rafmacalaba/data-use-mentions-extended huggingface dataset:
off‐farm employment shocks – are also important in explaining why poor households remain poor, and why some non‐poor households are more likely to fall into poverty in the next period.

The calculation of expected shocks matters quite a bit in the assessment of vulnerability. Setting all shocks equal to zero results in much lower vulnerability rates. But this is unrealistic. In any given year, across the country or each region, the expected value of a shock is not zero, since some households will indeed face a shock while others do not. Thus, we constructed expected welfare outcomes by setting the shocks equal to the regional annual average rainfall and price shocks observed from historical rainfall data, and to regional level average malaria and employment shocks. While fewer households expect to be vulnerable next period than were poor in 2013, overall the government should expect more households to be vulnerable than observed poor in 2013. The empirical analysis confirmed that a sizable number of rural households were vulnerable to poverty in Malawi in 2010 due to the potential realization of risks (the occurrence of shocks) between 2012 and 2013. The latter results primarily because both rainfall and price shocks were relatively low in 2013 versus their long‐term historical average, as well as the importance of off‐farm employment shocks on consumption per capita.

Of the variables included in the welfare outcome and variance equations, both household wealth and agricultural asset indices are the most important in protecting households from falling into poverty and reducing the severity of the fall when shocks occur. However, in country contexts similar to Malawi's where risk mitigation mechanisms are limited, shocks can force many households into poverty. In short, we found broad similarities between vulnerable and poor households in terms of explanatory characteristics. In both cases, vulnerable households are characterized by large families with high dependency ratios, and limited household wealth and agricultural assets, and located in more isolated communities with poorly developed socioeconomic infrastructure. While household wealth can be used 22
revenues, shared taxes (including land and building and personal income tax), and (partial) natural resource revenues SDA.

# **_Lumpsum Allocations_**

8. The sum of _lumpsum allocations_ were 6.2 trillion for local government and 1.2 trillion for provinces. Local governments were to receive 18.51 billion per local government (or 31,875 per head). Provinces received 46.1 billion per province or 10,063 Rps per capita. The introduction of a lump sum figure creates incentives for the formation of new regions, especially in the case of smaller provinces (Fitrani, et al. 2005).

- (2). LSi = (LS-weight * Total DAU) / N

# **_Proportional Wage Bill Allocation_**

9. For the _proportional wage allocation,_ 31.1 trillion of the local government and 2.1 trillion of the provincial DAU were allocated by the relative percentage share of each local government or province in the total actual wage bill. The DAU 2002 base data suggests that the total annualized wage bill in 2001 was 40.5 trillion for 336 local governments and 6.6 trillion for 30 provinces.

- (3). PWBAi = (Wage Billi/Total Wage Bill) * (Total DAU PWBA)

# **_Fiscal Gap Formula Allocation_**

10. The _fiscal gap formula amount_ incorporates measures for fiscal capacity and expenditure needs to arrive at an allocation based on fiscal gap (FGi).

- (4). FGi = ENi - FCi

11. Depending on the relationship between the expenditure needs and fiscal capacity indicators, fiscal capacity can be negative. The Indonesian formulation imposed the condition that no region show a negative FG (and hence have STX and SDA transfers reduced).

12. _Fiscal capacity_ includes estimated own revenues (PADi*), realized tax shares (SXTi includes PBB, BPHTB, PPh shares for each region), and 75 % of realized natural resource revenue shares (SDAi). Own revenues were
Distributed Lag model (ARDL) for the period 1994-2018. Results using annual data are in line with those using quarterly data, although the RER coefficient is poorly determined due to the small sample size (see Table A.1 in the Appendix).

Table 1: Long-run relationships for Pakistani exports

|VARIABLES<br><br>|Quarterly<br>(1)|
|---|---|
|lll<br>t<br>|-0.492***<br>(0.165)|
|llt<br>∗|1.046***<br>(0.076)|
|Quarterly dummies|Yes|
|Observations<br>|95|
|R<sup>2</sup>|0.881|

***, **, * imply significance at the 1%, 5% and 10% respectively. Newey-West SE are reported in parenthesis.

In column 1 of Table 2, we present the results for the error correction model. The estimated speed of adjustment is significant and negative for both quarterly and annual data. For quarterly data, about 28 percent of the disequilibrium from the long-term relationship is corrected in one quarter. Annual data estimates show that 73.5 percent is corrected within the year, in line with quarterly estimates (see Table A.2 in the Appendix). The significance of the coefficient implies that, as expected, deviations from equilibrium conditions are corrected, and its absolute value below one shows corrections are gradual. This provides further evidence for the finding of cointegration when using the two-step Engle-Granger test.<sup>18</sup> In Column 2 we present results when we separate deviations from the long-run equilibrium into positive and negative, allowing for different speed of adjustments to new equilibria when exports are above or below the long-run equilibrium modeled by equation (1). This parameterization of the ECM model is analogous to a Threshold

> 18 For robustness
Narshingdi (8 monitoring points), and rural areas of Jessore (16 points) and Rangpur (11 points). Average outdoor concentrations in the three locations are 36, 48, and 62 ug/m<sup>3</sup> , respectively. The overall average for 35 monitoring points is 50 ug/m<sup>3</sup> , which we have adopted as our estimate of outdoor pollution for this exercise.

In the previous paper, we have shown that pollution generated in cooking areas diffuses almost immediately into living areas.<sup>3</sup> As a result, pollution in both areas exhibits strong pollution cycles in response to fuel combustion for cooking. Drawing on information from continuous, 24-hour monitoring of PM10 in 27 households in Narshingdi, Figure 2 displays a typical daily pollution cycle as a 24-hour plot of the ratio of the hourly mean PM10 concentration to the daily mean concentration.<sup>4</sup> Its distinguishing features include two peaks during morning and evening cooking times, when pollution rises to over 3 times the daily average, and extensive periods in the afternoon and evening when pollution is substantially lower than the daily average. Daily indoor pollution cycles are also reflected in outdoor ambient cycles, as many houses emit cooking smoke. Figure 3 illustrates a typical ambient cycle, drawn from 24-hour monitoring at 7 points in rural villages of Jessore and Rangpur.<sup>5</sup> We combine mean indoor and outdoor pollution concentrations with the hourly ratios in Figures 2 and 3 to produce Table 3, which provides hourly estimates of PM10 in cooking areas, living areas and outside the house.

> 3   For illustrations of the close relationship, see Dasgupta, et al. (2004), Figure 2, p. 12.

> 4   Of course, households differ significantly in the timing of daily peaks; some have three rather than two, and there are also significant differences in peak levels and change gradients.  Figure 2 represents the central tendency
challenge to be able to estimate what the trade transactions cost is for MENA countries. This study uses the results from Zarrouk (2003) survey, which suggest that inefficiencies in trade facilitation amount to some 10.6% of the value of traded goods. This amount is taken as the total trade transactions cost arising from inefficiencies. However, in order to adopt the above methodology we break this into an indirect (iceberg) and a direct TTC (tax component). To obtain the iceberg component we adopt Hummels (2001) estimate that each day saved in shipping time, in part due to faster customs clearance, is equivalent to a 0.5 percentage point reduction in ad valorem tariffs. The results of the Zarrouk (2003) survey suggest that average clearance from ports in MENA countries ranges between some two to ten days, hence the indirect cost component of the TTC is estimated to be 3%. The remaining 7.6% is attributed to the average direct cost component. Given the generally higher documentary requirements needed for agricultural goods it is assumed that the direct trade transactions cost for agricultural goods is twice that of nonagricultural goods. Hence the direct TTC for agricultural goods is about 12%, whereas that for non-agricultural trade is about 4%.<sup>5</sup> As discussed earlier, the indirect TTC are introduced as technical progress in importing.<sup>6</sup>

# **Data**

The model used for this study draws data from the GTAP 6 Data base. Although this version of the GTAP database allows for 87 regions and 57 commodities, its coverage of MENA is rather limited. The database has separate data for Morocco and Tunisia, while the rest of MENA is aggregated into “the rest of North Africa” (RONAF), and “the rest of the Middle East” (ROMIDE). The current study uses a 13 region by 16 commodity aggregation, which captures all the MENA sub-regions, key trading partners and key commodities. Details
Figure A9: Share of Purchases where Store is Chosen for its Quality by Income <!-- Start of picture text --> 5 6 7 6 7 7 8 9<br>Log Expenditure PP, Constant 2010 USD Log Expenditure PP, Constant 2010 USD Log Expenditure PP, Constant 2010 USD<br>(a) Benin (b) Burundi (c) Comoros<br>5 6 7 7 8 9 6 7 8<br>Log Expenditure PP, Constant 2010 USD Log Expenditure PP, Constant 2010 USD Log Expenditure PP, Constant 2010 USD<br>(d) Dem. Rep. of Congo (e) Morocco (f) Rep. of Congo<br>.3 .3 .3<br>.2 .2 .2<br>.1 .1 .1<br>Purchased for Quality (%) 0 Purchased for Quality (%) 0 Purchased for Quality (%) 0<br>.3 .3 .3<br>.2 .2 .2<br>.1 .1 .1<br>Purchased for Quality (%) 0 Purchased for Quality (%) 0 Purchased for Quality (%) 0<br><!-- End of picture text --> Local polynomial fit of the share of households buying any product for its quality on household’s total expenditure per person (log). Each panel corresponds to one of the six countries, for which the expenditure survey asks respondents why they chose this place of purchase for each expenditure. The solid vertical line corresponds to the median household total expenditure, while the dotted lines correspond to the 5th and 95th percentile.

60
|-0.040**|-0.120|-0.045|0.177|0.035***|
||0.244|0.231|0.020|0.140|0.140|0.163|0.009|
|Observations|438|438|438|438|438|437|435|
|||**4**<br>|**. Pooled o**<br>|**utcomes of EC**<br>|**T-R1 and ECT-**<br>|**R2**<br>||
|ECT-1 and ECT-2|0.319***|0.473***|0.012|0.389***|0.389***|0.221**|0.046***|
||0.114|0.123|0.031|0.098|0.100|0.098|0.008|
|Observations<br>_Source_: Authors’ cal|911<br>culations from|913<br>CIMS (2020)|913|913|911|910|892|

_<mark>Note</mark>_ <mark>: Standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1. Inverse probability weighted regressions. For each estimation, overidentification tests shows that the IPW model balances the covariates. Psychological wellbeing outcomes are originally in a 5-point Likert scale format and are considered as continuous in the IPW estimation. The coefficients should</mark> 23
E in place . We construct this index at the district level, using the 2013 round of Economic Census and iimm<sup>can be interpreted as the likelihood of industryDDandjjlocate to generate spillovers for one another</sup> excluding establishments from Uttar Pradesh and Uttarakhand from the sample. mm The second measure captures the strength of input-output linkage between industries. We calculate it between sectors and as: DD Ijj<sup>UUDDTTiim</sup> , <u>m</u> , <u>m</u> , <u>m</u> ~~)~~ mii ii mii UUDDTT UUDDTT UUDDTT iimm<sup>= arg max</sup><sup>~~(~~</sup> kk kkmm kk mmkk kk iikk kk kkii U i where denotes the value of sector ∑UUDDTT used as inputs in the production of sector ∑UUDDTT ∑UUDDTT ∑UUDDTT , and ∑U UU denotes the ratio between inputs from sector UUDDTTiimm DD and the sum of all inputs in the production of jj . The overall kk UUkkii index is the maximum value of the four pair-wise shares. The underlying data are India’s input-output DD jj matrix developed in Singh and Saluja (2016) for 2013-14 that is consistent with National Accounts Statistics 2015 and produced using the official supply use table released in 2012-13. The input-output matrix comprises of 130 unique products and services, categorized by the 3-digit IO commodity classification. We concorded pair-wise shares denoted in this classification to the 3-digit NIC 2008 codes. The third measure is on the similarity between sectors in terms of the types of workers employed. We make use of the employment module of the 2011-2012 National Sample Survey of India (NSSO 2012), which reports detailed occupation and sector information for each worker. We construct i as the fraction of sector ’s employment in occupation . As the number of observations in the National Sample SShT TT Survey data is limited, we calculate this variable at 2-digit NIC 2008 level. The
# **3.2 Data Intensities**

For our measure of data‐intensity as defined in equation (1), we use information on software use from the 2011 US Census ICT Survey. These data are survey‐based and record at detailed 4‐digit NAICS sector‐level how much each industry and services sector spends on inputs from the ICT‐sector in terms of ICT equipment and types of computer software in million USD.

We take computer software expenditure to compute data‐intensity. The ICT Survey records two separate variables on software expenditure, namely capitalized and non‐capitalized. Non‐capitalized computer software expenditure is comprised of purchases and payroll for developing software and software licensing and service/maintenance agreements for software. Capitalized computer software expenditures cover capital expenditures of equipment and software itself. Although this proxy of software does not entirely capture the extent to which sectors use electronic data, it nonetheless is the closest kind of data‐use variable we can publicly find. Note however that inside firms, data‐based innovation is based on software and therefore provides a good reason to use software as a proxy. We take the year 2010 for our regressions and divide this software expenditure over labor, also for 2010, and use it for our data linkage variable.

Admittedly, this proxy for data‐intensity is not ideal. Currently there is no data on the extent to which data is used by sectors. There are only some guesstimates on how much data are used by countries, such as recorded by Cisco or Teleography, but even these sources provide data for only a handful of observations. Having said that, what is clear is that the transmission of data for innovation within and across borders over the internet is performed using software technologies. Software is needed to develop digital innovations in its simplest form and with the help of software data are transmitted. In addition, more technology advanced transmissions of data over the internet are done with the help of cloud computing technologies which in themselves are a form of software
Banco de Provincia de Buenos Aires omitted.

22 The independent variables in this model change, at most, every six months. Government deficit data is only available on a yearly basis, bank quality variables are available for June and December of each year.

23 See Central Bank of Argentina (1996). A Wald test of the null hypothesis that the sum of the two coefficients is zero is rejected at conventional levels (X<sup>2</sup> (l) = 5.84).

24 The most significant definitional changes occurred for different classifications of 'bad' loans. It seems unlikely that the slight definitional changes for normal loans drive this result, especially since the variable base hazard rate might help control for some of the change. However, the definitional changes make it impossible to draw strong conclusions. See Bolzico and Figueroa (1994) for a discussion of the changes in definitions and in reserve requirements.

13
6, another payment that many lower-income urban households make is to transfer funds to family members living outside the U.S. In the OCC survey, 20 percent of the respondents indicated that they had made at least one such remittance over the past year. Of those who did so, 45 percent wired the funds and 24 percent mailed a money order. No other method accounted for over 5 percent of typical means of transferring the money.

18
following Becker et al. (2015). Sample corresponds to children born between 1958 and 1962, or 1968 to 1972. Covariates include birth district FE, year of birth×1971 enrollment, year of birth×1971 number of children, year of birth×water sanitation program, year of birth dummies, following Duflo (2001). Primary completion takes the value of 1 if the child has completed primary and 0, otherwise. Father’s Edu represents father’s years of schooling, which was calculated based on the education level completed. Family background is measured by father’s years of schooling. The variable Inpres measures the number of Inpres schools per 1000 children at the district level divided by the highest number of schools received by one district. Data sources: Indonesia’s full count census 2000 and Duflo (2001).

65
OIC countries need to develop a regulatory and supervisory framework that supports wide financial inclusion based on sound risk management and with sufficient consumer protections. During this process, governments and regulators need to take the lead in supporting improvements in access to finance, especially through _Shariah_ -compliant financial tools, displaying the _Leadership_ called for in the G20 Principles (See Box 4). Financial inclusion, especially Islamic financial inclusion, should be considered as a goal alongside prudential regulation and financial system stability. The CGAP and World Bank Financial Access survey (2010) of financial regulators worldwide found that regions that include financial access in their strategies and mandate their financial regulators to carry such agendas are also the countries that reform the most. Regulators with a financial inclusion strategy are more likely to have more financial inclusion topics under their purview and more resources and staff dedicated to working on these matters.<sup>32</sup>

**Practical measures that can be taken by regulators** (as has been the case in Indonesia and Pakistan) include: licensing Islamic banks, supporting centers for training and certification on Islamic financial operations to staff and managers of financial service providers, and developing guidelines setting out requirements for licensing and appointment of _Shariah_ advisers to rule on _Shariah_ compliance. The political climate for Islamic finance also affects investor and lender decisions about whether to offer _Shariah_ -compliant products, and acts as an incentive or disincentive to its growth.

In addition, consumer protection is also increasingly important as access increases, so that new customers, and existing customers with access to new services, can take well informed decisions about how best to manage and use Islamic financial services. In some countries, customers stay away from Shariah-compliant services because of lack of education about authenticity and credibility of products. Consumer protection can help remove such perceptions and can lead to enhanced inclusion. Therefore, it is essential that consumer laws and regulations are developed and put in place: i) to protect against unfair or deceptive practices ii) to improve transparency through disclosure and
# **2. Data and measurement**

This study uses data for over 2,000 companies from nine Asian economies-Hong Kong, Indonesia, South Korea, Japan, Malaysia, Philippines, Singapore, Taiwan and Thailand-for the year 1996 as assembled in Claessens et al. (1998a).<sup>6</sup> We restrict the sample to firms with sufficient segment and financial data to construct empirical measures, particularly the diversification measure. Of the 2,980 firms with complete ownership data, 2,187 firms have one or several years of the necessary segment and financial data. Assuming that the ownership structures of the firms do not change substantially over time, we end up with a sample of 9,559 firm-years spanning from 1991 through 1996.7 For each of the companies in the database, we identify who the ultimate owners are, what their share of cash-flow and control rights is, and whether the company is affiliated with a business group. The corporate group information is supplied by the data sources detailed in Claessens et al. (1998a). The procedure of identifying ultimate owners is similar to the one used in La Porta et al. (1999b). An ultimate owner is defined as the shareholder who has at least 5 percent of the control rights of the company and who is not controlled by anybody else. If a company does not have an ultimate owner, we classify it as widely-held. We further classify ultimate owners into four types: widely-held corporations, widely-held 1998; Klein and Saidenberg, 1998).

> 6Worldscope contains fmancial, segment, and block ownership information on companies from 49 countries. The database has been used in several other international studies (for example, Fauver et al. (1998), La Porta et al. (1998a, b), Lins and Servaes (1998) and Claessens et al. (1998a, b)). As our starting point for the data collection, we use the Worldscope database, which generally provides the names and
**Indicator 4 (Strengthen capacity by coordinated support):** Afghanistan (-1.9) and Kosovo (- 2.8)

# **Indicator 6 (Strengthen capacity by avoiding parallel project implementation units):** Burundi (-1.9), Haiti (-2.2) and Sudan (-2.9) **Indicator 9 (Use of common arrangements or procedures):** Central African Republic (-1.9).

# **(3) FCS Partner Country Progress between 2005 and 2010**

Poor FCS aid effectiveness performance in 2010 identified in the previous analyses may not be surprising due to well recognized challenges. However, the question on whether FCS performance improved between 2005 and 2010 is unanswered. There are two hypotheses. One is that as FCS started from a weak base, progress would be better than for non-FCS. The other is that the challenges faced by FCS would prevent these countries from improving their performance.

The same methodology as used previously is employed to compare progress between 2005 and 2010. Changes in indicator values are standardized to enable comparison across indicators and countries. Thirty-two countries participated in the PD survey in both 2006 and 2011. Among these, three countries (Afghanistan, Burundi and Democratic Republic of Congo) are FCS and one country (Cambodia) graduated from FCS between the periods<sup>12</sup> . Figure 4 shows performance in 2010 (the horizontal axis) and progress between 2005 and 2010 (the vertical axis)<sup>13</sup> .

**Figure 4: 2010 Performance and Progress between 2005 and 2010**

<!-- Start of picture text --> 20<br>3rd Quadrant: Progress since<br>1st Quadrant: Both2010<br>2005 is better than average, but<br>performance in 2010 is worse Dominican Rep performance and progress<br>since 2005 are better than<br>than average 15<br
growth. Figure 3 plots real GDP growth in the top 10 destinations for OFWs. In addition to the differences in growth rates in 1997 during the Asian Financial Crisis, another period of high volatility was during the Global Financial Crisis, which by 2009 had affected some destinations more than others.

> 7 The Philippine Labor Force Survey is administered annually to a nationally-representative sample of households. The SOF is administered as a rider to the LFS if the household reports having any members working overseas, and contains information on migrant demographics, overseas occupation and location, and remittances (all reported by the household remaining behind in the Philippines).

11