rafmacalaba/datause-encoder-data huggingface dataset:
increase in book use, and book use continued to fall, until only 43 percent recorded information in March 2006, the last full month for which books were kept.

We then estimate the effect of keeping books on reported revenues, materials purchases, total expenses, revenue-expenses, home consumed goods, and profits. We do this for the subsample of firms not keeping books in the baseline survey (for whom the treatment would therefore be a change of status). Table 6 presents the resulting estimates. Bookkeeping is found to have a significant effect in the KMS, where books were kept for the relatively short period of one month and compliance was high. Purchases of materials and items for resale and total expenses were both 7000 Rupees higher with books, a statistically significant difference which is approximately 22 percent of the mean levels for those without books. The value of goods used in home consumption was also significantly higher, with the treatment effect equal to 28 percent of the mean level of those without books. Reported revenues were 5400 Rupees higher (14 percent at the mean), but the difference was not statistically significant. Profits were only 379 Rupees higher with books (8 percent of the mean), a statistically insignificant difference.

The allocation of books results in more modest and insignificant reported intent-to-treat estimates in the SLMS. However, while the KMS had 100 percent compliance with the book treatment, compliance was less frequent in the SLMS. We therefore also present the effect of the books treatment on those who take up the books (TOT estimates), using the assignment of books as an instrument for whether books are actually used. This does increase the magnitudes of the reported effects, although they remain insignificant. However, the signs and magnitudes again suggest that both revenue and expenses are underreported without books, with less overall effect on profits.

These bookkeeping exercises therefore suggest that both revenues and expenses are underreported when records are not kept. However, reported profits don’t change significantly when books are used, suggesting that recall and other such problems are not having major effects on profits. Table 7 shows that the use of books does not help increase the correlation between reported revenue-expenses and reported profits: In the 18
Table 1: Evolution of PSA payment scheme

|**Modality**|**Sub**<br>**group**|**2004 **|**2005 **|**2006 **|**2007 **|**2008 **|**2009**|**Sub**<br>**group**|**2010 **|**2011 **|**2012 **|**2013 **|**2014**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Hydrological||||||||||||||
||Cloud forest|400|400|410|430|450|470|Area 1|1100|1100|1100|1100|1100|
||Oak forest||||380|390|410|Area 2|700|700|700|700|700|
||Rain forest|300|300|320|330|340|360|Area 3|382|382|382|382|382|
|Biodiversity||||||||||||||
||All||||330|390|410|Area 4|550|550|550|550|550|
|||||||||Area 5|382|382|382|382|382|
|||||||||Area 6|280|280|280|280|280|

> 1 All payments are expressed in Mexican pesos.

Table 2: Sampled versus non-sampled communities in 2011-2014 cohorts

||(1)<br>**Not**<br>**sampled**|(2)<br>**Sampled**|(3)<br>**Normalized**<br>**Diferences**|
|---|---|---|---|
|Ln(community area, ha)|8.269|8.211|-0.037|
|Ln(common property area, ha)|7.759|7.809|0.020|
|Ln(members with rights to comm. land)|4.602|4.599|-0.003|
|Ln(members without rights to comm. land)|1.416|1.354|-0.027|
|Ln(area submitted, ha)|6.682|6.697|0.012|
|Ln(average slope in deg)|2.384|2.217|-0.153|
|Ln(average elevation in deg)|6.681|6.507|-0.079|
|Ln(km to any road)|7.772|7.643|-0.089|
|Ln(km to major city)|4.430|4.597|0.195|
|Ln(km to city _>_ 5,000)|3.229|3.227|-0.002|
|Mean canopy cover 2000|42.433|50.909|0.200|
|Percent indigenous in municipality|0.161|0.328|0.289|
|Deforestation risk (INE)|0.057|0.055|-0.025|
|Obs|1977|862|2839|

> 1 Table shows mean values in columns (1) and (2), and the normalized differences between these means in columns (3).

33
14 <!-- Start of picture text --> Consta Lv of Sevo (LOS) LOS E<br>8.000 - Constan Sped/ capacty)<br>7.000-<br>6,000 - 45 mph<br>£ / / ~~~~~~~~~~~~~~~~~~LOS C<br>5.000 - ~~~~~~~~~~~~~~~~~~~~~~Of Other vehcles<br>45,000-<br>i 4 °°Z \ ~~~~~~~~~~~~~~~~~52 mp<br>14000 - LS<br>3.000 - / / / - - ~~~~~~~~~~~~~~~(Ffee llow)<br>2.000- 8fp<br>1.000 Zo<br>0 2 3 4 5 6<br>Number of Lanes<br><!-- End of picture text --> <!-- Start of picture text --> Two and 4-lane road, Trnsportaton Reseach Board, 1985; single lane roads, Hoban, 1987, Table 11, figu<br>for India.<br>F;e 3: Rclaionship Between Road Capacity and Number of Lanes<br><!-- End of picture text --> costs will be **less than** the marginal costs of adding additional road capacity. This creates special pr6uiers in countries with low traffic densities. The lumpiness of investments means therc .s - - little traffic congesion a common feature of the road systems in most developing countries and congestion costs are close to zero. There are only limited sections of road where the network is operatng at capacity, unless investment is sub-optimal (or there are physical and/or environmental constraints restricting capacity) which allows congestion to build up beyond the point where further investment would be economically justified.
the 2001 Census classifications. Thus, while the formal definition of an urban area has not changed during this period, some sub-units of districts move from being rural areas to being urban areas with the 2001 Census compared to the 1991 Census when the sub-units begin to satisfy the urbanization criteria. This change can influence our measured urbanization levels, and Ghani et al. (2012b) describe exercises to ensure that these reclassifications do not have a material consequence for the measured patterns.

It is important to note that India uses a more demanding set of criteria than most countries to define urban areas (e.g., Bhagat, 2005; United Nations, 2001). For instance, substantial parts of U.S. metropolitan areas like Atlanta or Phoenix would be classified as rural in Indian statistical analyses because their population densities fall below 1000 persons per square mile. Thus, our measured urbanization for India will be lower than many international standards. This consideration does not affect, however, the longitudinal consistency of our trends for India.

# _Descriptive Tables_

Tables 1a and 1b present simple descriptive statistics or the urban and rural distribution of activities across districts over time and by their distance from the GQ network for the organized and unorganized sectors, respectively. Panel A of each table provides descriptive tabulations from the 1994 and 2000 data that come before the GQ upgrades, and Panel B provides similar tabulations for the surveys that follow the GQ upgrades. Columns 1-3 provide aggregates of manufacturing activity within each spatial grouping, averaging the two surveys, and Columns 4-6 provide similar figures for young establishments less than four years old. Within each panel, we tabulate activity by distance from GQ. There are nine nodal districts. Roughly one-third of districts fall within 0-10 or 10-50 km from the GQ network, with roughly two-thirds of districts over 50 km away from the network. In Table 1a, the broad trend is for declining urban shares in the organized sector, while the broad trend in Table 1b is for increasing urban shares in unorganized sector. Urbanization rates are strongest in the nodal cities, with the districts falling within 0-10 and 10-50 km from the GQ network being of relatively similar levels.

11
# **C. Attrition from Baseline**

Comparability at baseline is critical, but even if the two groups are comparable at baseline, it is possible that the treatments might cause different types of students to drop out of the study, making the groups of students that answer our one-year follow up survey incomparable. To check for this, we perform two exercises. First, we check the overall attrition rates in each group. If these are sufficiently low, then compositions of the groups cannot significantly change from baseline to treatment even if significantly different types of students attrit. Second, to asses how different the attritors are, we compare the kinds of students attriting in each group using the baseline characteristics of all of the students.

The first two rows of Table 4 provide the exact number of attritors and their percentage in the research group. Column 1 shows the values for the control group and columns 2-4 show the difference from this value and between the two treatment groups for San Cristobal. Columns 5-8 do the same for Suba. Overall, the attrition rate is very low at just less than 2 percent, and the differences in the number of children who dropped out are mostly in the single digits. Given this extremely low rate of attrition, only very large differences could generate changes in the comparability of the research groups.

Panels B through E then estimate these relative comparisons of background characteristics. The control columns (columns 1, 5, and 7) show the difference in characteristics between those students that attrit and those that remain in the sample at follow-up. The difference columns (columns 2-4, 6, and 8) then display the results of a slight modification of Equation 2 to show the difference between the research groups of the relative differences between attritors and stayers.

Again, these differences are all minor. The vast majority of the differences are extremely small – for example, the differences in the families’ poverty measures are negligible both in economic and statistical terms. The largest differences occur in the age of the head of the household for San Cristobal (3 to 7 years difference), the age of children in San Cristobal (2-3 years), and the years of education of students in Suba grades 9-11 (1.25 years). Overall the distributions are very similar, and especially given the underlying low rates of attrition, the few differences that do exist are arguably too small to generate confounding changes in the measured outcomes.

- 19 -
**Table 8: The Impact of Minimum Wages on Women’s Relative Employment Prospects**

||Depend<br>|ent Variabl<br>|e: % Female<br>|Prod. Worke<br>|rs<br>||
|---|---|---|---|---|---|---|
||OLS|FE|OLS|FE|OLS|FE|
||(’93-’06)<br>(1)|(’93-’06)<br>(2)|(’93-’06)<br>(3)|(’93-’06)<br>(4)|(’95-’97, ’06)<br>(5)|(’95-’97, ’06)<br>(6)|
|Log Minimum Wage|-0.021***|0.006*|-0.012**|0.007*|0.018**|0.009|
||(0.007)|(0.004)|(0.006)|(0.004)|(0.008)|(0.007)|
|Foreign-owned|||0.023***|-0.002|0.033***|0.010|
||||(0.006)|(0.004)|(0.006)|(0.007)|
|Government-owned|||-0.110***|0.001|-0.134***|0.004|
||||(0.007)|(0.002)|(0.008)|(0.005)|
|Exporter|||0.028***|0.002**|0.038***|0.004|
||||(0.003)|(0.001)|(0.004)|(0.002)|
|Log Firm Age|||-0.014***|-0.003**|-0.017***|-0.004|
||||(0.001)|(0.002)|(0.001)|(0.003)|
|Log L|||0.058***|0.031***|0.069***|0.031***|
||||(0.001)|(0.002)|(0.001)|(0.003)|
|Unskilled Ratio|||0.446***|0.104***|0.379***|0.104***|
||||(0.008)|(0.006)|(0.009)|(0.010)|
|Log V/L|||-0.055***|-0.003***|-0.056***|-0.005***|
||||(0.001)|(0.001)|(0.001)|(0.001)|
|% Primary|||||-0.027***<br>(0.008)|-0.015**<br>(0.006)|
|% Junior|||||-0.008<br>(0.008)|-0.017***<br>(0.006)|
|% High+|||||-0.147***<br>(0.009)|-0.046***<br>(0.007)|
|Province Dummies|Yes|No|Yes|No|Yes|No|
|Industry Dummies|No|No|Yes|No|Yes|No|
|Year Dummies|Yes|Yes|Yes|Yes|Yes|Yes|
|N|273531|273531|272372|272372|87815|87815|
|R<sup>2</sup>|0.029|0.000|0.327|0.017|0.342|0.0274|

_Note:_ *** p<0.01, ** p<0.05, * p<0.1, Standard errors, reported in parentheses, are heteroscedasticity robust and clustered by establishment. Minimum wages, wages and value-added are all measured in real terms. Minimum wage, educational variables and share of female workers are demeaned.

38
Feenstra 1994, Feenstra and Rose 2000, Hummels and Klenow 2005).

# Extended IV Results: Basic Robustness

Table S6a provides robustness checks in the same format as Table 2, and Table S6b provides reduced-form relationships. The instrumented elasticity is again very stable to the exclusion of high-profile countries, and sample composition is further considered below. The weighted elasticity is very stable in Column 4 to excluding industry 383 (Machinery, electrical), while the unweighted elasticity strengthens. This choice of reporting is again due to electrical machinery having the largest impact on the results, with, for example, very similar elasticities to Column 1 being observed if excluding industry 382 (Machinery, except electrical). Results are also again very similar with a winsorized sample. The instrumented elasticities are reasonably stable to variations on industry dimensions and samples in Columns 6-8 in terms of economic magnitudes, but the standard errors on the unweighted sample in Panel B become too large for definitive conclusions.

The last column again shows the results are robust to exporter-level clustering. The F statistics with this approach are 8.9 and 11.9 for Panels A and B, respectively. Employing bilateral-route clustering or bootstrapped standard errors substantially increases the precision of the results, with the standard errors declining in size by 60-70%. The F statistics also increase substantially. Bilateral-route clustering is more comparable to prior estimates in the trade literature, but the exporter-industry clustering is a conservative approach common in applied econometrics.

The primary IV estimations build a five-year lag structure into when the technology growth occurs in the United States to when the productivity growth happens abroad. Thus, growth in → technology flows over the 1975-1979 1980-1984 period towards an exporter-industry are used to predict labor productivity growth and export growth during the 1980-1984→1985-1989 period. This five-year lag matches discussions of rates of differential technology flows across countries. Table S5 compares this lag structure with contemporaneous technology flows in estimations similar to the first-stage and reduced-form specifications. The lagged estimator is stronger than the contemporaneous estimator when both are modelled independently or when modelled jointly. This pattern provides comfort in the estimation design and the proposed causal direction of the results.

Unreported estimations model additional fixed effects for distance-industry-year, where distance is an indicator variable for being more than the median distance from the United States.

10
has a relatively low elasticity of poverty reduction relative to economic growth. Overall, the number of poor (those living on less than US$1 per day) would decline by 32 million by 2015, a reduction of some 5 percent compared to the projected baseline level of 622 million, of which 21 million would be in Sub-Saharan Africa. The modest extent of reform by developing countries explains the smallness of the poverty impact from the Doha Round.

The remainder of the paper is organized as follows. Section 1 provides a brief overview of the model, which is followed by a brief section describing the base-year patterns of protection. The third section discusses some baseline issues. The fourth and fifth sections discuss results of the simulations, the sixth discusses poverty impacts, and the final section provides some concluding remarks.

# **1. Model Overview**

The model used for this analysis is the World Bank’s global dynamic computable general equilibrium (CGE) model, LINKAGE (van der Mensbrugghe 2004b). The model is a relatively standard GE model with a few distinguishing characteristics. First, it is recursive dynamic, with a 2001 base year, and typically solved through to 2015. The dynamics are driven by exogenous population and labor supply growth, savings-driven capital accumulation, and labor-augmenting technological progress. In any given year, factor stocks are largely fixed. Producers minimize costs subject to constant returns to scale (CRS), consumers maximize utility, and all markets clear with flexible prices. There are three types of production structures. Crop sectors reflect the substitution possibility between extensive and intensive farming. Livestock sectors reflect the substitution possibilities between ranch- and range-fed production. All other sectors reflect the 3
the small geographic units will be merged until one of the hard constraints is satisfied. The output is in vector format, and each generated pre-EA has necessary attribute information from the administrative boundary, total estimated population, area, and unique IDs (Random IDs and ID numbering using serpentine technique). **Table 4** presents the parameter calibration used in the preEA tool. The user can adjust the merging process’s priority parameters. For instance, when the population in the tool is given more weight, the created pre-EAs’ total population will be homogeneous and near the maximum population threshold. In contrast, raising the shape factor coefficient can achieve a more compact shape.

**Table 4: Parameter calibration in the preEA tool**

|Class|Maximum<br>population|Maximum area|Minimum<br>length shared<br>boundaries|Area<br>(coefficient)|Population<br>(coefficient)|Share factor<br>(coefficient)|
|---|---|---|---|---|---|---|
|Urban|1000|10km<sup>2</sup>|20%|0|1|2|
|Rural|800|10km<sup>2</sup>|20%|0|1|2|

**Post-Processing:** The quality of the input datasets primarily undermines the quality of the outputs from the preEA tool. Some of the generated pre-EA outputs need to be assessed and modified because of the limitation of the input datasets and the imposing of various constraints in the tool. This is also the primary rationale behind the output’s designation as pre-EA rather than EA. For example, pre-EAs with negligible populations and geographic areas were automatically merged with their neighbor via employing the “Eliminate Small Area” tool in the preEA tool package. Manual modifications were applied to pre-EAs spanning large geographic areas with high populations.

## **3.3 Further Manual Adjustments**

The national sampling frame created using the automatic approach requires additional modifications for several reasons. First, some adjustments are needed in areas where the existing datasets were inadequate to create smaller EAs. Second, the output contains several polygons with positive populations but no settlement of residents. The pre-EAs with positive populations that clearly fall within non-residential areas such as offices, buildings under construction,<sup>14</sup> recreational centers, factories, manufacturing zones, and agricultural lands are manually adjusted.

Appendix B discusses the use of boundary information produced by the approach for creating field and offline maps to supplement the survey data collection process.

# **4 Results: A National Sampling Frame – Pre-EAs for Armenia**

This section presents the sampling frame in which the pre-census enumeration areas (pre-EAs) serve as primary sampling units (PSUs).

> 14 Note that the judgments are based on satellite images from Google Street Maps available at the time of this study, i.e., April 2024. So, settlements and residential buildings recently built might have been overlooked in the construction of this sampling frame. However, these measurement errors are more likely to be random, so this limitation should not introduce systematic bias in the frame.

15
where the first set of brackets represents the returns effect and the second represents the covariate effect. We use both specifications to see whether results are sensitive to the choice of the counterfactual distribution used.

# **_Data_**

The 2002-2003 Pesquisa de Orcamentos Familiares (POF) survey in Brazil is a household budget survey designed to measure consumption, expenditures, and income. Unlike other Brazilian surveys, POF is representative at both the national and regional levels for metropolitan, urban (i.e. non-metropolitan urban), and rural areas. The 48,568 households (181,747 individuals) in the POF represented 48,534,638 households (175,331,798 individuals).<sup>5</sup> The regional breakdown of households in our sample is shown in Table 2.1 below.

**Table 2.1 Sample Households by Region**

||**Metro**|**Urban**|**Rural**|**Total**|
|---|---|---|---|---|
|North|2,472|2,452|1,957|6,881|
|Northeast|5,524|8,921|4,218|18,663|
|Southeast|2,578|4,254|1,835|8,667|
|South|1,423|3,666|1,023|6,112|
|Center West|1,851|4,779|1,615|8,245|
|**Total **|**13,848**|**24,072**|**10,648**|**48,568**|

The measure of standard of living that we use is the log of the “welfare ratio”, defined as the nominal per capita consumption deflated by the region-specific poverty line that summarizes the cost of meeting minimum livelihood needs. The 2007 World Bank study on measuring poverty in Brazil estimates separate poverty lines for twenty one different metropolitan, urban, and rural areas of each of Brazil’s five major regions using the Cost of Basic Needs method (see Table 2.2).<sup>6</sup>

> 5 When the sample size is a small fraction of the population, the finite population correction is close enough to unity and can be ignored (Deaton, pg 43). Thus, for the Brazil POF where 1 out of every 1,000 households were sampled, we chose to ignore the finite population correction for simplicity with the understanding that the calculated standard errors may be slightly larger than if the finite population correction were accounted for.

> 6 Specifically the CBN poverty lines used are based on the lower estimate of the adjustment to the food poverty line for basic nonfoods. For a detailed discussion of the construction of the region-specific poverty lines in Brazil see World Bank, 2007. Note that a welfare ratio equal to 1, or equivalently a log welfare ratio equal to 0, represents a household per capita consumption equal to the poverty line.

9
PPR Working Pager Series

||Ia|Athor||Contact<br>[f|
|---|---|---|---|---|
|WPS307|On the Accuracy of Economic<br>Observations: Do Sub-Saharan<br>Trade Statistics Mean Anything|Alexander J. Yeats|November 1989|J. Epps<br>33710|
|WPS308|Harmonizing Tax Policies in Central<br>America|Yalcin M. Baran|November 1989|T. Watana<br>31882|
|WPS309|Honduras-Public Sector Finances,<br>Issues, and Policy Suggestions<br>to Resolve Them|Yalcin M. Baran|November 1989|T. Watana<br>31882|
|WPS310|A Macroeconomic Consistency<br>Framework for Zimbabwe|Ali Khadr<br>Klaus Schmidt-Hebbel|||
|WPS311|Macroeconomic Performance<br>Before and After Disinflation in<br>Israel|Leonardo Lelderman<br>Nissan Liviatan|November 1989|R. Luz<br>61588|
|WPS312|Improving Public Enterprise<br>Performance: Lessons from<br>South Korea|Mary M.3hidtey|October 1989|R. Malcolm<br>61708|
|WPS313|The Evolution of Paradigms of<br>Environmental Management in<br>Development|Michael E. Colby|November 1989|<br>C. Evangelista<br>32645|
|WPS314|Primary Commodity Prices and<br>Macroeconomic Variables: A Long-<br>rur, Relationship|Theodosios Palaskas<br>Panos Varangis|November 1989|<br>D. Gustafson<br>33714|
|WPS315|Notes on Patents, Distortions,<br>and Development|Julio Nogues|||
|WPS316|The Macroeconomics of Populism<br>in Latin America|Rudiger Dornbusch<br>Sebastian Edwards|||
|WPS317|Price and Quality Competitiveness<br>of Socialist Countries' Exports|Zdenek Drabek<br>Andrzej Olechowski|||
|WPS318|Debt Buybacks Can lndice*e<br>Sovereign Countries' Willingness<br>to Raise Investment and Repz4y<br>Debt When Partial Debt Reiief is<br>Offeri': Theory and Tests|Sankarshan<br>Acharya<br>Ishac Diwan|||
|WPS319|Trenc.s in South-South Trade and<br>the Potential in Non-Discriminatory<br>Liberaliation o; Barriers|Refik Erzan<br>|||
# **FIGURE 2** PARALLEL MARKET DOLLAR PREMIUM

<!-- Start of picture text --> 120<br>100 .. ............................................<br>080<br>Wo: 60 _. ... .. \. . ................ ................. ......<br>0<br>0A<br>8 40 ....... . . . . . . . . . . . . . .<br>20 _. ...... . ... ... ....<br>3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4<br>19871 1988 | 1989 I 1990 1 1991 j 1992<br><!-- End of picture text -->

Quarter/Year Data not available for 4/1991 & 1/1992 74