rafmacalaba/datause-encoder-data huggingface dataset:
revenue. Furthermore, formal sector registration provides firms access to a VAT sales ID, 24 which offers greater domestic and international sales opportunities. Legislative reforms to the registration process have been shown in countries around the world to increase entry and small business employment (i.e. Mexico and Russia).<sup>25</sup> An example of legislative reform to encourage formal entrepreneurship and the growth of new and small firms is to introduce on-line electronic registration systems. Automating the registration process also helps provide lenders, suppliers, and customers greater access to information on the financial health, management, and ownership of registered firms, which encourages greater access to financing and growth.

In order to have a better understanding of the business registration process and the impact of different typologies of registries in the ease of doing business, the 2007 _World Bank Group Entrepreneurship Survey_ added a special section related to the business registries. Seventy-five countries participated in this section, providing valuable information about the registration processes, information requirements, and the availability of e-registries and e-distribution, among other issues.

# **_7.1   Business Registry Typology_**

In order to asses the different degrees of modernization of business registries, the survey collected information on the availability of electronic registration, which broadly includes the automation and computerization of local registrars, the ability to register over the Internet, and electronic distribution of data via the Internet. However, this does not necessarily include online authentication or integration of e-government services.<sup>26</sup> Figure 12 shows the deep disparity found between industrialized and developing countries. While on average only 32% of developing countries have implemented an electronic registry, more than 80% of the industrialized countries have already achieved complete automation. However, in most regions over 60% of countries make registrar information available over the internet. This discrepancy might be explained by the fact that electronic distribution is less expensive and difficult to implement and does not require electronic signature or security laws or complex e-government platforms.

> 24 For example, see Djankov, La Porta , Lopez de Silanes and Shleifer, 2002.

> 25 See Seira, Kaplan, and Piedra, 2007 and Yakovlev, E, E. Zhuravskaya, 2007, for studies on the effect of registration reform on entrepreneurship in Mexico and Russia, respectively.

> 26 For further information on EBR see “Implementing Electronic Business Registry (e-BR) Services,” World Bank, 2007.

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We explore the effect of this constraint in two ways. The first is to vary the size of transfers based on the PMT scores. The second is to reformulate the problem as one of optimizing the transfers as a function of the variables going into the PMT. Quite generally one can think of the informationallyfeasible transfers as a function of _m_ observed _x_ ’s. The policy maker only observes the covariates _x_ for each person; it is not known who is poor and who is not. However, the policy maker has a survey with much more information available for a sample. The problem is to choose the parameters of a score for assigning the real-world transfers based on the _x_ ’s, as given by: 0 0 _k_ (Here _xijt_  1 so that  _jt_ is the intercept—the transfer received by someone with _xijt_  0 for _k_ =1,..., _m_ .) In one version transfers are linear in the _x_ ’s, i.e.,  1. We call this the linear optimization. We also estimate a nonlinear version with  2, which introduces squared terms and interaction effects among the _x’_ s.<sup>36</sup> The choice of the score parameters  _kjt_ is made to minimize the Watts index in the sample survey data: The choice is constrained by the budget: We solve this problem numerically.<sup>37</sup> One start value we use for the optimization is the uniform 0 _k_ case obtained by setting  _jt_  _B jt_ and  _ijt_  0 for _k_ =1,..., _m._ Other start values are tested. When there are multiple local optima the solution for the parameters  _kjt_ that gives the lowest value of the poverty measure is chosen.

**_Data:_** The data for implementing these measures come from the World Bank’s well-known LSMS.<sup>38</sup> Table 2 lists the countries, years of survey, and numbers of households surveyed. In keeping

> 36 Glewwe (1992) recommended this in his formulation of the optimal targeting problem.

> 37 <mark>We use the “fmincon” program in Matlab.</mark> 38 The LSMS has designed and implemented household surveys across many countries since the 1980s. These are nationally representative multi-purpose surveys spanning a quite wide range of topics. Further information can be found at the <u>LSMS website. All surveys except for Ghana are LSMS-ISA surveys.</u>

15
2019 to January 2020). In comparison, 18.5 percent, 18 percent and 17.7 percent of participants in intervention group 1 (personal invitation), in intervention group 2 (personal invitation with peer group information) and in intervention group 3 (personal invitation with a labeled pharmacy voucher) respectively received both screenings. Finally, 34.3 percent of participants in intervention group 4 (personal invitation with a conditional pharmacy voucher) obtained both screenings.

**_Table 5: Screening status by study groups_**

|**Screening Status**|**Intervention**<br>**group one**<br>**Mean**|**N**|**Intervention**<br>**group two**<br>**Mean**|**N**|**Intervention**<br>**group 3**<br>**Mean**|**N**|**Intervention**<br>**group 4**<br>**Mean**|**N**|**Control**<br>**Group**<br>**Mean**|**N**|
|---|---|---|---|---|---|---|---|---|---|---|
|No Screening|74.8%|299|74.5%|298|74.3%|297|57.8%|231|91.2%|365|
|Diabetes only|1.3%|5|1.8%|7|2.5%|10|1.8%|7|0.7%|3|
|Hypertension only|5.5%|22|5.8%|23|5.5%|22|6.3%|25|4.5%|18|
|Both Hypertension<br>and Diabetes|18.5%|74|18%|72|17.7%|71|34.3%|137|3.5%|14|
|Observations|400||400||400||400||400||

Table 6 reports the estimates of the study’s impacts on the uptake of taking both screening tests using OLS regressions. The interventions had a statistically significant impact on the uptake of screening in all intervention groups. Column 1 only controls for facility and provincial fixed effects, column 2 further controls for the level of health facility, column 3 adds controls for age and gender of study participants, and column 4 controls for a broader of socio-economic variables collected during the endline survey. The results are similar across all four specifications and we therefore focus on the specification with the full set of controls. In Column 4, compared to the control group, intervention groups 1 to 3 led to an increase in the screening rate of about 15 percentage points (pp). The highest intervention impact, however, is measured among participants of intervention group 4 (personal invitation with a conditional pharmacy voucher). For them, the uptake of screening on both tests increased by 31.2 pp. Additionally, we conducted a test of equal treatment effects between the different intervention groups. In Column 4, the reported p-values at the bottom of the table indicate that the difference in effect sizes between intervention groups 1 to 3 was not significant, while the effect size in intervention group 4 was significantly different than in the other 3 intervention groups.<sup>3</sup> In Table 7, we used the OLS specification with the full set of controls to estimate the study’s impacts on the three other outcome variables: 1) taking the diabetes screening, 2) taking the hypertension screening, and 4) taking at least one of the two screenings. The results are quantitatively all similar for all outcomes. Therefore, in the remainder of the analysis, we focus on describing the program’s effect on taking both tests.

> 3 We ran the same analysis using logistic regressions: the marginal effects show very similar results to the coefficients (results available on request).

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|_lat_modified_<br>_lon_modified_|EA Longitude<br>(WGS84)<br>Modified<br>EA Latitude<br>(WGS84)<br>Modified||“Modified” refers to the offset of the<br>coordinates.<br>TZA: in waves 4 (extended panel<br>only) and 5, only households who<br>have not moved could be assigned<br>geocoordinates.<br>UGA: missing in waves 4, 5, 7, 8|
|---|---|---|---|
|_geocoords_id_|Geocoordinate<br>ID|Variable created by assigning<br>a numeric code to each<br>unique pairs of<br>geocoordinates. The first<br>number of the code<br>determines the country.|TZA: in waves 4 (extended panel<br>only) and 5, only households who<br>have not moved could be assigned<br>geocoordinates.<br>UGA: missing in waves 4, 5, 7, 8|

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Table 4: Difference-in-Differences Regressions (2006-2010)

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Top|Coded|Bottom|Coded|
||∆ln NTL pc|∆ln NTL pc|∆ln NTL pc|∆ln NTL pc|
||OLS|IV|OLS|IV|
|∆ln Market Access|0.100**|0.225**|0.0773*|0.194**|
||(2.19)|(2.37)|(1.73)|(2.10)|
|∆ln(1+Pal.Fat._<_5km)|0.0235|0.0277|0.0654***|0.0693***|
||(1.28)|(1.46)|(3.65)|(3.77)|
|∆ln(1+Isr.Fat._<_5km)|0.0800|0.110**|-0.0193|0.00888|
||(1.56)|(1.97)|(-0.39)|(0.16)|
|_N_|241|241|241|241|
|adj. _R_<sup>2</sup>|0.018|-0.012|0.050|0.023|

Notes: * _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01. Heteroscedasticity robust _t_ -statistics in parentheses. ∆ _ln NTL pc_ measures the change in the natural log of local lights emissions per capita between 2006 and 2010 (Source: NOAA) as described in section 4. Columns 1 and 2 use (conventional) top-coded data, while columns 3 and 4 use bottomcoded data (see section 4). ∆ _ln Market Access_ is the change in the natural log of market access between 2006 and 2010 calculated using: _MAit_ =<sup>�</sup> _j_<sup>_h_(</sup><sup>_Tijt_)</sup><sup>_Pjt_,where</sup><sup>_P_denotesdestinationpopulation,</sup><sup>_T_denotestraveltime,and</sup> _h_ ( _T_ ) = exp ( _−_ 2<sup><u>1</u></sup><sup>_T_2</sup><sup>_/θ_2)isadistancedecayfunctionwith</sup><sup>_θ_= 40(seeSection3fordetails).∆</sup><sup>_ln(1+Pal.Fat.<5km)_</sup> _(Isr.Fat.<5km)_ measures the change in the number of Palestinian (Israeli) fatalities within a 5km radius (Source: B’Tselem). In columns 2 and 4 ∆ _ln Market Access_ is instrumented using both the level and the first difference of _Checkpoints 10-25km_ , the number of checkpoints deployed in a radius between 10 and 25km away from the locality’s center (Source: UNOCHA).

35
A _cost-allocation_ is a vector: The “preliminary criterion of a satisfactory allocation” originally listed by Ransmeier (1942) in his presentation of the problem corresponds to the _Core_ of this cost game: If we consider the _savings-game_ ( _N_ , _v_ ) corresponding to this cost-game, we will have: These savings can be obtained by the subset _S_ of projects.

We have:

> _v_ ( _i_ ) = 0  and _v_ ( _S_ ∪ _T_ ) ≥ _v_ ( _S_ ) + _v_ ( _T_ ) in the TVA case study. This is equivalent to say that _v_ is _zeronormalized_ and _superadditive_ .

The allocation to be chosen will now be a _vector of savings_ : ( _x_ 1<sup>,...,</sup> _xn_ ) where: and, if we look for an imputation lying in the Core, it must be: for all _S_ ⊆ _N_ .

The Core of the TVA game is non-empty (the game is convex), but it consists of a set of infinite points, thus there is the problem of choosing between them.

Almost all the methods considered by the TVA charge each purpose a minimum cost called _separable cost_ , that is, the change in the cost of a multipurpose project due to adding a project to the set of the others: If the savings game is convex it follows that: and the allocation problem turns to be the allocation of the _non-separable costs_ : _NSC_ = _C_ ( _N_ ) − _SC_ A possibility is the _equal allocation_ of the _NSC_ among the projects: In terms of the savings-game, 5
Figure A.1: Spatial Lag Model: Distance from mine and agricultural and service sector employment <!-- Start of picture text --> 0.05<br>0.04<br>0.03<br>0.02<br>0.01<br>0<br>-0.01<br>0 - 10 10 - 20 20 - 30 30 - 40 40 - 50<br>Agricultural employment Service sector employment<br><!-- End of picture text -->

* Figure A.1 shows the result from Table A.6 Panel E. The x-axis measures distance from an active mine and the y-axis the coefficients for the 10km distance bins. The control group if further away than 50km, but within 200km.

# **A.2 Heterogeneous effects by distance**

The empirical strategy relies on a decreasing footprint with distance from a mine. As seen in Table A.5, there are large and highly statistically significant effects of mine openings up to 25 kilometers away, and there is a shift from agriculture to service sector jobs or to leaving the work force. The largest effects are found using a cut-off of 5 kilometers and the most statistically significant effects are for distances up until 15 kilometers. The probability of having a service sector job within 10 kilometers of an active mine is 2.8 percentage points higher and this gives a total effect (coefficient for _active_ – coefficient for _inactive_ ) of 4.0 percentage points. Similarly, the probability of working in agriculture within 10 kilometers of an active mine is 4.9 percentage points less ( _active_ ) and the effect of mine opening at this distance is a reduction of 16.2 ( _active_ – _inactive_ ) percentage points. At 50 kilometers away from the mine center point, we no longer find significant difference in outcomes comparing active and inactive mining communities. Women living within 50 kilometers of a mine are more likely to work, to work in agriculture, and less likely to work in sales, but there is no extra effect of the mine opening. Table A.4 shows the sample sizes broken down by treatment status ( _active_ , _inactive_ and _suspended_ ) for the different distances.

As shown in Table A.6, we get consistent results using continuous measures to the closest active mine. _Distance to closest active mine_ captures the distance in kilometers (scaled by a hundred) from the DHS cluster to the closest active mine (Panel A), limited to 200 kilometers (Panel B), or taken in logs (Panel C). The results from these regressions show that being further away from an active mine is correlated with less employment, less service sector employment, and less agricultural work for women. We also do a horse race with the logged distance to the closest mine regardless of activity ( _distance to closest mine_ ) and do the same with the logged distance to the closest active mine. In accordance with previous results, we find that the shorter the distance to an active mine, the larger the share of the female population engaged in services. We see the opposite results for agriculture - the distance to an active mine is positive, but the 31
That is the approach taken here. As we will see, even this conceptually straightforward approach faces some severe data limitations, which generate considerable uncertainty about the poverty impacts of demographic change. And even without these data problems, one would rightly question the assumptions underlying this approach. Most importantly, it does not allow for possible indirect effects on living standards of the high rates of mortality and fertility found among the world’s poor. A higher fertility rate for the poor may also entail lower future real wages for unskilled labor and hence higher poverty independently of the direct demographic effect; indeed, there is evidence for precisely this effect for India (van de Walle, 1985; Datt and Ravallion, 1998, both using state-level panel data). Or higher death rates for the poor may undermine future productivity of the survivors; for example, it has been argued that the orphans resulting from the sharp rise in adult deaths due to the HIV/AIDS epidemic have had lower schooling than they would have had otherwise.<sup>1</sup> An alternative empirical approach is to regress rates of economic growth and/or poverty reduction at country level on rates of population growth and/or their demographic covariates, with controls for other factors affecting growth. Following this approach, Kelley and Schmidt (2001) and Williamson (2001) present evidence of adverse effects of high population growth rates on rates of economic growth; Williamson argues that the key demographic variable is the growth rate of the dependent population. By this view, changes in the age composition of the population affect aggregate savings and (hence) growth. Eastwood and Lipton (1999, 2001) use instead changes over time in poverty measures as their dependent variable, for a cross-section of countries, and find evidence of adverse effects of higher fertility on measures of poverty; they find that these effects are transmitted thought both growth and distribution. However, while

> 1 See Ainsworth and Filmer (2002) who also warn against such generalizations, given the diversity in apparent impacts across countries.

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# **2 Economic Growth in Sub-Saharan Africa**

This section investigates the economic performance of Sub-Saharan Africa and its sources of growth during the period 1961-2014. Specifically, it reviews if economic growth in the region was driven by the accumulation of (human and physical) capital or by growth in total factor productivity (TFP). Before assessing the sources of growth, Figure 1 shows the annual average rate of growth of GDP per worker, physical capital per worker, and a human capital index for Sub-Saharan Africa as well as groups of countries in the region classified by their extent of natural resource abundance. Over the period 1961-2014, the average annual growth rate for Sub-Saharan Africa on physical capital was 1.37 percent, while that of human capital was 0.42 percent. Physical capital accumulates at a faster pace not only for the region as whole but also among all other country groups; namely, oil-rich countries (SSA-oil), metal-rich countries (SSAmetal) and non-resource rich countries (SSA-nrr). We should also point out that metal-rich countries in the region exhibit a small growth in human capital despite the fact that output and physical capital per worker contracts over the past half century. Oil rich countries in Sub-Saharan Africa, on the other hand, exhibit the largest growth rate of physical capital per worker (annual average rate of 3.31 percent), which is three times as fast as that of non-resource rich countries (0.97 percent).

**Figure 1. Growth per worker of output, physical and human capital, 1961-2014**

<!-- Start of picture text --> 3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>Sub-Saharan Africa SSA Oil Rich Countries SSA Metal Rich SSA Non-Resource Rich<br>(SSA) Countries Countries<br>Output Physical Capital Human Capital<br>percent<br><!-- End of picture text --> _Notes: Averages are population weighted averages. The human capital index_ H�exp�φ�s���� _is characterized by the relationship between human capital (H) and the years of schooling (s) as_ φ�s����φ� ∙s�� _for each country i in period t —where the returns to education (_ φ� _) are heterogeneous across countries. The returns to education are measured from the estimation of Mincerian wage regressions on schooling. We use the Mincerian returns estimated by Montenegro and Patrinos (2014) and the total years of schooling for each country and year to construct the human capital index H. Source: Penn World Tables 9.0 (Feenstra, Inklaar and Timmer 2015)._ Figure 2 plots the sources of growth for the Sub-Saharan Africa region as well as the different groups according to the extent of natural resource abundance —that is, oil rich, minerals and metals rich, and non-resource rich countries during the period 1961-2014. The average annual growth rate of output per worker in the region (1.1 percent) is _page 5_
have some discretion.<sup>11</sup> In this sense, the investor’s own investability criteria can come into play on whether to include these countries, on the margin. Again, there is a trade-off between tracking error relative to the index and the desire to beat the index, which by definition means varying the weights each country represents in the portfolio.

_Table 1: Number of countries by weight in key emerging market indices_

|||Weight(%)<br>||
|---|---|---|---|
||0-5|5-10|10+|
|EMBI Global|28|0|3|
|EMBI Global diversified|23|6|2|
|ELMI+|16|8|0|
|MSCI|19|4|3|
|S&P/IFC composite|24|7|2|

Note: The specific country weights are confidential information for most indices. Source: JP Morgan Chase, Morgan Stanley, and IFC.

In sum, the benchmarking of investment funds, either directly or indirectly, can impact the investability decision of managers by placing a country either in a “must” invest category or a “may” invest category, the latter offering a degree of discretion for investment. However, just because a country is not in an index does not preclude it from the investable universe; rather the appropriate “housekeeping” or “plumbing” issues then come into play to make an investor use his deviations from benchmark to invest in it.

# **6 “CANNOT” INVEST COUNTRIES**

As mentioned in the introduction, only a few countries are found to be explicitly or implicitly on the “cannot invest” list. The main criteria defining this set of “blacklisted” countries are related to the country’s standing in the international community, the quality of its plumbing / infrastructure, or criteria specified by clients. Consequently, the “cannot” list or the “non-investable” list comprises countries where either the end investor or the asset manager in practice has lost the discretion to invest.

> 11 All the emerging market indices mentioned have a number of minimum criteria which need to be fulfilled even before an issue from a country is included in the benchmark; completing an international bond issue is by no means a guarantee of inclusion even with a small weight.  For smaller countries, emerging and developed alike, the minimum criteria may be insurmountable: e.g., a minimum individual size of a series (for some, around US$ 250 million); specific types of fixed instruments only; and minimum total outstanding marketable debt.  Such requirements will be impossible for many small countries to fulfill.

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