rafmacalaba/datause-encoder-data huggingface dataset:
- Government of Vietnam – Donor Working Group (2000). _Vietnam: Managing Public Resources Better. Public Expenditure Review 2000_ . Hanoi: Vietnam Development Information Centre.

- Hanson, G. and A. Harrison (1999).  "Who gains from trade reform?  Some remaining puzzles", _Journal of Development Economics_ , 59, 53-73.

- Mammen, K and C. Paxson (2000).  "Women's Work and Economic Development." _Journal of Economic Perspectives_ , 14(4), 141-64.

Nguyen, N. (2002). _Vietnam: Trends in the Education Sector._ Hanoi: World Bank.

- Newman, C. (2002).  “Gender, Time Use, and Change: The impact of the cut flower industry in Ecuador.” _World Bank Economic Review,_ 16(3), 375-395.

- Poverty and Human Resources Division of the World Bank (2000).  "Vietnam Living Standards Survey, 1997-98: Basic Information."  Mimeo.

- Poverty Working Group (Joint Report of the Government-Donor-NGO Working Group).  1999. _Vietnam Development Report 2000: Attacking Poverty._

- Ranjan, P. (2001).  "Credit constraints and the phenomenon of child labor," _Journal of Development Economics_ 64, 81-102.

- Rosenzweig, M. (1988).  “Labor Markets in Low-Income Countries,” in H. Chenery and T.N. Srinivasan (eds.) _Handbook of Development Economics_ , Volume 1.  Elsevier Science Publishers.

- Smith, J., Thomas D., Frankenberg E., Beegle K., and G. Teruel (2002).  “Wage, employment and economic shocks: Evidence from Indonesia.” _Journal of Population Economics_ 15, 161193.

- Schultz, P.T. (1999).  “Women’s Role in the Agricultural Household:  Bargaining and Human Capital.”  Economic Growth Center Discussion Paper No. 803, Yale University.

- Sudman, S., N. Bradburn, and N. Schwarz. _Thinking About Answers_ .  Jossey-Bass Publishers: San Francisco, 1996.

- Schwarz, N. and S. Sudman Eds.. _Answering Questions: Methodology for Determining Cognitive and Communicative Processes in Survey Research_ .  Jossey-Bass:  San Francisco, 1996

Thomas, D., K. Beegle, and E. Frankenberg (2000). “Labor Market Transitions of Men and Women During an Economic Crisis: Evidence from Indonesia.” _Rand Labor and Population Working Paper_ 00-11.

41
# **Appendix Figure 1D - Japan The main variables during 1997-2010 for firms from Japan**

<!-- Start of picture text --> 7.00% Gross Savings 10% Gross Capital Formation<br>6.50% 9%<br>6.00% 8%<br>5.50% 7%<br>5.00% 6%<br>4.50% 5%<br>4%<br>4.00%<br>3%<br>3.50%<br>2%<br>3.00%<br>1%<br>2.50%<br>0%<br>2.00%<br>-1%<br>4%<br>Excess Savings Non-Operating Assets<br>25%<br>3%<br>20%<br>2%<br>1% 15%<br>0%<br>10%<br>-1%<br>5%<br>-2%<br>0%<br>-3%<br>75% Leverage 18% Liquidity Holdings<br>70% 16%<br>14%<br>65%<br>12%<br>60% 10%<br>55% 8%<br>6%<br>50%<br>4%<br>45%<br>2%<br>40% 0%<br>Median<br>Asset-Weighted Average<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br><!-- End of picture text --> Excess Savings, Gross Savings, Gross Capital Formation, Non-Operating Assets, Leverage, and Liquidity Holdings, during the period 1997-2010, according to median and asset-weighted average, for the sample of manufacturing firms from Japan.
countries still strive to implement even a "simple" correction of many major environmental externalities, it appears likely that we cannot expect first-best policies consisting of several interdependent market interventions anytime soon to become the norm. Until this changes, using state-owned enterprises might be a promising alternative.

# **References**

- Acemoglu, D., P. Aghion, L. Bursztyn, and D. Hemous (2012). The environment and directed technical change. _The American Economic Review 102_ (1), 131–166.

- Bárcena-Ruiz, J. C. and M. B. Garzón (2006). Mixed oligopoly and environmental policy. _Spanish Economic Review 8_ (2), 139–160.

- Barnett, A. H. (1980). The pigouvian tax rule under monopoly. _The American Economic Review 70_ (5), 1037–1041.

- Belloc, F. (2014). Innovation in state-owned enterprises: Reconsidering the conventional wisdom. _Journal of Economic Issues 48_ (3), 821–848.

- Bondarev, A., P. Dato, and F. C. Krysiak (2021). Green technology transitions with an endogenous market structure. _WWZ Working Paper_ .

- Bortolotti, B., V. Fotak, and B. Wolfe (2019). Innovation at state-owned enterprises. _BAFFI CAREFIN Centre Research Paper_ (2018-72).

- Buchanan, J. M. (1969). External diseconomies, corrective taxes, and market structure. _The American Economic Review 59_ (1), 174–177.

- Carraro, C. and A. Soubeyran (1996). Environmental policy and the choice of production technology. In _Environmental policy and market structure_ , pp. 151–180. Springer.

- Dato, P. and F. C. Krysiak (2021). Optimal mix of policy instruments and green technology transitions. _WWZ Working Paper_ .

- De Fraja, G. and F. Delbono (1990). Game theoretic models of mixed oligopoly. _Journal of Economic Surveys 4_ (1), 1–17.

28
military spending, we repeated the estimations with squared and, in separate estimations, even cubic heterogeneity terms included. However, we found no evidence for non-linear relationships.

We followed Goldsmith (2003) and controlled for the previous year’s value of the dependent variable. One can argue that military budget decisions are subject to bureaucratic inertia (Goldsmith 2003; Gupta, de Melo, and Sharan 2001). Results on our main variables of interest are little affected in terms of the sign of the coefficient and statistical significance, even though the coefficient size is of course reduced by the inclusion of a lagged dependent variable.<sup>9</sup> With our ethnic heterogeneity variables being invariant over time, we cannot compute a fixed effects model. However, we tried to capture some crude cross-regional heterogeneity by regional dummy variables, where the regional classification follows that of the World Bank (2004). With the exception of the region of North Africa and Middle East, which often showed a higher level of militarization, there was little evidence for systematic regional differences. Our main results were hardly affected with the exception of the heterogeneity variables derived from Montalvo and Reynal-Querol (2003) and Alesina et al. (2003), which sometimes became (marginally) insignificant, while maintaining their negative coefficient sign. In these estimations, the average weighted militarization of contiguous countries often became statistically significant as well. This is not surprising given that militarization of contiguous countries is correlated with regional levels of militarization.

Next, we limited our analyses to a sub-sample of only developing countries. The results on diversity remain very similar. The share of the population urbanized had little effect on the results when added to the models, nor was this variable statistically significant, contrary to the findings of others (Davoodi et al. 2001). The same is true for the level of aid to gross national income, which might ease the governmental budget constraint. One might wonder whether oil wealth might allow governments to achieve greater levels of militarization. Adding a dummy variable taking the value of one if oil exports reach one third of total GDP (Fearon and Laitin 2003), suggests no impact on military expenditures or military personnel, but oil has a positive and statistically significant effect on arms imports. This concurs with the fact that major oil exporters, such as the Gulf countries, have been major arms importers over the last decade or so. The results on the remaining variables were hardly affected, however. The same is true if we add a dummy variable for the 20 largest arms-producing countries based on information from SIPRI

> 9 A lagged dependent variable can potentially mask any causal effects by the other explanatory variables (Achen 2000).

15
consistently shown that changes in market shares between firms are important drivers of aggregate productivity. In particular, start-ups and the growth of young firms are known to be crucial in job creation and productivity growth (Haltiwanger, Jarmin, and Miranda, 2013; Foster, Haltiwanger, and Krizan, 2001). However, the role of entry and exit in accounting for aggregate productivity growth varies across countries. For example, Foster, Haltiwanger, and Krizan (2001) find that plant entry and exit account for 25% of US productivity growth. On the other hand, Brandt, Van Biesebroeck, and Zhang (2012) find that entry and exit account for approximately 70% of Chinese productivity growth. Asturias et al. (2017) argue that entry and exit are especially important during periods of rapid aggregate growth. However, since the Great Recession, there has been a growing concern that the recent decline in entry rates (i.e., “start-up deficits”) may affect long-term economic growth.<sup>3</sup> Our decomposition results based on Foster, Haltiwanger, and Krizan (2001) and Melitz and Polanec (2015) show that reallocations along with entry and exit helped increase the productivity of the Korean manufacturing sector. However, most of the productivity growth was driven by the efficiency gains among continuing plants. In spite of the very high rates of entry and exit, the contribution of these plants (i.e., net-entry effects) was relatively small. However, the contribution of entrants varied across industries. The fast growth of entrants played an especially 3 Entry, exit, and plant-level dynamics between growing and declining industries may provide some empirical support for investment dynamics and slow but sustained productivity growth observed in miracle economies. The neoclassical model implies that investment rates jump at the beginning of the “reform” but decrease monotonically back to the long-run value. However, in countries with growth miracles, investment rates responded slowly, building to peak and then falling back over time. The evidence regarding the industrial shift implies that such growth processes, which involve entry and growth of entrepreneurs, may occur over long time horizons, shifting from one industry to another.

4
**Table H5: Relationship between food security measures and weight-for-age z-scores (Tanzania): Limited Controls Version**

|VARIABLES|(1)|(2)|(3)<br>Weight-for-|(4)<br>age z-scores|(5)|(6)|
|---|---|---|---|---|---|---|
|Per capita kcal|-0.017<br>(0.041)||||||
|1-SSR||0.049<br>(0.038)|||||
|1-SSEXR|||0.089**<br>(0.037)||||
|DDS||||0.142***|||
|||||(0.037)|||
|PC expenditure|||||0.045||
||||||(0.041)||
|Food share||||||-0.098***<br>(0.038)|
|Rural|-0.244**|-0.230**|-0.217**|-0.148|-0.204*|-0.189*|
||(0.109)|(0.105)|(0.106)|(0.107)|(0.109)|(0.106)|
|Dodoma|0.064|0.062|0.051|0.156|0.024|0.087|
||(0.396)|(0.392)|(0.392)|(0.387)|(0.395)|(0.386)|
|Arusha|0.451|0.472|0.484|0.448|0.425|0.463|
||(0.392)|(0.387)|(0.389)|(0.381)|(0.394)|(0.387)|
|Kilimanjaro|0.546|0.609|0.599|0.502|0.496|0.503|
||(0.393)|(0.389)|(0.385)|(0.381)|(0.391)|(0.385)|
|Tanga|0.576|0.599|0.580|0.520|0.499|0.560|
||(0.414)|(0.408)|(0.407)|(0.404)|(0.411)|(0.400)|
|Morogoro|0.526|0.570|0.523|0.491|0.443|0.512|
||(0.397)|(0.394)|(0.392)|(0.387)|(0.399)|(0.388)|
|Pwani|0.459|0.488|0.445|0.458|0.409|0.415|
||(0.423)|(0.413)|(0.415)|(0.410)|(0.417)|(0.414)|
|Dar es salaam|0.673*|0.681**|0.647*|0.601*|0.580|0.599*|
||(0.354)|(0.346)|(0.346)|(0.340)|(0.358)|(0.344)|
|Lindi|0.611|0.626|0.649|0.696*|0.566|0.610|
||(0.402)|(0.397)|(0.396)|(0.391)|(0.401)|(0.393)|
|Mtwara|0.316|0.341|0.335|0.324|0.236|0.315|
||(0.385)|(0.379)|(0.378)|(0.372)|(0.384)|(0.373)|
|Ruvuma|0.065|0.108|0.089|0.092|0.016|0.076|
||(0.374)|(0.371)|(0.369)|(0.362)|(0.372)|(0.365)|
|Iringa|0.424|0.452|0.460|0.413|0.367|0.418|
||(0.370)|(0.366)|(0.365)|(0.360)|(0.370)|(0.361)|
|Mbeya|0.542|0.563|0.566|0.504|0.481|0.510|

92
requires an approach that allows gender inequality within and outside household to be identified. For this, a modeling framework that takes household production seriously is essential.

It was also noted at the outset that an important advantage in setting out such a modeling framework is that it makes clear the kinds of data that are required for analyzing the questions at hand. In addition, it makes clear the relevant unit of analysis and, therefore, for whom the data need to be collected. Available household datasets typically provide information on many of the key variables of the models set out above – market wage rates, exogenous incomes and labor supplies. However these are not sufficient. Time use data and information on domestic output are also required.

A fundamental feature of the modeling approach outlined is that the household is treated as the relevant economic unit, analogous to a small economy in which individuals engage in production and exchange. Thus the decisions of individual members cannot, in general, be modeled independently of the wage rates and incomes of other members. In particular, unless information on wages is collected for every active member, it is not possible to construct a household budget constraint.

The household survey datasets used by economists to estimate the standard demand/labor supply model typically provide information for all active members. However, time use surveys often do not. They are sometimes designed to include only one or two randomly selected members of each household interviewed. They also frequently fail to distinguish between labor and non-labor incomes, so that it is not possible to compute or estimate a gross wage for each active member. These and other deficiencies seriously limit the usefulness of many time use survey datasets as an input to policy analysis.

The aim of this section is to examine some of the main limitations of currently available time use surveys, and to show how many of the problems could have been avoided and, in some cases, without incurring a significant increase in cost. Two very different time use surveys for developing countries are selected for analysis. The first is the Statistics 42
43 (Heath,1987:278). Admittedly, these figures are not a perfect measure of the programme's kmpact: easier access to Conasupo warehouses may enable producers to lever middlemen Into paying them an acceptable prico, a benefit enjoyed both by participants and non-partIcipants In PACE. However, a further reason for low participation rates Is that, In the absence of thuely credit facilitles, some producers are driven to **seek** loans from middlemen, committing their harvest In advance (invariably at a prico wel below the official lovel).

95. A study In Zittcuaro, Michoacgn, demonstrates that, even In areas with a good warehouse network and excellent road access, most ejidatarios are disinclined to sell their grain to the state. The quality controls operated by Conasupo entail payment deductions for grain that Is dirty, damp or pest-ridden. Ejldatarios argue that private traders Impose less domanding product standards. A further objection concerns the great expenditure of time Involved In selilng to Conasupo (there are long queues at the warehouses In the kmmodlate aftermath of the harvest) and producers' understandable preference for on-the-spot payment In cash.

96. By and large, In this area the programme seemed to cater for producers with more than two tonnes to sell. The author concludes that only farmers with more than two tonnes to market (a minority of the farmors In this area) do fInd It worthwhile to sell to Conasupo. The low level of direct sales from farmer to state Is loss a reflection of problematIc access to the warehouses than It Is a consequence of the llmited number of farmers producing a significant marketable surplus of maize. This In turn Is attrlbutable to low yields (exacerbated by Ineffective credit policies) and Inadequate price incentives: these factors appear to pose more fundamental constraints than the nature of marketing facilities (Heath,1 987).
capital.<sup>2</sup> A distinguishing feature of the PROGRESA data is that they are based on an experimental design, with randomization of the coverage of the program at the locality level. The empirical methodology consists of comparing conditional means of key outcome variables (such as labor market participation, hours of leisure, and poverty rates) between households living in villages covered by the PROGRESA program (treatment villages) and households living in comparable villages that are out of the program (controls). An additional advantage offered by the design of the sample, is that we can also examine the potential effects of the program on the labor supply of the non-eligible households living in the treated communities (i.e. the communities covered by PROGRESA).

We investigate three main questions. First, we examine whether being eligible or ineligible for PROGRESA benefits affects adult labor force participation. Second, we study the effects of PROGRESA on adult leisure hours. Leisure may be an important determinant of welfare and beneficiary households may choose to increase their welfare by using the cash transfers of the program to “buy” more leisure. Finally, we analyze the impact of PROGRESA on poverty measures based on household income. Poverty measures offer the advantage of being simple, albeit imperfect, summary measures of the effects of the program on the communities with both eligible and ineligible households exposed to the program. Significant labor supply response among households in communities covered by the program may result in small impacts of the program on poverty.

The organization of the paper is as follows. Section 2 describes briefly the PROGRESA program and the data used. Section 3 illustrates the econometric specification and estimation approach behind our results. Section 4 shows and discusses the results regarding the impact of PROGRESA on adult labor force participation and leisure. The impact of PROGRESA on

> 2 Skoufias (2005) provides a detailed discussion of PROGRESA, the evaluation design and a summary of the impacts of the program estimated by a large team of researchers.

3
groups (Angrist et al., 2002). While we investigate this in more detail in section five below (and find the attrition rates similar), the distribution of non-attriting students is presented in the last three columns of Table 1. Like the original baseline tabulation, the distribution of students is still evenly distributed between the research groups even at follow-up, despite the high attrition rate.

Both the baseline and follow-up questionnaires followed the same format. Three different questioners were administered respectively to students, math and Spanish teachers and the principal of the school. The students’ questionnaire was selfadministered by the students with the assistance of the survey team. The variables included in the students questionnaire were socioeconomic characteristics (age, gender, family structure, number of siblings, work status, and allowance, among others,); school outcomes (attendance last week, number of hours of study outside the school, grades in math and language, repetition, and dropout spells in the past, among others); and attitude towards the school and the content of classes, including the use of computers. Finally, the student survey included a shortened version of the national Colombian exam, the _Saber._<sup>_3_</sup> For the purpose of analysis, the scores on these tests are normalized relative to the grade-specific control group distribution of scores.

Information was also collected from all math and language teachers present at the school the day of the visit. We collected background information (age, gender, education, experience, etc.), information on their knowledge of computers, and their use of

> 3 Four different tests were used, depending on the student’s grade, grouping them as follows depending on their grade at the time of the survey: 1) third and fourth, 2) fifth and sixth, 3) seventh and eighth, and 4) ninth, tenth and eleventh.  These are national Colombian exams normally administered to students in the odd grades from grade three to grade nine.  As a result, we simply administer the exam normally given to a particular grade to that grade and the subsequent grade with the exception of grade eleven which receives the grade nine test (since there is no grade eleven test).

- 10 -
- 17 -

_Q1_ -0.715 (0.132) *** -0.693 (0.215) *** _Q2_ -0.526 (0.124) *** -0.430 (0.191) ** _Q3_ -0.530 (0.110) *** -0.062 (0.179) _Q4_ -0.386 (0.101) *** 0.022 (0.170) ln _HHS_ -0.061 (0.070)<sup>-0.123 (0.123)</sup> ln _MINCSB_ -0.011 (0.027) 0.019 (0.049) ln _ROOM_ 0.180 (0.068) *** 0.189 (0.118) _HOME_ -0.074 (0.063) 0.155 (0.132) _POWR_ 0.206 (0.112) * -0.116 (0.196) _TAPW_ 0.190 (0.105) * -0.148 (0.192) _TOIL_ 0.050 (0.075)<sup>-0.058 (0.134)</sup> <u>Constant 10.986 (0.380)</u> *** <u>0.515 (1.301)</u> Obs. 858 No. of district 8 8 dummies Wald chi2 580.96 Inter-equation correlation parameters: _λ_ -0.426 (0.100) *** _ρ_ 0.766 (0.039) *** Endogenous selection test: Wald chi2 13.77 ***

# **VI. ROBUSTNESS**

37. Related to the selection endogeneity, one may wonder whether the instruments are statistically valid. To formally test this question, the instrumental variable (IV) regression is performed. Although this is a linear model, it is still valid to use. Not surprisingly, the results are similar to the above ( **Table 5** ). Commuting increases wages, and the proximity to taxi-be routes has a positive impact on the commuting choice. Human capital is important to earn more. Women are more likely to use buses to commute, and yet, they earn less than men.

The conventional endogeneity test statistics are estimated at 8.57 and 7.70, respectively. Thus, the exogeneity hypothesis can easily be rejected: The commuting selection is endogenous. On the other hand, the overidentifying restriction test statistics are 4.48 and 5.70, which cannot reject the hypothesis that there is no correlation between the selection status and the error term. Therefore, our proposed instruments are valid to control for the endogeneity of the commuting choice in the system.