rafmacalaba/datause-encoder-data huggingface dataset:
these variables will consist of the individual level characteristics such as age and education that are identically defined in both the DHS2000 and the sentinel dataset, and the same commune/district means that were inserted into the sentinel dataset for the probit model) to obtain the predicted probability of being HIV/AIDS positive for each woman in the DHS2000. Third, individual-level HIV status indicators are aggregated up to our desired geographic level to obtain the estimate of HIV/AIDS prevalence for respective sub-population groups. The level of geographic aggregation is restricted to the level at which DHS2000 data is nationally representative, which is a district unit in our case.<sup>14</sup> The section below describes in a greater detail each step involved in the analysis.

# **2b. The prediction model**

The first concern is to develop an accurate empirical model of p _i_ , the individual HIV status observed in 2001 sample of women attending ANC sites. Since the dependent variable is a dichotomous one, the estimation is performed using maximum likelihood probit. The specification of the model in general terms can be presented as: where **X** _i_ is a vector of individual level characteristics, and **X** r is a vector of district/subdistrict characteristics affecting a person’s chance of being HIV infected, α and β are the vectors of parameter estimates. Φ denotes the standard normal cumulative distribution function.

The choice of **X** _i_ variables is restricted to those individual characteristics observed in the 2001 sentinel survey data which also occur and are identically defined in the DHS 2000 data. These variables are individual’s age and educational status. Since the age distribution for women in the DHS sample is from 15 to 49 years, we also restrict the

> 14 Note that using unit record census data as a base for prediction would allow obtaining the accurate estimates of HIV prevalence for much smaller administrative units. However, we do not have access to such data. It is also important to bear in mind that aggregation does not need to be geographical. One can, for example, estimate prevalence by age groups, etc.

7
holders of transfer rights over the land. These patterns can vary by country context, however—in Tanzania, for example, users tend to not have transfer rights. We also find that users are rarely in the joint cluster in comparison to owners (only 2.2 percent of users in Tanzania and 1.7 percent in Ethiopia).

**Table 11: Percentages of Land Users and Owners in Each Clusters**

**Ethiopia**

||**Cluster Categ**<br>|**ory Based on Holding Tr**<br>|**ansfer Rights**<br>|
|---|---|---|---|
||Mostly Exclusive|Mostly Joint|Mostly Not Holding|
|Using but not an owner|19.0%|1.7%|12.3%|
|Reported owner|29.8%|26.0%|11.2%|
|Total number of obs.|6189|||

Notes: Percentages sum to 100%.

## **Tanzania**

||**Cluster Categ**<br>|**ory Based on Holding T**<br>|**ransfer Rights**<br>|
|---|---|---|---|
||Mostly Exclusive|Mostly Joint|Mostly Not Holding|
|Using butnot anowner|4.6%|2.2%|16.5%|
|Reported owner|28.2%|13.9%|34.6%|
|Total number of obs.|1681|||

<u>Notes: Percentages sum to 100%.</u>

# **6.2 Clustering Using a Simpler Specification (Hold versus Not Hold)**

When narrowing the categorization of rights and decision-making roles from mostly exclusively/jointly/not held, to simply whether rights and decision-making are “held” versus “not held”, we continue to find that transfer rights tend to cluster together and explains a large part of the variation in the first dimension. Figure 9 demonstrates the MCA results for Ethiopia (using all variables). Separately, we found similar results for Tanzania and Malawi, further underscoring the importance of transfer rights in categorizing different types of landowners.

28
# **5.  Educational differentials in migration**

The literature also emphasizes the importance of human capital as a precondition of migration (Schultz 1982; Todaro 1969). For example, one percent of national sampling survey conducted in 1995 shows that among all the migrants, 30.8 percent had received senior high and higher formal education, comparing to 11.3 percent for the same category of national average (Yang 1997). Zhao (1999) further found that better educated people in rural areas were not necessary to leave countryside to migrate to other provinces and likely to shift their jobs from agriculture to the non-agricultural sector in towns within own region. One of reasons lies in the fact that educated rural people regard migration as a loss of their social status.

Thus, it is unclear whether the development of towns attracts a large part of educated labor force, and whether development of towns attracts educated laborers from its own region who otherwise heads to other regions as suggested by Zhao. In Table 9, we represent the findings on educational attainment of those who migrated into towns, and of general population in towns in three provinces.

Table 9 Educational attainment of town in-migrants (6 years old or above) by migration status: 2000

|Province||1|2|3|4|5|6|7|
|---|---|---|---|---|---|---|---|---|
||||(|%)|||||
|Zhejiang|in-migrant|4.15|28.89|43.91|12.76|5.00|3.94|1.33|
||town pop|8.03|35.32|38.23|11.67|3.29|2.66|0.79|
|Henan|in-migrant|3.58|17.59|37.37|15.19|13.65|8.95|3.67|
||town pop|5.94|27.12|41.31|14.17|5.39|4.92|1.16|
|Sichuan|in-migrant|3.80|20.09|29.79|24.26|12.79|7.18|2.09|
||town pop|6.19|32.20|36.21|12.94|6.96|4.41|1.09|

Sources: calculated from Tabulation on 2000 Population Census of the P. R. China, Table 1-8b, and Table L7-6-1b and Table L7-6-2b.

note: 1, literate; 2, primary school; 3, secondary school; 4, high school; 5, vocational school; 6, college; 7, university or above.

In representing the findings on educational attainment of those who migrated into towns, and of general population in towns in three provinces (Table 9), we have found that in-migrants generally possess higher level of educational attainment than those of general population. In Zhejiang, for example, there were 17.8 percent of all in-migrants who have completed high school or professional technical school, whereas only 14 percent of town general population who have completed high school or professional technical schools.

10
publicly available data alone is sufficient to establish that the market is being used in a significant way as a “crypto vehicle currency” to make fiat currency payments (at home or abroad) or to send capital abroad.

Of course, there is substantial anecdotal evidence on the use of crypto for transactions purposes, and it is well known that Bitcoin is the medium of choice on the Dark Web, not to mention ransomware. But systematic analysis has been lacking to support that claim. Our methodology allows us to systematically establish a lower bound on the use of Bitcoin as a medium of exchange. It is quantitative, rather than anecdotal. In principle, the same algorithm can potentially be applied to data from any P2P exchange, should the researcher or regulator obtain (or demand) similar data to ours. This includes not only P2P exchanges that mainly match buyers and sellers (and hold Bitcoins in escrow until both sides confirm the fiat money payment has cleared), but centralized P2P exchanges as well.<sup>9</sup> Table 1 provides some descriptive statistics of the core data set.

**Table 1: Descriptive Statistics**

|Number of Trades|45 528 193|
|---|---|
|USD Trade Volume|USD 11.0 billion|
|Average Trade Size (USD)|242|
|Average Trade Size (BTC)|0.03734 Bitcoin|
|Largest Trade Size Recorded|USD 2.3 million|
|Number of Fiat Currencies|135|

Source: LocalBitcoins.com API, authors’ calculations

> 9 In a centralized exchange, typically catering only to a single country, the exchange serves as an intermediary, buying and selling on its own account, with price moving to match demand and supply.  Coinbase is the largest such exchange in the United States. Again, it is important to emphasize that although “on-chain” transactions are public and therefore directly observable, the vast majority of off-chain transactions are proprietary information, making the LocalBitcoins data set – which is publicly available –unique at present.

5
as a content hub. In contrast, high-tech innovators, while facing similar constraints of small domestic markets, see potential to go global...” It is not surprising then that in most countries the bulk of the income from the creative content industries is derived from the domestic market rather than export sales. In the US, exports of copy-righted products accounted for only 11% of total sales in 2001. In the UK and Australia, the corresponding figures are 9% and 6.3% respectively.<sup>34</sup>

# **Social and political constraints**

Unlike efficiency infrastructure, innovation infrastructure has a larger lifestyle component. While the development of the physical side of the innovation infrastructure may be constrained mainly by the financial resources that a country has, that of the cultural side has to take place within the parameters of the country’s larger political and social objectives. Being a sovereign, multi-racial city-state appears to have imbued policy makers with a deep sense of vulnerability and limited the options available to them. A slow, incremental approach will likely remain the preferred route in any move towards greater political openness. This may put Singapore at a disadvantage compared with some other Asian cities which can adopt a far more experimental approach in setting social limits.

Recent experience appeared to bear out such a cautious approach. Prime Minister Lee Hsien Loong, who chaired the ERC, conceded that the government faced many political and social constraints in its attempt to “re-make” Singapore.<sup>35</sup> Other anecdotal evidence tells the same story. The easing of the bar-top dancing rules, for example, were followed by a list of “what not to do” from the police department. The government also felt compelled to clarify its position on the employment of gays in the civil service and affirmed its basic objection to gay lifestyle, after disclosing that gays are not discriminated in the civil service.

A conservative approach to political and social openness could arguably affect innovation in the creative content industries more severely than the other two segments. Those involved in creative content innovations tend to attach much greater importance to the opportunities of working within a more liberal political and social milieu. Many of them may also define

> 34   See ERC Creative Industries Report, 2002.

> 35 Speech at the Economic Society of Singapore in April 2003.  A recent speech by Lee at the Singapore Harvard Club (Lee, H L, 2004) setting out the landscape for political opening in the coming years further confirmed the same cautious stance that the government would take.

24
Table 9: Linearity of Relationship Between Productivity and Technology Sophistication

||ln(Sales|per worker)|
|---|---|---|
||(1)|(2)|
|_Kj_|0.23<sup>_∗∗∗_</sup>|0.24<sup>_∗∗∗_</sup>|
||(0.01)|(0.01)|
|_Hj_|0.19<sup>_∗∗∗_</sup><br>(0.04)|0.24<sup>_∗∗∗_</sup><br>(0.04)|
|_Sj_|0.56<sup>_∗∗∗_</sup><br>(0.04)||
|_Sj_ * D(High Sophistication)|-0.03<sup>_∗_</sup><br>(0.02)||
|D(1.5 _≤_<br>_Sj ≤_2.5)||0.31<sup>_∗∗∗_</sup><br>(0.04)|
|D(2.5 _≤_<br>_Sj ≤_3.5)||0.75<sup>_∗∗∗_</sup><br>(0.05)|
|D(<br>_Sj ≥_3.5)||1.11<sup>_∗∗∗_</sup><br>(0.07)|
|Constant|6.02<sup>_∗∗∗_</sup>|6.77<sup>_∗∗∗_</sup>|
||(0.18)|(0.17)|
|N|13046|13046|
|R-squared|0.41|0.40|
|Sector FE|Yes|Yes|
|Country FE|Yes|Yes|

Notes : D(.) are binary variables that take the value 1 if the establishment/country satisfies the condition in parenthesis and 0 otherwise. High Sophistication represents that the establishment has above-median _Sj_ ; the different intervals for _Sj_ represent that the establishment’s _Sj_ is in the given interval. All regressions are estimated using establishment-level sampling weights. *, ** and *** denote 10%, 5% and 1% significance respectively.

55
Gaddis, Isis and Stephan Klasen (2014) “Economic Development, Structural Change, and Women’s Labor Force Participation: A Reexamination of the Feminization U Hypothesis.” _Journal of Population Economics_ 27(3): 639–81.

Goldin, Claudia (1995), “The U-Shaped Female Labor Force Function in Economic Development and Economic History.” In: Schultz TP Investment in Women’s Human Capital and Economic Development. University of Chicago Press; 1995. pp. 61-90.

Gonzales, Christian, Sonali Jain-Chandra, Kalpana Kochhar, and Monique Newiak (2015) “Fair Play: More Equal Laws Boost Female Labor Force Participation” _IMF Staff Discussion Note_ SDN/15/02 Klasen, Stephan (2002), “Low Schooling for Girls, Slower Growth for All? Cross-Country Evidence on the Effect of Gender Inequality in Education on Economic Development.” _World Bank Economic Review_ 16(3): 345–73 Klasen, Stephan and Dana Schüler (2011) “Reforming the Gender-Related Development Index and the Gender Empowerment Measure: Implementing Some Specific Proposals.” _Feminist Economics_ 17(1): 1–30 Klasen, Stephan and Francesca Lamanna (2009) “The Impact of Gender Inequality in Education and Employment on Economic Growth in Developing Countries: New Evidence for a Panel of Countries.” _Feminist Economics_ 15(3): 91–132.

Knowles, Stephen, Paula K. Lorgelly, and P. Dorian Owen (2002) “Are Educational Gender Gaps a Brake on Economic Development? Some Cross-Country Empirical Evidence.” _Oxford Economic Papers_ 54(1): 118–49.

Lagerlof, Nils-Petter (2003), “Gender Equality and Long-Run Growth” _Journal of Economic Growth_ 8:403-426 Lopez-Claros, Augusto and Saadia Zahidi (2005), Women’s Empowerment: Measuring the Global Gender Gap. Geneva: World Economic Forum.

Maitra, Pushkar (2004) “Parental bargaining, health inputs and child mortality in India.” _Journal of Health Economics_ , 23(2), 259–291.

McElroy, Majorie B. (1990) “The empirical content of Nash-bargained household behavior.” _Journal of Human Resources_ , 25, 559–583.

North, Douglass C. (1990) Institutions, Institutional Change, and Economic Performance. New York: Cambridge University Press.

Social Watch (2005) Roars and Whispers Gender and Poverty: Promises vs. Action. Montevideo: Social Watch.

24
expanding and contracting firms relative to stable firms. Tests for linear effects would miss these relationships.

# **3. Addressing these concerns with the Enterprise Survey data**

This paper uses the Enterprise Survey (ES) database to test for how significant these concerns are in practice across 79,000 surveys in 105 countries. In face-to-face interviews, the survey collects detailed information on many aspects of the investment climate in which a firm operates as well as information about the firm’s own characteristics and performance. The concept of investment climate compiles issues related to costs and risks of investing in and operating a business. The same questionnaire covering all these aspects faced by managers is implemented in each country, with a standardized sampling methodology, making the data comparable across countries (see data appendix for details).

Questions include both qualitative subjective questions on various potential constraints to business performance and quantitative measures. Managers are asked to rank how constraining a set of issues are for the operation and growth of their business. The list is found on Table 1, column 1, with higher average values indicating more firms report the issue as a significant constraint.<sup>6</sup> More than three-fifths of firms report tax rates as being above their average level of constraint. This is closely followed by policy uncertainty, access to finance, corruption, tax administration and informality. Issues that earned low rankings were telecommunications and transportation.

> 6 The constraints are grouped thematically, and ordered from most to least constraining.  The same ordering is then kept for all the tables.

10
|**Table 1: Business Service Provider Survey Data**|All Pro|viders|Me|ans by Provid|er Type|
|---|---|---|---|---|---|
||Mean|S.D.|HR|Accounting|Marketing|
|_Provider Characteristics_||||||
|Age of firm (years)|11.5|8.5|11.4|14.2|8.8|
|Has more than one branch|0.38|0.49|0.37|0.52|0.24|
|Number of full-time professionals|10.9|16.8|10.9|13.1|8.6|
|Annual sales (000s of USD)|148|357|85|173|162|
|Number of customer per month|29.6|47.0|28.3|42.1|17.8|
|Daily rate (USD)|285|692|210|233|387|
|_Quality information_||||||
|Offers money-back guarantee|0.02|0.15|0.00|0.00|0.06|
|Will re-do work if not satisfied|0.53|0.50|0.60|0.43|0.58|
|Share of staff with tertiary degree|0.73|0.25|0.72|0.75|0.70|
|Share of staff with skill certification|0.39|0.25|0.37|0.45|0.34|
|Belongs to a professional organization|0.84|0.37|0.82|0.97|0.71|
|_Main way of getting customers_||||||
|Word of mouth|0.66|0.48|0.59|0.68|0.68|
|Advertising|0.16|0.37|0.24|0.08|0.19|
|Walk-ins|0.08|0.27|0.07|0.08|0.09|
|Bidding on contracts/RFPs|0.14|0.34|0.09|0.15|0.16|
|_Main reasons they think SMEs do not use_||||||
|Cost: too expensive for firms to afford|0.61|0.49|0.62|0.60|0.62|
|Returns: not worth it for small firms to use|0.25|0.43|0.24|0.25|0.26|
|Knowledge of returns: firms don't know the returns|0.75|0.43|0.74|0.77|0.74|
|Uncertain quality: firms can't tell quality|0.40|0.49|0.40|0.40|0.39|
|Uncertain returns: firms don't know if it will work for them|0.31|0.46|0.24|0.35|0.31|
|Knowledge of providers: Firms don't know providers|0.33|0.47|0.38|0.34|0.28|
|Sample Size|271||68|102|101|

20
# 3. **Compliance versus Noncompliance**

The previous section showed the significant amount of noncompliance with India’s Factories Act. Further, it showed that noncompliance is a phenomenon that is much larger than the phenomenon of adjusting out of regulation. In this section we will focus specifically on compliance versus noncompliance. For this exercise our universe is all firms with 10 workers or more, either in the ASI data set or in the NSSO data set.<sup>11</sup> Those in the ASI data set are compliant, and those in the NSSO data set are noncompliant.

|**Compliant**<br>**Table 3: Compliance under the Factories Act, 1948, in 2009-10***|**Non-Compliant**|
|---|---|
|Number of Firms<br>114,278|256,993|
|Share of Firms<br>30.8%|69.2%|
|Total Employment**<br>11,300,000|4,543,668|
|Share of Employment<br>71.3%|28.7%|
|Mean Employment<br>98.8|17.7|
|Median Employment<br>30|13|
|Median Labor Productivtiy (Rupees)****<br>151,754|59,820|
|Median Capital-Output Ratio***<br>0.2|0.5|
|Median Capital-Labor Ratio (Rupees)***<br>166,727|68,947|
|Median Wage Rate (Rupees)<br>61,247|39,460|
|Median Gross Value Added Share in Total Output<br>20.0%|59.2%|
|Ownership Type (%)||
|Sole Propreitory<br>24%|86%|
|Partnership<br>32%|12%|
|Limited Co<br>41%|0%|
|Others (Co-operatives, SHG etc)<br>3%|2%|
|Location (%)||
|Rural<br>39%|43%|
|Urban<br>61%|57%|
|Maintain Books of Accounts (%)<br>100%|50%|
|<br>Median Age (years)<br>12<br>* Usage of power is ignored and only criteria of 10 or more employees is considered for registration un<br>**Includes unpaid family membes/helpers working in the firm<br>***Capital here is defined as the value (market value for NSSO and book value for ASI) of assets at the e<br>****Laborproductivityis totalgross value added divided bytotal employment|6<br>der the Factories Act,1948.<br>nd of the year|

11 Recall that in the ASI there are a significant number of enterprises who have less than 10 workers, although these are very small and do not account for a large share of employment among ASI firms (only 1.5%, see Appendix B). These enterprises are excluded from the analysis of this section.

10
ZIMBABWE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Annual funding gap

Zimbabwe‘s infrastructure funding gap amounts to $584 million per year in the case of the idealistic scenario, or about 13 percent of GDP once efficiencies are captured. Every infrastructure sector except ICT is facing a funding gap (table 19). By far the biggest funding gap, even after accounting for significant efficiency potential, is found in the power sector, followed by water and sanitation. The funding gap could be fully funded from the available resources, assuming that huge efficiency gains are captured, under the minimalistic scenario set out above or the recent Governmental Action Program for Infrastructure. According to the action program, Zimbabwe would require $962 million of public and $460 million of private funds annually to meet most pressing infrastructure needs, including rehabilitation of existing stock and upgrades.

Adopting lower-cost technologies could substantially reduce the cost of meeting the posited infrastructure targets, and eliminate the funding gap. The overall savings from these measures would amount to $226 million or 38 percent of the country‘s total infrastructure funding gap, underscoring the importance of technology choices.

## **Table 19. Funding gaps by sector**

### <u>US$ millions</u>

||ICT|Irrigation|Power|Transport|WSS|Total|
|---|---|---|---|---|---|---|
|Spending needs idealistic scenario|(75)|(47)|(1,242)|(218)|(427)|(2,009)|
|Spending needs intermediate scenario|(75)|(47)|(1,156)|(110)|(342)|(1,729)|
|Spending needs minimalist scenario|(36)|(18)|(665)|(187)|(313)|(1,219)|
|Existing spending*|51|2|378|103|85|618|
|Reallocation potential within sectors|24|0|0|31|0|55|
|Efficiency gains|0|0|576|72|104|752|
|Funding gap idealistic scenario|**—**|**(44)**|**(288)**|**(13)**|**(239)**|**(584)**|
|Funding gap intermediate scenario|**—**|(45)|(202)|**—**|(153)|(304)|
|Funding gap minimalistic scenario|**—**|(16)|**—**|**—**|(124)|-|

_Source:_ Derived from Foster and Briceño-Garmendia (2009).

_Note:_ Potential overspending across sectors is not included in the calculation of the funding gap, because it cannot be assumed that it would be applied toward other infrastructure sectors. WSS = water supply and sanitation; ICT = information and communication technology.

* traced to needs. — = Not available.

# What else can be done?

The funding gap can be addressed only by raising additional financing or, alternatively, by adopting lower-cost technologies or less-ambitious development targets. Zimbabwe may have realistic prospects for increasing the flow of resources to infrastructure, and to power and water in particular, both from the public and private sectors, and from ODA. But due to its economic and political turmoil, Zimbabwe has not attracted as much private finance into infrastructure as other African peers. Over the early 2000s, Zimbabwe has captured private investment commitments worth only around 0.4 percent of GDP, predominantly in the ICT sector. Most of Zimbabwe‘s peers have done significantly better in this area.

49