rafmacalaba/datause-encoder-data huggingface dataset:
One way to provide some additional flexibility is to modify the original Swiss formula by introducing an additional parameter, _b_ , into equation (5) to obtain: We call parameter _b_ a flexibility parameter as it allows the shape of the relationship between the initial and final tariffs to change<sup>3</sup> . As can be seen from the second term in equation (4), the original Swiss formula is a special case of equation (4), with _b_ = 1. As will become evident, the impact of tariff reductions on peak tariffs can be softened by raising the _a_ parameter. As _b_ increases, the formula tends to increase the reduction in the lower tariffs, allowing for higher maximum rates with the same target reduction in the average tariff. The third expression in (4) shows that the flexible formula can be seen as a combination of a proportional cut and a Swiss formula. If _b_ > 1, then one can see the approach as first imposing a proportional cut on _t0_ to obtain _t_ <u>0</u> ; then subject _b_ _t_ <u>0</u> to a Swiss formula. Clearly, there is an entire family of flexible Swiss formulas, _b_ distinguished by their _b_ values<sup>4</sup> . Comparing these formulas directly is difficult since changing _b_ changes both the curvature of the line and the average depth of cut. like Microsoft EXCEL’s Solver can be used to find the combinations of _a_ and _b_ consistent with the targeted reduction in average tariffs and hence allow formulas with different curvature, but the same depth of cut in average tariffs, to be compared.

To avoid formulas that increase the value of some tariffs, the flexibility parameter, _b,_ should be one or above. This extended Swiss formula retains the key feature of the original Swiss formula that all tariffs are reduced below a ceiling given by _a_ . Figure 2 illustrates, for an example with tariffs ranging from 0% to 90%, the percentage reduction in each tariff rate needed given a target of a 50% reduction in the average tariff. As shown, there is some scope for trading off cuts in higher tariffs with cuts in lower tariffs through adjustments to the compensation parameter. This family of curves clearly provides much greater flexibility to negotiators who had, hitherto, to choose one or the other approach.

> 3 The first expression in (4) shows its similarity to the original Swiss formula. The second shows the source of the asymptotic feature of the formula—division by a rectangular hyperbola in the tariff rate. 4 This might be called a Swiss-Army-knife approach to tariff reduction.

7
Our second contribution is empirical. We show that teacher subject content knowledge has a large and significant contemporaneous effect on student performance. Our preferred specification implies that a 1 standard deviation (SD) increase in teacher content knowledge increases student learning by 0.06 SD in the short run (after one year of teaching). This implies that moving a student from a teacher at the 5th to the 95th percentile of the teacher content knowledge distribution increases student learning by 0.2 SD in one year. These effects do not persist fully, however, with about 60 percent of the short-run effect persisting from one grade to the next (an estimate that is similar to those derived from value-added models in other contexts).<sup>3</sup> As a result, being taught for four years by teachers whose content knowledge is 1 SD higher than the average student knowledge would increase by 0.12 SD, equivalent to two and a half times the short-run effect.

The empirical results allow us to assess the relative magnitude of the effects and to carry out various policy simulations. First, we investigate the extent to which shortfalls in teachers’ content knowledge account for the large learning gaps observed within and across countries. Second, we ask by how much learning would increase if students were moved from teachers with lower to higher content knowledge, recognizing that this entails a potential cost through larger classes. Finally, we consider the longer run implications of complementary policy reforms aimed at improving teacher content knowledge along with incentives that increase the time devoted to teaching.

Our analysis suggests that raising teacher effective content knowledge to the typical official requirement for primary teachers in Africa (10 years) would, by itself, reduce the observed effective schooling gap after four years by around one-third. Around 20 percent of the gap in learning between the lowest and the highest ranked country in terms of student learning is explained by differences in teachers’ content knowledge. The simulation results show that there are potentially substantial cost-savings that could result from assigning more students to better teachers, even if there are negative class-size effects. Last, they show that ensuring that all incoming teachers have the officially prescribed effective years of education (that is, they master the material covered in the officially prescribed years of education), along with increasing the time spent on teaching to the mandated schedule, would almost double student learning within the next 30 years. Either of these reforms alone would have substantially smaller effects.

> 3 See Kane and Staiger (2008); Jacob, Lefgren, and Sims (2010); Rothstein (2010); and Andrabi et al. (2011).

3
Notes: All models include firm and year fixed effects. Standard errors clustered at the firm level are parentheses. ***, **, and * indicate significance at 1 percent, 5 percent, and 10 percent. The reference categories for the exporter, age, and firm size groups are “Non-exporters”, “Age: 1-5 years”, and “Firm size: 1-5 employees”, respectively.

Previous empirical literature – both theoretical and empirical –indicates that competition can drive greater productivity growth. It does this through: i) within-firm effects (where competition acts as a disciplining device, encouraging firms to become more efficient and to innovate), and ii) acrossfirm effects whereby competition ensures that more productive firms increase their market share at the expense of the less productive. Aghion et al (2008) provided the baseline study of the relationship between markups and productivity growth in South Africa through a study of publiclylisted companies. They find that higher markups have a large negative impact on productivity growth for publicly-listed firms in South African manufacturing industries.

Our results confirm that competition is positively related to productivity growth in South Africa. The results indicate that higher markups – and by extension lower competition – negatively impact productivity growth. The results are highly significant across model specifications, as presented in Table 3. Our results suggest that a 10 percent reduction in average markups in manufacturing industries is associated with a 0.1 percent increase in productivity growth. To put this in context, this productivity gain would have taken South Africa’s negative productivity growth for manufacturing industries into positive territory over the period between 2010 and 2014.

The results also suggest that South African exporters have lower productivity growth than nonexporters, particularly in the case of firms with over 25 percent of their sales coming from export. While this may appear contrary to the theoretical prediction of learning-by-exporting, it could be in line with the theory that more productive firms self-select into export – assuming that more productive firms have less room for productivity growth. It could also be the case that exporting firms have been able to compete despite their low productivity growth due to support from the South African government directed at export sectors.<sup>21</sup> Future work could explore these potential explanations further.

> 21 Between the years 2006 and 2012, foregone tax revenue from tax incentives amounted to around 1.3 percent of GDP on average (World Bank, 2016). And figures appear similar from 2013 to 2016 according to the 2018 Budget Review. The majority of these tax expenditures are directed towards the Automotive Production and Development Program (APDP) which is aimed at exporters, and towards the diesel refund scheme available for primary producers

17
Table 2: Output <u>gap</u> decomposition: _tsi_ = 0 <u>(percent)</u>

|Year|Output|TFPGa|Intersectoral|Intersectoral|∆Aggregate|
|---|---|---|---|---|---|
||Gap|p|_K_|_L_|Capital|
|1998|20.00|5.47|1.32|1.38|11.82|
|1999|21.20|6.43|0.41|1.05|13.31|
|2000|28.46|8.22|1.60|0.95|17.69|
|2001|22.79|5.64|0.10|0.72|16.33|
|2002|19.60|4.52|0.61|0.34|14.14|
|2003|19.85|4.82|0.03|0.55|14.45|
|2004|22.30|4.16|-0.41|0.80|17.75|
|2005|31.20|4.33|-2.94|1.74|28.07|
|2006|35.29|6.83|0.06|1.53|26.86|
|2007|38.02|11.12|-0.55|1.31|26.14|

Notes: Since aggregate labor supply is inelastic, ∆Aggregate Labor=0 for any counterfactual policy scenario.

# **4.3 Allocative Efficiency and Corporate Tax Rates**

In this section, we analyze how different levels of tax rates affect our economy by considering different counterfactual flat tax rate policies. The equations implied by the model portray the mechanisms through which intrasectoral reallocation of resources occurs due to different tax policies. Profit tax rates affect firms’ marginal products, as can be seen from equations (5)-(8). Since profit tax rates interact with firm-level wedges, flat tax rate policies will have heterogeneous effects.

Our counterfactual exercise is the following. We set the corporate tax rate equal to _t_<sup> ̄</sup> for all firms (i.e., _tsi_ = _t_<sup> ̄</sup> _∀i_ ). In these counterfactual scenarios, all firms face the same output and capital wedges implied by the data as well as a flat tax rate _t_<sup> ̄</sup> . We compare these counterfactual economies to the observed Chilean economy and measure changes in allocative efficiency with respect to the data.

20
# **Table B5: The Effect of News About Wahid’s Impeachment on Share Prices**

||D<br>|ependent var<br>|iable: shar<br>|e price retu<br>|rns<br>||||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
||1<br>February,<br>2001|13<br>February,<br>2001|21<br>March,<br>2001|1 May,<br>2001|14<br>May,<br>2001|30<br>May,<br>2001|Pooled|Pooled|
|Suharto Dependence|0.612|-0.451|-2.534|0.880|-0.232|-0.211|0.217|0.172|
||(0.509)|(0.550)|(1.892)|(1.347)|(0.521)|(0.640)|(0.313)|(0.315)|
|NR JCI|||||||0.537|0.246|
||||||||(0.442)|(1.362)|
|NR JCI* Suharto|||||||||
|Dependence||||||||0.0906|
|||||||||(0.422)|
|Constant|-2.847|2.676|11.33|-3.142|0.709|1.215|-1.195|-1.048|
||(1.788)|(2.043)|(7.722)|(3.587)|(1.650)|(2.486)|(1.046)|(1.067)|
|Observations|46|46|46|46|46|46|276|276|
|R-squared|0.034|0.017|0.043|0.018|0.004|0.003|0.007|0.008|

_Note:_ The dependent variable is the return on the price of security i during the news episode listed in the column heading. Suharto Dependence is a measure of political connectedness, notably a score from 1-5 provided by the Castle Group. NR JCI is an indicator of the return on the Jakarta Stock Exchange Composite Index net of broader Southeast Asian and serves as a measure of event severity. The specific events studied are: 1 February 2001 -- Wahid gets first parliamentary censure because of corruption charges.

13 February 2001 -- Parliament makes first call on Wahid to share power with Megawati Sukarnoputri.

21 March 2001 -- Defense minister Mahfud M.D. claims Wahid is ready to step aside if there are constitutional reasons for doing so.

1 May 2001 -- Wahid gets second censure.

14 May 2001 -- Megawati says impeachment proceedings against Wahid are “unstoppable.” 30 May 2001 -- Parliament decides on impeachment moves against Wahid.

52
# **Additional robustness checks**

The National Rural Employment Act (NREGA), a large-scale public employment scheme rolled out in rural India in 2005, has affected rural labor markets during our study period, including by increasing wages (Imbert and Papp, 2015; Berg et al., 2018). While NREGA does not necessarily interfere with our identification strategy, we check the robustness of our findings with respect to this scheme. In Appendix Table A11 we present a split-sample analysis of Table 5. The first two columns are the districts where NREGA started in 2006 (Phase 1). The third and fourth columns are the districts where NREGA started in 2007 (Phase 2). The fifth and sixth columns are the districts where NREGA started in 2008 (Phase 3). We note similar effects in the first and last set of districts, but appear to lack power for the middle set. Chari et al. (2019) notes that while the exact criteria are not known, the assignment of districts into phases was intended to prioritize the most ‘backward’ districts. This might have resulted in the stronger result in the first set, which might be more rural as well.

Another concern relates to demand-side channels. Structural transformation is associated with demand shifting away from staple food crops (Herrendorf, Rogerson and Akos<sup> ́</sup> Valentinyi, 2014). This change in demand may impact the land market and also correlate with migration flows. To check this possibility, we use the standard IV specification and the acreage of cash crops (as a percentage of the cultivated acreage) as the dependent variable in Table A12.<sup>26</sup> There is no statistically significant relationship between migration and cash crop prevalence, suggesting that these types of correlated effects are unlikely to be a threat to the main results.

# **Absentee landlords and farm yields**

Finally, we examine the relationship between high-skilled migration and crop yields. The direction of this relationship is theoretically ambiguous. The Mar-

> 26This is the percentage acreage of crops which are not rice, wheat, maize or sorghum using ICRISAT data.

33
tion and empowerment are differentially related to displacement status and context within the same cases, and that such manifestations and relations are inconsistent across locations and over time.

This heterogeneity points to the importance of identifying and addressing the barriers and enablers of participation in political and social life, which policymakers can use to develop policies and programs that stimulate participation and cultivate perceptions of empowerment. While the three case studies in this paper are insufficient to draw definitive conclusion on such barriers and enablers, we find that opportunities and settings significantly shape IDPs engagement and participation. This heterogeneity is an important finding in and of itself: based on our data and analysis, international actors should pursue different interventions in Iraq, the Philippines, and Uganda. Instead, inconsistencies across our cases highlight the need for locally-responsive and contextually appropriate interventions to promote empowerment among IDPs. Such local ownership will allow interventions to be more responsive to perceived needs among IDPs, which we show likely vary across time and space even within the same displacement context.

Despite the heterogeneity in our results across contexts, we do find some important trends within cases that can inform targeted interventions by the international community. In Uganda, we find that patterns of engagement change over time, likely as a result from the transition from a period of peace negotiation immediately after the violence to a period when the peace talks have collapsed but the conflict has moved (with the LRA) beyond the country’s borders. Opportunities for return and resettlement likely prompted higher levels of social and political engagement among IDPs. Our data reflect this changing set of priorities for IDPs as the stage of the conflict changes. Policy and programming in such fluid contexts should regularly seek input from local communities to ensure that interveners remain aware of priorities among IDPs. Our results also highlight that the initial period of resettlement and return after prolonged displacement is likely a fruitful period for the international community to engage IDPs by capitalizing on their improved perceptions of empowerment.

In the Philippines and Iraq, we find that the settings of displacement have different effects on engagement and participation among IDPs. Based on the matching models, we show that campbased displacement is in general negatively related to personal empowerment when compared to observationally similar urban displaced populations. But we should caution that city-based displacement is not a panacea: in the Philippines, we find mixed relationships between urban status and perceptions of empowerment when comparing urban IDPs to rural IDPs using matched samples.

Again, inconsistencies of relationships across displacement settings highlight the importance of understanding and responding to nuances of the specific case. Survey data suggests that varying 25
blacksmiths, electricians, barbers, tailors, real estate agents, social workers, counseling, banking, doctors, restaurant and hotel business, and so on, and the miscellaneous sector includes other small activities. Source: HIES 2000, 2005 and 2010

**Table 4: Salient characteristics of rural microenterprises: 2000-2010**

|**Enterprise characteristics**|**2000**|**2005**|**2010**|
|---|---|---|---|
||**(N=1,427)**|**(N=1,426)**|**(N=1,909)**|
|Years of operation|9.5|9.2|10.0|
|If the enterprise is registered (%)|9.9|14.7|18.2|
|If the enterprise is home-based (%)|11.9|14.2|16.7|
|Months of operation per year|10.3|10.7|10.9|
|Number of workers|1.6|1.7|1.4|
|Share of hired labor in total workforce (%)|11.2|10.3|8.7|
|Owner’s sex (1-Male, 0=Female)|96.4|94.8|95.5|
|Owner’s age (years)|44.5|46.1|46.7|
|Owner’s education (years)|3.2|3.5|3.5|

Note: Owner’s characteristics are that of the head of the household that owns the enterprise. Source: HIES 2000, 2005 and 2010

**Table 5: Distribution of the sources of startup capital of the rural microenterprises:**

|**200**|**0-2010**|||
|---|---|---|---|
|**Share of different sources (%)**|**2000**|**2005**|**2010**|
||**(N=1,425)**|**(N=1,423)**|**(N=1,902)**|
|Own resource (asset, inheritance, savings, etc.)|78.2|78.5|79.6|
|Loans from microcredit|3.4|5.9|8.2|
|Loans from commercial banks|0.8|0.6|1.0|
|Loans from informal moneylenders|2.4|0.6|1.2|
|Loans from relatives/friends|5.5|4.0|5.6|
|Others|9.7|10.4|4.4|

Source: HIES 2000, 2005 and 2010 36
diversification strategies) and the extent to which the growth rates of income and consurnption generated by the macro component of IMMPA following a given shock differ among the various income groups on which it is based. If, for instance, the intra-household distribution as given in the survey is such that most of the income of each composite unit is generated by the household head, treating the household as a homogeneous unit and applying the same growth rate of income to each member should not result in significant errors.

Several recent studies have attempted to drop the assumption of a stable within-group distribution to analyze the poverty and distributional effects of policy and exogenous shocks in applied general equilibrium models; they include Cockburn (2001), Decaluwe, Dumont and Savard (1999), and Decaluwe, Patry, Savard, and Thorbecke (1999). Individual data in these studies are included directly in the general equilibrium model and (assuming that the within-group distribution follows a well-defined statistical distribution, such as the lognormal or a beta distribution) micro-simulation techniques are used to exploit intra-group information. This approach has the benefit of allowing the analyst to distinguish, in the evolution of poverty indicators, the specific contribution of three factors: changes in the poverty line (when it is treated as endogenous), average income variations, and income distribution. It provides therefore a potentially important direction for future research.<sup>3 4</sup> At the same time, however, it must be recognized that it is relatively complex to implement (particularly in conjunction with a fully specified macroeconomic model, like ours), because it requires manipulating a sizable amount of data. Moreover, whether changes in the intra-group distribution matter a lot appears to be shock dependent; indeed, in some of the simulation results reported by Decaluwe, Dumont and Savard (1999), such changes only account for a small proportion in changes in poverty measures. A possible way of testing for whether this type of techniques should be used is to compare the aggregated results of two (or more, where feasible) household surveys and test statistically (using parametric or non-parametric tests) for any evidence of a shift in intra-group distributions. This test is not, however, fully satisfactory, because evidence of stability across surveys cannot necessarily be construed as providing definite support to any model-based experiment-particularly those involving "atypical" shocks.

It is also worth noting that in the artificial survey that we constructed,

> 34Another approach, based also on micro-simulation techniques, is pursued by Robilliard, Alatas, Bourguignon, and Robinson (2001).

56
Table 1 presents changes in the distribution of formal jobs in Mexico between 1999 and 2014 according to Economic Censuses. As can be seen, the bulk of jobs are in services, commerce and manufacturing. Although employment has increased in all sectors over the period, the greatest increases have occurred for commerce and services which relative shares have jobs, gone up while the relative shares of all the other sectors have down. The decrease in the relative gone share of manufacturing employment from 30 t0 24 percent is particularly noticeable. In 2014, more people were actually employed in the commerce sector than in manufacturing, showing that Mexico is following a trend of tertiarization similar to that of developed economies in previous decades.

Table 1: Sectoral distribution of formal jobs in Mexico (1999 – 2014)

|Sector|1999|share|2009|share|2014|share|
|---|---|---|---|---|---|---|
|Agriculture|174,127|1.27|180,083|0.90|188,566|0.87|
|Mining|113,189|0.83|14,2325|0.71|166,548|0.77|
|Manufacturing|4,1754,00|30.47|4,661,062|23.17|5,073,432|23.51|
|Commerce|3,792,466|27.68|6134758|30.50|6,389,648|29.61|
|Services|5,257,100|38.37|8,762,918|43.56|9,537,235|44.20|
|Other|190,033|1.39|235,688|1.17|220,929|1.02|
|Total|13,702,315|100.00|20,116,834|100.00|21,576,358|100.00|

_Source_ : Economic Censuces (inegi) 1999, 2009 and 2014.

Figure 1 plots the location of establishments in the denue database in 2004 and 2014 for all firms (top panel) and for manufacturing firms (bottom panel) respectively.<sup>5</sup> It reveals a high concentration of economic activity in the center of the country and in the surroundings of Mexico City, with a greater concentration of manufacturing firms than for the other sectors, but no visually noticeable change over the past decade.

# **.2 Roads 3**

In this paper, we focus on the later waves of road construction and improvement that occurred since the mid-eighties. To have a consistent measure of road extent and road types over time, we constructed a geo-referenced database for the period 1985-2016. This was done by importing historical road type and road extent information from the American Automobile Association (aaa) paper maps into the 2014 road geometry published by DeLorme. Because the DeLorme road geometry is network-enabled and topologically correct, it allows for clean travel time computations needed to measure access to markets (see below). Using this fixed we then information on road and extent for the reference or geometry, imported type years

> 5On Figure 1, establishment locations are overlayed on roads. See the following subsection for details regarding the roads data construction.

6