.029)|(0.027)|
|Works outside of home (1=Yes)|0.576|0.005|0.016|0.001|
||[0.494]|(0.042)|(0.046)|(0.038)|
|Any Savings (1=Yes)|0.599|0.024|0.043*|0.032|
||[0.490]|(0.023)|(0.023)|(0.021)|
|Total Savings (NRs).|3161.273|901.440**|1171.483**|929.787**|
||[7916.744]|(433.792)|(469.584)|(450.135)|
|Logged Savings|4.506|0.335*|0.497***|0.387**|
||[3.868]|(0.179)|(0.179)|(0.164)|
|Taken out loan (1=Yes)|0.343|0.008|0.005|0.009|
||[0.475]|(0.021)|(0.021)|(0.019)|
|Clustered Standard Errors||Yes|Yes|No|

Notes: All columns report difference‐in‐difference estimates. "ITT" indicates that everyone whose score qualified them for a given training event is included in the "treatment" group. Standard errors (reported in brackets) clustered at the event level where possible. Self‐employment and location of work were not asked in 2010. *,**, and *** denote significance at the 10% level, 5% level, and 1% level.

31
Each of the three data-generating processes used in our Monte Carlo simulations provides a plausible economic representation of observed trade flows, and can be parameterized to represent the frequency of zero trade flows observed in real trade data. The resulting data sets differ from those used by SST (2006) in containing true zero observations, and in being based on economic models that jointly determine whether to trade and, if so, the volume of trade. They are _not_ constant-elasticity models because constant-elasticity models cannot generate finite probability mass at zero trade. Instead of moving from a constant-elasticity model to a model containing zeros by transforming the latent variables from constant-elasticity models with a negative-binomial distribution as in SST (2011), we use limited-dependent variable models whose properties have been thoroughly explored in the literature.

The paper is organized as follows. In the next section we provide a brief review of the relevant literature. Section III discusses the econometric problems associated with zero trade flows. Potential approaches to estimating the gravity equation in the presence of frequent zero trade flows are considered in Section IV. Section V covers the DGPs of our Monte Carlo simulations and the simulation results. Section VI provides an empirical implementation of the gravity estimation using country-level trade data and section VII discusses reconciling Monte Carlo and empirical findings. Finally, the paper concludes in Section VIII.

# **II. Review of Relevant Literature on Estimating the Gravity Model**

Eaton and Tamura (1994) appear to have been the first to provide a systematic approach to estimation of the gravity equation as a limited-dependent model. Their model builds on the Tobin (1958) model, where non-limit observations arise when the latent dependent variable (including the error term) crosses a threshold value.<sup>2</sup> Another popular approach to estimation uses the Heckman (1979) model to deal with the presence of zero trade flows (see, for example, Francois and Manchin (2007) and Lederman and Ozden (2007)). The third approach that we consider builds on the Heckman model to take into account the extensive margin of firm involvement in trade (HMR 2008).

> 2 Note that in the Tobit model the effect of the coefficient estimate on the explanatory variable x cannot be interpreted as the effect of x on the dependent variable y. Rather, as McDonald and Moffitt (1980) correctly point out, it is the effect of x on two components of y: “(1) the change in y for those observations above the limit, weighted by the probability of being above the limit; and (2) the change in the probability of being above the limit, weighted by the expected value of y if above” (pg. 318-319).

3
For a better understanding of the changes in inequality and poverty presented so far, the final result we present in this section looks at the incidence of fiscal interventions across deciles of the distribution, as a share of market income plus pensions. Results are shown in Figure 6. The strong progressive profile of the education and health transfers, with benefits relatively more concentrated in the bottom deciles, explain the sharp reduction obtained in the Gini of Final income. In addition, these in-kind transfers lift the impact of fiscal policy in the bottom of the distribution. If we consider only in-cash transfers, we observe that they are not sufficient to offset the burden of taxes across all deciles of the distribution. But once in-kind transfers kick in, they have a boosting effect on the final position of low-income households, making them net receivers of fiscal resources. From decile 4 and above, households are net payers to the fiscal authority.

**Figure 5. Poverty rate before and after fiscal policy in Turkey and peer countries**

<!-- Start of picture text --> Market Income (M.I.) Disposable Income Consumable Income<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Argentina Brazil Chile (2013) Mexico Croatia Poland Russia USA (2016) Turkey<br>(2012) (2009) (2014) (2014) (2014) (2014) (2016)<br><!-- End of picture text --> Source: Argentina (Rossignolo 2017); Brazil (Higgins and Pereira 2017); Chile (Martinez-Aguilar et al. 2016); Croatia (Inchauste and Rubil 2015); Mexico (Scott et al. 2018); Poland (Goraus and Inchauste 2016); Russia (Popova et al. 2018). Turkey: Own estimates based on Turkey 2016 HBS. Note: Argentina, Brazil, Chile, Mexico, and Russia are based on a $4/day poverty line in 2005 PPP. Croatia and Poland are based on $5/day poverty line in 2005 PPP. Turkey is based on 5.5/day poverty line in 2011 PPP. The figure shows the poverty rate for each income concept described in Figure 1.

**Figure 6. Distribution of taxes and transfers across income deciles in Turkey, 2016**

22
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