rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 3: Among Firms Offered Money, Which Ones Formalized?**

|Marginal effects fromprobit estimation of Registration amongsample of|fered 10,0|00,20,000|or 40,000 t|reatments|
|---|---|---|---|---|
|(1)|(2)|(3)|(4)|(5)|
|20,000 Rs Treatment<br>-0.0216|-0.0240|-0.00650|-0.0169|-0.0490|
|(0.0886)|(0.0892)|(0.0877)|(0.0886)|(0.0937)|
|40,000 Rs Treatment<br>0.307***|0.311***|0.302***|0.311***|0.312***|
|(0.0901)|(0.0908)|(0.0917)|(0.0903)|(0.0924)|
|Colombo<br>0.132|0.0859|0.143*|0.127|0.162*|
|(0.0831)|(0.0859)|(0.0833)|(0.0844)|(0.0914)|
|Retail<br>0.00266|0.0370|-0.0204|0.00226|-0.0403|
|(0.102)|(0.105)|(0.101)|(0.100)|(0.100)|
|Manufacturing<br>-0.0361|-0.0241|-0.0296|-0.0294|-0.105|
|(0.0813)|(0.0822)|(0.0816)|(0.0819)|(0.0844)|
|More than 2 paid workers<br>0.0515|0.0867|0.0744|0.0694|0.0112|
|(0.0795)|(0.0804)|(0.0805)|(0.0808)|(0.0859)|
|Says would register and sees benefit to doing so<br>0.0377|0.0609|0.0444|0.0450|0.0434|
|(0.0769)|(0.0779)|(0.0775)|(0.0766)|(0.0794)|
|log monthly sales in December 2008<br>-0.0259|-0.0255|0.00358|-0.0288|-0.0377|
|(0.0421)|(0.0417)|(0.0531)|(0.0415)|(0.0462)|
|Operate on publicly owned premises<br>-0.269***|-0.278***|-0.265***|-0.270***|-0.234***|
|(0.0744)|(0.0716)|(0.0754)|(0.0737)|(0.0849)|
|Education of Owner (years)|-0.0126<br>(0.0132)||||
|Digitspan recall of owner|0.0388||||
||(0.0303)||||
|Owner knows cost of registering|0.118<br>(0.0792)||||
|Expects to have 15 or more employees in 5 years||-0.156**|||
|||(0.0748)|||
|Profits in December 2008 exceed income tax threshold||-0.0805<br>(0.0993)|||
|Hyperbolic discounter|||-0.0422||
||||(0.0952)||
|Risk seeker|||0.0284||
||||(0.0185)||
|Log Business Assets in December 2008||||0.0499|
|||||(0.0305)|
|Observations<br>181|181|181|181|163|
|Note:|||||
|Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1|||||

28
# **Appendix 1 (continued)**

|**Variable**|**Description**|**Source**|
|---|---|---|
|Lending rate|Lending interest rate (%) in 2008|WDI|
|Agents (credit)|An index of agent operations allowed in terms of loans. See Kendall et al. (2010) for details.|_Financial Access_database|
|Places to submit loan<br>application|Locations to submit loan applications (out of 5)|Beck, Demirguc-Kunt, and<br>Martinez Peria (2008)<br>|
|Minimum loan amount|Lowest amount of consumer loan banks make expressed as a percent of GDP per capita in 2004|Beck, Demirguc-Kunt, and<br>Martinez Peria (2008)|
|Fees - consumer loan|Fee banks charge on consumer loans expressed as percent of GDP per capita in 2004|Beck, Demirguc-Kunt, and<br>Martinez Peria (2008)|
|Days to process loan<br>application|Number of days banks take to process a typical consumer loan application in 2004|Beck, Demirguc-Kunt, and<br>Martinez Peria (2008)|
|Branch approval|Equals one if the Supervisor/Regulator approval is required to open each bank branch and zero<br>otherwise in 2008|_Financial Access_database|
|Interest spread|Lending rate minus deposit rate (%) in 2008|WDI|
|Contract enforcement<br>procedures|Number of procedures in enforcing a contract|DB|
|Contract enforcement<br>time|Length of time to enforce contracts (days) in 2008|DB|
|Contract enforcement<br>cost|Total enforcement cost, including legal fees, assessment and court fees expressed as percentage<br>of total debt in 2008|DB|
|Documents required|The average number of documents (in the 5 largest banks) required to open a checking account in<br>2008.|Banking the Poor (2008)|
|Min. balance to open<br>checking|Minimum balance required to open a checking account expressed as a percent of GDP. It<br>combines two databases. When data were available in both data sets, the variable takes the latest<br>value.|Banking the Poor (2008); Beck,<br>Demirguc-Kunt, and Martinez<br>Peria (2008)|
|Fees - checking<br>account|Fees associated with maintaining a checking account expressed as percent of GDP per capita in<br>2004|Beck, Demirguc-Kunt, and<br>Martinez Peria (2008)|
|Government bank<br>share|Percentage of banking system assets in banks 50% + owned by government|Barth, Caprio, and Levine (2004)|
|Foreign bank share|Percentage of bankingsystem assets in banks 50% + owned byforeign entities|Barth,Caprio,and Levine(2004)|

51
# **List of Boxes**

|Box 1.1: Main Data Sources ............................................................................................................ 4|
|---|
|Box 2.1: Effects of the Decline in Agricultural Production on Rural Non-farm Enterprises ........ 11|
|Box 3.1: The Success Story of a Small Entrepreneur ................................................................... 16|
|Box 4.1: The Vodacom's M-PESA Financial Services .................................................................. 27|
|Box 4.2: Will the Machinga Complex Solve the Problem of Itinerancy in Dar es Salaam? ......... 31|
|Box 4.3: Main Constraints Identified by VIBINDO, 2008 ............................................................ 33|
|Box 4.4: Major Constraints Listed by the Sampled Enterprises in the Urban FGDs, 2009 ........... 33|
|Box 4.5: Major Constraints Listed by the Sampled Enterprises in the Rural FGDs, 2009 ............ 34|
|Box 5 1: Regulating Household Enterprises in Uganda ................................................................ 43|
|Box 5 2: Studying the Impact of Microfinance and Training on Small Enterprises ...................... 44|
|Box 5 3: Recent Media Report on the Massive Default Rate of the PEF (JK Billions) ................ 45|
|Box 5 4: Training of HE and MSE Women in Food Preparation .................................................. 48|
|Box 5 5: Government Financing of Rural Skills Development ..................................................... 48|

iv
One of the most impressive dimensions of China’s stellar export performance is the rapid diversification of the exports of its products. Since the 1980s, products “Made in China” have pervaded all sectors of world trade, including those that are typically considered to belong to the specialization areas of more developed countries, such as high-tech electronics and computers (Rodrik 2006; Schott 2008). China’s rapid export upgrading is especially puzzling because the production of goods requires capabilities and products that vary considerably in their knowledge requirements (Hausmann and Hidalgo 2011). Because countries can only diversify by building on what they already have, China’s export diversification suggests a particularly efficient ability to capitalize on its existing productive knowledge and to exploit the links between products. Recent work has argued that the main factor behind successful upgrading is the consistency of the new industries with the country’s latent (and evolving) comparative advantages (Lin 2012).

We empirically test this proposition using export data for more than 100,000 Chinese exporters over the 2000–6 period to investigate the link between export performance and the consistency of products with the local productive structure. The density of links between a product and the local product space is calculated by combining information on the local pattern of specialization and the intrinsic relatedness of the product. We thus contribute to the recent revival of literature on the role of structural transformation as an engine of economic growth (McMillan and Rodrik 2011). The main argument is that not all products have the same degree of relatedness (and, hence, the same position in the product space); as a result, products have different potential, notably as platforms for jumping up to new economic activities (Hausmann and Klinger 2007a; Hidalgo et al. 2007). Greater link density between products is predicted to yield positive spillovers, such as knowledge externalities and
equations (30, 31) and 61 and 62 from the exogenous variable autoregressions (35, 36), only three parameters remain to be estimated (ps, b and g). Estimation by nonlinear least squares (NLLS) is straightforward and can be implemented In most standard regression packages. This equation therefore uses the RE commodity price theory to impose two restrictions on equation (28). Unfortunately, it does not appear possible to obtain a similarly restricted version of equation (43).

# E2. The Gilbert-Palaskas Model

Sl. The underlying philosophy of the approach adopted by Ghosh _et al._ (1987) and Gilbert and Palaskas (1990) is to attempt to obtain a natural generalization of the traditional 'myopic' commodity price equation (17) to allow for rational expectations. In the traditional approach the change in the commodity price is related to the current market imbalance Qt-t, equal to the change in stock Ast. This suggests that in a forward looking model the current change in price should be related to a discounted sum of present and future market imbalances. Two questions arise:

- i) At what rate should expected future imbalances be discounted? and

- ii) Should one model expected future imbalances or the innovations in these imbalances (i.e. the unanticipated components of the imbalances) as affecting the commodity price?

52. The fundamental equation in the Gilbert and Palaskas model is 32
an average of 194 percent while they underestimate the largest quintile's plot sizes by an average of 11 percent. These findings are consistent with previous studies showing that self-reported area measurement suffers from regression-to-mean biases: overestimation for smaller plots and underestimation for larger plots (e.g., Carletto et al., 2013; Carletto et al., 2015; Dillon et al., 2019; Abay et al., 2019; Gourlay et al., 2019). There is not much difference between reporting biases in the post-planting and post-harvest periods, except for the last quintile. We investigate this fact further in the next sections. Considerable average overestimation is one striking feature of selfreported plot areas.

**Table 2: Discrepancies between self-reported (SR) and GPS-based plot size measures**

|Plot size<br>quartile|Observations|Mean<br>SR:PP|Mean<br>GPS|Mean<br>SR:PH|Relative bias<br>(%):PP|Relative bias<br>(%):PH|
|---|---|---|---|---|---|---|
|0-25%|387|0.28|0.10<sup>***</sup>|0.28|193.73|198.52|
|25-50%|389|0.46|0.26<sup>***</sup>|0.47|74.03|76.85|
|50-75%|390|0.66|0.54<sup>***</sup>|0.66|23.93|23.23|
|75-100%|380|1.09|1.22<sup>***</sup>|1.14|-10.57|-6.30|
|Total|1,546|0.62|0.53|0.64|18.27|21.10|

**Notes:** GPS stands for area measurement using handheld Global Positioning Systems, while SR stand for self-reported plot size in acres. PP stands for post planting visit while PH stands for post-harvest visit. These are quartile-specific mean and relative biases as a percent of (mean) GPS-measured plot size.<sup>***</sup> Represents statistical test differences between GPS values and self-reported value in the post-planting survey.

Figure 1 shows that rounding is a major source of measurement error in self-reported plot area. Moreover, Figure 1 shows that heaping at focal points remained pervasive even after the true plot areas and the extent of measurement error were shared with treatment group farmers. About half of the self-reported plot sizes assume either of the four rounded values (0.5, 1.0, 1.5 or 2.0) in the post-planting and post-harvest interviews.<sup>11</sup> The slight differences in the distribution of plot sizes between the post-planting and post-harvest rounds appears to be only for those plots greater than 1 acre, where rounding at 1.5 and 2 acres slightly diminishes in the post-harvest survey. Appendix Table A2 reports Kolmogorov-Smirnov tests for differences between each pair of distributions in Figure 1. These non-parametric tests suggest that we cannot reject the null hypothesis of no significant differences between self-reported values across control and treatment groups, and between self-reported pre-treatment and post-treatment values within the treatment group. However, we can clearly detect statistically significant differences between self-reported

> 11 The corresponding share associated with GPS-measured values is less than 3 percent.

9
Within the informal sector, there is a marked heterogeneity in net earnings outcomes. The outcomes depend in part on whether a worker is self-employed or wage-employed, in agriculture or in other sectors. There are clear differences, for example, in the earnings levels by distinct subgroups within the informal sector (Figure 4). There are also disparities in the changes in earnings levels over time. The self-employed agricultural <!-- Start of picture text --> Figure 4. Real Net Monthly Earnings<br>by Selected Sub-Groups of Informal Sector Workers<br>450<br>Self-employed non-<br>400 agriculture<br>350<br>300 Wage-employed non-agriculture<br>250<br>Wage-employed<br>agriculture<br>200<br>Self-employed<br>150 agriculture<br>100<br>2001 2002 2003 2004<br>Source: LSMS and staff estimates.<br><!-- End of picture text --> workers within the informal sector, for example, have the lowest reported net earnings in absolute terms. They have also experienced a decline in earnings over the 2003 to 2004 period, with net earnings levels currently below the 2001 period.

Not surprisingly, earnings inequality is much higher in the informal sector, at least in the FBH. For the BH labor market as a whole, the Gini coefficient of the formal sector earnings distribution is 0.31 while the informal sector distribution is 0.35, based on nominal earnings. However, most of the aggregate difference in distribution appears to be driven by FBH (Gini coefficient is 0.30 in the formal sector and 0.37 in the informal sector, using nominal earnings). Overall consumption inequality is virtually the same in the two sectors, and essentially unchanged over the last four years at 0.26, suggesting the mitigating role of transfers, among other things.<sup>9</sup> For the panel sample of workers, wage inequality fell over the last four years, by 2-3 points, while preserving the gap between formal sector and informal sector wage inequality. Even more striking is the disparity in wage inequality between formal sector and informal sector workers within selected industries. For example, within the agriculture sector, the Gini coefficient of formal sector distribution is 0.28 while the informal sector is 0.39.

**III. Labor Market Transition in Bosnia and Herzegovina** Is there a core of long-term unemployed in BH? The incidence of longterm unemployment (long term unemployment as a proportion of total unemployment), depending on its magnitude, may indicate substantial social costs. Long-term unemployment may lead

|Table 2. Long-Term U<br>(In percent of all un|nemployed<br>employed)<br>|||
|---|---|---|---|
||2001|2002|2003|
|_Duration of unemployment_||||
|Less than 1 month|1.9|1.4|2.32|
|1-3 months|13.0|5.0|5.77|
|4-6 months|8.5|6.9|6.5|
|7-11 months|6.7|4.1|5.63|
|1 year or more<br>Source: LSMS and staff estimates.|69.8|82.5|79.8|

> 9 This is consistent with the consumption inequality reported in the _Poverty Assessment_ (World Bank 2003) _._ The disparity in wage inequality between the formal and informal sectors is also broadly consistent with the disparity reported in the BH labor market study (World Bank 2002).

8
# **5. Results**

In this section we present our main results. Many outcome variables are measured at the household level. However, several of these outcomes are also reported separately by different individuals within households which allows us to explore gender-differentiated impacts. In Tables 4 to 13 we present the main impact estimates for the Land Titling Program. All tables follow a similar format. Column 1 provides the sample size for the pooled regression and Column 2 the mean of the dependent or outcome variable of interest in the control group for the third survey round. Columns 3 to 5 report the estimates for each survey round separately which correspond to the coefficients on the interaction terms in Equation 1. Column 6 reports the estimates from the pooled regression and Columns 9 to 11 show the results from estimating the SAR regression model on those respondents for whom household-level GPS data were available.<sup>14</sup> We start our examination of impacts with the most proximate indicator that the program might be working: individuals reporting that the land was registered and their perceptions of security. Table 4 shows these variables. Land in the treatment area is significantly more likely to be reported as registered by the respondents, and this coefficient increases across our three rounds for women. Although it is hard to capture in quantitative data, there is some suggestion that perceptions of security have also improved. A significant fraction of both men and women are worried that they will lose their plot if they leave it fallow (about a third in the control group in round 3). For men we see significant reductions of this for the pooled estimate, although this effect may have tapered off by the

> 13 The distance decay can also be formulated as a power function and the elements of W matrix can be inverse 1 distances with a distance decay factor, , ∅= , where i denotes the distance between units and i 14 The GPS measurements of plots in our data are incomplete and, as mentioned above, we rely on a repeated γγ dd dd ii jj. cross section of plot-level data. Therefore, we only present SAR estimates for household- or individual-level regressions where we are using the location of the dwelling as the geographic coordinates. We do not estimate SAR regressions for plot-level outcome variables.

17
ments to estimate travel demand and supply. Second, counterfactual trip information extracted from Google Maps allows comparison of similar trips under different traffic scenarios, which helps attenuate endogeneity issues by breaking any correlation between the dependent variable and the error term.<sup>2</sup> The authors find a negligible welfare loss associated with traffic congestion, estimated to be less than 1 percent of the daily wage in Colombia. This result is consistent with mobility patterns in urban India, where the examination of traffic data collected via Google Maps (Akbar et al., 2018) or smartphone applications (Kreindler, 2018) also points to small welfare effects of congestion.

In a recent research, Yang et al. (2020) combine nearly 400 million real-time observations of speed and traffic volume data with a plausibly exogenous policy shock inducing a variation in traffic congestion (driving restrictions based on the last digit of the license plate number) to estimate the marginal external congestion cost in Beijing. Using traffic microwave sensors, they collected real-time traffic flows at two-minute intervals for the city’s major roads in 2014. The authors find that the marginal external congestion cost can rise up to $0.30 per vehicle-km while the deadweight loss of road congestion represents about half percent of Beijing’s GDP. The authors find that this welfare loss would increase about three fold if social and environmental externalities such as accidents and air pollution were included in the calculation.

# B. Mobility, Employment and Productivity

There is an established literature that uses traditional data (mainly travel surveys) to the links between and economic outcomes. For exam- analyze reciprocal mobility ple, Bhat and Koppelman (1993) use the Dutch Mobility Panel to show that employment and household income have a on car significant influence ownership, trip generation,

> 2 Potential identification challenges may arise from trip selection and the simultaneous determination of trip cost and flows. To overcome these issues, the authors combine a number of empirical strategies, including controlling for weather shocks, enlarging the study area to multiple road segments, and considering several counterfactual time costs for similar trips under various scenarios.

9
**Table 2: Intra‐regional and Inter‐regional GVC Imports by World Regions, 2014**

||**EAS**|**ECS**|**LCN**|**MEA**|**NAC**|**SAS**|**SSA**|**TOTAL**<br>**IMPORTS**<br>**FROM**<br>**WORLD**|**Share of**<br>**Intra-**<br>**regional**<br>**Imports**|
|---|---|---|---|---|---|---|---|---|---|
|**EAS**|1,132,505,425|174,331,826|6,933,197|9,397,836|95,928,700|10,380,906|1,685,414|**1,431,163,304**|**79.13%**|
|**ECS**|403,344,353|1,219,972,652|8,895,265|22,929,535|63,956,216|27,760,844|5,864,669|**1,752,723,532**|**69.60%**|
|**LCN**|100,885,823|34,250,745|36,008,876|1,055,878|107,771,641|4,927,574|268,978|**285,169,514**|**12.63%**|
|**MEA**|115,396,696|71,046,409|1,173,348|39,853,338|22,789,776|9,453,694|959,278|**260,672,538**|**15.29%**|
|**NAC**|429,709,060|124,021,608|173,198,436|6,236,400|167,259,268|15,739,249|1,960,914|**918,124,936**|**18.22%**|
|**SAS**|56,130,444|9,738,020|278,777|2,828,815|3,239,340|5,416,451|158,392|**77,790,239**|**6.96%**|
|**SSA**|42,276,074|21,863,028|984,448|6,079,575|5,273,939|4,930,635|7,034,538|**88,442,236**|**7.95%**|
|**TOTAL**<br>**MIRRORED**<br>**EXPORTS**|**2,280,247,875**|**1,655,224,288**|**227,472,346**|**88,381,376**|**466,218,880**|**78,609,352**|**17,932,183**|**4,814,086,300**||
|**Share of**<br>**Intra-**<br>**regional**<br>**mirroed**<br>**Exports**|**49.67%**|**73.70%**|**15.83%**|**45.09%**|**35.88%**|**6.89%**|**39.23%**|||

<u>Notes: Values expressed in thousand current US$ and calculated on the total number of 256 countries/geographies with available data on GVC Exports in at least some years of</u> the period 1988‐2014 (HS 1988/92 nomenclature, six‐digits, mirror data). All trade data is drawn from Comtrade on WITS, and data on countries' region and income group in each year comes from WB classifications.

<u>Source: Trade: World Bank MC‐GVC database (http://wits.worldbank.org/WITS/WITS/AdvanceQuery/GVC/GVCQueryDefination.aspx?Page=GVCIndicator).</u> 24
# Appendix A: Source Data

## Population census data

This paper relies on data collected by eight Pacific Island governments’ National Statistics Offices (see Table A1) through their national population and housing censuses, and disseminated by the Statistics for Development Division (SDD) of the Pacific Community (SPC) through their PopGIS platform (https://prism.spc.int/regional-data-and-tools/popgis2).

### **Table A1: Pacific country census years and National Statistics Offices**

|**Country**|**Censusyear**|**National Statistics Office**|
|---|---|---|
|Fiji|2007|Fiji Bureau of Statistics|
|Solomon Islands|2009|Solomon Islands National Statistics Office|
|Vanuatu|2009|Vanuatu National Statistics Office|
|Kiribati|2010|Kiribati National Statistics Office|
|FSM|2010|FSM Division of Statistics|
|Tonga|2016|Tonga Statistics Department|
|RMI|2011|RMI Economic Policyand PlanningStatistics Office|
|Tuvalu|2012|Tuvalu Central Statistics Divison|

The data on the PopGIS site are encapsulated in maps for each PIC, illustrating variation in the results to select census questions across different parts of the country. Although the unit-record data for each individual and household are not available (and so it is not possible to consider cross-tabulations of responses across questions, for example), the geographical disaggregation is generally very detailed, and it is particularly helpful for this study focused on spatial aspects of development in the PICs.

Table A2 describes the geographical disaggregation of the data published on the PopGIS platform. Included in this table is a note on the nature of the overlap of geographical area boundaries with islands; with the exception of Vanuatu, which publishes a purposeful disaggregation of the census results by island on PopGIS, the data cannot be manipulated to present results at the island-level.

## Geospatial data

The geospatial data we used were from several different sources. The PopGIS platform provides ‘shape files’ (the geo-coded outlines for specific regions) for each country, corresponding to the geographical disaggregation of the data. The locations of cities and towns of interest were pinpointed using OpenStreetMap and Google Maps; some coordinates were automatically generated using the name of the town, others were manually identified. Distances were calculated using QGIS software.

28