rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 14: Evolution of Fund E’s Net Amounts Transferred (% of AUM)**

<!-- Start of picture text --> 0.35<br>0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>-0.05<br>-0.1<br>-0.15<br>-0.2<br>Oct-08 Apr-09 Oct-09 Apr-10 Oct-10 Apr-11 Oct-11 Apr-12 Oct-12 Apr-13 Oct-13 Apr-14 Oct-14 Apr-15 Oct-15 Apr-16<br><!-- End of picture text --> To study whether the stress caused by members switching funds affected investment strategies, this section uses monthly information of flows and portfolio holdings of Fund E provided by the Chilean Pension Supervisory Agency during 2008-2016. More specifically, the following asset classes were analyzed: Indexed Treasury Bonds<sup>24</sup> , Nominal Treasury Bonds, Indexed Central Bank Bonds, Time Deposits, Bank Bonds and Corporate Bonds. Taken as a whole, these investments represent 80% of Fund E’s portfolio. To follow the empirical strategy from the previous section, assets were classified according to their duration<sup>25</sup> and liquidity.<sup>26</sup> The evidence suggests that larger flows into and out of Fund E are related with more investments in low duration and high liquidity instruments. In particular, after 2011, Fund E investments have become more concentrated in assets with duration below 1 year and in securities with high trading volume. These results, which are consistent with the Colombian evidence, suggest that fund managers increase investments in short term instruments in order to meet liquidity needs.

> 24 Indexation to inflation is achieved by using Unidades de Fomento (UF), which is an account unit that is monthly adjusted to reflect changes in the consumer price index. As of December 2016, a UF was roughly equivalent to 40 US dollars.

> 25 Less than one year, between 1 and 5 years, and more than five years.

> 26 Liquidity is measured by transaction volume of each asset.

20
Table 3: Dependent Variable: Arms imports. Add-on: interaction with the oil price.

|(1)<br>SIPRI|(2)<br>SIPRI|(3)<br>COMTRA|(4)<br>DE<br>COMTRADE|(5)<br>Import share|(6)<br>Import sha|(7)<br>re<br>GDP share|(8)<br><br>GDP share|
|---|---|---|---|---|---|---|---|
|Discovery in past 5 years<br>0.608<sup>_∗∗_</sup>|0.633<sup>_∗∗_</sup>|0.340|0.394|0.188|0.286|0.178|0.288|
|(0.223)|(0.268)|(0.240)|(0.259)|(0.215)|(0.236)|(0.248)|(0.264)|
|_×_ Oil price<br>0.646<sup>_∗_</sup>|0.686|-0.119|-0.033|-0.095|0.065|-0.244|-0.065|
|(0.376)|(0.405)|(0.301)|(0.325)|(0.275)|(0.295)|(0.339)|(0.358)|
|Nb of previous discoveries|-0.019||-0.043||-0.079||-0.088|
||(0.160)||(0.128)||(0.099)||(0.110)|
|N<br>3408|3408|3392|3392|3392|3392|3436|3436|
|R-sq<br>0.72|0.72|0.72|0.72|0.42|0.42|0.37|0.37|
|The dependent variable|is in in|verse hy|perbolic sine.|The log of|oil price|is centere|d around|
|$80.<br>The regressions in|clude|country|and year fxe|d efects.|Robust|standard|errors in|
|parenthesis, and * stand<br>*** at the 1% percent le|s for st<br>vel.|atistical|signifcance at|the 10%|level, **|at the 5%|level and|

such arms deals we could have expected corruption and lack of democratic accountability to accentuate the discovery effect on arms deals. This is not what the results suggest. Instead we find a positive and significant effect only in the most democratic and least corrupt countries (these results are similar using the World Governance Indicators’ measure of corruption). This may be because a minimum threshold of state capacity is required to generate revenues from the oil find. These interactions are not statistically significant however. Hence I do not want to stress these results, especially as there might not be much relevant difference in institutional quality across countries in the sample. As for the role of military heads of state, the results are different whether I use SIPRI or COMTRADE data. The latter suggests that discoveries trigger arms imports only in countries where the head is a military officer, while SIPRI data suggests the opposite. I guess it is cautious not to make any conclusions about the role of military heads here.

In Tables 4 and 5 I check if giant discoveries trigger arms imports from all or only some countries among the major arms exporters. I estimate both the baseline model and the model where the discovery dummy is interacted with the oil price to check for effects when the oil price is high. According to SIPRI data, arms imports from the United States, the Russian Federation, Germany, France, and China account for around 75% of all imports during 18
The ATT estimates for comparison of Jalanidhi and other community managed schemes indicate no significant differences across various indicators related to availability and reliability, household satisfaction and household cost of service (Table 12). Jalanidhi schemes, however, experienced fewer breakdowns compared to other community managed schemes. Jalanidhi schemes also perform significantly better in certain dimensions of institutional sustainability. Jalanidhi schemes have a larger proportion of females in executive committees and significantly higher bank balances compared to other community managed schemes. Similarly, a higher proportion of Jalanidhi schemes have female treasurers and maintain records regularly.

**Table 12: PSM Comparison of Jalanidhi with Other Community Managed Schemes: Selected**

**<u>Indicators</u>**

|**Variable**|**Jalanidhi**|**Other**<br>**Bottom-up**|**ATT**|**SE**|**P-value**|
|---|---|---|---|---|---|
|**Availability & Reliability Index**||||||
|No Irregular Supply|0.62|0.53|0.10|0.13|0.443|
|No Low Pressure|0.65|0.55|0.10|0.16|0.539|
|No Seasonal Shortage|0.49|0.54|-0.05|0.13|0.723|
|**Household Satisfaction Index**||||||
|Overall Satisfaction|3.5|3.5|0.04|0.15|0.820|
|Regularity of Supply|3.4|3.2|0.19|0.23|0.415|
|**Household Cost of Service Index**||||||
|HH Opinion on Contribution|2.1|2.2|-0.10|0.14|0.489|
|HH Opinion on Tariffs|2.0|1.9|0.11|0.07|0.108|
|**Operation & Maintenance Index**||||||
|Number of Breakdowns Last Year|3.471|5.9|-2.4***|0.72|0.001|
|Scheme Facing No Water Shortage|0.532|0.462|0.07|0.14|0.601|
|**Institutional Sustainability Index**||||||
|Proportion of Female in BG Executive<br>Committee|0.322|0.251|0.07+|0.04|0.098|
|Maintenance of Records (Yes/No)|0.163|0.006|0.16**|0.05|0.004|
|CurrentBalanceinthe SavingsAccount|15,967|3,096|12,871***|2,568|0.000|

� p�0.1, * p<0.05, ** p<0.01, *** p<0.001 The ATT estimates of the aggregate performance indexes indicate that Jalanidhi schemes perform better than the KWA-BM schemes in Availability and Reliability, Household Satisfaction, O&M and Overall Performance (Table 13). The ATT estimate for availability and reliability Index is around 1.0 which means that that the respective z-score for Jalanidhi scheme are on an average higher than the z-scores of comparable KWA schemes by 1. Since the difference between z-scores is not readily interpretable, the mean z-scores of the matched Jalanidhi and KWA schemes have been converted to percentile rankings. ATT can then be interpreted as the average difference in percentile rankings between similar Jalanidhi and KWA schemes. Figure 1 shows that for overall performance, availability and reliability, household satisfaction and O&M, Jalanidhi schemes on average ranks 30 to 40 percentile points higher 19
in jobs requiring such skills is greater than the rate at which technology has been able to automate them. This could be a result of the structural changes in the Chinese economy—or of globalization channeling more jobs with routine cognitive tasks to China. Globalization might also be behind the increasing demand for routine and nonroutine manual tasks between 2000 and 2010. As rapid technological progress and automation in developed countries reduce the number of manual jobs outsourced to China, and as China itself advances in automation of manual tasks, the demand for these tasks is likely to fall. In fact, between 2010 and 2015, the share of jobs involving manual tasks fell drastically. The evolution of nonroutine cognitive tasks follows the opposite pattern. The share of these tasks fell in 2000–2010 but then rose in 2010–2015.

Figure 2.5 Demand for non-routine interpersonal and cognitive skills has steadily increased in China, based on the change in occupational employment shares <!-- Start of picture text --> Change in occupational employment shares<br><!-- End of picture text --> <!-- Start of picture text --> Routine manual<br>Routine cognitive<br>Non-routine manual physical<br>Non-routine manual interpersonal<br>Non-routine cognitive analytical<br>Non-routine cognitive interpersonal<br>2000 2005 2010 2015<br>Year<br>.06<br>.04<br>.02<br>0<br>-.02<br>-.04<br>-.06<br>Change in occupational employment shares<br><!-- End of picture text --> _Source_ : Du and Park 2017.

Trends in population and implications for upper secondary enrollment While China is the second largest economy in the world, it has also been experiencing a dramatic demographic change since the 1980s. As shown in Figure 2.6, the newborn population peaked at the turn of the 1990s but plummeted by 60 percent in the mid-1990s. Despite a small revival in the number of newborns at the turn of the century, a long-term decline was anticipated after 2015. The newborn population in 2015 was only a little over half the 1989 level.

14
symmetry, etc., limit the number of free parameters to be estimated.<sup>11</sup> Even though ordinary least squares could be used to estimate any one of these two functions, if having additional information, it can be used to improve the estimate efficiency. Applying Shephard’s lemma ensures that there is a group of factor demand equations that can be derived from the cost function. In this way, we can obtain as many additional equations to cost function as thre are productive factors (n) involved in the estimation of costs, without introducing any additional parameter. The maximum likelihood method can be used to estimate the unknown parameter, specifying that n+1 equations bear normal additive errors. Although cost functions can be estimated alone, it is clearly more efficient to estimate parameters from the n+1 equations system.<sup>12</sup> Lastly, it is worth mentioning that estimating these functions at the approximation point (generally the sample mean) represents usual practice in the empirical paper and there are basically two reasons for this. First, this results in an immediate estimation of the gradient at the approximation point and Hessian elements. Second, multicollinearity between linear, square and cross terms is avoided because independent variables variations are magnified. In fact, if expansion is carried out around zero values, multicolinearity problems arise, although estimates are the same _(Jara-Díaz, 1983)._

# **5. Production and cost functions in ports**

The literature on port production and cost structure is about 25 years old. While it addresses many of the general issues raised in the theory survey above, of particular interest is the set of contributions focusing on economies of scale and, in some cases, economies of scope. Two different approaches can be distinguished within this group of papers. The first approach is represented by the studies using production functions, such as C _hang (1978), Reker et al., (1990)_ and _Tongzon (1993)._ The second approach encompasses the studies that estimate cost functions, whether single-productive as in _Kim and Sachis (1986)_ and _Martínez Budría (1996)_ , or multi-productive as in _Jara-Díaz et al. (1997), Martínez Budría et al._

> 11 Non-normalized quadratic function has m + n + 1 parameters more than the translog function restricted to linear homogeneity in prices.

> 12 From n equations of factors derived demand only n – 1 are linearly independent. Zellner (1962) iterative process consists of a two-phase estimation procedure with one equation less to obtain maximum likelihood estimations of the complete system. For further details, see _Christensen and Greene (1976)_

11
from, data compiled in WHO’s annual reports on Global Tuberculosis Control (World Health Organization 2007). While the model calculates incidence, prevalence, and deaths, only deaths are used in the analysis of economic effect (Figure A.2).

<!-- Start of picture text --> Incidence rate Prevalence rate Death rate<br>150 400 40<br>Estimated<br>Fitted 300 30<br>100<br>target 200 20<br>50<br>100 10<br>0 0 0<br>1990 2003 2015 1990 2003 2015 1990 2003 2015<br><!-- End of picture text -->

**Figure A.2.** TB model fitted (white bars) to WHO estimates (black bars) of incidence, prevalence, and death rates per 100,000 population in the Western Pacific Region, 1990 and 2003. Grey bars are the MDG target values for 2015 and the corresponding white bars for 2015 are the expected outcome of implementing the Global Plan (scenario 3). The third panel shows the standard errors on calculated death rates, as derived from multivariate uncertainty analysis.

29
63 Table D.1: Employment Share of Urban Tradables by City Size, Cross-Section, c. 2000

|Dep. Var. = MFGFIRE_a,_00|Coef.|SE||Coef.|SE|
|---|---|---|---|---|---|
|Capital City_a_(CAP)|-5.46**|(2.44)|URB_c_|0.03|(0.05)|
|Pop. Size CAT_a_= 2|-0.66|(1.42)|URB_c_* Pop. Size CAT_a_= 2|0.07**|(0.03)|
|Pop. Size CAT_a_= 3|1.03|(2.35)|URB_c_* Pop. Size CAT_a_= 3|0.08*|(0.04)|
|Pop. Size CAT_a_= 4|3.69|(3.99)|URB_c_* Pop. Size CAT_a_= 4|0.04|(0.06)|
|Pop. Size CAT_a_= 5|5.79|(4.41)|URB_c_* Pop. Size CAT_a_= 5|0.01|(0.07)|
|Pop. Size CAT_a_= 6|13.09***|(4.10)|URB_c_* Pop. Size CAT_a_= 6|-0.06|(0.06)|
|Pop. Size CAT_a_= 7|8.59*|(4.50)|URB_c_* Pop. Size CAT_a_= 7|0.03|(0.07)|
|Pop. Size CAT_a_= 8|11.70*|(5.98)|URB_c_* Pop. Size CAT_a_= 8|0.05|(0.10)|
|Pop. Size CAT_a_= 9|21.76***|(5.35)|URB_c_* Pop. Size CAT_a_= 9|-0.09|(0.08)|
|Constant|16.24***|(4.06)||||

_Notes:_ Obs. = 6,865 urban agglomerations. R2 = 0.20. The dependent variable is the employment share of MFG+FIRE in each urban agglomeration _a_ belonging to country _c_ circa 2000. The other variables are also defined in 2000. Robust SE clustered at the country level in parentheses. * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01.

Table D.2: Additional Correlations for the Long-Difference Regressions

|**Dependent Variable:**|**Urban**|**ization Rate (%**|**) in 2020**|
|---|---|---|---|
||(1)|(2)|(3)|
|NRXGDP (%) (Mean 1960-2020)|1.02***|0.74***|0.74***|
||[0.239]|[0.236]|[0.275]|
|MFGSERV (%) (2020)|1.09***|||
||[0.195]|||
|DEINDU (%) (1980-2020)|-0.04|-0.70|-0.88|
||[0.343]|[0.480]|[0.628]|
|MFG (%) (2020)||1.57***|1.06***|
|||[0.279]|[0.395]|
|SERV (%) (2020)||0.41*||
|||[0.244]||
|FIRE (%) (2020)|||0.69**|
||||[0.327]|
|SERV (non-FIRE) (%) (2020)|||0.57*|
||||[0.303]|
|Beta Coef. MFGSERV|0.57|||
|Beta Coef. MFG||0.62|0.22|
|Beta Coef. SERV||0.16||
|Beta Coef. FIRE|||0.14|
|Beta Coef. SERV (non-FIRE)|||0.16|
|Controls|Y|Y|Y|

_Notes:_ Obs. = 115. We control for initial conditions c. 1960 – i.e., the urban share and the value of the variables c. 1960 (except for FIRE and SERV (non-FIRE) since there is no existing data for the FIRE sector in the 1960s – and add the controls for area, population, small islands, and urban definitions. Robust SE in parentheses.
42 and the last 12 months are used frequently, periods that are probably too long for items such as outpatient care and medicines. Multipurpose surveys are better in that spending data are gathered via a health module that varies recall period by type of service. Third, variations in comprehensiveness probably exist across surveys. A review of 100 survey questionnaires cited in Wagstaff et al. (2018a) found that, in 80% of surveys, questions were asked about spending on pharmaceutical products, hospital services, medical services, and paramedical services.

# _Measuring income_

Because, as mentioned above, total consumption increases after a health shock and pushes sick households up the consumption distribution while income does not, catastrophic (and impoverishing) payments computed with reference to income are easier to interpret, especially when looking at inequalities in catastrophic payments (Wagstaff et al. 2018a). It is customary to distinguish four main components in the measurement of income: (i) wage income from labor services; (ii) rental income from the supply of land, capital, or other assets; (iii) self-employment income; and (iv) current transfers from government or nongovernment agencies or other households. It is sometimes claimed (cf. e.g. Deaton and Zaidi 2002) that developing-country surveys (including the LSMS surveys) do well on (i) but less well on the other components of income. Recent initiatives such as the FAO RIGA project (Quiñones et al. 2009) are changing this. Sometimes income is not measured at all in developing-country surveys, so using it is not an option. The 2018 HEFPI database uses income rather than consumption for all high-income countries, and for certain upper middle-income countries. For most developing countries, we use consumption. Many of the highincome country datapoints come from ex-post data harmonization efforts like the LIS – see below.

# _Measuring consumption_

Surveys have differed a great deal in the level of detail of their consumption modules. The LSMS surveys, which have been designed and implemented with the explicit objective of measuring living standards, have included somewhere in the region of 20 to 40 food items and a similar number
Budget Survey (EBCNV). Moreover, Section 4 is devoted to the presentation of the new sample design in the case of the 2021 Household Budget Survey in Tunisia. Three scenarios are taken into account: i) PSUs are selected with Simple Random Sampling (SRS) within the 24 governorates (explicit strata) in Tunisia; ii) PSUs are, first, stratified among the 24 governorates and then further stratified (implicitly) with systematic sampling by ordering the lists according to some economic and geographical variables, which consequently make the list a **geographical serpentine** ; iii) PSUs are, first, stratified among the 24 governorates, and then further stratified (implicitly) with systematic sampling by ordering the lists of PSUs according to the average per-capita consumption expenditure estimated by the poverty mapping method presented in Section 3.

This third scenario represents the primary, original contribution of this paper, and the design effect gain is compared with other scenarios. Such gain in the design effect has an impact on both the estimate variance (for higher precision in the estimates), and in the optimal sample size (i.e., for a smaller budget).

Finally, Section 5 concludes the paper. After summarizing the main results of the paper, the section aims to briefly present some variants of the proposed methodology when Census and/or Household Budget Surveys are not available for recent years in order to perform the poverty mapping exercise.

# **2. Theory of design effects, variance estimation, and resampling techniques**

This section aims to present the theory behind the estimation of standard errors in complex surveys such as HBSs. Since the _implicit stratification_ proposed is based on poverty estimates and per capita consumption expenditure performed with the World Bank ELL method (described in further detail in Section 3), here the notation adopted is a mixture of the notation for sampling techniques in Wolter (1985) and the notation for poverty mapping in ELL (2003), as follows.

We have a _population_ divided in L strata (l: 1, 2, ...L) and clustered in Ml clusters (m: 1, 2, ..., Ml); in each cluster m there are Km households (k: 1, 2, ...., Km) where 5
# **Data**

The firm-level data come from the World Bank’s numerous Investment Climate Surveys (ICS) and Business Environment and Enterprise Performance Surveys (BEEPS) conducted in various countries in various years, which are listed in the Data Appendix. There is substantial overlap between the ICS and BEEPS questionnaires, but there are differences in sampling approaches.

# The surveys

The ICS tend to focus on manufacturing firms; the BEEPS are drawn from a broad range of economic activities including services (actually the BEEPS database is slightly skewed towards services). We restricted the coverage of the BEEPS data to firms in the manufacturing sectors.

The coverage of the BEEPs and ICS data in terms of the sampling of firms is also different. The BEEPS used quota sampling, whereby 10 percent of selected firms are small, with the number of employees in the 2-49 range; another 10 percent were firms with 250-999 employees, and the rest were randomly selected in between those two extremes. The ICS survey sampling approaches differed across countries. In some cases, quotas by sector and size were used. In others, existing industrial census shares by industries and size were used as benchmark sampling quotas. Thus, there might be some selectivity of the sampled firms, which might raise doubts about the randomness of the sample. If we were attempting to draw conclusions for each country specifically, these cross-survey and cross-country differences in sampling would be important impediments. Our objective is more modest, as we want to draw general conclusions about the probability of product innovation by incumbent firms from around the world. It is possible that selective sampling for each country but for numerous countries can yield representative samples for the world, because the statistical requirements for representativeness are less restrictive than for smaller sub-samples. Thus we interpret our statistical results with caution, and discuss results with different samples.

# Multi-level explanatory variables and hypotheses

Three levels of analysis are used in our empirical model, namely firm-, sector- and country-level variables. The first set includes the dependent variable: the introduction of a new product. The surveys asked managers whether the firm had introduced a new product during the past two years. Hence our dependent variable is a dichotomous dummy variable.

6
Instituto Nicaraguense de Seguridad Social y Bienestar (Nicaraguan Government). 1985. _Anuario Estadistico_ 1985. Nicaragua: INSSB.

Instituto Nicaraguense de Seguridad Social y Bienestar. Memoria de Actividades 1989. Managua, Nicaragua: Direccion General de Planificacion; 1990 October.

Instituto Nicaraguense de Seguridad Social y Bienestar (Nicaraguan Government). Anuario Estadisticos 1985 and 1989. Nicaragua: INSSB; 1985.

Instituto Mexicano de Seguridad Social. 1993. _Anuario Estadistico._ Mexico: IMSS.

Instituto Venezolano de Seguro Social. Various years. _Anuario Estadistico._ Caracas, Venezuela.

IDB (Inter-American Development Bank). 1993. Sistemas de Seguridad Social en la Region: Problemas y Alternativas de Solucion: Argentina: Unpublished.

IFC (International Finance Corporation). 1994. Emerging Markets Data Base. Central Capital Markets Department. (Provided on diskette by IFC staff).

ILO (International Labour Office). Various years. _Yearbook of International Labour Statistics._ Geneva, Switzerland.

-. 1987. _L'investissement des Fonds de la Securite Sociale dans les Pays en Developpement._ Geneva, Switzerland.

-. 1989. Indonesia: Extension of Social Security Protection Project, Pensions Planning Component.

-. 1992a. _The Cost of Social Security._ Geneva, Switzerland.

_-. 1993. Report to the Government of the People 's Republic of China on Social Security Reform._ Geneva. Switzerland.

-. Forthcoming. _The Cost of Social security._ Geneva, Switzerland.

IMF (International Monetary Fund). Various years. _International Financial Statistics._ Washington. D.C.

IMSS. 1993. Instituto Mexicano de Seguros Sociales, _Anuario Estadistico._ INSSB. 1990. Instituto Nicaraguense de Seguro Social y Bienestar, _Memoria Anual._ IPSP. 1987. Instituto de Prevision Social de Paraguay, Estadisticas.

IPRES. Institution de Prevoyance Retraite du Senegal. Rapport de Gestion 1986, 1989. Senegal.

131