rafmacalaba/datause-encoder-data huggingface dataset:
# A On-line Appendix: Estimation Details

This appendix provides more details on the estimation procedures introduced in section 3 of the paper. We estimate the parameters related to the rm and worker decision problems using Argentine data. We combine a panel of rms with a panel of workers.

## A.1 Data

### Firm data

The estimation of the rm problem requires panel data with detailed information on the investment decision of rms. In particular, to t the capital adjustment cost model, we need data on purchases of new capital as well as on sales of installed capital. We use the Encuesta Industrial Anual (EIA, or Annual Industrial Survey), which meets these requirements. In the EIA panel, we have data for the period 1994-2001. The EIA dataset provides information on revenue, value of production, employment, wages, costs, intermediate inputs, inventory stock, and both gross expenditures and gross sales of capital.

We express all monetary variables in real terms using dierent deators. For wages we use the consumer price index; for investment, capital and intermediate inputs we use the general level wholesale price index; and for revenue, sales and prots a wholesale price index at a four digit level of disaggregation according to the ISIC classication.

To construct the real investment series, we generate an initial measure of the real capital stock at the plant-level and then complete the series using the perpetual inventory method according to the rule _Kijt_ +1 = (1 _− δ_ ) _Kijt_ + _Iijt_ . Real investment is dened as _Iijt_ = _Eijt − Sijt_ , where _Eijt_ is real gross expenditures on capital equipment, and _Sijt_ is real gross retirements of capital equipment.

Since our dataset does not contain information on the book value of capital, we approximate the initial capital stock of the rm as the average across years of the ratio between the amount of capital depreciation declared by the rm and the estimated depreciation rate. We deate our measure of initial capital stock by the general level of the wholesale price index. We use depreciation rates estimated by the Bureau of Economic Analysis (BEA) as described before. Our depreciation rates include both in-use depreciation (which reects declines in the eciency of the asset because of aging or wear and tear) as well as retirements or discards (which reects, for example, obsolescence).

40
**<mark>Table 6. Regression: Latrine Use Behavior</mark>**

|VARIABLES|(1)<br>Use latrines|(2)<br>Use latrines|
|---|---|---|
|Social norms (SN+SE)|0.119***<br>(0.025)||
|SN: Pro‐latrine use||0.089***<br>(0.026)|
|SE: Pro‐latrine use||0.075***<br>(0.023)|
|HH income/month|0.000|0.000|
||(0.000)|(0.000)|
|Age|0.001|0.001|
||(0.002)|(0.002)|
|Education|0.042***|0.040***|
||(0.015)|(0.015)|
|Male|‐0.193***|‐0.166***|
||(0.059)|(0.060)|
|Constant|0.591***|0.574***|
||(0.120)|(0.120)|
|Observations|201|198|
|R‐squared|0.210|0.231|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 <mark>Each respondent was also presented with 4 vignettes. The situations depict a typical village and varied either social empirical expectations (most people in the village defecated in the open or used a latrine) or social normative expectations (most people found it acceptable to defecate in the open or found this to be wrong). The vignette then asked where a typical resident (matched to the gender of the respondent), who had access to a latrine, would defecate. Vignettes, in this case, were used to test a counterfactual—norms of latrine use vs. norm of open defecation. This survey experiment is used to measure how social empirical and social normative expectations affect behavior. In this case the behavior is another villager, rather than the respondent him or herself, in order to mitigate social desirability bias in the responses. The survey experiment with vignettes provides further evidence on the existence of a social norm beyond the correlations in the regression table above.</mark> <mark>We conduct simple factorial analysis without interaction effects, representing each of the above factors with a dummy variable. Simple regression results are shown in Table 7. The results suggest that the likelihood of latrine use on the part of a villager would increase by 18.9% if he or she moved to a village where most people used latrines, and by 21.7% if most people in the village found it wrong to defecate in the open. We also test for robustness using a multilevel model with a random intercept, since each individual is asked to respond to 4 vignettes. The results were similar. Overall, the analysis of the vignettes confirms the importance of both social normative and social empirical expectations for individual behavior.</mark> 13
Figure 3.2. Argentina: Real Overvaluation of the Peso <!-- Start of picture text --> 70<br>60<br>so =;iOvervaluation<br>e 20 -g, *<br>10 f<br>X, Y K X X e CDNH;<br>en CD a_ aO o _ ew 0 c<br>-10<br>-20<br>-30<br><!-- End of picture text --> Source: Alberola, L6pez ancl Serven (2003) Figure 3.3. Relative Productivity <!-- Start of picture text --> 2.0<br>1.5<br>1.0<br>0.5 /0\<br>0.0 *<br>1990 199 1992 1993 1994 1995 \996 1997 1 1999 2000<br>-0.5<br>-1.0<br>-1.5<br>-zo2<br><!-- End of picture text --> Source: Alberola, L6pez and Serven (2003) 21
6

**Table 4: Infant and under-age three mortality by asset index for nine African countries**

|_Forfive-year coho_|_rtsofchildr_|_en born o_|_neandthr_|_eeyearspr_|_iortothe_|_survey, _|_respectiv_|_ely.Per1 _|_000 bi_|_rths._||
|---|---|---|---|---|---|---|---|---|---|---|---|
||_Ghana_||_Keny_|_a_|_Madag_|_ascar_||_Mali_||_Senegal_||
|_SurveyYear_|_1988_|_1993_|_1988_|_1993_|_1992_|_1997_|_1987_|_1995_|_1986_|_1992_|_1997_|
|_Cohortatrisk_|_'83-'87_|_'88-'92_|_'83-'87_|_'88-92_<br>|_'87-91_|_'92-'96_|_'82-86_|_'90-'94 _|_81-85_|_'87-'91_|_'92-'96_|
|_Infantmortality_||||||||||||
|<br>Poorestquintile|120|90|78|90|121|128|173|157|114|<br>96|101|
|Third quintile|92|85|76|56|109|103|168|156|96|<br>76|70|
|<br>Richest quintile|74|48|_55_|_45_|88|73|102|98|81|38|47|
|_RatioQl/Q5_|_1.6_|_1.9_|_1.4_|<br>_2_|_1.4_|_1.8_|_1.7_|<br>_1.6_|_1.4_|<br>_2.5_|_2.1_|
|**_Under-age-threem_**|**_ortality_**|||||||||||
|Poorestquintile|160|152|93|128|200|191|318|266|224|<br>169|157|
|Thirdquintile|138|108|83|67|176|166|237|256|175|136|120|
|Richestquintile|113|80|60|54|135|85|184|148|114|<br>60|66|
|_RatioQl/Q5_|_1.4_|_1.9_|_1.6_|_2.4_|_1.5_|_2.2_|_1.7_|<br>_1.8_|_2.0_|<br>_2.8_|_2.4_|
||_Tanza_|_nia_||_Uga_|_nda_||_Z_|_ambia_||_Zimb_|_abwe_|
|_SurveyYear_|_1991_|_1996_||_1988_|<br>_1995_||_1992_|_1997_||_1988_|<br>_1994_|
|<br>_Cohortatrisk_|_'86-'90_|_'91-'95_||_'83-87_|_'90-'94_||_'87-'91_|_'92-'96_||_'81-'87_|_'89-93_|
|**_Infantmortality_**||||||||||||
|<br>Poorestquintile|114|116||141|107||134|143||66|57|
|Thirdquintile|97|89||115|100||129|101||69|54|
|Richestquintile|76|66||103|73||72|103||37|39|
|_RatioQJ/Q5_|_1.5_|_1.8_||_1.4_|<br>_1.5_||_1.9_|_1.4_||_1.8_|<br>_1.5_|
|**_Under-age-threem_**|**_ortality_**|||||||||||
|<br>Poorestquintile|156|144||189|182||217|224||84|71|
|Thirdquintile|152|138||184|168||187|184||92|70|
|Richestquintile|127|91||158|100||103|147||36|53|
|_RatioQl/Q5_|_1.2_|_1.6_||_1.2_|_1.8_||_2.1_|_1.5_||_2.3_|<br>_1.3_|

Source: Sahn et al. (1999).

# **_Income inequality_**

We turn now to income inequality and to the issue of whether episodes of growth in the 1 990s in Africa were associated with widening income distributions. On the one hand, increasing reliance on markets and the withdrawal of the state might be expected to increase income inequality (people with low levels of education, and limited access to public services and markets being less likely to take advantage of the opportunities growth presents). On the other hand, the previous tendency for the state to tax agriculture and the rural sector heavily, and the removal of such state intervention, might result in improved national income distributions.
The Enterprise Survey dataset provides firm-level data on access to bank finance and other firm characteristics. The survey includes various questions on access to bank finance. First, the survey asks firms if they have a loan, line of credit or overdraft.<sup>9</sup> Second, the survey identifies firms that currently have a loan. Third, the survey asks firms to report the share of working capital and, separately, of fixed assets financed by banks. Fourth, for firms that have a loan, the dataset includes information on the interest rate and maturity of the most recent loan. Finally, the Enterprise Survey asks firms to rate the severity of access to finance as an obstacle for their operations and growth. The Enterprise Survey also includes information on other firm characteristics such as size, ownership, exporter status, and sector, which we include as control variables in our estimations.

In order to be able to control for country fixed effects in our estimations, we only consider countries with at least two Enterprise Surveys since 2002, when these surveys were published with standardized country data matched to a common set of questions to allow crosscountry analyses. In addition, because our primary interest is to study the impact of the introduction of collateral registries for movable assets on access to finance, we are only able to include countries with such reforms that have at least one enterprise survey before the reform and at least one after the reform. Our final sample includes 73 countries. Among these countries, 7 introduced a collateral registry for movable assets (Bosnia, Croatia, Guatemala, Peru, Rwanda, Serbia, and Ukraine) and 7 implemented other types of collateral reforms without introducing a registry for movable assets (Armenia, Georgia, Kyrgyzstan, Mauritius, Poland, Romania, and Vietnam).<sup>10</sup> We also pick a sample of 7 countries from our sample of non-reform countries: –

> 9 Appendix 1 describes in detail how we generate the dependent variables.

> 10 The collateral registry is one of the items in the Doing Business “Getting Credit” index, which in addition contains 7 components that pertain to movable collateral laws and two components pertaining to bankruptcy laws. Some of the collateral reforms outside from the introduction of a registry include: allowing out-of-court enforcement

7
**Table** 3t **The** effect of student background characteristic* on student expectations, perceived parental support, perceived ability, attitudes **and** effort in Thailand, 1981-82

|Independent<br>variab<br>les|YMOREED|PARENC|YPERCEV|<br>YFUTURE|<br>YDESIRE|<br>YMWHKL|
|---|---|---|---|---|---|---|
|Sex (2- boy)|-.14***|<br>.27***|-.10**|-.09**|-.21***|<br>.04|
||(4.35)|(8.54)|(3.19)|(2.99)|(8.78)|(.31)|
|Age|-.00|-.02|.03|.02|.03|.00|
||(.08)|(.66)|(.88)|(.55)|(1.36)|(.03)|
|Age squared|-.00|.00|-.00|-.00|-.00|-.00|
||(.34)|(.84)|(.8_)|(.66)|(1.51)|(.15)|
|Father's occupation|.12***|<br>-.06**|.00|.04*|.05**|<br>-.09|
||(6.66)|(3.30)|(.24)|(2.39)|(3.28)|(1.20)|
|Mothers education|.12***|<br>-.07**|<br>-.03|.02|.06***|<br>.23*|
||(5.91)|(3.30)|(1.50)|(1.14)|(3.68)|(2.54)|
|Calculator at home|.05|.00|-.02|.03|.01|.13|
||(1.31)|(.10)|(.48)|(.84)|(.21)|(.85)|
|District PCI (1000 bh)|.01*|.01|-.00|-.00|.01|.02|
||(2.07)|(1.44)|(1.94)|(1.26)|(1.37)|(.85)|
|Lang. of inst. athome|.00|-.16|-.21*|-.07|.03|-.52|
||(.01)|(1.77)|(2.39)|(.85)|(.39)|(1.35)|
|Dist. PCI x Lang of inst.|.00|.00|.00*|.00|-.00|.00|
||(.01)|(1.95)|(2.50)|(.42)|(1.46)|(.83)|
|Constant|3.72|3.24|1.83|.89|2.99|4.61|
|Adjusted R-squared|.06|.04|.00|.01|.04|.00|
|N|3628<br>3|597<br>3|599|3582|3617|3574|

***p < .001, ** p < .01, * p <.05.

Note: Unstandardized regression coefficients; t-statisties in parentheses.

- 26 -
**Figure 4. Distribution of wealth**

<!-- Start of picture text --> Asset Index Density, Non-Camp Households Asset Index Density, Camp Households<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Asset Index Asset Index<br>June (Gini=0.3325) June (Gini=0.2183)<br>February (Gini=0.3267) February (Gini=0.1970)<br>Before Earthquake (Gini=0.3346) Before Earthquake (Gini=0.2446)<br>kernel = epanechnikov, bandwidth = 4.6918 kernel = epanechnikov, bandwidth = 3.0994<br>.05<br>.02<br>.04<br>.015<br>.03<br>.01<br>.02<br>.005 .01<br>0 0<br><!-- End of picture text --> Source: Own computations using June 2010 (EFSA II) surveys. Notes: The sample is restricted to the households residing in the six strata that cover areas directly affected by the earthquake. Weights are calculated using preearthquake assets.

Figure 5 presents the distribution of wealth losses in percentage of pre-earthquake wealth. The percentage of asset loss among households who lost assets is around 25%. This percentage does not seem to vary a lot according to wealth quintile.

**Figure 5. Wealth losses distribution**

<!-- Start of picture text --> Percentage of Asset Index Loss Percentage of Asset Index Loss, by Asset<br>Index Quintile (if Asset Loss>0)<br>35%<br>30%<br>25%<br>20% February<br>15% June<br>10%<br>-100 -50 Loss Percentage0 50 100 5%<br>February 0%<br>June 1 2 3 4 5<br>kernel = epanechnikov, bandwidth = 4.1864 Quintile<br>.04<br>.03<br>.02<br>.01<br>0 Percentage Lost<br><!-- End of picture text --> Source: Own computations using June 2010 (EFSA II) surveys. Notes: The sample is restricted to the households residing in the six strata that cover areas directly affected by the earthquake. Asset index weights are calculated using pre-earthquake assets.

# **5.3.Directly Affected Areas**

Table 15 presents descriptive statistics for households living in affected areas. The food consumption score is calculated based on the number of different food groups consumed by the household, to represent diversity, and the number of times a week they are consumed. Notably, we observe that the average food consumption score is 55.79, with a standard deviation of 19.75. A majority of households is above the limit food consumption thresholds (the limit consumption threshold being 42 and the critical threshold being 26).

40
WPS3850

# **Do Regional Trade Pacts Benefit the Poor?**

## **An Illustration from the Dominican Republic-Central American Free Trade Agreement in Nicaragua**

### **Maurizio Bussolo***

### **and**

### **Yoko Niimi****

#### **_Abstract_**

The main objective of this paper is to provide an _ex-ante_ assessment of the poverty and income distribution impacts of the Central American Free Trade Area agreement on Nicaragua. A general equilibrium macro model is used to simulate trade reform scenarios and estimate their price effects, while a micro-module maps these price changes into real income changes at the individual household level. A useful insight from this analysis is that even if the final total impact on poverty is not too large, its dispersion across households – due to their heterogeneity of factor endowments, inputs use, commodity production, and consumption preferences – is significant and should be taken into account when designing compensatory policies. Additionally, growth and redistribution decomposition shows that, at least in the short to medium run, redistribution can be as important as growth. The main policy message that emerges from the paper is that Nicaragua should consider enlarging its own liberalization to countries other than the U.S. to boost trade-induced poverty reductions.

#### World Bank Policy Research Working Paper 3850, February 2006

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

***** World Bank, 1818 H Street, NW, mailstop MC2-200, Washington, D.C. 20433, USA. Tel: +1-2024582619, Fax: +1-202-5222578, email: mbussolo@worldbank.org.

****** World Bank, 1818 H Street, NW, mailstop MC3-303, Washington, D.C. 20433, USA. Tel: +1-2024585912, Fax: +1-202-5221159, email: yniimi@worldbank.org.

1
**Table 4.** **<u>Correlations between actual and fitted HAZ-score</u>**

||Fitt<br>|ed HAZ-scor<br>|e_with_<br>|
|---|---|---|---|
|Actual HAZ-score|Predicted income<br>only (i)|Asset index<br>only (ii)|Predicted income<br>and asset index(iii)|
|A d ROC|0.5978|0.5985|0.6033|
|rea uner curve|**0.6627**|**0.6669**|**0.6730**|
|Searman rank correlation|0.1889|0.1861|0.1981|
|p|**0.2551**|**0.2496**|**0.2652**|

Source: Own computations using HLCS 2001 and DHS 2000 (2005 in bold)

# **3.4.Evolution of Asset-Poverty over Time**

For the purpose of the temporal comparison of assets, all of the household asset indices used in the analysis are calculated on an individual basis by dividing indices by household size. In order to factor in asset-related economies of scale within the household, indices were also calculated on a per household basis and for assets divided by the square root of household size. Results did not qualitatively change with the use of these different definitions of asset indices, so that they prove to be robust to the choice of equivalent scales. Asset-based poverty headcount (P0), poverty gap (P1), and poverty severity (P2) indices are presented in table 5 for various thresholds. The asset-based poverty lines are the 20<sup>th</sup> , 40<sup>th</sup> , 60<sup>th</sup> and 80<sup>th</sup> percentiles of the 1995 distribution of the asset index. In Table 5, results have been split according to a rural/urban division so that the evolution of the index can be observed in different areas. It appears that asset-based poverty decreased between 1995 and 2000 and remained fairly constant between 2000 and 2005. Moreover, it mainly declined in the North-East, Artibonite, Centre, and Grand’Anse departments, especially in rural areas.<sup>15</sup> These trends are comparable to those observed from chronic malnutrition rates, which have also decreased over time from 32% in 1995 to 23% in 2000 and 24% in 2005.

**Table 5. Changes in asset-based poverty between 1995 and 2005**

|||Poverty|headco|unt(P0)<br>Pove|rty gap|(P1)|Povert|yseverit|y (P2)|
|---|---|---|---|---|---|---|---|---|---|
|Percentile||1995|2000|2005<br>1995|2000|2005|<br>1995|2000|2005|
|20th|National|0.20|0.15|0.17 0.139|0.111|0.125|0.1175|0.0966|0.1088|
||Urban|0.01|0.01|0.02 0.007|0.004|0.009|0.0052|0.0030|0.0066|
||Rural|0.31|0.23|0.28 0.214|0.172|0.201|0.1811|0.1499|0.1762|
|40th|National|0.40|0.31|0.34 0.258|0.198|0.221|0.2068|0.1586|0.1797|
||Urban|0.06|0.02|0.06 0.026|0.011|0.028|0.0165|0.0072|0.0195|
||Rural|0.59|0.48|0.53 0.389|0.304|0.348|0.3145|0.2446|0.2854|
|60th|National|0.60|0.52|0.51 0.378|0.305|0.323|0.2951|0.2311|0.2524|
||Urban|0.21|0.14|0.18 0.072|0.038|0.066|0.0399|0.0187|0.0394|
||Rural|0.82|0.74|0.73 0.552|0.456|0.492|0.4395|0.3518|0.3930|
|80th|National|0.80|0.75|0.71 0.520|0.450|0.450|0.4064|0.3367|0.3491|
||Urban|0.56|0.46|0.45 0.206|0.151|0.172|0.1082|0.0701|0.0952|
||Rural|0.94|0.91|0.89 0.698|0.620|0.634|0.5751|0.4882|0.5166|

Source: Own computations using DHS 1995, 2000, 2005

> 15 These results are not reported here but are available upon request.

11
Table 1: Comparative evolution of Income and Exports Per Capita. (1990 Geary Khamis Dollars)

||GD|P per c|apita|Growth<br>GDP|p|Exports<br>er capit|a|Exports<br>Growth|
|---|---|---|---|---|---|---|---|---|
||1870|1913|1990|1870-1913|1870|1913|1990|1870-1913|
|Denmark|1927|3764|17953|1.6|166|501|7642|3.3|
|Finland|1107|2050|16604|1.4|177|528|5222|3.9|
|Netherlands|2640|3950|16569|0.9|478|702|9346|2.3|
|Norway|1303|2275|16897|1.3|129|349|9145|3.2|
|Sweden|1664|3096|17695|1.5|171|475|6543|3.1|
|UK|3263|5032|16302|1.0|417|923|3363|2.8|
|Australia|3801|5505|16417|0.9|281|704|2732|4.8|
|Canada|1620|4213|19599|2.2|194|515|4934|4.1|
|New Zealand|3115|5178|13994|1.2|344|729|||
|USA|2457|5307|21866|1.8|62|197|1765|2.2|
|Argentina|1311|3797|6581|2.5|124|257|372|5.2|
|Brazil|740|839|4812|0.3|87|80|235|1.9|
|Chile|1153|2653|6380|2.0|85|201|802|3.4|
|Colombia||1236|4917||48|51|242|2.0|
|Mexico|710|1467|4917|1.7|26|158|341|5.4|
|Peru|676|1037|3000|1.0|78|94|156|5.3|

Source: Maddison (1995) and Bravo-Ortega (1999).

5
15 association with performance nor are predictive of treatment effect heterogeneity in Ekiti, and neither drives the point estimate of the difference in treatment effects between states to zero. In short, our observable attributes at the facility level are unable to explain why the treatment had significant effects in Ekiti but not Niger. This may suggest the importance of institutional, managerial, and perhaps even behavioral variables that are not routinely collected in surveys.

# _CONCLUSION_

In this paper we provide quantitative experimental evidence regarding the effect of an incentive designed to encourage bureaucrats to perform better at work. Specifically, we introduced a public social recognition intervention for accounting staff in health facilities over a four-week period in two Nigerian states, Ekiti and Niger, and tested its effect on how well they tracked their resources. As has been shown, despite being part of the same country and despite parallel program implementation through the same organization, the two states exhibited substantially divergent results.

The social recognition intervention had a substantial and statistically significant positive impact on performance in Ekiti but essentially no effect in Niger. The positive outcome in Ekiti was seen across multiple sub-dimensions, especially regarding completeness of the target cash book form, albeit with less effect on the existence of substantiating documentation. This is encouraging given the relatively low cost of such an intervention.

The quantitative analyses strongly suggest that contextual factors at the state, community and facility levels may play a key role in determining the viability and effectiveness of our interventions in particular facilities. Recent work shows that the same intervention can have different effects in the hands of different implementers – a regularity across low-, middle-, and high-income countries (Bold et al. 2013; Banerjee, Karlan, and Zinman 2015; Allcott 2015). This project attempted to test for contextual factors while keeping the implementer constant. Our analysis found that the reason for the heterogeneous effects between the two states is not explained by facility-level variables of the kind usually collected in facility surveys, including measures of human capital and facility-level productivity. Explaining heterogeneity and