rafmacalaba/datause-encoder-data huggingface dataset:
# **Panel B Upper Secondary School Total Enrollments, Residuals from Trend**

<!-- Start of picture text --> Low Change in Violence High Violence Increase<br>2000 2005 20102000 2005 2010<br>Year<br>5<br>0<br>-5<br>-10<br><!-- End of picture text --> Source: INEGI (2014).

Note: Municipalities were divided into “high violence increase” and “low change in violence” according to their homicide rate trend percentile from 2007 to 2011. Municipalities classified as violent (471 total) represent 24.9% of our sample while non-violent municipalities (945 totals) represent 51% of our sample.

Although the difference-in-difference approach used in the regressions reported in Table 1 allows us to disentangle the effect of violence from cofounders that are either fixed in time or change at the same pace in all the country, it does not allow us to disentangle from cofounders that change at the municipality level. Table A.1 in the Appendix also shows that these results are not being driven by confounders that vary at the state level, by presenting the results of regressions with the addition of state-by-year dummy variables.

Given that our finding from the effects shown in Table 1 is that there is at most a small impact in the number of students enrolled in a municipality, we would like to control for variables that may be biasing the effect downwards. One potential issue is that governments (at the federal, state or municipality level) may be responding to the increases in violence by augmenting spending in social services. Such higher level in social spending could increase enrollments if, for example, it includes construction of new schools or improvement in roads that lead to schools. This, in fact, has been occurring as evidence of the recently launched National Program for the Social Prevention of Violence 18
did not have to undertake any training before taking on their duty. Hence, there was a high degree of variation in the quality of school supervision.

3. Specifying the role of school directors and providing them with training. Similar to school supervisors, many of the school directors lack the necessary skills on how to manage a school. Directors had difficulty identifying the strengths and weaknesses of their school, and in turn were limited in their capacity to design and monitor school improvement plans. For example, school directors seldom set measurable and reachable goals in crucial indicators to monitor progress.

4. Reinforcing teachers’ knowledge in the identified academic areas posing challenges. The program provided teachers with training and special courses to strengthen their knowledge in subject areas identified as challenging during the diagnosis step of the program.

Due to reasons unrelated to the program’s performance, PAE was canceled in December 2011, one year after the pedagogical interventions were in place. A difference-in-difference and a regression discontinuity approach is used to answer the following research questions:

1. Does PAE increase learning outcomes among participating schools?

2. If there is a positive effect of PAE, then is this explained by the accountability plus diagnosis intervention or by the pedagogical intervention or both?

3. Did PAE have a differentiated effect across boys and girls or among students with relatively disadvantaged initial conditions?

# **2.2 Dataset and Recent Trends**

This study uses and merges student learning outcomes as measured by ENLACE with administrative school census data collected by federal and state education authorities (known as the _Formato 911_ ). Since 1998, this school census is collected at the beginning and end of each school year, and lists, among other entries, the number of teachers, students, classrooms, computers, the average years of education of teachers, and the geographic location of each school. With a unique school identifier ( _Clave de Centro de Trabajo, CCT_ ), it is possible to merge this school census data with the results from ENLACE into a single data base. In addition to learning outcomes, ENLACE includes socioeconomic information for each school based on their geographical location.<sup>5</sup>

> 5The National Population Council (Consejo Nacional de Poblaci ́on, CONAPO) ranks all localities (an administrative and / or geographic entity often more disaggregated than a municipality) in Mexico according to a marginality index, a weighted average of literacy, access to basic public utilities, household infrastructure and average wages. Rankings range from very high marginalization, high marginalization,

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# **1. Introduction**

Poor households build livelihood structures that include coping mechanisms to mitigate the many risks they face. Included are informal forms of insurance based on a willingness to help extended family members and neighbors in time of need with the expectation of receiving assistance when needed. In the absence of formal insurance and risk markets, informal mechanisms are crucial and can work well when risk events are idiosyncratic. However, informal insurance systems can fail when needed most in the face of a systemic risk event affecting all members of the informal insurance network. Governments can and do help prop up informal systems by responding to crises and by building all‐purpose safety nets.

In rural areas, households often choose to produce much of their own food. This livelihood choice provides a measure of protection against a loss of other sources of income and can be especially effective in the face of rising food prices. However, the protection comes at a cost, since the strategy can obligate families to devote limited land and labor resources to activities and production technologies that are less profitable under normal circumstances. In turn, this makes it harder for families to generate higher incomes, accumulate wealth and human capital, and escape reinforcing poverty traps.

In this essay we focus on surging food prices, a cardinal risk that can undermine the capacity of poor households to meet minimal nutritional needs. Specifically, we examine potentially permanent effects of the food price crisis of 2007‐08 on child nutrition intakes, distinguishing between food producers and non‐producers. To bypass measurement problems inherent in consumption data, we use standardized child anthropometric nutrition‐status measures such as the height‐for‐age and weight‐for‐age z scores, and we calculate local food price indices to take into account spatial differences in transmission of global price shocks and diet compositions. The analysis uses Indonesian household panel data collected in 2007 and 2010. The first round of 2007 was fielded from the second to third quarter, immediately before the initial food price run‐up late in 2007. The follow‐up survey was conducted in 2010 after the crisis had subsided. The timings offer us an ideal setting to assess the impact of food price spikes on child growth. The geographic coverage of the survey over seven provinces in five macro regional islands lets us take advantage of significant variations in village‐level food price changes.

We find disheartening evidence that soaring prices had a significant and negative impact on child growth among non‐farming households, despite government‐backed support programs targeting the most vulnerable. At the same time, a corresponding effect on child growth was undetectable for food‐producing households, suggesting that some measure of self‐sufficiency remains a prudent course of action for poor rural households. The result remains robust when
_Forestvds_ is forest cover in village _v_ , district _d_ and state _s_ , and _Treatmentvds_ is an indicator equal to one if a new road was built in village _v_ . _popvds_ is the population of village _v_ and _Ts_ is the treatment threshold used in state _s_ .<sup>11</sup> _μd_ and _νd_ are district fixed effects; we find virtually identical results with fixed effects at higher or lower geographic scales. We also add controls for village characteristics in 2001, before any roads were built; like the fixed effects, these are unnecessary for identification but improve precision. Controls include baseline forest cover, indicators for village amenities (primary school, medical center and electrification), the log of total agricultural land area, the share of agricultural land that is irrigated, distance in kilometers from the nearest town, the illiteracy rate and the share of inhabitants that belong to a scheduled caste. This is a cross-sectional regression where _β_ 1 identifies the effect of new roads on forest cover in a given year. To further improve precision, we stack outcome data from 2010 through 2013 and cluster standard errors at the village level.<sup>12</sup> Appendix Figure A2 shows regression discontinuity balance tests for a set of variables measured in the baseline period; Appendix Table A2 presents the regression estimates on these tests using Equation 1. None of the regression discontinuity estimates are significantly different from zero at baseline. Appendix Figure A3 shows that the density of the running variable is continuous around the treatment threshold (McCrary, 2008).

> 11The treatment threshold varies with state because some states used a threshold of 500 and others were using a threshold of 1000. States used the lower treatment threshold when they had few villages with population over 1000 that did not already have roads. Officials at the National Rural Roads Development Agency provided us with information on which states were using which cutoffs, which we then verified in the data. Madhya Pradesh used both the 500 and 1000 treatment thresholds for roads built in the same period; we include separate fixed effects for the set of villages in the neighborhood of each threshold. Because the optimal regression discontinuity bandwidth is close to 100, there is no overlapping between these two groups. Few villages around the lowest population threshold of 250 received roads so we do not use this threshold for analysis.

12Running the test in any of these years separately or for similar sets of years produces virtually identical point estimates with marginally higher standard errors, and thus does not change any of our conclusions. We exclude years before 2010 because the first stage for road construction is too weak; the majority of the roads built before 2010 were built by states that did not follow the treatment threshold rules. The first stage is comparable in size from 2010 through 2013 (see Panel B of Figure 1, which shows the first stage in each year).

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|**Table 8. Robustness c**|**heck: Capi**<br>|**tal account o**<br>|**penness**<br>||||
|---|---|---|---|---|---|---|
|2nd stage:<br>|[1]<br>High cap<br>Output info|[2]<br>ital account o<br>rmality<br>|[3]<br> penness|[4]<br>Low capit<br>|[5]<br>al account<br>|[6]<br>openness|
|Real GDP per capita (in logs)|-2.07|-1.84|-0.72|-5.75**|-5.88***|-4.24|
||(1.57)|(1.62)|(2.20)|(2.24)|(2.26)|(6.83)|
|Control of Corruption|0.90|0.90|0.80|-0.73|-0.77|-0.28|
||(0.62)|(0.64)|(0.79)|(0.81)|(0.80)|(2.31)|
|Regulatory Quality|1.20**|1.22**|1.33**|-0.81|-0.83|-0.61|
||(0.52)|(0.52)|(0.52)|(1.09)|(1.08)|(1.70)|
|Rule of Law|-1.07|-1.04|-0.77|1.56*|1.52*|2.03|
||(0.65)|(0.67)|(0.74)|(0.90)|(0.87)|(2.36)|
|Commercial bank branches|-0.14***<br>|-0.16***<br>|-0.22***<br>|-0.33<br>|-0.29<br>|-0.81<br>|
||(0.04)|(0.04)|(0.07)|(0.26)|(0.20)|(2.17)|
|1st stage: Bank|ingsector d|evelopment|||||
|Commercial bank branches in the region|1.01***<br>(0.23)|||0.35**<br>(0.16)|||
|Distance-discounted commercial bank branches (regional)||0.88***<br>(0.18)|||0.38**<br>(0.16)||
|Distance-discounted commercial bank branches (global)|||3.45***<br>(0.96)|||0.30<br>(0.71)|
|Observations|1,087|1,087|1,089|1,068|1,068|1,068|
|R-squared|0.99|0.99|0.99|0.98|0.98|0.95|
|Kleibergen-Paap rk LM statistic|17.35***|18.59***|14.08***|4.37**|5.26**|0.20|
|Cragg-Donald Wald F statistic|19.85|22.94|12.91|4.80|5.39|0.18|

Notes: Standard errors clustered at the country level in parentheses, *** p<0.01, ** p<0.05, * p<0.1. Here common controls and constant terms are omitted for brevity. All regressions include both country fixed effects and year fixed effects. The dependent variable at the second stage is DGE-based output informality (in percent of GDP). “High (low) capital account openness” are countries where Chinn-Ito index is above (below) median. Commercial bank branches are per 100,000 adults. GDP per capita is in constant 2010 U.S. dollars. See sections 2 and 3 for detailed data descriptions and empirical methods. Data are between 2004 and 2018.

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# **I. Introduction**

The motivation for this study is to understand how developing countries can stimulate and sustain strong export growth. Export growth is important for a number of reasons. First, it can generate income growth when domestic demand lags. This is especially important in small economies, where foreign markets are likely to be the main engines for growth (Bhagwati 1996, Krueger 1998). Second, robust export growth yields both more jobs and better jobs. Manufacturing exports are typically labor intensive, so the demand for labor rises with them. Exporting firms also create jobs that pay higher wages and offer better working conditions than otherwise similar import-competing firms.<sup>1</sup> Third, strong export growth tends to induce a more efficient production structure. This happens through compositional shifts, as the most productive exporting firms tend to grow most rapidly when exports boom.<sup>2</sup> Finally, strong export growth helps prevent balance of payments crises. While many studies have examined the effect of openness on growth and productivity,<sup>3</sup> our focus is different—it is on how exports take-off.

The advantage of exploring export surges is that their dynamics may provide insight into how export-led growth begins. For example, if export expansions are the result of domestic productivity shocks, we should see an increase in investment and GDP before the surge. In this case, export-led growth is a misnomer in the sense that the expansion is domestically driven and exports simply follow a productivity shock. If, on the other hand, export expansions are the result of trade liberalization or real exchange

> 1 Bernard and Jensen (1995) report detailed statistics for the United States.  A number of papers followed their approach and find similar results in both developing and developed economies.  Shank, Schnabel and Wagner (2007) provide a summary of these papers in Appendix A, and offer similar evidence for Germany. Bernard, Jensen, Redding, and Schott (2008) also provide a summary.

> 2 See Bernard, Jensen, Redding, and Schott (2008) for a summary of the literature.

> 3 See Winters (2004) for a summary of the literature on trade and growth.

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3 of behavioral incentive schemes in an important and novel policy context, but also a next step in the assessment of external validity in field experiments.

# _Existing literature_

Management is at least as important for civil service as for corporations. For instance, rules that insulate bureaucracy from political interests, increasing its autonomy, should enhance effectiveness. In a study of the Nigerian civil service, Rasul and Rogger (2017) support this conjecture, finding that, amid widespread problems in a country where 38% of public sector projects never start, compared to 31% that actually finish, autonomy for bureaucrats is significantly associated with the likelihood of project completion. They also find, perhaps surprisingly, that management practices that control bureaucrats, in an effort to reduce corruption, may make things worse. In particular, a one standard deviation increase in incentives for monitoring corruption is associated with a 14% decline in project completion. This may follow from the fact that high-powered incentives on corruption could have the effect of increasing risk-aversion on the part of individual bureaucrats, and reduce their incentives to act on unmonitored activities, such as inputs to project completion. Bureaucrats are always multitasking, and when they know that one aspect of their performance is being watched closely, they may exert more effort on that dimension, at the expense of others.

If observability of bureaucratic action is a fundamental problem, perhaps involving local political leaders and citizens, through decentralization, can solve it. Decentralization might facilitate effectiveness by promoting more transparency and accountability for individual bureaucratic performance. There is evidence for this idea. For instance, data from Brazil show that the adoption of participatory budgeting increases health and sanitation spending, which in turn lowered mortality rates in those municipalities (Gonçalves 2014). On the other hand, decentralization can be associated with a “race to the bottom” as well as with the formation of regional inequalities (Bardhan 2016). Differences in local-level accountability structures can also impair service delivery – Khemani (2005) argues that differences in local-level accountability explained substantially greater non-payment of salaries to primary health care providers in Kogi state than Lagos state, also in Nigeria.

From the point of view of individual motivation, there is a more fundamental problem with decentralization as a solution. A number of tasks that bureaucrats perform, perhaps most,
the path to end poverty, it is essential to protect countries from crises and facilitate a country’s quick recovery in case of a crisis, and ensure any policy that accelerates growth and improves sharing of the growth is implemented in an environmentally and fiscally sustainable manner.

These requirements to achieve the end of extreme poverty target are consistent with the WBG goals. The other goal of WBG encourages a developing country to improve growth of the less well-off in the country. Also, the WBG goals need to be achieved in an environmentally and fiscally sustainable manner. Only an addition is that the above requirements for ending extreme poverty target include improving sharing of economic growth between rich and poor countries. With this additional condition, the pursuit of WBG goals makes the dream of the world free of poverty possible.

A challenge is that policies to improve sharing of growth within and across countries are little known. As Dollar et al. (2013) show, the past data fail to show any policy that can effectively reduce within country inequality although the above studies suggest learning from recent experiences in LAC is promising. How to improve sharing of growth across countries is even less known. It is urgent to explore policies to improve sharing of growth within and across countries.

Finally, it is very important to update the analysis above whenever source data are updated. Our proposed framework uses publicly available data only and produces poverty projections that are fully consistent with global and regional poverty estimates in the past. For example, to calculate growth rates, we used household expenditure (or income) per capita data available in PovcalNet. In this way, all growth estimates are fully consistent with the global and regional poverty rates published in MDGs and other official global poverty databases. Furthermore, by using only publicly available data, anyone who is interested in the projections in this note can replicate our results. Such replicability with publicly available data is critical for transparency. We include in the annex a detailed explanation of how all projections were conducted using the publicly available data.

However, as a result, like all other statistics, all projections conducted in this paper are subject to changes since the source data are being updated continuously. This implies that once the source data are updated, all projections also need to be updated. In this sense, we would recommend that this analysis be repeated when the source data are updated. Frequent repetition of this analysis will continuously give us a solid grasp of where we stand in terms of the target of ending extreme poverty and help update intermediate targets.

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# **Table A.4.5 Marginal Effects for Chilean Model.**

||**Patents**|**Product**<br>**innovation**|**Process**<br>**innovation**|
|---|---|---|---|
|**Firm Characteristics**||||
|Export active|**_0.05_**|0.01|**0.16**|
|Firmage (lnage)|**0.05**|**_0.04_**|0.00|
|Firmsize (lnsize)|0.00|-0.03|**_0.05_**|
|**Collaboration on innovation with**||||
|Universities|**0.37**|**0.29**|0.19|
|Consultants|-0.02|0.12|0.16|
|Suppliers|**0.09**|**0.31**|**0.35**|
|Other fimrs|0.01|-0.01|0.09|
|**Absorptive capacity**||||
|R&Ddepartment|**0.08**|**0.19**|0.12|
|Innovation intensity|0.00|**0.03**|**0.07**|
|Investmentin innovationeducation|**0.09**|**0.27**|**0.23**|
|Permanentinnovationactivities|-0.01|**0.28**|**0.32**|
|**Human capital**||||
|Knowledge of managersratedhigh|-0.04|-0.05|-0.01|
|Knowledge ofsupervisorsratedhigh|**_0.04_**|**0.14**|**0.16**|
|Knowledge ofworkersratedhigh|**0.07**|**-0.18**|**-0.22**|
|Managers educationand trainingratedhigh|-0.04|**0.20**|-0.04|
|Supervisors educationand trainingratedhigh|0.01|-0.08|0.08|
|Workereducationand trainingratedhigh|-0.04|0.10|0.08|
|**Sector– Manufacturing**||||
|Food,Beverages andTobacco|Ref|Ref|Ref|
|Textile, WearingApparelandLeather|0.00|0.04|0.00|
|Wood and wood products,furniture|**0.10**|**_-0.14_**|0.06|
|Paper, printing and publishing|0.04|-0.05|0.02|
|Chemicals, petroleum, coal,rubberand plastics|0.02|0.00|0.02|
|Non metallic mineral products, except products of<br>petroleumand coal|-0.04|**0.27**|0.03|
|Basicmetal industry|0.05|**_-0.30_**|-0.04|
|Fabricatedmetalproducts,machinery and equipment|-0.02|0.01|0.04|
|Other manufacturingindustries|-0.01|-0.12|-0.25|
|Observations|4570|4570|4570|
|R2|0.25|0.33|0.37|
|LogLikelihood|-164|-311|-295|

Note: Bold is denoting significance in a 5% level. Bold + Italic denotes significance in a 10% level.

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