rafmacalaba/datause-encoder-data huggingface dataset:
Although it would be desirable to expand our analysis to a panel setting, we are currently constrained by data limitations (in particular the Doing Business, Logistics Perception Index, and MAcMap datasets).

# **_4.2 Estimation Results_**

Table 5 presents our baseline estimation results. The first column covers all HS Chapters, while the second excludes raw materials (Chapters 1-27) and the third excludes in addition basic manufactures (Chapters 1-83). We find that coefficients generally carry the expected signs and are statistically significant at the 5% level. However, results are noticeably clearer for the trade policy variables in the last two columns when raw materials are excluded. The reason is probably that the markets for agricultural goods and raw materials are often still heavily distorted through different economic policy interventions that are not adequately captured by ad valorem tariffs. For our analysis we therefore focus on the estimation results with this sector excluded.

Moving down column 2, we find that both importer and exporter market size (GDP) are positively associated with bilateral trade, with an income elasticity approaching unity. Similarly, higher bilateral tariffs are associated with reduced trade: it is approximately the case that a 1% cut in applied tariffs is associated with a 2.8% increase in trade. The same applies to non-tariff barriers, although the elasticity is less than half as strong. Finally, the two variables of main interest, namely the ETI and ITI, both have estimated coefficients that are strongly positive and statistically significant. Column 2 suggests elasticities of 6.8 and 8.9 respectively. Indeed, the effects for all dimensions of trade policy, including transparency, would appear to be even stronger on the basis of column 3.

These results suggest that the impact of transparency might be stronger for manufactured goods than for raw materials. To test this hypothesis more extensively, we re-estimate the gravity model separately for differentiated and homogeneous goods. We identify these products using the classification scheme due to Rauch (1999), who divides all products at the 4 SITC digit level into three groups: goods traded on an organized exchange, reference priced goods, and differentiated products. We consider the first two as homogenous products and the later group as bringing together heterogeneous products. Running the same gravity equation on both groups yields the results which are presented 23
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Dix-Carneiro and Kovak Trade Reform and Regional Dynamics

# Figure 10: Capital Adjustment

Panel A: Regional log Number of Formal Establishments - 1992-2010 <!-- Start of picture text --> 6<br>5<br>4<br>3<br>2<br>1<br>0<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>-1<br>-2<br><!-- End of picture text --> Panel B: Regional log Average Formal Establishment Size (Number of Workers) - 1992-2010 6 <!-- Start of picture text --> 5<br>4<br>3<br>2<br>1<br>0<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>-1<br>-2<br><!-- End of picture text --> Each point reflects an individual regression coefficient where the dependent variable is the change in log number of establishments or log average establishment size, calculated from 1991 to the year listed on the x-axis using RAIS. For all years, the independent variable is the regional tariff change reflecting tariff changes from 1990-1995 (described in the text), with state fixed effects and pre-trend control. Positive estimates imply larger declines in the number or average size of formal establishments in regions facing larger tariff declines. Vertical bar indicates that liberalization was complete in 1995. Dashed lines show 95 percent confidence intervals. Standard errors adjusted for 112 mesoregion clusters.

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# **Table 22. Second Stage Regressions Formal Labor Markets: Female**

**Worked for a salary in the past 12 Tried to find a job in the past 12 months months (1 if she worked for a salary) (1 if she tried to find a job)**

||**OLS**|**2SLS**|**OLS**|**2SLS**|
|---|---|---|---|---|
|Violent Shock|0.0007<br>(0.0142)|0.0620<br>(0.0492)|0.0109<br>(0.0098)|0.0903**<br>(0.0420)|
|Observations|3,311|3,311|3,311|3,311|
|R-squared|0.0719|0.0719|0.0648|0.0648|
|Hansen Test||0.524||0.900|
|Estimated effect of a standard deviation increase in probability of violent<br>shock(in standard deviations of dependent variable)||0,058||0,105|
|Cluster by community|||||
|Regional Fixed Effects|||||
|*** p<0.01, ** p<0.05, * p<0.1|||||

Source: CLS-WILL: first wave – household questionnaire.

51
**Box 2: What the PMR survey means by “command and control” or coercive regulation**

The contrasting use of “command-and-control” and “incentive-based” regulation appear to have been brought into common usage by Schultze who wrote in a 1977 lecture about economic efficiency: “We tend to see only one way of intervening – namely removing a set of decision from the decentralized and incentive-oriented private market and transferring them to the command-and-control techniques of government bureaucracy” (page 6) In this context, the PMR attempts to measure the extent to which the cost of new regulation is assessed, and whether alternatives are considered before implementing new regulations. About half of the indicator weights are allocated to the following two questions (the full make-up of the indicator is provided in Annex table A2.5):

# **Regulators are required to assess alternative policy instruments (regulatory and non-regulatory) before adopting new regulation. (Current answer: no).**

**Explanation.** The use of a wide range of mechanisms for meeting policy goals, not just traditional regulatory controls, helps to ensure that the most efficient and effective approaches are used. Approaches may include green taxes and subsidies, voluntary agreements, information programs such as eco labeling, self-regulation, permit-trading schemes, and performance-based regulation (where a sector or industry must comply with a standard but can broadly choose how to meet it).  Note that the question only refers to whether the obligation exists as a specific provision in a specific legislative act, not whether the spirit of it is in fact respected.   A positive answer to the question would require the existence of a normative act explicitly ruling out regulation as the default option

# **Guidance has been issued on using alternatives to traditional regulation. (Current answer: no).**

**Explanation.** We understand that the first steps have been made to implement regulatory impact assessment (RIA) which is a step in the right direction. However, RIA has not been legislated to make it mandatory for a wide range of regulatory interventions and, most importantly, the RIA guidelines contain no provisions on **how** to use alternatives to traditional regulation. Lack of know-how and guidance how to use alternatives may prove to be a surmounting obstacle in an environment which is heavily used to more traditional coercive kinds of intervention.  Box 3 offers a discussion of alternatives to traditional regulation.

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# **1. Introduction**

People often make individual choices which differ from those that would maximize social welfare and even their own long‐run utility. Monetary incentives that seek to change this kind of behavior have become increasingly popular. In fact, behavioral interventions across a wide range of areas – from contributions to public goods to education and health – nowadays include financial incentive schemes.

The emerging literature predominantly concludes that financial incentives for healthy behavior appear to be effective in the short run. Yet, it is unclear whether induced changes disappear or even reverse when incentives are removed. There are two conflicting theories concerning the long‐run effects of cash rewards: The “motivation crowding theory” claims that monetary incentives may reduce helpful behavioral motives, such as intrinsic motivation. Therefore, monetary incentives may even increase unhealthy behavior through, for instance, the signal that changing current behavior is difficult or not attractive. The alternative hypothesis (“habit‐formation”) explains a sustained change in habitual behavior by a positive correlation between past and current consumption, i.e., the development of behavioral automaticity.<sup>1</sup> In this paper, we test whether financially induced healthy behavior is sustainable which would provide strong evidence in favor of the habit formation theory (cf., Charness and Gneezy 2009). Using data from a randomized experiment, we investigate the long‐run effects of financial incentives for weight reduction of obese people. Targeting body weight is particularly interesting in the context of habit formation because many obese individuals fail in their weight‐loss attempts and the majority among those who succeed soon regain weight (Crawford et al., 2000). Furthermore, we test whether monetary incentives can make a change in habitual behavior sustainable. Stretching short‐run success may be important for the sustainability of the effects because habit formation may take time. This is especially relevant for eating habits (and perhaps exercise habits) given the widespread phenomenon of weight cycling, i.e., repeated loss and regain of body weight. To examine the impact of financial incentives to sustain behavior change, we estimate the effect of monetary incentives for maintaining a previously achieved target weight on the change of body weight over time.

The importance of finding effective means to fight the obesity pandemic is difficult to overestimate. The obesity pandemic is one of the major health problems of developed economies and associated with considerable economic costs. Obesity increases morbidity, reduces life expectancy, and deteriorates life satisfaction (for a comprehensive overview, see Sassi, 2010). Through negative effects on the probability of being employed (Morris, 2007; Reichert 2015) and wages (Han et al., 2009) as well as positive effects on the risk of early retirement (Houston et al.,

> 1 See Gneezy et al. (2011) for a review of the literature.

2
To study the empirical Chenery-Syrquin relationship, we use agricultural GDP and income per capita expressed in 1995 US$ obtained from the World Development Indicators 2003 of the World Bank.<sup>9</sup> The data cover the period 1960-2002. Figures 1 is a scatter plot of agriculture’s GDP share as a function of income per capita. Fitted regression lines are drawn for the whole sample and for LAC. The two sub-samples do not seem to have different β ’s . However, this graph is inappropriate for examining international heterogeneity in the β ’s because it does not control for long-run heterogeneity in agricultural GDP shares and GDP per capita.

Table 1 presents econometric estimates of β across several specifications and estimations methods. All estimations use annual data. The first column presents the estimate of β derived from the pooled data. Columns (2)-(4) present estimates from country fixed-effects regressions, whereas columns (5)-(10) present results from two-stage regressions with fixed effects (Baltagi 2002). Consequently all the estimates of β under columns (2)-(10) correspond to the effect of development (increases in the log GDP per capita) on agriculture’s GDP share over time, within countries. The instrumental variables for the IV regressions are the 5-15 years lagged observations of log GDP per capita. The p-value of the Sargan test for the validity of the instruments are listed toward the bottom of Table 1. The null hypothesis of the Sargan test is that the instruments are not correlated with the errors, and thus a p-value above 0.05 indicates that the we cannot reject the null and thus the instruments are valid. This is the case for regressions (6) – (10). In all of them we observe a negative and significant β .

Models (3)-(4) and (6)-(8) also include a time trend, which is also statistically significant. Moreover, regression (8) includes dummy variables identifying high-income and LAC countries

> 9 See Appendix C for data definitions and primary sources.

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This analysis also helps clarify the importance of focusing on gross capital flows as opposed to net capital flows. We document these patterns by systematically analyzing the cyclical behavior of gross capital flows: the capital inflows by foreign agents ( _CIF_ ) and the capital outflows by domestic agents ( _COD_ ). Positive _CIF_ and _COD_ both associate with increases in gross international investment positions. To construct _CIF_ and _COD_ , we use balance of payments data from the International Financial Statistics of the International Monetary Fund, from 1970 to 2009 for 103 countries. _CIF_ equals net purchases of domestic assets by non-residents and is thus equal to the sum of all liability inflows. _COD_ equals net purchases of foreign assets by domestic agents and is thus equal to the negative of the sum of all asset inflows, including international reserves. Hence, net capital flows are equal to the difference _CIF-COD_ .

Our main findings are the following. (i) Over the last four decades, the volatility of gross capital flows ( _CIF_ and _COD_ ) has been large and increasing, especially relative to the much lower volatility of net capital flows. This reflects the increasingly positive correlation between _CIF_ and _COD_ . (ii) Gross capital flows are pro-cyclical. In other words, during expansions foreign agents increase their purchases of domestic assets and domestic agents increase their purchases of foreign assets. During crises, especially during severe ones, there is a reduction in gross capital flows, with reductions in both _CIF_ and _COD_ . However, _CIF_ tends to fall more during crises as these tend to be associated with lower net capital flows. The 2008 financial crisis is a clear example of such _retrenchment_ , i.e. a simultaneous decline of both _CIF_ and _COD_ , but we show that retrenchment was a feature of previous episodes as well. (iii) A decomposition of gross capital flows reveals interesting heterogeneity in the behavior of their components around crises. In the case of _CIF_ , its reduction is due to declines in all its components for all country groups. In the case of _COD_ for developed countries, its reduction is due to declines in equity, portfolio debt, 4
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<u>http://www.dol.gov/ilab/iclre/evaluations/egyptEval.htm)</u>

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27
Figure <u>3.1: Household Activity and Labor Status in Dispersed Rural Areas, 2003 (percent)</u> <!-- Start of picture text --> 80<br>70 Farm Households<br>60 Nonfarm Households<br>50<br>40<br>30<br>20<br>10<br>0<br>Salaried Worker Self-Employed Employer Family&Unpaid Worker<br>Percentage of labor force<br><!-- End of picture text --> _Source_ : Own calculation based on RHS 2003.

The majority of jobs in dispersed rural areas are informal in nature. The share of the rural workforce engaged in the formal sector is affected by crises and stagnation in production, as these events tend to increase both unemployment and informality. In 2003, only 28.3 percent of Argentina's heads of household in dispersed rural areas was engaged in the formal labor market.

The rural nonfarm sector is very important for income, employment and poverty reduction in Argentina. The significance of rural nonfarm activities is being increasingly recognized in the development literature and in applied programs.<sup>11</sup> Today’s industrialized countries have highly diversified rural areas, with agriculture as only one of many economic activities. Also, rural nonfarm incomes and employment have expanded rapidly in middle income countries. According to Reardon, Berdegué, and Escobar (2001), in the 1990s, incomes in the rural nonfarm sector accounted on average for around 40 percent of rural incomes in Latin America. In Argentina both the poor and nonpoor receive a large share of their incomes from the rural nonfarm sector; 46 and 32 percent respectively (see below).

The rural nonfarm sector is key to solve a series of issues in rural areas. According to Lanjouw and Lanjow (2001) these are: first, the rural nonfarm sector has a potential to absorb a growing rural labor force; second, the rural nonfarm sector can slow down ruralurban migration; third, the rural nonfarm sector’s contribution to national growth; and fourth the rural nonfarm sector can promote a more equitable distribution of income. In the following subsection nonfarm employment and incomes is addressed in more detail.

Rural nonfarm activities account for an important share of employment in rural areas in Argentina. In 2003, about 25 percent of the rural working population declared nonfarm activities as their primary source of employment (see above). These figures are likely to be highly conservative estimates of the importance of RNF activities because they do not take into account seasonality and do not consider secondary occupations. Also, the 11 See Lanjouw and Lanjouw (2001) and Reardon, Berdegué and Escobar (2001) for two recent surveys.

12