rafmacalaba/datause-encoder-data huggingface dataset:
**Table 3**

**GMM Results for equation (5) - Dependent variable:** EL

|**IndependentVariables**|**Parameter**<br>**Estimates**<br>**Model1 (TSS)**|**Parameter**<br>**Estimates**<br>**Model 2(COD)**|
|---|---|---|
|State|-0.0607**<br>(0.013)|-0.0729**<br>(0.013)|
|Coll|-0.0843***<br>(0.000)|-0.1195***<br>(0.000]|
|Fjv|-0.0421*<br>(0.094)|-0.0581<br>(0.136)|
|Textile|0.2423***<br>(0.000)|0.1586***<br>(0.007)|
|Chemical|0.1159**<br>(.016)|0.1321***<br>(0.000)|
|Profemp|0.0003|0.0000|
|(profitability)|(.119)|(0.869)|
|Lref|0.0059***|0.0067***|
|(refund)|(0.001)|(0.001)|
|Ratwtop|-3.8832*|-5.5717***|
|(pollutioncontroleffort)|(.057)|(0.003)|
|Cmpaccon|0.0504**|0.0725**|
|(socialimpact)|(0.023)|(0.012)|
|Bentss|-0.0094*||
|(TSSviolation)|(.099)||
|Bencod||0.0202*|
|(CODviolation)||(0.093)|
|Insp|-0.0007|-0.0033**|
|(numberofinspections)|(.283)|(.050)|
|Testoverid.rest.|14.0825<br>(0.295)|13.1836<br>(0.356)|
|#observations|382|378|

Note: (...) are p-values. Standard errors are robust standard errors. Test overid. rest. is the test for the validity of over-identifying restrictions. (***), (**), and (*) means significant at the 1%, 5%, and 10% level, respectively.

A firm which has suffered environmental accidents, conflicts with residents on environmental issues or is the object of citizen complaints is more likely to pay for the full pollution charge. A firm which has received a refund from the levy funds in the previous year appears more willing to pay for the levy charged in the current year. A 1% increase in discharges of ratio of total suspended solids to its standard brings about a decrease of the order of 0.007% in the levy payment ratio. On the contrary, a 1% increase 17
i) The length and complexity of the distributed lags in the production of primary commodities implies that there is little to be gained from attempting to impose restrictions on these lag distributions in the way suggested by the REH. It will probably continue to be preferable to use unrestricted and apparently _ad hoc_ distributed lags of actual prices in these relationships.

Hi) There is also little to be gained from attempting to build structural speculative stock demand models and then solving for price _via_ the market clearing identity. This is both because stock data are usually of poor quality, and because the speculative demand theory is too partial as an explanation of these stock series for the resulting implied price models to be of value.

iii) The large amount of work undertaken over recent years which utilizes the Muth price model has produced singularly little by way of achievement. In particular, there is little evidence that fit is improved by attempting to model market expectations of future supply and demand conditions; and a great deal of evidence that the interest rate effects implied by this class of models are exaggeratid.

iv) It may anyway be misconceived to see speculative stockholding as the central determinant of intertemporal pricing in non-agricultural markets where stocks are predominantly held by producers and when An element of monopolistic behavior is evident.

On the positive side, I conclude v) Recent work which takes explicit account of the non-negativity constraint on stocks has provided a superior theoretical framework than that previously available within which commodity price theory may be developed. Furthermore, there does 59
_Table 11: Moldova – Random loss shock_ <!-- Start of picture text --> Random Loss Shock<br>Targeting → Household<br>Eligibility ↓<br>Budget ↓ BeneficiariesOriginal Means Testing (Ex-Ante Poor) Means Testing (Post Shock Poor) Universal PMT Ex-ante PMT Post Shock Perfect Loss Indicator Imperfect Loss Indicator PMT Ex-ante or Perfect Loss Indicator PMT Post Shock or Perfect Loss Indicator<br>(1a) (2) (3) (4) (5) (6) (7) (8) (9) (10)<br>Poverty Rate Pre-shock: 0.120; Post-shock: 0.152<br>(1) 0.05% GDP 0.000 -0.002 -0.002 0.000 -0.001 -0.001 0.000 0.000 -0.001 -0.001<br>(2) 1.0% GDP -0.015 -0.037 -0.034 -0.005 -0.011 -0.012 -0.009 -0.009 -0.011 -0.012<br>Poverty Gap Sqr. Pre-shock: 1.344; Post-shock: 3.095<br>(1) 0.05% GDP -0.072 -0.040 -0.057 -0.009 -0.017 -0.016 -0.092 -0.089 -0.037 -0.032<br>(2) 1.0% GDP -0.779 -0.613 -0.959 -0.172 -0.289 -0.274 -1.210 -0.979 -0.661 -0.592<br>Gini Pre-shock: 0.320; Post-shock: 0.337<br>(1) 0.05% GDP -0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000<br>(2) 1.0% GDP -0.009 -0.008 -0.008 -0.002 -0.005 -0.005 -0.008 -0.007 -0.006 -0.006<br>Coverage<br>(1) Avera ge Transfer $ 124.95 $ 41.71 $ 32.96 $ 5.09 $ 42.15 $ 33.23 $ 116.20 $ 154.05 $ 31.14 $ 26.33<br>(2) Avera ge Transfer $ 2,499.04 $ 834.28 $ 659.26 $ 101.73 $ 842.91 $ 664.65 $ 2,323.91 $ 3,484.51 $ 622.86 $ 526.60<br>Beneficiaries 109,169 327,009 413,824 2,681,733 323,663 410,471 117,396 88,551 438,007 518,077<br>Ex-ante poor 0.29 1.00 1.00 1.00 0.49 0.55 0.02 0.01 0.51 0.56<br>Ex-ante non-poor 0.00 0.00 0.04 1.00 0.07 0.10 0.05 0.03 0.12 0.14<br>Ex-ante Vulnerable 0.01 0.00 0.04 1.00 0.13 0.17 0.04 0.03 0.17 0.21<br>Post-shock poor 0.23 0.79 1.00 1.00 0.39 0.45 0.23 0.14 0.61 0.65<br>Note: impacts are color-coded from dark red (highest impacts) to dark blue (lowest impact). Not all households declare<br>labor income.<br><!-- End of picture text --> Note, again, the importance of focusing attention on the right indicator. In particular, the use of the poverty headcount indicator delivers misleading results: in Albania, for instance, under the poverty headcount welfare metric using a PMT indicator would still indicate larger impacts than using a loss indicator. This is because the excessively simple metric used by the poverty headcount indicator, which focusses on an extremely narrow section of the income distribution around the poverty line, ignores any distributional impacts that happen below the line.

26
Table 4. Intermediate Indicators of Empowerment: Society Domain

||**Sub-domain: Family**||
|---|---|---|
|**Presence of rules (institutions)**|**Enactment of rules (institutions)**|**Existing data**<br>**sources/indices**|
|_Formal_<br>•Ratification of Convention on the<br>Rights of the Child (CRC) and<br>Convention on Elimination of all<br>Forms of Discrimination Against<br>Women (CEDAW) (1, 2)<br>•Number of legislative responses to the<br>Convention on the Rights of the Child<br>(CRC) and Convention on<br>Elimination of all Forms of<br>Discrimination Against Women<br>(CEDAW) (1, 2)|_Formal_<br>•Number of cases filed in the formal justice system<br>enforcing children’s rights legislation per year (3)<br>•Number of formal justice cases filed against<br>violators of women’s rights legislation per year (2, 3)<br>_Informal (cultural)_<br>•Percentage of cases in which rules governing duties<br>and entitlements relating to accumulation and<br>redistribution within households and kinship groups<br>diverge from joint utility maximizing rules<br>•Number of women working in occupations socially<br>defined as male occupations as percentage of total<br>women working<br>•Percentage of women able to travel alone outside of<br>community in the previous year<br>•Percentage of men able to travel alone outside of<br>community in the previous year<br>•Percentage of females accessing formal institutions<br>in the previous year<br>•Percentage of males accessing formal institutions in<br>the previous year<br>•Number of community advocacy and awareness<br>campaigns against domestic violence and sexual<br>abuse in the previous year|(1) Office of the UN High<br>Commissioner for Human<br>Rights<br>(2) World Bank Country Policy<br>and Institutional<br>Assessment<br>(3) US State Department –<br>Country Reports on Human<br>Rights Practices<br>Where no data source is<br>identified, data must be<br>collected using the national<br>survey or in-depth research<br>techniques.|
||**Sub-domain: Community**||
|**Presence of rules (institutions)**|**Enactment of rules (institutions)**|**Existing data**<br>**sources/indices**|

72
Database by computing an array of firm-level measures on Argentine exporters’ characteristics and dynamics. The information is computed for (1) all exporters; (2) exporters that import intermediate and capital goods; (3) other exporters; and (4) importers.<sup>1</sup> This paper shows the progressive retreat of exporters in Argentina from the global economy in the last decade. After controlling for the size and the level of development, Argentina’s exporters underperform in comparison with their counterparts in other countries along the following dimensions. First, the number of exporters is lower than in similar countries. Between 2007 and 2017, the number of exporters decreased by 30 percent (around 5,100 firms ceased exporting). Second, export value is disproportionally concentrated in few firms in Argentina. The top 5 percent largest exporters account for 90 percent of the country’s overall export value. Third, there is little firm churning in Argentina. Entry and exit rates in export markets are among the lowest in our sample of countries. Fourth, Argentina displays a disproportionally high survival rate for new exporters.

These findings are consistent with an environment of high entry cost into exporting, linked to a wide-ranging and ever-changing array of policies featuring an anti-export bias and import substitution. Simple average most favored nation (MFN) tariffs rose from 10.4 percent in 2006 to 13.7 percent in 2017. Furthermore, Argentina’s use of nontariff measures increased during the same period. These measures are mainly related to registration requirements, import and export taxes and procedures, and nonautomatic import licensing.<sup>2</sup> Moreover, the clarity of the trade policy regime has been undermined by the apparent lack of transparency in the application of the regulatory framework governing trade. Surprisingly, the findings on large average firm size, more concentration at the top of the export distribution, low turnover rates, and high survival rates situate Argentina closer to the behavior of higher-income countries. Fernandez, Freund,

> 1The Exporter Dynamics Database is available at https://www.worldbank.org/en/research/brief/exporterdynamics-database. The indicators for all exporters are part of the database; other partitions of the information are used only for this paper.

> 2Non-automatic import licensing is a procedure introduced, for reasons other than SPS or TBT reasons, where approval is not granted in all cases. The approval may either be granted on a discretionary basis or may require specific criteria to be met before it is granted.

3
widespread use of contract labor. Like Besley and Burgess, Ahsan and Pagés found that <u>de jure</u> legal amendments and de facto use of contract labor hurt formal sector workers and impose substantial costs on society. In their words: “India must find alternative way (sic) to improve labor conditions for the majority of workers. While traditionally portrayed as labor advances against the abuse of capital, current labor regulations favor no one. The answer to this conundrum does not involve a complete deregulation of the labor market. Instead, it requires better regulations (and appropriate enforcement) so workers’ fundamental rights can be protected and jobs can be created.” This literature is a nice example of rigorous empirical study combined with sensitive welfare economics. Perhaps the one limitation of this work is that while the studies’ authors have told readers what they think India should not have done, they have not told readers what they think India should now do in this regard.

# **<u>Limitations of Existing Empirical Research: the Case of Social Returns to Education</u>**

As an indication of the needs in this area, it will be helpful to consider one particular policy decision: whether to invest additional social resources in education. The conclusion I shall reach is that despite the vast amount of research effort that has been devoted to this area, we actually know quite little about what should be done in a given country context.

# <u>Earnings functions as the analytical starting point</u>

The starting points for policy research pertinent to the question of whether to invest additional resources in education are the related literatures on earnings functions (also called wage equations) and rates of return to education. As reviewed by Willis (1986) and World Bank (1995), the earnings function studies show similar patterns of earnings differentials in various countries. Other things equal, earnings generally are higher for those workers with more schooling and more labor market experience, for men compared with women, for formal sector workers compared with informal sector workers, etc. Focusing on the schooling variable, George Psacharopoulos is well-known for a long series of studies summarizing studies of returns to education. His most recent compilation of evidence (Psacharopoulos and Patrinos, 2004) reaffirms three of his earlier findings about returns to education as conventionally measured: they are higher in 47
and the measure of risk tolerance (RISKPREF). Among self employed men - after controlling for autocorrelation in contributions between consecutive periods, as well as for the measured and unobservable variables that determine contribution density - a greater tolerance for risk significantly (10% level) increases the likelihood of contribution **62** to the pension system.

# VII. **Conclusion**

In the preceding sections I have applied a simple analytical framework borrowed from the economics of insurance literature to examine individual and household strategies to mitigate the risk of poverty in old age in Chile. Although the analysis has abstracted from the important inter-temporal, life-cycle element of savings and insurance decisions, it has nevertheless provided valuable insights into other dimensions of such decisions. The results can be summarized by answering the questions posed as hypotheses in Section IV.

Are there structural factors that limit access of certain groups to saving and pooling in the formal pension system? After controlling for sector of employment, and the industry of employment, the variables capturing expectations and preferences for alternative investments have a significant impact on whether workers contribute. Including the demand variables constructed from the PRIESO data in the analysis, leave most of the access variables statistically insignificant. This said, workers in large firms are still significantly more likely to be contributing, raising concern for workers employed in small firms. There also appear to be barriers preventing women in rural areas and those working in agriculture from formal cover.

Does a rise in the (perceived) probability of old-age increase incentives to save in the formal system? The strong positive significance of subjective life expectancy on the contribution density of affiliated men, suggests that peoples' perceptions of the likelihood of facing a period of life without the ability to work, influence their demand for cover from the formal system. It bears emphasizing that respondents' expected mortality has a substantial explained, rational component.

Does greater prevention (which lowers the relative costs of pooling) lower the incentives to save in the formal system? Although there were few forms of prevention that could be included in the empirical analysis, investing in productive assets - tools, vehicles or a small business - can postpone loss of earnings ability. There is evidence that workers who choose to invest in productive assets are less likely to contribute to the pension system. Even among the self employed, holding productive assets lowers the likelihood of contribution.

Do formal pension benefits received by elderly household members increase the likelihood that individuals save in the formal system through a demonstration effect? The amount of income households receive in contributory pensions has a positive effect on the contribution density of men, but no significant effect on that of women. Benefits received by resident elderly women are likely to have been earned through the

> **62** The p-value of the estimated coefficient on RISKPRF is 0.085.

30
``` Table 4 Regression Results for Living Room Pollution ``` ``` Dependent Variable: Living Room PM10 Concentration (ug/m3) ```

||`(1)`|`(2)`|`(3)`|`(4)`|
|---|---|---|---|---|
||`High-Dust`|`Season`|`Low-Dust`|`Season`|
||`OLS`|`IV`|`OLS`|`IV`|
|`Kitchen PM10`<br>|`0.610`|`0.516`|`0.107`|`-0.114`|
|`(ug/m`<sup>`3`</sup>`)`|`(12.82)**`|`(7.11)**`|`(3.20)**`|`(1.96)`|
|`Living Room`|||||
|`Wall Materials`<br>`(Thatch Excluded)`|||||
|`Brick`|`6.531`|`1.570`|`-2.404`|`-9.590`|
||`(0.90)`|`(0.18)`|`(0.39)`|`(1.33)`|
|`Mud`|`-22.002`|`-23.942`|`-15.390`|`-20.568`|
||`( (3.54)**`|`(3.57)**`|`(3.25)**`|`(3.98)**`|
|`Tin`|`-46.856`|`-41.781`|`-11.883`|`-11.217`|
||`(5.13)**`|`(4.41)**`|`(2.39)*`|`(2.12)*`|
|`Living Room`|||||
|`Roof Materials`<br>`(Thatch Excluded)`|||||
|`Tin`|`11.058`|`11.266`|`-15.207`|`-13.454`|
||`(2.29)*`|`(2.10)*`|`(2.22)*`|`(1.78)`|
|`Living Room`|`-22.543`|`-19.087`|`-0.701`|`-0.209`|
|`Fan On`|`(2.54)*`|`(2.04)*`|`(0.11)`|`(0.03)`|
|`Constant`|`31.199`|`52.077`|`75.277`|`104.777`|
||`(2.82)**`|`(3.20)**`|`(10.71)**`|`(9.96)**`|
|`Observations`|`225`|`225`|`250`|`250`|
|`R-squared`|`0.60`|`0.59`|`0.15`||

``` Robust t statistics in parentheses
* significant at 5%; ** significant at 1%
``` As before, our results indicate the importance of seasonality. Regression fits differ substantially between the two seasons, with R<sup>2</sup> near .60 during the high-dust season and .15 during the low-dust season.<sup>8</sup> The constant effects are again highly significant and very different, but for the living room their role is reversed. In the IV estimates, the highdust-season constant is 52 ug/m<sup>3</sup> , while during the low-dust season it doubles to 105 ug/m<sup>3</sup> .

During the high-dust season, diffusion of kitchen pollution is a large, highly significant determinant of living-room pollution. Ceteris paribus, living room pollution increases by

> 8  R2 could not be computed for the low-dust-season IV equation.

12
**Figure 2: A Two-Way Relationship between Security and Development**

<!-- Start of picture text --> Rule of law Investment Economic growth<br>Climate<br>Infrastructure; etc.<br>Security Monopoly Revenue State Revenue Development<br>of violence control<br>Health / education;<br>political stability<br>Social and<br>Safety Human Social<br>outcomes<br>Capital<br><!-- End of picture text --> _Source: authors’ summary._

# **3. Public Finance in the Security Sector**

In this section we apply a public finance framework to the security sector, which considers three key levels of fiscal outcomes: aggregate fiscal discipline, allocative efficiency of fiscal resources, and operational or technical efficiency (World Bank, 1998). Using this framework, we show that, by and large, principles of good public finance management can and should be applied to the security sector, at all three of these levels.

## **3.1. Aggregate Fiscal Discipline**

The objective of fiscal sustainability is founded on two rationales. First, to make the budget an effective policy instrument, budgets have to be realistic. Otherwise they become irrelevant since they are never implemented. Second, there is solid evidence that poor fiscal policy—notably in the form of large budget deficits for unproductive purposes or financed by loose monetary policy—has a negative impact on inflation and growth, and hence on poverty. In fact, since there is also evidence that macroeconomic instability can be a source of conflict (World Bank, 2003a), fiscal discipline can be viewed as a necessary condition for a country to achieve its security objectives. Finally, a focus on fiscal discipline is critical since an obvious risk mitigation strategy would be to build very large security forces, which— beyond the potential governance and security (e.g. coups d’etat) related issues—would jeopardize fiscal stability and hence macroeconomic stability, thereby defeating the risk mitigation strategy. Applying this principle generates three lessons.

First and foremost, and applicable to prioritization of spending within the security sector (see below) as well as aggregate security sector expenditures, the security budget and the budgets of its main components (defense, interior, justice, etc.) should be prepared against **clear strategies** —at the overall, sector, and subsector levels—and corresponding policies. The security strategy in turn needs to be an integral part of the broader national development strategy. These principles are applicable to all parts of the public sector, and security should be no exception. This issue is reviewed in more detail in the next section.

Second, as for the budget as a whole and as with any other sector, security sector policies and expenditures must be **affordable** , both in the short run (annual budget) and in terms of 11
showed more variability in their choice of _B_ , with five firms choosing not to shield any part of past revenues, and four firms choosing to shield between 30 and 90 percent of prior average revenue levels. The resulting share of revenue to be paid, _m_ , ranged from 0.29 to 5.85 percent. This was equivalent to a mean (median) of 8.2 percent (5.1 percent) of average monthly profits according to the profit data firms reported at the time of application. The entrepreneur’s contract choices meant that they retained a relatively large share of the incremental revenue stream, which has the advantage of lowering moral hazard concerns, but also perhaps suggests that our growth projections were conservative in their view.<sup>15</sup>

# **3.3 Contract performance**

The nine investments totaled Rs. 4,310,000 ($34,206). Table 3 summarizes the repayments for each investment. Firms reported less revenue than they had predicted, so that the total repayment due from revenue-sharing was approximately Rs. 1.3 million less than the interest that would be paid on an 18% declining-balance loan over this period, with every single investment yielding less than the 30.1 percent nominal three-year return of such a loan. However, the larger problem was enforcement of repayment, conditional on the revenues that firms reported. Only two of the firms (the vehicle repair firm A, and the motor coil rewinding firm C) made all payments on time and yielded positive returns. A third firm (firm G) experienced some repayment issues, but was able to complete the payments required with some delays.

The remaining six firms all paid back substantially less than the capital invested. Four of the six paid their contractual revenue-sharing and principle payments for at least seven or eight months before falling into arrears. The other two firms fell into arrears before even making their first principle payment, and ended up paying only two months of principle repayments in total, and only a few months of revenue sharing. The three-year contracts ended between February and November 2016. At the end of the contracts, we had only received Rs. 2,259,383 ($17,932) in total payments, yielding a return of -47.6 percent. The total amount outstanding was Rs. 2,501,929 ($19,857).

KCG Equity sought to enforce re-payment and chase up arrears during the contract period through multiple avenues. The first involved in-person visits to firms and discussions, and several review

> 15 We did not vary the expected return, however, and so at least for this pilot, did not induce firms to reveal information about expected returns in a manner that allowed us to capture a larger share of the investment gains.

18