rafmacalaba/datause-encoder-data huggingface dataset:
Using respondent’s municipality of residence I match homicide rates by municipality-year. For the LAPOP sample, homicide rates have a mean value of 0.75 and standard deviation of 0.52 (with a maximum value of 3.1 homicides per 1,000 inhabitants). Crime statistics merged to the Latinobar ́ometro sample are fairly similar: mean value of 0.8 and standard deviation of 0.57 (and maximum of 4.2).

I also take into account differences across municipalities and years in central government unconditional transfers (FODES, by its acronym in Spanish) per capita received the year before the implementation of the survey.<sup>8</sup> Data comes from the Instituto Salvadore ̃no de Desarrollo Municipal.<sup>9</sup> FODES represent the main financial resource for municipalities. The central government allocates 6% of the national budget to municipal governments, 80% of these funds are intended to fund investment and 20% expenses. Importantly, the allocation mechanism for these funds is based on population (50% ), equity (25%), poverty (20%), and territorial size (5%) of each municipality. For the LAPOP sample, FODES Transfer per 1,000 inhabitants (in 1992) has a mean value of USD 45, a standard deviation of USD 39, and maximum of USD 206. On the other hand, Latinobar ́ometro sample shows a mean value of USD 46, a standard deviation of USD 46, and maximum of USD 478.

# **3 Empirical Strategy**

Exploiting several waves of two public opinion surveys (i.e., LAPOP and Latinobar ́ometero) from 2004 to 2018, I estimate the following equation: where _yimt_ is one of each of the reported attitudes by individual _i_ , living in municipality _m_ , and year _t_ . The variable _Econmt_ is the proxy of economic activity (NTL density) or per capita household income for a restricted sample. Both vary at the municipality level and over time. _X_ is a vector of individual-level controls such as gender, age, years of schooling, rural and employment status.<sup>10</sup> This set of individual-level controls is constrained by data availability and are included to improve the precision of the estimates. The vector _Mmt_ includes two time-varying measures at the municipality level: annual homicide rates (per 1,000 inhabitants) and Central Government unconditional

> 8I used 1992 population counts to normalize the amount of government transfers.

> 9Retrieved at https://www.transparencia.gob.sv/institutions/isdem/documents/otra-informacion-de-interes

> 10Since Latinobar ́ometero does not survey individuals in rural areas, I use a “small town” indicator when performing the analysis with this public opinion survey.

13
Table 3: Descriptive Statistics for Unemployed

||All Unemployed|
|---|---|
|Sample Size|3,606|
|Population|1,810,468|
|P(_s ∈U_)|0.185|
|E(_tus_)|51.53|
|SD(_tus_)|53.15|

Author’s calculations based on ECH, Second Quarter 2003, 13 Metropolitan Areas. Unemployment duration in weeks Table 4: Transitions across Labor Market States (Population Values)

|From _\_To|Unemployment|Formal Sector|Informal Sector|
|---|---|---|---|
|Unemployment|1,435,620|106,308|141,453|
|Inactive|124,238|196,693|261,717|
|Formal Sector|101,282|2,590,524|192,138|
|Informal Sector|149,328|191,894|4,294,197|

Author’s calculations based on ECH, Second Quarter 2003, 13 Metropolitan Areas. All statistics weighted using expansion factors. Informality using Firm size and Occupation definition. By adding the number of rows in each column I recover the total number of individuals in each labor market state in the second quarter of 2013.

Table 5: Annual Discrete Transition Matrix

|From _\_To|Unemployment|Formal Sector|Informal Sector|Other|Sum|
|---|---|---|---|---|---|
|Unemployment|0.72|0.05|0.07|0.16|1.0|
||(0.004)|(0.0026)|(0.0020)|(0.0030)||
|Other|0.04|0.06|0.08|0.82|1.0|
||(0.002)|(0.0042)|(0.0051)|(0.0010)||
|Formal Sector|0.03|0.81|0.06|0.10|1.0|
||(0.0021)|(0.0030)|(0.0010)|(0.005)||
|Informal Sector|0.03|0.04|0.93|0.00|1.0|
||(0.0024)|(0.0018)|(0.0022)|(0.004)||

Author’s calculations based on ECH, Second Quarter 2003, 13 Metropolitan Areas. All statistics weighted using expansion factors. Informality using Firm size and Occupation definition. Standard errors in parentheses Table 6: Estimated Hazards

||Unemployment|Formal Sector|Informal Sector|
|---|---|---|---|
|Estimated Hazards|0.28|0.19|0.07|
||(0.0041)|(0.0030)|(0.0022)|

Author’s calculations based on ECH, Second quarter 2003, 13 Metropolitan Areas. All statistics weighted using expansion factors. Informality using Firm size and Occupation definition. Standard errors in parentheses 38
# **7. CONCLUSION AND NEXT STEPS**

The DSI seeks to provide a set of indices for benchmarking service delivery performance in two key social sectors in Africa, education and health, in order to track progress within and across countries over time. It seeks to enhance effective and active monitoring and evaluation of service delivery systems and to become an instrument of public accountability and good governance in Africa. The ultimate objective of the DSI index is to help policymakers, citizens, donors and other stakeholders in enhancing the quality of service provision and ultimately improve population outcomes.

The main perspective adopted by the DSI index is one of citizens accessing services and facing potential shortcomings in those services available to them. The DSI index is thus presented as a Service Delivery Report Card on education and health. While emphasizing the experience of an average citizen at the service provider level, the index accounts for the various components of the supply side of service delivery and the multi-actors and multi-steps involved in service delivery systems. It assembles information from composite micro level surveys in education and health.

The current version of the DSI encompasses only primary levels in the education and health sectors. In future versions of the DSI, it would be very useful to have such service delivery report cards developed for secondary and tertiary levels and encompass other key social sectors such as water and sanitation.
even with a small number of clusters. This may be especially relevant in the context of the first experiment, which had fewer schools per treatment arm than the second experiment. Second, given the large array of hypotheses considered throughout the analysis, we also provide _p_ -values that are adjusted for multiple hypothesis testing across different families of outcomes (List et al., 2019).

# **3.1 Children’s Outcomes**

Table 1 and the first row of Table 2 display the impacts of the _Original_ modality on children’s outcomes, as measured by individual test scores collected two years after the introduction of the mentoring program in each experiment, respectively. For the first experiment, the outcome variables shown in Table 1 are based on administrative records of sixth graders in a national standardized test. For the second experiment, we collect our own measures of cognitive and socio-emotional skills (first to fourth columns of Table 2), as the national standardized test was terminated in 2014.<sup>7</sup> In spite of the differences in measurement of the outcome variable, the separate analyses of the two experiments show consistently inconclusive evidence regarding the effectiveness of the _Original_ modality of the mentoring intervention. Depending on the outcome, the effect of the program in the first experiment ranges from positive to negative and is not statistically different from zero. The effect size of the estimated treatment effect on the overall index for student achievement (column 4 of Table 1)—a Generalized Least Squares (GLS)-weighted average across the three subject tests that increases the power of the analysis (O’Brien, 1984)—is negative, small and imprecise.<sup>8</sup> Effect sizes are consistently positive and slightly more precise in the second experiment, although none of the estimated coefficients gets close to the conventional significance levels. The impact on the GLS-weighted overall index for student achievement across the two cognitive measures and the socio-emotional score is 0.12

> 7Another national standardized test was administered by the National Institute for the Evaluation of Education (INEE) starting in 2015, the PLANEA National Plan for Learning Evaluation. While the national test that we employ in the first experiment (ENLACE) was administered to all Mexican students in grades three through six through the year 2013 (see Appendix A.1), PLANEA scores are collected only on sixth graders in a random sample of students within schools.

> 8The GLS weighting procedure increases efficiency when compared to other summary indices by ensuring that outcomes that are highly correlated with each other receive less weight, while outcomes that are uncorrelated and thus represent new information receive more weight. This procedure is more powerful than other popular tests in the repeated-measures setting. Also, missing outcomes are ignored when creating the GLS-weighted score. Thus this procedure uses all the available data, but it weights outcomes with fewer missing values more heavily.

11
processes and products that embody foreign knowledge and that would otherwise be unavailable or very costly (Helpman 1997; Grossman and Helpman 1991).

The literature identifies two types of externalities generated through trade: pure knowledge spillovers and rent spillovers. Pure knowledge spillovers arise mostly through licensing agreements or through firms that are multinational. Rent spillovers occur when the prices of imported intermediate and capital goods do not fully reflect the costs of innovation embedded in them, so that part of the rents from innovation are transferred from the innovating firm to trading partners (Keller 2021).

International trade also allows countries to exploit economies of scale and network effects in areas where they have a comparative advantage (Helpman 1981; Helpman and Krugman 1985; Krugman 1979). Trade causes output to expand and, in the presence of increasing returns to scale, firms’ fixed costs are spread over a larger number of units produced. This results in more efficient production at smaller average cost.a similar mechanism, the output expansion associated with trade may also allow greater product variety, which can enhance productivity (Feenstra 2010). In addition, innovations resulting from international trade often allow workers to acquire new human capital through learning by doing as workers take up new tasks. This also boosts productivity and helps countries move up the product-quality ladder (Lucas 1993).

By increasing competition, trade also promotes productivity growth by reallocating resources toward more efficient firms as the least productive firms are encouraged to exit (Bernard et al. 2007; Melitz 2003). Since entering foreign markets imposes an up-front cost for exporting firms, only relatively productive firms can generally engage in exporting. Once they have entered a new market, exporting firms can expand and attract workers and capital, thus tending to force out firms limited to the domestic market by inferior efficiency. In addition, by raising competitive pressures in the domestic market, international trade lowers firms’ markups over marginal cost and encourages organizational change and production upgrades to boost within-firm productivity (Melitz and Ottaviano 2008).

# II.2 Empirical literature

The relationship between international trade and long-term output growth has been investigated by a large empirical literature, using cross-country and firm-level data. In addition to aggregate effects, studies have identified specific channels through which trade integration boosts productivity, capital accumulation, and employment growth—the fundamental drivers of long-term economic growth.

Trade and output growth. Most cross-country studies have found a positive link between international trade and output growth.<sup>2</sup> However, the direction of causality and the role of third factors remain matters of debate. Some studies find clear growth-enhancing effects of trade liberalization (Dollar 1992; Bhagwati and Srinivasan
At least two alternative hypotheses can be advanced to explain this **result. The** first is that, contrary to what was argued earlier, debt forgiveness does indeed free resources from debt servicing that can be used to finance a larger import bill. However, a regression of import capacity on ODA debt forgiveness for all the sample countries during 1989-93 (excluding Nigeria) does not support this hypothesis. In fact, the **ODA** debt relief variable turned out to be insignificant for all groups except the low **group, but** this result appears to be spurious in the sense that when adding pure grants to the same regression ODA debt forgiveness becomes insignificant. (These regressions are reported in Appendix 5.) A different explanation can be found by looking at triangular relationships. In particular, as suggested by some of the results above, it could be that on an incremental basis, the high group gets additional funding from different sources along with the increase in ODA debt forgiveness. This possibility is analyzed further in Tables 9 and 10.

**Table 9. Impact of incremental pure grants on other financial transfers for the high group**

|________-<br>___<br>_--_<br>_____<br>;_|_First period_<br>__ -__1984-88_|_Second period_<br>_1989-93_|
|---|---|---|
|Multilateral net transfers|0.066|0.014|
||(0.490)|(0.176)|
|Bilateral net transfers|0.129|0.045|
||_<br>(0.958)|(0.427)|

Note: t-statistics are in parentheses.

*: Significant at 10 percent on one-tail test

> **: Significant at 5 percent on one-tail test

The results in Table 9, when compared with those in Table 7, show that for the high group total grants are more significantly and positively correlated to multilateral net lending than pure grants, especially during 1989-93 ($0.06 per $1 of additional grants, with a marginal significance level-one-tail--of 9 percent; see Table 7, last column). This result is consistent with the hypothesis posed above, that as the debt burden is lessened by ODA debt forgiveness by bilateral donors, multilateral net flows tend to increase (either through increases in disbursements or decreases in repayments).

19
**Table 5. Business entry and the strength of legal institutions supporting business regulations**

|VARIABLES|(1)<br>newbiz|(2)<br>newbiz|(3)<br>newbiz|(4)<br>newbiz|(5)<br>newbiz|(6)<br>newbiz|(7)<br>newbiz|(8)<br>newbiz|
|---|---|---|---|---|---|---|---|---|
|GDP per capita (log)|2.047**|2.057**|1.737**|1.844**|1.806**|1.501*|1.575*|0.865|
||(2.467)|(2.472)|(2.264)|(2.347)|(2.175)|(1.736)|(1.706)|(0.969)|
|FDI||-0.00268|-0.00375|-0.00380|-0.00420|-0.00420|-0.00341|0.00259|
|||(-0.797)|(-1.211)|(-1.219)|(-1.331)|(-1.330)|(-1.056)|(0.997)|
|Political freedom and civil|||||||||
|<br>rights (FH)|||0.216|0.234|0.286|0.299|0.369|0.399*|
||||(1.189)|(1.271)|(1.389)|(1.454)|(1.541)|(1.964)|
|Trade (X and M)||||0.00670|0.00649|0.00626|0.00778|0.00127|
|||||(1.300)|(1.165)|(1.124)|(1.231)|(0.212)|
|Corruption (CPI)|||||0.0750|0.0731|-0.0673|-0.0454|
||||||(0.529)|(0.516)|(-0.376)|(-0.274)|
|Lagged GDP growth||||||0.0336|0.0375|0.0460|
|||||||(1.256)|(1.163)|(1.602)|
|Country risk (ICRG)|||||||0.0169|0.0219|
||||||||(0.428)|(0.612)|
|Credit to private sector||||||||0.0183***|
|||||||||(5.284)|
|DTF (legal)|0.0202|0.0205|0.0387*|0.0388*|0.0394*|0.0401*|0.0564**|0.0507**|
||(0.935)|(0.942)|(1.918)|(1.892)|(1.827)|(1.861)|(2.040)|(2.111)|
|Constant|-16.29**|-16.37**|-15.11**|-16.72**|-16.90**|-14.35*|-16.55**|-10.93|
||(-2.277)|(-2.280)|(-2.287)|(-2.440)|(-2.342)|(-1.916)|(-2.011)|(-1.384)|
|Observations|832|829|821|808|782|781|646|548|
|R-squared|0.035|0.036|0.047|0.050|0.053|0.055|0.064|0.152|
|Numberofcod2|123|123|122|121|120|119|94|93|

t-statistics in parentheses *** p<0.01, ** p<0.05, * p<0.1

**Notes:** ***(**)(*) denotes significance at the 1(5)(10) percent level. Standard errors reported in parentheses. This table reports results from OLS panel regressions of new business creation on the distance to the legal regulatory frontier. Business creation is defined as number of new businesses per 1,000 people in working-age. Country fixed effects are included in order to capture unobserved between-country differences. The estimations reported include controls for lagged GDP per capita growth, per-capita GDP, trade openness, corruption, credit to the private sector, FDI inflow, economic and political risk as well as civil freedoms. All specifications include time dummies to control for any global changes in the macroeconomic environment.

21
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38
TABLE U CONTENTS

|EXECUTIVE SUMMARY||
|---|---|
|I. INTRODUCTION<br>. . . . . . . . . . . . . . . . . . . . . . . . .<br>The Economic Case for Property Taxation . . . . . . . . .|1<br>1|
|II. POLICY TARGETS.|5|
|A. Raising the EffeCLive Rate .....<br>. . . .. . . . .<br>..<br>Reduce Central Controls over Rates . . . . . . . . . . .<br>Index Valuations ..............<br>..<br>..|5<br>5<br>5|
|B. Broadening the Base . . . . . . . . . . . . . . . . . . . .<br>Reduce Tax Rate Progressivity . . . . . . . . . . . . . .<br>Reduce Sectoral Differentiation . . . . . . . . . . . . .<br>Increase Rate on Owner-occupied<br>Residential Property.<br>.<br>Increase Rate on Vacant Land . . . . . . . . . . . . . .|6<br>6<br>7<br>8<br>8|
|*II. PROCEDTURAL<br>TARGETS . . . . . . . . . . . . . . . . . . . . . .|10|
|A. Discovery and Identification . . . . . . . . . . . . . . . <br>Use Simple, Single Purpose Maps . . . . . . . . . . . . .|11<br> 12|
|B. Valuation . . . . . . . . . . . . . . . . . . . . . . . . . <br>Defining Value<br>..<br>Use Definition that Exploits Best Market Data.<br>. . <br>Calculating Value<br>..<br>For Common Types of Property, Use Mass Appraisal . <br>Addressing Data Constraints . . . . . . . . . . . . . . . <br>Use Construction Cost Data to Value Buildings.<br>. . <br>Use Real Estate Agent Informants to Value Land<br>Use Points System to Evaluate Low Value Property<br>Perform Individual Valuations on Unique, High<br>Value Properties<br>.<br>.20<br>Use Hypothetical Market Value to Value Rent<br>Controlled Property . . . . . . . . . . . . . .|14<br>14<br> 14<br>16<br> 16<br> 18<br> 18<br>19<br>20<br> 20|
|C. Updating Property Information . . . . . . . . . . . . . . . <br>Cross Reference to Update Data on Physical<br>Characteristics<br>and Ownership . . . . . . . . . . . .|22<br> 22|
|D. Billing and Collection<br>..<br>The Legal Framework . . . . . . . .<br>. . . . . . . . . . <br>Define Owner as Liable, But Define Ownership Broadl<br>Define Notification as Delivering the Bill to<br>the Property . . . . . . . . . . . . . . . . . .|24<br> 24<br>y 24<br> 25|
|Increase Penalties for Late Payment . . . . . . . . <br>Triage Enforcement Against Longstanding Delinquents|26<br>26|
|**ion and Country Portfolios**<br>**uth Wealth)**|**Vary Default Probability (**α**)**|-1<br>-0.8<br>-0.6<br>-0.4<br>-0.2<br>0<br>0.2<br>0.4<br>0<br>0.01<br>0.02<br>0.03<br>0.04<br>0.05<br>0.06<br>α<br>ev<br>-e*v*<br>-l<br>nfa*|
|---|---|---|
|**at**<br>**o**|||
|**ti**<br>**S**|||
|**Figure 7: Inefficient Renego**<br>**(Fraction of**|**Vary Fraction of Capital Lost in Default (**λ**)**|-1<br>-0.8<br>-0.6<br>-0.4<br>-0.2<br>0<br>0.2<br>0.4<br>0<br>0.005<br>0.01<br>0.015<br>0.02<br>λ<br>ev<br>-e*v*<br>-l<br>nfa*|