rafmacalaba/datause-encoder-data huggingface dataset:
# Figures

<!-- Start of picture text --> A: Wheat B:stocks<br>1970 1980 1990 2000 2010 1970 1980 1990 2000 2010<br>Year Year<br>Lowess US wheat Prod. Stocks<br>C: Aid D: Conflict<br>1970 1980 1990 2000 2010 1970 1980 1990 2000 2010<br>Year Year<br>Least Regular 2nd Quartile Least Regular 2nd Quartile<br>3rd Quartile Most Regular 3rd Quartile Most Regular<br>80 800<br>70 600<br>60 400<br>(millon MT) 50 200<br>(Million Bushels)<br>US Wheat Production 40 0<br>End of Year Gov. Wheat Stocks<br>150 .5<br>100 .3.4<br>50 .2<br>(1,000 MT)<br>0 .1<br>Wheat Aid Receipts Experiencing Conflict<br>Proportion of Countries<br><!-- End of picture text --> FIGURE 1: TIME TRENDS IN KEY NQ VARIABLES _Notes:_ Panel A shows US wheat production over time. Panel B is US government holdings of wheat stocks over time; the vertical lines represent Farm Bill years that substantially changed government wheat procurement. Panel C is the average physical volume of US wheat food aid shipments within each quartile of regularity of aid receipts over time. Panel D is proportion of conflict by each quartile of aid receipt regularity over time. The data for Panels A, C, and D are taken from the NQ replication file. Data for panel B is taken from the Farm Service Agency and National Agricultural Statistics Service, USDA (2006).

26
We estimate that during 2000-2008, annual private business spending on intangibles in Brazil averaged about 4 percent of GDP. While this is significantly lower than comparable rates for the US, Japan and the UK, which hover around 11 percent, and China at almost 8 percent, it is not too far below estimates for other developed countries such as Italy and Spain at around 5-6 percent. Also, Brazil‘s business investment in intangibles is only slightly lower than that in fixed assets (which ranged between 4-9 percent of GDP during this period). Thus, our results suggest that intangible investment is an important part of the Brazilian economy. We also find that total intangible investment is increasing, rising from about 3 percent of GDP in 2000 to 5 percent in 2008. However, in recent years, tangible investment appears to be growing even faster. It is too early to say if this signals a divergence between tangible and intangible investment, which would be contrary to the experience of advanced economies.

<!-- Start of picture text --> Figure 1: Aggregate Expenditures on Intangibles (% of GDP)<br>2.5%<br>2.0%<br>1.5%<br>1.0%<br>0.5%<br>0.0%<br>2000 2002 2004 2006 2008<br>COMPUTERIZED INFORMATION INNOVATIVE PROPERTY ECONOMIC COMPETENCIES<br><!-- End of picture text --> The CHS classification of intangibles consists of three main categories: computerized information, innovative property, and economic competencies. Of these, Brazil spends the most (2.3 percent of GDP in 2008) on innovative property, which comprises business research and development, copyrights, architectural and engineering designs, new financial products and mineral exploration. Next (at 1.6 percent of GDP) are economic competencies, which include brand equity development through advertizing and market research, employee training and organizational improvement.<sup>7</sup> Lastly, business computer software and database investments total about 1 percent of GDP. However, spending on

> 7 This is an underestimate because of missing data on labor training expenditures in service sector firms.

4
**Table A3.IU:  Father's Education and  Educational Expenditure on Children**

|||**URBAN**<br>**IHDS**|**INDIA**<br>**2005**||
|---|---|---|---|---|
||**(6-22) Year**|**s Children**|**(8-23) Year**|**s Children**|
|Father's Sch.|384.4|338.7|403.9|348|
||(14.9)***|(14.40)***|(17.4)***|(16.9)***|
||[22.2]***|[20.4]***|[24.1]***|[22.6]***|
||{28.2}***|{26.4}***|{30.9}***|{28.8}***|
|Father's Sch. * Daughter|-34.0|-37.5|-36.5|-40.2|
|Dummy|(20.5)*|(20.3)*|(23.9)|(23.5)*|
||[19.0]*|[18.4]**|[21.9]*|[21.1]*|
||{18.7}*|{16.7}**|{23.9}|{19.6}**|
|Daughter Dummy|-290.2|-189.9|-325.1|-174.3|
||(136.9)***|(138.7)|(164.4)**|(162.9)|
||[125.0]**|[118.9]|[144.0]**|[138.6]|
||{142.2}*|{123.9}|{171.0}*|{148.0}|
|Intercept|784.8|2763.5|917.3|3220.7|
||(105.1)***|(148)***|(124.9)***|(175.6)***|
||[157.5]***|[271.2]***|[185.7]***|[332.3]***|
||{223.2}***|{279.4}***|{255.2}***|{338.3}***|
|No. of Children||-673.1||-777.7|
|||(36.0)***||(41.9)***|
|||[75.9]***||[88.8]***|
|||{83.1}***||{99.8}***|
|No. Observations|13086|13086|11051|11051|
|||**NSS**<br>|**1995**<br>||
||**(6-18) Year**|**s Children**|**(8-18) Year**|**s Children**|
|Father's Sch.|108.4|108.6|110.4|110.9|
||(2.817)***|(2.789)***|(3.070)***|(3.039)***|
||[7.819]***|[7.467]***|[7.436]***|[7.021]***|
||{7.898}***|{7.735}***|{7.781}***|{7.644}***|
|Father's Sch. * Daughter|-11.3|-11.5|-11.2|-11.2|
|Dummy|(3.906)***|(3.860)***|(4.295)***|(4.238)***|
||[2.766]***|[2.788]***|[3.114]***|[3.121]***|
||{4.559}**|{4.497}**|{4.682}**|{4.616}**|
|Daughter Dummy|-39.4|-14.3|-41.55|-11.14|
||(26.12)|(26.15)|(29.00)|(29.03)|
||[16.95]***|[17.48]|[21.47]|[22.19]|
||{26.77}|{29.61}|{30.19}|{33.90}|
|Intercept|505.9|923.0|543.6|1004.0|
i POLICY RESEARCH WORKING PAPER 1379

# Summary findings

Arguments for financing infrastructure development through gr :rnment subsidies and foreign borrowing meet with increasing skepticism. Numerous "white elephants" subsidized by governments have strengthened doubts about the cfficacy of public finance, and the debtservicing problems of the 1980s have weakened arguments for foreign borrowing.

Recent innovative suggestions for financing infrastructure investments in developing countries have a back-to-the-future quality. At the heart of the nineteenthcentury debate on financing infrastructure development - - especially railways lay certain concepts: relying on private finance, encouraging the growth of domestic financial markets, and choosing financial instruments that minimize the risk of dependence on foreign funds. Eichengreen reviews the historical record in an attempt to glean lessons for developing countries today.

In the nineteenth century, much as in many of today's less developed and less liberalized economies, not all the informational and contractual preconditions for efficient private or commercial finance of infrastructure projects prevailed. In somc regions, it was difficult to tap investors at home or abroad. Many countries lacked the private institutions (such as universal banks) and public ones (such as regulatory agencies) needed to facilitate monitoring, to discipline management, and to ensure an adequate flow of information to investors.

Irn places as diverse as Canada, India, Spain, and the United States, getting enough finance often required that the government provide collateral (land grants) and bond - guarantees especially where asymmetric information caused credit rationing.

The main lesson: Exploiting nontraditional approaches to financing infrastructure investment requires acdon on two fronts: liberalizing and developing domestic financial markets, and reforming administrative mechanisms that ensure accountability from enterprises enjoying government subsidies or guarantees.

- - This paper a product of the Office of the Vice President, Dceclopment Economics is one in a scries of background papers prepared for _World Development Report 1994_ on infrastructure. Copies of this paperarc available free from the World Bank, 18 18 H StreetNW, Washington, DC 20433. Please contact the World Development Report office, roomT7-101, extension 31393 (48 pages). November 1994.

_The Policy Resarch Working Paper Series disseminates the fsndings of uork in progress to encourage the exchange of ideas about development isses. An objective of the series ss toget the fidngs out quickly, even if the presentations are ess than fuily polished. The papers carry the names of the authors and should be used and cited accordingly. The fndings, inetrprerarions, and conclusions are the authors' own and should not be attributed to the World Bank, its Executive Board of Directors, or any of its member countries._ Produced by the Policy Research Dissemination Center
# **Figure A.7. Predicted Poverty Gap Based on Within-Year Imputation**

<!-- Start of picture text --> Bangladesh 2018/19 Ethiopia 2018/19<br>2 18<br>1.5 16<br>1 14<br>.5 12<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Tanzania 2020/21 Malawi 2019/20<br>6 19<br>5.5<br>18<br>5<br>17<br>4.5<br>4 16<br>3.5<br>15<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Nigeria 2018/19 Vietnam 2016<br>20 3.5<br>18<br>3<br>16<br>2.5<br>14<br>2<br>12<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Estimated poverty rate (%) Estimated poverty rate (%)<br>Estimated poverty rate (%) Estimated poverty rate (%)<br>Estimated poverty rate (%) Estimated poverty rate (%)<br><!-- End of picture text -->

**Note:** Estimates are obtained by imputing from sample 1 into sample 2. 100 simulations are implemented. The standard errors are calculated using 100 bootstrap replications and are adjusted for complex survey design. Larger hollow symbols indicate that the estimates are statistically insignificantly different from the true poverty gap. Dashed lines represent the true poverty gap. Dotted lines represent confidence intervals of the true poverty gap. Estimates are obtained using the normal linear regression models.

64
**Figure 4: Simulated enrollment inequality impacts of reducing the distance to schools**

<!-- Start of picture text --> Simulation: Simulation:<br>Distance to primary school reduced to zero Distance to primary school reduced to zero<br>3.0 3.0<br>45 degree line 45 degree line<br>2.5 2.5<br>Niger 92<br>2.0 2.0<br>Mali<br>Burkina Faso<br>1.5 Mali 1.5 C.A.RNiger 98<br>Chad<br>1.0 1.0<br>0.5 0.5<br>0.5 1.0 1.5 2.0 2.5 3.0 0.5 1.0 1.5 2.0 2.5 3.0<br>Actual male/female ratio Actual rich/poor ratio<br>Simulation: Simulation:<br>Distance to primary and secondary school reduced to zero Distance to primary and secondary schools reduced to zero<br>3.0 3.0<br>45 degree line Senegal 45 degree line<br>2.5 2.5<br>Morocco<br>2.0 C.A.R Niger 92 2.0 Niger 98 Nigeria Morocco<br>C.A.R Mali<br>Benin<br>Niger 92<br>1.5 Chad Niger 98 1.5 Haiti Dominican RepBurkina Faso<br>Chad<br>Mali Cameroon<br>1.0 1.0<br>0.5 0.5<br>0.5 1.0 1.5 2.0 2.5 3.0 0.5 1.0 1.5 2.0 2.5 3.0<br>Actual male/female ratio Actual rich/poor ratio<br>Simulated rich/poor ratio<br>Simulated male/female ratio<br>Simulated rich/poor ratio<br>Simulated male/female ratio<br><!-- End of picture text --> _Note_ : The vertical distance from the 45<sup>o</sup> line shows the change in inequality associated with reducing the distance to school to zero. Labeled countries are those for which the absolute value of the change is greater than 0.1.

_Source_ : Author’s calculations from DHS data.

It is clear from the figure that the models imply little impact of increasing the availability of primary schools on gender inequality: almost all points lie on the 45-degree line. The only exceptions are Mali and Niger (1992) where reducing the distance to the nearest primary school to zero is associated with more than a 0.1 decline in male/female inequality.

The top right panel of Figure 4 shows the equivalent simulation for rich/poor inequalities. Again, most of the points lie on the 45-degree line, which indicates little impact of distance on inequality. For a handful of Western and Central African countries there is more than a 0.1 drop in rich/poor inequality associated with distances to primary schools equal to zero (Chad, Central African Republic, Niger 1998, Burkina Faso, and Mali).

The simulation suggests that the inequality impacts of increasing the availability of both primary and secondary schools would be larger in absolute terms: substantial deviations from the 20
Table C.4: Average Technology Sophistication by Country and Sector

||A|BF EXT||G|BF EXT||S|SBF EXT||
|---|---|---|---|---|---|---|---|---|---|
||AGRI|MANF|SVC|AGRI|MANF|SVC|AGRI|MANF|SVC|
|Overall|2.51|2.51|2.63|2.37|2.54|2.76|2.71|2.40|2.24|
|Brazil (BR)|3.23|3.03|3.19|3.17|3.05|3.44|3.34|2.98|2.62|
|Vietnam (VT)|2.70|2.72|2.69|2.52|2.72|2.76|2.91|2.73|2.44|
|Senegal (SN)|1.59|1.78|2.02|1.41|1.86|2.09|1.87|1.48|1.66|
|Gap: BR - SN|1.64|1.25|1.17|1.76|1.19|1.35|1.47|1.50|0.96|
|Relative Gap|41%|31%|29%|44%|30%|34%|37%|38%|24%|
||A|BF GAP||G|BF GAP||S|SBF GAP||
||AGRI|MANF|SVC|AGRI|MANF|SVC|AGRI|MANF|SVC|
|Overall|0.57|0.76|0.74|0.61|0.79|0.79|0.53|0.79|0.56|
|Brazil (BR)|0.71|0.91|0.81|0.85|0.90|0.84|0.53|1.07|0.72|
|Vietnam (VT)|0.68|0.87|0.77|0.73|0.84|0.83|0.58|1.09|0.54|
|Senegal (SN)|0.33|0.51|0.65|0.24|0.63|0.69|0.48|0.22|0.42|
|Gap: BR - SN|0.38|0.40|0.16|0.61|0.27|0.15|0.05|0.85|0.30|
|Relative Gap|10%|10%|4%|15%|7%|4%|1%|21%|8%|

Note: AGRI, MANF, and SVC represent agriculture, manufacturing, and services, respectively. Overall is the average of Brazil, Vietnam, and Senegal. For the columns on SSBF, the sample is restricted to the firms having sector-specific technologies. Technology measures are weighted by the sampling weights.

74
# **Table 7.1. LS results**

_Dependent variable: Share of stages with unofficial payments in total (procedures No. 1, 2A, 2B,_ _<u>3A and 3B)</u>_

||Model 7.1.1|Model 7.1.2|Model 7.1.3|Model 7.1.4|<br>Model 7.1.5|
|---|---|---|---|---|---|
|Constant|-0.219**<br>(0.095)|-0.248**<br>(0.103)|-0.318**<br>(0.137)|-0.243**<br>(0.111)|-0.182<br>(0.132)|
|Number of stages by law|0.014***<br>(0.004)|0.011***<br>(0.004)|0.015***<br>(0.004)|0.015***<br>(0.004)|0.011***<br>(0.004)|
|Number of documents obtained by<br>respondent||0.005<br>(0.004)||||
|Number of documents per stage|||0.077<br>(0.070)|||
|Ln of total time divided by number of<br>stages bylaw(templates)||||0.012<br>(0.018)||
|Ln time required to complete a<br>procedure with a landplot|||||0.009<br>(0.019)|
|Procedure 2A|0.017<br>(0.045)|0.005<br>(0.048)|0.003<br>(0.048)|0.019<br>(0.046)|-0.022<br>(0.048)|
|Procedure 2B|0.128**<br>(0.051)|0.131**<br>(0.053)|0.132**<br>(0.053)|0.122**<br>(0.052)|0.090<br>(0.054)|
|Procedure 3A|0.120**<br>(0.054)|0.120**<br>(0.057)|0.118**<br>(0.057)|0.113**<br>(0.055)|0.072<br>(0.058)|
|Procedure 3B|0.091*<br>(0.054)|0.085<br>(0.056)|0.083<br>(0.056)|0.086<br>(0.055)|0.038<br>(0.058)|
|Kaliningrad oblast|0.179**<br>(0.070)|0.202***<br>(0.077)|0.209***<br>(0.079)|0.172**<br>(0.073)|0.169**<br>(0.079)|
|Saint-Petersburg|0.346***<br>(0.104)|0.418***<br>(0.114)|0.430***<br>(0.116)|0.301***<br>(0.112)|0.447***<br>(0.147)|
|Leningrad oblast|0.220**<br>(0.100)|0.258**<br>(0.109)|0.268**<br>(0.114)|0.216<br>(0.102)|0.212**<br>(0.105)|
|Moscow|0.301***<br>(0.079)|0.338***<br>(0.088)|0.349***<br>(0.094)|0.300***<br>(0.081)|0.331***<br>(0.089)|
|Moscow oblast|0.239***<br>(0.091)|0.257***<br>(0.097)|0.269***<br>(0.100)|0.235**<br>(0.093)|0.249**<br>(0.099)|
|Sverdlovsk oblast|0.111<br>(0.082)|0.109<br>(0.091)|0.120<br>(0.093)|0.104<br>(0.085)|0.155*<br>(0.092)|
|Tomsk oblast|0.046<br>(0.072)|0.059<br>(0.078)|0.069<br>(0.080)|0.050<br>(0.075)|0.050<br>(0.080)|
|Khabarovsk krai|0.036<br>(0.073)|0.070<br>(0.081)|0.082<br>(0.083)|0.039<br>(0.076)|0.043<br>(0.081)|
|Irkutsk oblast|0.250***<br>(0.073)|0.286***<br>(0.080)|0.296***<br>(0.083)|0.255***<br>(0.076)|0.259***<br>(0.081)|
|Rostov oblast|0.353***<br>(0.077)|0.374***<br>(0.084)|0.384***<br>(0.087)|0.364***<br>(0.081)|0.409***<br>(0.087)|
|Perm oblast|-0.087<br>(0.077)|-0.057<br>(0.085)|-0.047<br>(0.087)|-0.095<br>(0.081)|-0.064<br>(0.085)|
|Novosibirsk oblast|0.013<br>(0.070)|0.029<br>(0.077)|0.041<br>(0.080)|0.003<br>(0.075)|0.011<br>(0.080)|
|Nizhny Novgorod oblast|0.120<br>(0.076)|0.140*<br>(0.082)|0.151<br>(0.084)|0.115<br>(0.079)|0.139<br>(0.086)|
|Sakhalinsk oblast|0.040<br>(0.100)|0.092<br>(0.115)|0.103<br>(0.117)|0.033<br>(0.102)|0.026<br>(0.109)|
|Adj. R|0.277|0.277|0.276|0.270|0.288|
|N|405|375|375|398|358|
|Mean of the dependent variable|0.20|0.22|0.22|0.20|0.20|

*, **, *** Significance level of 10%, 5%, and 1%, respectively

27
# **<u>White Women</u>**

|||||||||Sim|ulated y|ears of|schoolin|g||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||0|1|2|3|4|5|6|7|8|9|10|11|12|13|14|15|16|17|Total|
|0|4575|97|41|110|267|19|17|26|71|0|9|52|2|3|1|19|28|3737|9074|
|1|0|805|0|5|14|2|2|4|9|0|0|5|0|1|1|5|8|282|1143|
|2|31|21|1791|16|54|3|8|5|18|1|5|21|1|0|1|8|10|549|2543|
|3|48|27|10|2871|45|4|4|9|24|1|2|26|3|3|2|12|22|1004|4117|
|4|98|67|28|20|7898|43|32|24|72|4|27|44|7|7|1|44|96|2494|11006|
|5|41|14|6|11|8|2060|2|6|6|1|3|4|0|1|0|5|9|611|2788|
|6|21|11|2|5|6|6|1648|1|7|1|2|4|0|1|0|1|5|514|2235|
|7|36|21|9|16|12|17|8|1801|5|5|5|2|0|0|0|1|5|496|2439|
|8|33|32|15|24|24|33|28|21|4539|4|4|16|4|4|1|10|49|1274|6115|
|9|17|12|10|6|19|37|12|17|23|1124|5|5|1|1|0|2|9|267|1567|
|10|18|10|10|8|16|37|49|38|37|19|1513|0|3|2|2|0|8|326|2096|
|11|86|47|40|60|117|122|112|124|214|60|76|7146|12|23|21|59|188|1715|10222|
|12|5|0|3|2|5|9|13|9|14|9|11|6|452|2|5|0|4|93|642|
|13|4|2|3|4|3|5|15|5|14|2|6|2|0|590|0|1|3|99|758|
|14|4|3|3|6|21|17|10|10|40|5|8|27|5|6|629|6|21|113|934|
|15|24|7|6|17|50|27|21|36|95|13|37|117|15|32|13|2096|80|409|3095|
|16|2|0|0|1|2|6|2|3|10|2|3|0|2|2|2|0|918|138|1093|
|17|2|0|0|1|1|3|0|0|2|1|0|0|1|0|1|0|3|388|403|
|Tota|5045|1176|1977|3183|8562|2450|1983|2139|5200|1252|1716|7477|508|678|680|2269|1466|14509|62270|
documented the acute effects of COVID-19 restrictions on the livelihoods and wellbeing of FSW, there has been limited understanding of how these effects subsequently evolved. Our results underscore that there were severe economic impacts that ensued after restrictions were put into effect at the onset of the COVID-19 pandemic, and reinforce the need for social protection mechanisms for key vulnerable populations and informal workers.<sup>26,27</sup> However, our study also demonstrates how demand, income, and economic security improved after restrictions were lifted and pandemic concerns receded, underscoring that informal employment in the context of COVID19 is not a static experience and continues to fluctuate as restrictions shift and personal risk tolerance changes. In the Tanzanian context, the government’s limited public health response and approach to reopening may also have mitigated income shocks for FSW and reduced COVID-19 related fear and stigma;<sup>28</sup> nonetheless, the health implications in terms of COVID morbidity and mortality of this approach should not be underestimated.

There are some important limitations to this study. While efforts were made to support participants in reflecting on their work during surveys and interviews, poor recall may have affected the ability of participants to correctly report their experiences. In addition, quantitative and qualitative data collection occurred at different time points. Wave 2 surveys were conducted among a subset of participants with access to a mobile phone and do not include perspectives from the full sample, limiting our ability to explore longitudinal effects over critical time points. There was also substantial attrition in wave 3, and the balanced sample may be less representative of the overall study sample.

In conclusion, this study improves our understanding of how the experiences of FSW evolved over the course of an extended pandemic shock. We demonstrate that while ways of meeting clients and working changed, the financial hardship experienced early in the pandemic largely dissipated in the second year. However, despite improvements in economic security and wellbeing, there is a critical need to integrate social protection mechanisms for FSW and other key vulnerable populations as part of public health emergency preparedness and response measures to ensure that sex workers are not further marginalized and are able to meet basic needs during crisis periods.

14
the nature of informality better and to provide empirical support for the theories that best depict the reality on the ground, with an eye for what policies might be most appropriate in a setting of heterogenous firms and workers. The remainder of this section reviews previous studies on the informal sector and informal employment in Indonesia and ends with recent developments relating to informality.

# **The Informal Sector in Indonesia**

As in other countries, measuring the size of the informal sector in Indonesia is a challenging task. One of the key estimates of the size of the informal sector comes from Rothenberg et. al (2016). Using the 2013 IMK Survey, they found that 98 percent of firms with fewer than 10 employees are unregistered. Using Hsieh and Olken’s (2014) previous finding that 95 percent of firms in Indonesia have fewer than 10 employees, Rothenberg and co-authors (2016) projected that 93 percent of Indonesian firms are informal.

Informal enterprises in Indonesia share a number of characteristics that align with previously documented patterns in other countries. For instance, more than 90 percent of informal enterprises in the country are either micro or small firms – suggesting a substantial overlap between MSEs and the informal sector. This contrasts with the situation in Malaysia and Vietnam, both of which have a thriving MSE sector characterized by much lower rates of informality at 9 percent and 27 percent, respectively (Nugroho, 2021). Notably, an estimated 64.5 percent of MSMEs in Indonesia are owned by women, highlighting the important role of women in this space (Susanti et al., 2023).

In addition to firm size, informal enterprises in Indonesia are also characterized by low levels of productivity. For example, estimates from the 2009 ISS showed that labor productivity in the informal sector ranged from 9 percent (in the city of Yogyakarta) to 15 percent (in the province of Banten) of productivity in the formal sector (ADB & BPS, 2011). This reflects the fact that most informal enterprises are primarily labor intensive, rely on outdated technologies, and have little or no access to public goods and services such as electricity (Babbit et al., 2015). Human capital 13