rafmacalaba/datause-encoder-data huggingface dataset:
(consistent with other BOP flows).<sup>49</sup> Direct investment does not have a split in the BOP by sector, but the debt portion of direct investment inflows can be allocated with some assumptions. Direct investment debt inflows between affiliated parties are only recorded as direct investment debt if at least one party is a non-financial firm. Thus for inflows, we can attribute all direct investment debt to the Corporate sector if we assume that such lending from offshore non-financial firms to onshore banks is negligible.

**Portfolio Equity:** Portfolio equity captures investment in equity securities not included in direct investment.<sup>50</sup> It is broken down by institutional sector and, in principle, asset and liability flows are defined for all sectors. Note, however, that liability flows for central banks and general government should equal zero regardless of data reporting.<sup>51</sup>

**Portfolio Debt:** Portfolio debt consists of all debt securities not captured under direct investment. It is separated into asset and liability flows, and then disaggregated by institutional sector.

**Financial Derivatives:** Financial derivatives tend to be a quantitatively small category of gross flows, covering derivatives and employee stock options. Financial derivatives that are associated with reserve asset management are excluded. Both asset and liability flows offer breakdowns by institutional sector.<sup>52</sup> Due to its small size and sparse data, we ignore this component in our analysis.

**Other Investment:** Other investment captures all other investments not included in the

> 49This is one of the main differences between the BPM5 and BPM6 versions of the data.

> 50Equity not in the form of securities is not captured here.

> 51Some countries report positive equity liability flows for the government or central bank, but we believe this is equity from state-owned or quasi-public enterprises (banks or corporates) that was mis-recorded.

> 52Some countries may report financial derivatives on a net basis only. See 6th edition BOP manual paragraphs 6.60 and 8.34.

53
# **II.5 Productivity**

<!-- Start of picture text --> 120<br>100.<br>80<br>60 _<br>40<br>20<br>0 _ I _ I. I2M I<br>- o o o ° 2° ><br>|U1986 01995 01998<br>Figure 7: Main Lines Per Employee.<br>Source: Intemanonal Telecommunication Union (2001)<br>Note: Data for Sub-Saharan Afnca (SSA) excludes South Africa.<br><!-- End of picture text -->

In 1986, there were 17 main lines per employee in Malawi - higher than in any of the other case study countries or the average for SubSaharan Africa (12 main lines per employee) (see Figure 7). However, over the next decade, MPTC's relative performance declined and by 1995, MPTC, with 9 main lines per employee, was very inefficient by both regional and international standards. In comparison, by this time, the average number of lines. per employee had more than doubled to 25 main lines elsewhere in SSA. Furthermore, the average for SSA was very by international standards: in 1995, there were 134 main lines per employee in Latin America, 97 in East Asia, and 29 in South Asia. The number of main lines per employee, which increased to 16 by 1998, appeared to recover following corporatization. However, it remained far below the number for the other case study countries and far below the average for Sub-Saharan Africa (34 main lines per employee). 12

# **IIJ. THE 1998** COMMUNICATIONS SECTOR POLICY STATEMENT AND LAW.

In 1998, the Government of Malawi enacted the 1998 Telecommunications Law and Policy, liberalizing many areas of telecommunications. The _1998 Communications Sector Policy Statement_ (Ministry of Information, 1998) is the basis for the ongoing reform process. The objectives are: 17
the baseline for which the nominal food consumption expenditures are less than the value of the transferred food basket (i.e. $150 pesos) as a means of identifying the fraction of households who might be constrained by the transfer to consume more food than they would like (i.e. the extra-marginal households). Table 1 reveals that there are no households in the sample who are constrained by the transfer to consume more food than they would like. However, as is shown in appendix A, this is not the case for individual food items.<sup>19</sup>

# _Food and total consumption_

The survey collects information on the quantity of food consumed (including the quantity consumed out of own production and food gifts or donations including those of the PAL at follow-up) in the last seven days for 61 food items. The monthly value of food consumed is obtained by multiplying the quantity of food consumed of each food item multiplied by the median unit value of the same food item at the locality level.<sup>20</sup> The unit value of each food item is derived from the additional questions on the value and quantity purchased (and not necessarily consumed) in the last seven days. The total value of household food consumption per month is defined as the sum of the value of food consumed<sup>21</sup> , and the value of meals consumed away from home. Total consumption expenditures are defined as the sum of food consumption and expenditures for goods other than food.

When examining impacts on food consumption, the dependent variable _Y_ ( _i_ , _t_ ) in equation (1) is the natural logarithm ( _ln_ ) of the (nominal) value of food consumption per capita

> 19 In appendix A we conduct a more detailed investigation of the extent to which the PAL food basket “over-provides” individual food items in relation to the consumption pattern of households in the sample in the baseline round.

> 20.(The section erased was stated above already) However, we do not have the market price for all the food items that are included in the list of foods consumed (either some items are not included in the market price list or the definition of the food item is different).

21 Deaton and Zaidi (2002) stress that in cases where the amount of food consumed can be distinguished from food purchased it is the value of food consumed that should go into the consumption aggregate.

16
**Table 10: Mean Customer and Billing Cycle Level Own-Price and Net Income Elasticities**

|**Service**|**Own-Price Elasticity**|**Net Income Elasticity**|
|---|---|---|
|Heatingand Cooling|-0.344|1.336|
|Lighting|-1.378|1.167|
|Domestic end-use|-2.719|0.794|
|Business end-use|-1.194|1.341|
|Compositegood|-0.947|1.012|

**Table 11: Average marginal prices in FY2016-17 under current price schedule and MOP’s recommended tariff**

|Income<br>quintile|e=-0.1|e=-0.2|e=-0.3|e=-0.4|
|---|---|---|---|---|
|1|12.21|14.24|16.27|18.30|
|2|140.59|142.19|143.65|146.26|
|3|172.06|175.24|204.59|208.00|
|4|171.31|173.38|173.45|177.52|
|5|213.35|214.88|226.03|230.99|

**Table 12: Deadweight loss estimates (in rupees per month) by income quintile**

|Income<br>quintile|e=-0.1|e=-0.2|e=-0.3|e=-0.4|
|---|---|---|---|---|
|1|3.10|6.19|9.29|12.39|
|2|2.57|5.14|7.71|10.28|
|3|3.22|6.43|9.65|12.86|
|4|1.97|3.94|5.91|7.88|
|5|3.03|6.06|9.09|12.12|

**Table 13: Deadweight loss estimates (as a share of total expenditure) by income quintile**

|Income<br>quintile|e=-0.1|e=-0.2|e=-0.3|e=-0.4|
|---|---|---|---|---|
|1|1.4%|2.8%|4.3%|5.7%|
|2|1.0%|2.1%|3.1%|4.2%|
|3|1.6%|3.3%|4.9%|6.6%|
|4|1.0%|2.1%|3.1%|4.2%|
|5|1.7%|3.3%|5.0%|6.6%|

51
|**Table 13d. Impact of Improved**<br>|**External M**<br>Overall<br>_change from b_|**arket Acces**<br>_ase year, %_|**s**||||||
|---|---|---|---|---|---|---|---|---|
||average|Northwest|Central|South|Volga|Urals|Siberia|Far East|
|**Social Welfare Indices (ev/GDP)**|||||||||
|sigma=0|-0.3|-0.1|-0.1|0.0|-0.1|0.2|-0.4|-0.2|
|sigma=1|0.0|-0.1|0.0|0.0|0.0|0.3|-0.3|-0.1|
|sigma=4|0.0|0.0|0.0|0.1|0.0|0.4|-0.3|-0.1|
|sigma=+inf|0.0|0.0|0.1|0.1|0.0|0.4|-0.3|-0.1|
|ev/C|0.1|0.0|0.1|0.1|0.0|1.0|-0.5|-0.1|
|**Aggregate trade**|||||||||
|Regional terms of trade (% change)|0.0|0.2|0.2|0.0|0.0|-0.2|-0.2|0.0|
|Regional exports (% change)|-0.1|0.0|0.1|0.0|0.0|-0.8|-0.3|-0.3|
|Real exchange rate (% change)|-0.6|-0.6|-0.5|-0.5|-0.6|-0.5|-0.9|-0.6|
|International exports (% change)|0.5|1.1|1.4|0.5|1.0|-0.3|1.0|-0.6|
|**Return to primary factors**(% change)|||||||||
|Unskilled labor|-0.1|-0.2|0.0|0.0|-0.1|0.5|-0.6|-0.4|
|Skilled labor|0.3|0.2|0.3|0.3|0.2|0.9|0.0|0.1|
|Aggregate capital earnings|0.1|0.1|0.1|0.1|0.0|0.1|-0.2|0.1|
|Regional mobile capital|0.4|0.9|0.7|0.4|0.5|-0.1|0.6|0.4|
|Energy sector resources|-2.6|-2.0|0.3|-2.0|-2.3|-2.9|-1.9|-1.8|
|Specific capital in domestic firms|-0.2|-0.6|-0.6|-0.3|-0.4|1.4|0.0|0.4|
|Specific capital in multinational firms|1.7|0.5|0.7|3.0|2.0|5.2|4.5|2.4|
|**Factor adjustments**|||||||||
|Unskilled labor (% changing sectors)|0.7|0.9|0.5|0.4|0.6|1.0|1.2|1.0|
|Skilled labor(% changingsector)<br>Source: Authors' calculations.|0.8|0.9|0.5|0.4|0.6|1.4|1.2|1.3|

51
Table 4: Largest changes in protection and prices due to the EUSAA, by both sector and subsector<sup>_†_</sup>

|ISIC|Sector|Protection<br>|Price<br>|
|---|---|---|---|
|||change (%)|change (%)|
||**Without exclus**|**ion list**||
|A|Agriculture|-8.9|-0.3|
|B|Fishing|0.0|0.0|
|C<br>|Mining and quarrying<br>|-44.1<br>|-0.6<br>|
|D<br>|Manufacturing<br>|-27.4<br>|-1.5<br>|
|E|Utilities|0.0|0.0|
|F–Q|Services|-6.0|-0.1|
|742|Architectural<br>|-100.0|-34.6|
|181|Apparel|-38.6|-11.9|
|342|Motor vehicle bodies<br>|-73.3|-11.8|
|314|Accumulators and cells|-48.6|-10.4|
|192|Footwear<br>|-26.1|-7.7|
|322<br>|Tele/radio transmitters<br>|-88.2<br>|-6.9<br>|
|343|Motor vehicle parts|-50.1|-6.4|
|12|Animal farming|-97.1|-5.8|
|341<br>|Motor vehicles<br>|-24.4<br>|-5.1<br>|
|332|Optical instruments|-45.2|-3.8|
|191<br>|Leather<br>|-17.7<br>|-3.8<br>|
|293|Domestic appliances|-18.0|-3.1|
|331|Medical appliances|-55.0|-2.7|
|221|Publishing|-86.3|-2.6|
|313|Wire and cable|-69.0|-2.5|
|242|Other chemicals|-70.5|-2.3|
|173|Knitted fabrics|-15.1|-1.8|
|289|Other fabricated metal<br>|-33.3|-1.7|
|323<br>|Tele/radio receivers<br>|-10.2<br>|-1.7<br>|
|361|Furniture|-11.7|-1.7|
||**With 20% exclu**|**sion list**||
|A|Agriculture|-2.9|-0.1|
|B|Fishing|0.0|0.0|
|C|Mining and quarrying|-42.2|-0.6|
|D<br>|Manufacturing<br>|-5.6<br>|-0.4<br>|
|E|Utilities|0.0|0.0|
|F–Q|Services|-0.6|0.0|
|12|Animal farming|-94.6|-5.6|
|221|Publishing|-83.6|-2.5|
|331<br>|Medical instruments<br>|-46.6<br>|-2.3<br>|
|322|Tele/radio transmitters|-20.8|-1.7|
|101|Coal mining|-100.0|-1.5|
|103|Peat extraction|-100.0|-1.5|
|352<br>|Locomotives<br>|-66.4<br>|-1.4<br>|
|242|Other chemicals|-40.8|-1.4|
|289<br>|Other fabricated metal<br>|-17.9<br>|-1.0<br>|
|172|Other textiles|-16.6|-0.9|
|142|Other mining|-74.4|-0.9|
|312|Electricitydistribution|-34.9|-0.9|
|152|<br>Dairy|-27.7|-0.8|
|313|Wire and cable|-17.2|-0.7|
|319<br>|Other electrical<br>|-11.9<br>|-0.7<br>|
|315|Lighting equipment|-8.7|-0.7|
|291|General purpose machinery|-19.9|-0.6|
|252|Plastics<br>|-20.2|-0.6|
|232|Refned petroleum|-56.3|-0.6|
|343|Motor vehicle parts|-3.9|-0.6|

> _†_ For comparability, one-digit ISIC sectors are reported. Subsectors are chosen and ordered by those with the largest price changes, assuming elasticity parameters of _σ_<sup>_P_</sup> = 0 _._ 5, _σ_<sup>_DS_</sup> = 1 _._ 0, and _σ_<sup>_ES_</sup> = 1 _._ 5.

16
help boost competition and innovation. In fact, in Indonesia, the road freight sector, for example, could benefit substantially from reducing restrictions on foreign participation, which would bring the sector into line with those of comparator countries, such as Malaysia, thus better positioning local firms to produce at competitive prices.

Although in this paper we report evidence suggesting downstream vertical spillovers from service sector reform on manufacturing performance, it is likely not only that manufacturers benefit, but also that service users across the economy also benefit. It is known from the literature that these spillovers tend to increase with the amount of interaction between foreign and domestic firms. To maximize the positive spillovers, the government could consider incentivizing interaction between foreign service providers and domestic firms, either those that use their services or that supply inputs to them. One option would be for the government to substitute restrictions on foreign ownership with requirements for new foreign entrants to undertake, for example, explicit supplier development programs.

# **References**

Amity, M. and J. Konings (2007), “Trade Liberalization, Intermediate Inputs, and Productivity: Evidence from Indonesia”, American Economic Review, Vol. 97, No. 5, pp. 1611-1638.

Arnold, J., B. Javorcik, M.Lipscoom and A. Mattoo, (2010), “Services Reform and Manufacturing Performance: Evidence from India”, CEPR Discussion Papers, 8011, C.E.P.R. Discussion Papers.

Arnold, J., B. Javorcik, and A. Mattoo, (2011), “Does Services Liberalization Benefit Manufacturing Firms? Evidence from the Czech Republic”, Journal of International Economics, Vol. 85, No.1, pp. 136-146.

Arnold, J., A. Mattoo, and G. Narciso (2008) “Services Inputs and Firm Productivity in SubSaharan Africa: Evicdence from Firm-Level Data”, Journal of African Economies, Vol. 17, No.4, pp. 578-599.

Aw, Y.-B., X. Chen, and M. Roberts (2001), “Firm-level evidence on productivity differentials and turnover in Taiwanese manufacturing”, Journal of Development Economics, Vol. 66, No.1, pp. 51-86.

Blalock, G. and P. Gertler (2008), “Welfare gains from foreign direct investment through technology transfer to local suppliers”, Journal of International Economics, Vol. 74, No. 2, pp. 402-421.

Blalock, G. and P.Gertler (2009), “How firm capabilities affect who benefits from foreign technology”, Journal of Development Economics, Vol. 90, No. 2, pp. 192-199.

Blalock, G. and D. Simon (2009), “Do all firms benefit equally from downstream FDI? The moderating effect of local suppliers’ capabilities on productivity gains”, Journal of International Business Studies, Vol. 40, pp. 1095-1112.

18
proportional to the quantity purchased. This applies to regulated and non-regulated prices for goods where, to different levels of consumption, correspond different levels of prices. This is typically the case of utilities such as electricity or water where the price is set according to the consumed quantity.

For simplicity, assume that we have two goods, electricity, which we call good 1, and the rest of goods which we call good 2. Further, assume that the price of electricity is defined by two blocks of consumed quantities as follows:

# **The price schedule**

|Block|Price|
|---|---|

In this case, the _marginal price_ for a given consumer refers to the price of the last block of consumption. In what follows we will also consider the most common case where consumers pay the marginal price for each block for the quantities consumed in each block and not the marginal price of the last block on the whole consumption. This is similar to progressive taxation on income where tax payers pay the lowest tax rate for the first block of income and increasingly higher tax rates for subsequent blocks of income as income increases.

It should be noted here that, in the case of goods characterized by block tariffs such as utilities, at least two different types of tariffs exist. Similar to Komives et al. (2006) we call the first type _Increase Block Tariffs_ (IBT). This is where the household pays the price for the block of consumption in which it falls for all consumed units. The second type is _Volume Differentiated Tariff_ (VDT). In this case, the household pays different prices on their consumed units for each block covered by consumption. To be noted here that, when the number of blocks increases, this makes the VDT type converging towards the IBT type. In this paper, we focus only on the VDT type. This is justified by the fact that this structure is the most common type of tariff structure used in developing countries.

# **Estimation of consumed quantities**

With multiple pricing, the estimation of the quantities consumed by the household is also a little more complicated given that household surveys typically contain information on expenditure but not on quantities consumed of a good such as electricity. Households report expenditure as shown on the electricity bill but not the kWh consumed. However, the quantities consumed can be easily estimated.

Let _B_ = the number of tariff blocks with ; the upper kWh consumption within block and the upper expenditure threshold in block Then, consumption of electricity in kWh for each household belonging to each bracket can be calculated as follows: 24
household expenses, a bad management practice, are disproportionately low productivity, cutting across all three firm types.

**Figure 20: Profile of Urban Informal Firms by Financial Services and Management**

<!-- Start of picture text --> Low-educated Self-employed High-educated Self-employed Employers<br>1 1.0 1.0<br>0.8 0.8 0.8<br>0.6 0.6 0.6<br>0.4 0.4 0.4<br>0.2 0.2 0.2<br>0 0.0 0.0<br>accountBank Mixed w/ HHexpenses Uses bankinginst Mobilemoney accountBank Mixed w/ HHexpenses Uses bankinginst Mobilemoney accountBank Mixed w/ HHexpenses Uses bankinginst Mobilemoney<br>Bottom 40% Bottom 90% Top 10% Bottom 90% Top 10%<br><!-- End of picture text --> Source: Own elaboration based on IES. Notes: Cross-country averages using firm weights. The number of countries used for each variable is the same as the one indicated in previous figures.

# **7. Urban**

To assess how urban spaces, infrastructure, and services interact with the labor market, we consider which urban factors are correlated with different job types. We use the GMD to explore correlations between job type and the urban variables including access to electricity, water, improved sanitation, internet, and mobile phones. The results from the GMD should be interpreted with caution since the survey presents information about public services at the respondent’s home, which is not necessarily the location of the business.<sup>62</sup> The IES, on the other hand, provides insights into which urban goods and services informal firms need and use at their workplace. Finally, we use big data to proxy the correlation between urban spaces and informal job types in urban Mozambique, including poverty, urban density and cell tower density. Again, the urban variables observed where workers live, which may not be where they are working.

# **7.1. Access to Urban Infrastructure and Services**

A small share of owners of informal firms identifies a business need for public utilities, with significant differences by firm type. Less than 20 percent of firm owners sampled in the IES state that they need water or electricity services (Figure 21). However, low average electricity needs are driven by the large LE-SE sector; 40 percent of the other two firm types

> 62 The three country IES sample found that 60% of informal businesses operated outside the household premises. We do not have similar data for the 26-country GMD.

Page **37** of **60**
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consumption. In case the Nordin’s difference is negative, it is assumed to take a small but positive number to avoid logarithms of zero.<sup>17</sup> Two weather-related variables are selected: monthly average of maximum temperature and monthly precipitation. These data are available and presumably considered to be most relevant to the electricity demand in Albania. Besides two weather-related variables, monthly household income and the number of household members are included in _X_ 1 and _X_ 2. To control the extent to which individual households rely on electricity and gas for their basic home appliances, a number of dummy variables representing electricity use and the number of home appliances held by each household are also included.<sup>18</sup> A dummy variable for urban areas is introduced to control for a systematic heterogeneity between urban and rural areas. Urban residents seem to use more electricity; their income is higher, and they own more electric appliances.

The model includes a variable that represents frequency of power outages, which is measured by the average duration of power outages per day. Households experienced about 3 hours and half of power outages during the sample period. For obvious reasons, electricity cannot be consumed if power outages occur. This captures some undervaluation of the demand in the observed data.

As instrumental variables, two block rates, a threshold between the two blocks and the normative amount of consumption applied to those who have no meter. Recall that the block rates and threshold are common to the whole nation but vary from year to year. In addition, the normative amounts can be different among districts and between urban and rural classifications. It also differs between the summer and winter season. The analysis uses the only summer tariff schedules to make them consistent with the sample period.

> 17 An alternative is to use the Nordin’s difference variable as it is. The main estimation results have not change much even if the logarithm is not taken.

> 18 The home appliance ownership is represented as the number of appliances, rather than dummy variables. Our approach is more informative because some households own multiple appliances. But the main estimation results have been found robust even if the binary variables are used instead.
firm revenues which go to bribes for various activities). This reflects both that construction firms rely on governments for a considerable part of their business, but also that they tend to make larger payments (as a percentage of contract values) when they bribe. Construction firms in Eastern Europe believe that a typical payoff made for securing a government contract in their industry is around seven percent of the contract value.

Construction firms in BEEPS also report themselves more likely to make payments for licenses and permits (although this does not take up an abnormally large share of their bribe budgets). Similarly, Figure 5, based on data from global enterprise surveys, suggests that across a range of countries, construction permitting appears to be a regulatory area particularly prone to corruption –in most countries the process is notably more corrupt than the process to obtain an operating license or the process of labor inspections, for example.

A significant part of corruption in corruption was not captured by BEEPS, which did not report on government or SOE employee theft as a form of corruption. We have evidence from Indonesia that, where government officials are responsible for the construction of infrastructure, such theft can be significant. A physical audit of a community-driven development program that focused on road construction in Indonesia found that an estimated 24 percent of expenditures were ‘lost’ in materials theft, probably orchestrated by village heads who oversaw projects (Olken, 2004).

Survey evidence suggests the diversity of types of corruption present in the sector –from bribes designed to manipulate budgeting decisions, project selection, tender specifications, procurement outcomes or contract negotiations and renegotiations, through bribes designed to cover poor quality construction practices and outcomes, to the theft of materials. The evidence suggests that corruption may be a significant part of a system of decision-making that leads to poor construction, limited occupational safety and low returns to government infrastructure investment.

# **Variation in Measures of Corruption at the Country and Sector Level**

It is worth noting three additional features about the survey data on the extent of corruption in construction. First, there is little significant correlation between crossindustry general estimates of corruption and estimates of corruption given by the subset of construction industries at the national level (Kenny, 2006). Second, it appears that there is considerable variation within countries as to the comparative level of corruption in different parts of construction-related regulation. Figure 5 suggests that businesses in India and Cameroon, for example, are prone to significant corruption in all three areas of obtaining licenses, construction permits and labor inspections. On the other hand, Benin and Lebanon both see significantly more corruption in construction permitting than in labor inspection.

3