rafmacalaba/datause-encoder-data huggingface dataset:
saving for old age, such as livestock, gold, and stuffing money under the mattress. It could also be indicative of less liquid means of saving for old age, like home ownership.

Figure 2 presents the gender distribution of saving for old age around the world and by region. The global gender gap in terms of saving for old age is in the magnitude of 2.5 percentage points (10.5%), with 26% of males saving for old age, versus 23.5% of females, globally. Notably, this is lower than the 9 percentage point gap in account ownership around the world. The gap is largest in South Asia, and smallest in EAP, which also has the highest absolute savings rates for old age.

Figure 3 then presents the educational gaps in terms of saving for old age. The global education gap is wider between tertiary and secondary education, compared to the respective gap between secondary and primary education (or less). Specifically, 37.9% of individuals with tertiary education save for retirement, compared to 27% of individuals with secondary education and 22.2% of individuals with primary education or less. The savings-for-old-age gap seems to be much wider between the tertiary and secondary education groups, compared to the gap between secondary and primary education or less. The pattern is remarkably strong in LAC, in which 25.3% of individuals with tertiary education save for old age, compared to 11.9% and 8.6% of individuals with secondary and primary education or less, respectively. The exception in this pattern is EAP.

Figure 4 presents the age distribution of saving for old age around the world. The distinction is between six groups, namely those aged 18-25, 26-35, 36-45, 46-55, 56-65 and 65+, respectively. The global age distribution of saving for old age in the 2014 Global Findex data suggests that 10.1% of individuals aged 18-25 save for old age. The figure for those aged 26-35 rises to 21% and to 31% for those aged 36-45. 33.3% of the group aged 46-55 save for old age and so do another 36.9% of the 56-65 group. The figure for those aged 65+ is close to that for the 56-65 group at 36.5%. In EAP, a near doubling of the proportion of people saving for old age arises at age 26-35, at 31.9% compared to 16.3% at age 18-25. In comparison, in ECA, the tiny figure of 3.6% at age 18-25 rises at 6.2% at age 26-35 and 8.2% at 36-45. A rather major rise to 14.2% of the population occurs at age 45-55, with a further 20.8% of the age group 56-65 saving for old age. The figure is the 9
- D. Surveys should contain at least one summary question that allows the importance of constraints to PSD to be weighed against each other, including constraints not covered in detail. We have found rankings questions to serve this purpose well.

# IV. How can surveys be oriented towards their target population?

- A. Each survey should he preceded with an explanation of the survey's purpose, its ultimate value to they respondent, and the confidentiality of responses.

- B. Language should be readily comprehensible to the people surveyed.

- C. Language and discussion of issues specific to the group being sampled may increase their enthusiasm for the interview process.

- D. Information requested must be readily available to the respondent within the period of an interview, and not demand time-consuming research.

# V. How should surveys be implemented?

- A. Selection of firms should be done in as neutral and unbiased a manner possible - hence the biases of each source of information (government Ministries, trade associations, yellow pages) should be carefully considered and efforts should be made to compensate for them.

- B. Questionnaires must be tested to assure that questions elicit desired information, are comprehensible to respondents, do not take too long, and do not arouse negative reaction.

- C. Interviewers must be trained to pose questions in a professional, friendly and neutral manner and record responses accurately and objectively. They should be knowledgeable enough and sufficiently trained to follow up interesting points that arise in the course of the interview.

- D. Completed questionnaires should be monitored to ensure consistent and legible completion.

- E. The confidentiality of firms sampled must be protected and assured: under no circumstances should any government agency be aware of the identity of respondents or should any government agent be present at the interview. Similarly, the identity of firms must be excluded from any reporting of results. Firms must be assured of the confidentiality of their responses both in writing and verbally at the outset of the survey. This approach is vital to receiving candid and accurate responses.

- F. Interesting anecdotes and firm characteristics should be recorded by interviewers, so that helpful details are not lost.

17
doubling _S_ and _V_ in a labor market exhibiting IRS in matching will result in less than doubling of steady state unemployment, which is intuitively plausible as more of both newly unemployed and vacancies is likely to result in better matching. As this example based on the steady-state unemployment equation indicates, the assumption of IRS in matching appears much more natural than that of CRS. In other words, there appears to be little rationale for assuming that doubling of both _S_ and _V_ has to result in doubling of steady state unemployment.

From an empirical standpoint, our theoretical analysis indicates that there is a major advantage in using the matching function because its parameters may be estimated on disequilibrium data series and yet one may infer from its coefficients the causal impact of inflow and vacancies on steady-state unemployment.

In view of the unemployment problem in the transition economies, the literature on the matching of unemployed and vacancies in these economies has grown rapidly. It has also produced contradictory results, in part because the studies use different methodologies and data. Methodologically, the studies differ especially with respect to the specification of the matching function and treatment of returns to scale, the inclusion in equation (1) of other variables that might affect outflows and the extent to which they use static or dynamic models, and with respect to whether and how they account for endogeneity of explanatory variables. In terms of data, the studies differ in whether they use annual, quarterly or monthly panels of district-level or more aggregate (regional) data and whether they cover short or long time periods. None of the studies accounts explicitly for the varying size of the unit of observation (district or region) which, as we show presently, may generate biased estimates of the returns to scale in many studies.<sup>13</sup>

> 13 The principal studies in this area are Burda (1993), who uses  monthly Czech and Slovak district-level data from 1990 to1992, Boeri (1994), who uses 1991-93 regional panel data for the Czech Republic, Hungary  Poland, and Slovakia, Svejnar, Terrell and Münich (1994, 1995), who  use annual 1992 and 1993 data from the Czech and Slovak Republics, Lubyova and van Ours (1994), who use 1990-93 monthly data for Slovakia and 1991-93 data for the Czech Republic, Boeri and Scarpetta

− 13 −
possible that there are trends in world-wide averages of governance, either improvements or declines. If this is the case, it would inappropriate to rescale the mean of governance to zero in each period as we do. However, as we have documented in the past, and also later in this paper, we do not find much evidence from our underlying individual data sources that there are trends in global averages in governance. As a result, we think the assumption of a constant global average of governance in our aggregate indicators is reasonable.

The second consideration is that the sample of countries covered by our governance indicators has expanded since 1996, and quite considerably for some of our indicators (see Table 2 and the accompanying discussion in the next subsection). If the new countries added each year were broadly representative of the worldwide distribution of governance, this too would pose no special difficulties. However, for some of our indicators, we find that countries added in later years score on average somewhat higher than countries that were continuously in the sample. This in turn means that it would be inappropriate to impose a global average governance score of zero in earlier periods for the smaller set of countries for which data is available, since our earlier estimates did not include the better-than-average performers added later. It also means that some countries in our aggregate indicators in the earlier years showed small declines in some dimensions of governance over time that were driven by the addition of betterperforming countries in later years.

We address this issue with a simple re-scaling of the aggregate governance indicators. We take our 2005 indicators which, depending on the governance component, cover between 203 and 213 countries as representative of the world as a whole, as a benchmark. Consistent with our choice of units for governance, the estimates for 2005 have zero mean and standard deviation of one across countries. We next consider the countries that were added in 2005 relative to 2004. We then adjust the world-wide average score in 2004 so that it would have a mean of zero _had we included the 2005 scores for those countries added in 2005 relative to 2004_ . As a specific example consider Political Stability and Absence of Violence, where between 2004 and 2005 we have added 6 countries, mostly small islands in the Caribbean. Their average score in 2005 is 1.07 which is considerably above the world average of zero. We therefore slightly lower the scores of all of the 207 countries in 2004 by a factor of 0.03 in 9
**Table 1. Results from OLS regression explaining difference in log ratio of pre-assistance consumption to national poverty line by Sudanese refugee household characteristics.**

|**Dependent**<br>**variable**<br>|(**log) ratio of per capita pre-assistance consumption t**|**o international p**|**overty line**|
|---|---|---|---|
|**istics**|Age of household head|0.003|(0.002)|
|**cter**|Female-headed household|-0.067|(0.061)|
|**hara**|Household head is married|0.224|(0.236)|
|**old c**|Household head is widowed/divorced/separated|0.215|(0.256)|
|**useh**|Share of dependents(youngand elderly)|-0.515***|(0.114)|
|**Ho**|Household size|-0.116***|(0.012)|
|**Human capital**<br>|Head has completed at leastprimaryeducation|0.142*|(0.086)|
|**rship**|Household head has access to land|0.026|(0.066)|
|**wne**|Number of large cattle owned byhouseholds|-0.040|(0.052)|
|**ets o**|Number of small ruminants owned byhouseholds|0.008|(0.009)|
|**Ass**|Valuable initial assets|0.190|(0.245)|
|**Social capital**|Family connection within the camp|0.033|(0.068)|
||Social connection outside of the camp|0.137**|(0.056)|
|**ket**<br>**s**|Household head works in agriculture|-0.108|(0.117)|
|**mar**<br>**ome**|Household head works in industry|0.061|(0.114)|
|**bor**<br>**outc**|Household head works in services|0.114|(0.112)|
|**La**|Household head is self-employed|0.132*|(0.074)|
||Constant|-0.246|(0.265)|
||Observations|593||
||Adjusted R-squared|0.210||

Source: EHCVM 2018, Authors’ calculations While understanding the correlates of the pre-assistance poverty gap is crucial, it provides only limited insight into the magnitude of the impact of each identified correlate on the cost of basic needs for refugees. To address this, an additional descriptive assessment is offered of the main correlates by comparing the average cost of basic needs assistance for refugees that would be needed to bring refugees to the poverty line (FCFA 317) with different household characteristics. Assuming away the potential problem of selection bias between groups of refugee households, this comparison enables us to rank the identified correlates based on the magnitude of their reduction effects on the cost of basic needs, from the highest to the lowest premium.<sup>10</sup> Figure 9 shows the daily costs of basic needs per Sudanese refugee in FCFA that need to be covered under the current scenario to ensure these refugees can meet their basic

> 10 We view these effects as correlation rather than causal effects.

13
increases initially and then decreases beyond a point where comparative advantage has grown higher and the export of the product more intensive in the factor (Wells 1961). However, the optimal or typical factor supply for an industry is a dynamical outcome resulting from differentials in productivity across countries, products and global market demand (section 3); it is not a primary mechanism.

The RFI does not provide an explicit predictive model that could relate change in endowments to changes in RCAs or export composition. The "skeleton" approach in the product space decomposition is also based on a series of simplifications and does not provide either countryspecific information on its non-revealed potential or the linkages between composition and endowments.

Both models are univariate. The RFI takes one factor at a time, even though many factors enter into a production function simultaneously. Economic complexity is also one dimensional. Neither the RFI nor the product space decomposition include a metric on how well they explain the structure of the composition matrix—the equivalent of an goodness of fit in a linear regression—making it difficult to assess quantitatively the explanatory value of the theory.

Another problem in interpreting the RFI/PRODY is that, being a weighted average of factor variable by country, it is essentially the first iteration towards a trivial fixed point, which is the simple average of the factor index value across countries. In fact for actual trade data it is easy to check that the rate of convergence is relatively fast.

**<u>3 What Export Composition Tells about Factor Endowments: Product Space and Latent</u> Comparative Advantage**

Although the original composition space has many dimensions—more than sixty if one retains a classification with two digits—it is expected that trade composition is determined by a much smaller number of factors of endowments. Hence the points representing countries in the composition space should be close to a manifold (or sub-space) with relatively few dimensions within the much larger composition space (Fig. 2).

This manifold, dubbed “Product Space”,<sup>4</sup> is identified from the trade data through a geometric concept of dimensionality reduction. Modern econometric techniques yield a minimal set of latent factors for each country that best explain the structure of the trade matrix. The implementation of Product Space and latent factors is formalized in the forthcoming sections.

Countries have coordinates in the product space that represent latent factors and correspond to their projection from the composition space onto the product space. Products have dual coordinates in the same space. Thus the problem exhibits symmetry between countries and products. These product coordinates are export elasticities to each of the country’s factor endowment variables.

> 4 The author chose to use the same vocabulary as HHR to refer to the geometrical object capturing the structure of export composition However, the parameterization of product space is very different in the two models: “skeleton” network in HHR vs. a continuous low dimensional manifold here.

7
This seems to indicate that the costs of congestion in India are either much smaller than in the U.S., the agglomeration forces are much larger than in the U.S., or that there are some frictions, policies, and a general lack of infrastructure in medium-density cities that prevents them from growing faster, therefore favoring concentration in high-density areas. It is not obvious to us why Indian individuals should dislike congestion less than Americans or should benefit more than Americans from agglomeration economies. These forces seem to be more technological and universal. Therefore, the likely culprits are restrictions to economic growth in intermediate-density cities or districts. Our findings for China, an emerging economy that has suffered less from a lack of general infrastructure, support this interpretation. Similar to the U.S., congestion in the Chinese service sector is strong in locations with high employment density.

What is therefore preventing medium-density locations in India from growing and taking full advantage of agglomeration forces? Why is their evolution, relative to low and high density areas, so different from in advanced economies? This paper identifies this specific issue as a major question in India’s spatial development. Although our comparisons with the U.S. and China point to some possible explanations, having confident answers to what the sources of these distortions are will lead to better informed and more effective urban and regional policy.

As an endnote, the success story of Bangalore  the Silicon Valley of India  is one of the notable exceptions to our general findings: that district has a density level of similar magnitude as the hightech clusters in the U.S. Interestingly, it traces its history back to the so-called Electronics City, set up in the 1970s as an industrial park 18 kilometers south of the city. Perhaps this particular example points to a promising way to eliminate the growth restrictions that we have uncovered in many other intermediate-density districts.

# **References**

Caselli, P. and Paternò, F., 2001. “ICT Accumulation and Productivity Growth in the United States: An Analysis Based on Industry Data,” Temi di Discussione 419, Banca d’Italia.

Chun, H., Kim, J.-W., Lee, J. and Morck, R., 2005. “Information Technology, Creative Destruction, and Firm-Specific Volatility,” unpublished manuscript.

11
Table 2: The Logit Regression of Stunting, Wasting and Underweight for Children Under 5 (odds ratio presented below)

|Variables|Stunting|Wasting|Underweight|
|---|---|---|---|
|~~Per capita expenditure : quintile 1 (poorest) as comparison~~||||
|group||||
|quintile 2|0.966|0.908|1.018|
||(0.126)|(0.154)|(0.143)|
|quintile 3|0.734**|0.639**|0.769*|
||(0.101)|(0.121)|(0.120)|
|quintile 4|0.934<br>|0.795<br>|1.126<br>|
||(0.137)|(0.156)|(0.182)|
|quintile 5|0.758*|0.655*|0.823|
|Per capita protein intake: quintile 1 as comparison group|(0.121)|(0.144)|(0.150)|
|quintile 2|1.232*|1.046|1.029|
||(0.153)|(0.173)|(0.141)|
|quintile 3|1.131|1.021|0.962|
||(0.142)|(0.174)|(0.136)|
|quintile 4|0.971|1.168|0.882|
||(0.125)|(0.202)|(0.130)|
|quintile 5|0.947|0.847|0.779*|
|Region: Southern as the comparison group|(0.125)|(0.157)|(0.119)|
|Highlands|1.964***|0.374***|0.616***|
||(0.230)|(0.0678)|(0.0860)|
|Momase|1.253**|0.748**|0.938|
||(0.125)|(0.0997)|(0.107)|
|Islands|0.844|0.914|1.097|
|Household head highest education: no formal education as the||||
|comparison group|(0.107)|(0.146)|(0.151)|
|primary|0.981|0.677***|0.716***|
||(0.102)|(0.0946)|(0.0819)|
|above primary|0.781**|0.672***|0.583***|
||(0.0808)|(0.0937)|(0.0679)|
|Urban|0.772***|0.967|0.715***|
||(0.0752)|(0.130)|(0.0807)|
|Male|1.304***|1.092|1.181*|
||(0.101)|(0.116)|(0.105)|
|Children having had diarrhea|1.089|0.912|0.950|
||(0.183)|(0.217)|(0.184)|
|Children having had malaria|1.390**|0.950|1.333*|
||(0.209)|(0.195)|(0.215)|
|Age in month|1.007***|0.988***|1.006**|
||(0.00230)|(0.00311)|(0.00261)|
|Constant|0.655**|0.557***|0.492***|
||(0.108)|(0.118)|(0.0896)|
|Observations|2,875|2,785|2,875|
|Standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1||||

17
As in Zhou and Ji (2020), we adopt a parallel tempering Markov chain Monte Carlo algorithm (PTMCMC). Several Markov chains are used in parallel to investigate a target distribution at varying temperatures (e.g., Swendsen and Wang, 1986; Geyer, 1991; Earl and Deem, 2005). The basic idea is that, in general, it is hard to generate samples that can escape from high likelihood regions, travel quickly through low likelihood regions, and find other high likelihood regions. PTMCMC overcomes this challenge using multiple chains with different temperatures. It does so by utilizing low temperature chains to more efficiently sample higher-likelihood areas of the parameter space while employing higher temperature chains to more widely investigate the posterior space. Over the course of the sampling process, colder chains traverse flat sections of the parameter space by exchanging position with higher temperature chains.

Recall that θ denotes all model parameters and hyperparameters. The joint posterior distribution of θ is given by where Y= {y0 ... yT} is observation variables and p(θ) is the set of prior distributions. φ 2 2 is the density of normal distribution with mean η and variance σγ . η and σγ are included in θ as one of the parameters. While all parameters involve in determining the current 2 state, in particular, η and σγ governs the shape of the sampling distribution. The joint posterior distribution with a temperature is given by We follow Zhou and Ji (2020) in preparing J= 10 chains with the temperature of the j- th chain set to Δj = 1.5<sup>10−j</sup> . As a first step, all J chains are independently updated using the Gibbs sampler. We then swap θj and θj+1 for j= 1,2, ... J−1 with probability: MCMC simulations are allowed to run for 50,000 iterations. The initial 20,000 draws are discarded as an initial burn-in period.

Prediction inference is conducted by the following posterior predictive distribution.

7
**Table 3: Gender Heterogeneity in Subsamples in Long-Run Impacts**

|||Tota|l Profits||Other|Total|Profits|
|---|---|---|---|---|---|---|---|
||Base<br>Profits<br><$100|Base<br>Profits<br>>=$100|Base<br>Sector is<br>Commerce|Base<br>Sector not<br> Commerce|Household<br>Business|No other<br>Household<br>Business|Other<br>Household<br>Business|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Assigned to PersonaI Initiative * Male|107.5**|173.1**|129.5|142.2***|0.084|134.7*|135.5*|
||(52.3)|(76.7)|(142.9)|(48.3)|(0.059)|(71.8)|(77.0)|
|Assigned to PersonaI Initiative * Female|32.9|22.8|50.6|-20.8|0.054|-0.56|45.4|
||(27.6)|(81.7)|(47.1)|(57.0)|(0.054)|(49.1)|(94.7)|
|Assigned to Traditional Training * Male|8.60|105.0|-170.0*|124.6***|0.090|72.5|66.2|
||(30.5)|(69.4)|(103.2)|(45.5)|(0.059)|(57.6)|(67.1)|
|Assigned to Traditional Training * Female|7.36|-21.7|1.32|-4.10|0.027|46.2|-113.8|
||(18.4)|(76.1)|(35.7)|(69.4)|(0.052)|(50.7)|(70.0)|
|Sample Size|690|647|624|713|1115|608|507|
|Control Mean Men|142|363|330|246|0.59|239|320|
|Control Mean Women|83|274|175|136|0.33|168|217|
|p-value: PI Men=Women|0.207|0.180|0.600|0.029|0.704|0.121|0.461|
|p-value: Traditional Men=Women|0.972|0.219|0.117|0.121|0.425|0.732|0.064|

Notes: Dependent variable is total monthly profits, winsorized at the 1st and 99th percentiles, except in column (5), where it is a dummy variable for whether the household has a business owned by someone other than the entrepreneur in our sample. Columns 1 and 2 split the sample by whether baseline profits are below or above $100 in real 2021 USD, where 57% of femaleowned firms and 44% of male-owned firms are below; Columns 3 and 4 split the sample based on whether the baseline sector is in commerce (77% of female-owned firms and 22% of male-owned firms) or not; since column 5 shows no treatment impact on operating another household business, columns 6 and 7 split the sample based on whether there is another business in the household at follow-up.

All regressions control for baseline profits, a dummy for gender, and for baseline profits interacted with gender. Robust standard errors in parentheses. *, **, *** denote significance at the 10, 5, and 1 percent levels respectively.

23