rafmacalaba/datause-encoder-data huggingface dataset:
**Table 1. Dispersion of TFPR**

|Country|Moro|cco|US|France|South Korea|China|India|
|---|---|---|---|---|---|---|---|
|Data Year|2013|2007|1997|2005|2012|2005|1994|
|S.D ln(TFPR)|0.77|1.01|0.49|0.48|0.55|0.63|0.67|

_Note_ : S.D. = standard deviation. Industry groups are weighted by their value-added shares. The database year is indicated next to each country name. Values for the U.S., China, and India comes from Hsieh and Klenow (2009), values for France are from Bellone and Mallen-Pisano (2013), and numbers for South Korea comes from Oh (2016).

protection vis-`a-vis international competitors could play a sizable role. Indeed, industries more opened to competition (international and domestic) as “Machinery” and ”Textiles“ present lower levels of market distortions compared to more protected industries with relatively little competition such as “Food”.<sup>13</sup> Second, the composition of firms for each sector can also explain the cross-sector difference in market distortions. Thus, the share of firms that are exported-oriented, small, politically connected, public, and old, could play a key role to explain these results. For instance, the high level of market distortions in “Food” could be explained by its relative high share of small firms (which have on average higher credit constraints) and/or by the industry high percentage of politically connected firms (Saadi, 2016), which could generate negative distortions to the remaining firms. In addition, the high level of resource misallocation in “Chemicals” could be explained by the strong state ownership in the phosphate industry.

**Table 2. Dispersion of TFPR by Industries**

|Industry|Food|Confectionery|Textiles|Chemicals|Machinery|Other|
|---|---|---|---|---|---|---|
|S.D ln(TFPR)|1.06|0.85|0.62|1.13|0.60|0.87|

_Note_ : S.D. = standard deviation. Results obtained using the WBES2007 database. Results for “Machinery” obtained using the WBES2004 database.

To further investigate the role of firm characteristics and resource misallocation we run weighted least squares (WLS) regressions of TFPR (relative to industry means) on a range of dummies accounting separately for different firm features. This simple methodology has been used in this context by Hsieh and Klenow (2009) and Busson et al. (2013) and results should not be interpreted in terms of causation. Table 3 below present the estimation results.

Regression (1) shows that “old firms” present 21 percent lower TFPR. High entry barriers and low competition would allow these businesses to continue to operate despite low profitability. Thus, more resources should be allocated to “young firms” to increase global TFP. In regression (2) results indicate that small companies have 18.9 percent higher TFPR than medium size firms and TFPR for large firms is not significantly different than the one for medium size firms.

> 13In 2013, for example, the Competition Council identified a tacit pricing agreement between market participants in the dairy products sector and reported a high level of concentration.

14
# **7 (c) Afghanistan**

<!-- Start of picture text --> 25000<br>Technical potential (23,000 MW)<br>MEW<br>20000<br>ADB<br>Timilsina & Toman<br>15000<br>10000<br>5000<br>0<br>2020 2025 2030 2035 2040<br><!-- End of picture text --> _Note and source: ‘MEW’ refers to the capacity requirement calculated based on load forecast provided in MEW (2017). This forecast goes until 2023, we used the same annual average growth rate of load for 2020-2023 for the rest of the period. ‘ADB’ refers to capacity requirement based on reference case load forecasts available in ADB (2013). The available load forecast goes up to year 2032. We extrapolated to 2040 using the same growth rate for 2025-2032. Required capacity to load in both forecasts is assumed to be 2 based on installed capacity to peak load ratio in India at present. The ‘Timilsina & Toman’ refers to capacity requirement used in Timilsina and Toman (2016) study._ The WECS load forecasts for Nepal, especially the high economic growth and policy intervention scenarios are highly optimistic. The capacity requirement forecasts based on load forecasts made by the state electricity authority, Nepal Electricity Authority (NEA) are very close to WECS’s BAU forecasts.<sup>6</sup> The capacity projections used in Timilsina and Toman (2016) are on the lower side as they are based on NEA’s earlier (i.e., 2010) load forecasts that were available when the study was carried out.

> 6 Capacity requirements are estimated based on the load forecasts assuming the same peak load to capacity requirements ratios as used in Timilsina and Toman (2016).

16
**ANNEX II: LIQUIDITY REPORTS**

# **(1)  Questionnaire on Management of Liquidity Risk**

This questionnaire is a joint effort by the Banking, Finance and Insurance Commission (BFIC) and the National Bank of Belgium (NBB) to integrate general questions on liquidity and collateral management. The document is meant to provide guidance for a first presentation by the management of the large Belgian financial conglomerates in this area of risk management. This first presentation will be attended by both BFIC and NBB staff members--in the context of the joint interest of banking supervisors and central banks in liquidity and collateral management practices—and, thus, in order not to duplicate the groups’ efforts. Both the BFIC and NBB will, on an individual basis and each within their own field of competence, touch upon more specific central bank and supervisory issues at a later stage.

The questionnaire is based on a survey on liquidity management in cross-sector and crossborder financial groups, and has been prepared by a working group of the Joint Forum (international forum of bank, securities and insurance supervisors). The questionnaire encompasses five building blocks: the group’s and individual entities of the group’s collateral and liquidity management practices, the sources of liquidity risk and sources of funding for the group, contingency funding plans, the group’s experience in terms of liquidity stress-testing, and a final set of questions regarding regulatory considerations. We would appreciate it if a short written reply to this questionnaire could be provided in the context of the presentation. The reply to this questionnaire will be treated as a confidential document within the BFIC and the NBB. A part of the reply to this questionnaire will nevertheless be used –anonymously--in the context of the Joint Forum study.

# **1.  Liquidity risk and collateral management within financial groups.**

_One question of interest to global supervisors is: How do liquidity strains spread within financial groups? This question, in turn, leads to other questions on how liquidity is managed at the group level versus subsidiary levels._

1.1 How are liquidity and collateral management implemented at the group level and at the level of different entities? While determining the extent of centralization of liquidity risk management within financial groups in mind, please outline the structure of the group with respect to the management of liquidity and collateral.  Which divisions play an essential role in liquidity and collateral management and what are their respective roles? We are particularly interested in the roles played by a central unit and those played by subsidiaries and other business units located both in the home country and in foreign countries, with respect to:

- Managing cash and collateral on a day-to-day basis

- Establishing risk limits

- Monitoring liquidity positions against limits

- Identifying sources of liquidity risk

- Maintaining creditor relationships
- **26** -

states may stem from greater consumption llnkages as consumers channel rising Incomes Increasingly Into nonfoods.

40. In sum, the state-level comparisons generally support the notlon that growth In agriculture goes hand-in-hand wlth development of rural towns and growth In rural nonfarm activity. To quantify the magnitude of the growth multipliers requires more formal modelling approaches.

# IV. ESTIMATING RURAL AGRICUTURAL GROWTH MULTPLERS

# A. Econometric Model

41. Agricultural growth multipllers can be estimated In several ways. This section uses cross-section district and state data In order to estimate econometrIcally the Indirect rural employment and Income generated by agricultural growth. An alternative method Involves use of Input-output and consumptlon parameters to model the linkages. Since this second method Is better able to project urban as well as rural linkages, It Is used In Sectlon IV for that purpose and as a check on the econometric estimates of the rural growth multipliers.

42. The following model Is an adaptatlon of the economic base model developed by regional scientists (e.g., Rlchardson, 1985). It assumes that agricultural output Is constrained by technology, land and agroclimate, but that rural nonfarm activity Is constrained only by demand. Improved agricultural technology Increases farm output and hence the demand for nonfarm Inputs and consumer goods. Since agricultural output varles across reglons, the following relationship allows a rough estimate of the growth multiplier: RNFY - a + b AGY, where RNFY Is rural nonfarm Income, AGY Is agricultural Income and b - dNFY/dAGY Is the agricultural Income multiplier.
- 43 -

affect gas turbine efficiency adversely. The assumptions **are** worth reporting in - detail since they illustrate how much information is required Table 3.1 gives the comparative values for large power systems: lable 3.L : Comparative Assessment of Generation Costs for Large Power Systems

||Combined<br>Cycle Gas|Coal Fired<br>Steam|Fuel-oil<br>Fired Steam|
|---|---|---|---|
|Capacity (MW)<br>Plant factor (%)<br>Energy Supply (Gwh)<br>Unit Cost ($/kw)<br>Total investment cost<br>|450<br>65<br>2562<br>600<br>|500<br>65<br>2847<br>1000|500<br>65<br>2847<br>900|
|(Million$)<br>|310<br>|575|518|
|0 and M Unit cost (c/kwh)<br>|<br>0.5<br>|0.5|0.3|
|Heat content (BTU/unit)<br>Thermal efficiency (%)<br>|1<br>46<br>|25<br>38|5.9<br>39|
|Fuel price ($/unit)<br>|2.5<br> <br>|40.0<br>|15.0|
|Fuel price rise (%/year)<br>Annual fuel cost<br>|<br>1.0<br>|1.0|1.0|
|(Million$)<br>Implementation<br>period<br>|47.5<br>|40.9|63.3|
|(years)<br>|3|5|5|
|Economic Life (years)<br>|20|25|25|
|Generation cost (¢/kwh)<br>|4.2<br>|4.9|5.5|
|Discount rate (%)|10.0|10.0|10.0|

(No allowance has been made for flue gas desulfurization units for coal burning.) Source: Moore and Crousillat [19911 The assumptions for the relative fuel prices and thermal efficiencies are particularly important. The sensitivity analysis showed that under almost all circumstances the combined cycle plants would be least cost. Oil fired were not economic at prices even as low as $10/barrel for oil ($20 was taken to be the constant oil price in the base case). Coal firing became competitive for gas prices of $3.3/MMBTU (base case $2.5) or coal price. of $20/ton (base case $40/tcn). A rise in capital costs of gas of 10% and gas prices of $3.0 also favored coal. The critical parameter, given the experience of combined cycle in developing countries, is the plant factor. If this were about 15% points lower
Mabsout, Ramzi and Irene Van Staveren. 2010. “Disentangling Bargaining Power from Individual and Household Level to Institutions: Evidence on Women's Position in Ethiopia.” _World Development_ 38 (5): 783-96.

Maluccio, John and Rafael Flores. 2005. Impact evaluation of a conditional cash transfer program : the Nicaraguan Red de Protección Social. Research Report 141. Washington, DC: International Food Policy Research Insititute.

McPeak, John G. and Cheryl Doss. 2006. “Are Household Production Decisions Cooperative? Evidence on Pastoral Migration and Milk Sales from Northern Kenya.” _American Journal of Agricultural Economics_ 88 (3): 525-41.

Prabhu, Vimalanand S., 2010. “Tests of Intrahousehold Resource Allocation Using a CV Framework: A Comparison of Husbands' and Wives' Separate and Joint WTP in the Slums of Navi-Mumbai, India.” _World Development_ 38 (4): 606-19.

Panda, Pradeep and Bina Agarwal. 2005. “Marital violence, human development and women's property status in India.” _World Development_ 33 (5): 823-50.

Patel, Ankur., William Leonard, Victoria Reyes-Garcia, Thomas McDade, Tomas Huanca, Susan Tanner, and Vincent Vadez. 2007. “Parental Preference, Bargaining Power, and Child Nutritional Status: Evidence from the Bolivian Amazon.” Working Paper No. 31. Tsimane’ Amazonian Panel Study Evanston, Ill., U.S.A.: Department of Anthropology, Northwestern University.

Qian, Nancy. 2008.” Missing Women and the Price of Tea in China: The Effect of Sex-Specific Earnings on Sex Imbalance,” _The Quarterly Journal of Economics_ 123 (3): 1251-85.

Quisumbing, Agnes R. and John A. Maluccio. 2003. “Resources at Marriage and Intrahousehold Allocation: Evidence from Bangladesh, Ethiopia, Indonesia, and South Africa.” _Oxford Bulletin of Economics and Statistics_ 65 (3): 283-327.

Quisumbing, Agnes R. 1994. “Intergenerational transfers in Philippine rice villages: Gender differences in traditional inheritance customs,” _Journal of Development Economics_ 43 (2): 16795.

Rangel, Marcos A. 2006. “Alimony Rights and Intrahousehold Allocation of Resources: Evidence from Brazil.” _Economic Journal_ 116 (513): 627-58.

Rao, Vijayendra. 1997. “Wife-beating in rural South India: A qualitative and econometric analysis,” _Social Science & Medicine_ 44 (8): 1169-80.

39
We obtain the economic rent of global pasture land and the expenditures on animal feed, from GTAP v.7 database. The initial amount of animal feed comes from the USDA FAS PSD database.<sup>4</sup> The elasticity of substitution of animal feed for pasture land is taken from Golub et al. (2009). The livestock technology index and value shares of agricultural land and fertilizers in 2004 are calibrated from known values of livestock output, animal feed, and the pasture land as described in Rutherford (2002). Non land livestock farming costs are given by where _c_<sup>_l_</sup> 0<sup>captures the non-land cost per ton of livestock, and</sup><sup>_ξ_</sup> 0<sup>_l_captures the</sup> adjustment costs of pasture land conversion. We obtain the non-land cost of livestock farming from GTAP v.7 database, and calibrate the adjustment cost parameter to match recent trends in grain and livestock production (Taheripour et al. 2013).

# C.1.6 Food Processing Sectors

The growth of technology indices in food processing sectors is described by the following equation: where the parameters _θ_ 0<sup>_i,y_</sup> and _κi,y_ reect the initial level and annual growth rate in technologies of the food processing sector. We calculate the level of technology index of the grain and livestock processing in 2004 using GTAP v.7 database. For grains, we dividing the output of processed grains and crops (GTAP sectors 21, 23-25) by the output of grains and cereals (GTAP sectors 1-8). For livestock, we dividing the output of processed livestock and diary (GTAP sectors 19-20, 22) by the output of livestock products (GTAP sectors 9-11). We set the growth rate of the technology in the food processing sector equal to the economy's TFP. We obtain the non-land food processing costs from GTAP v.7 database.

> 4see http://www.fas.usda.gov/psdonline, last accessed in August 2013.

18
Table 1: **Industry Labor Demand for** Blue Collar Workers in Chile (1982-95) (standard errors in parenthesis)

||Ln L t-I|Ln WBt|Ln WBt- 1|Ln WWt|Ln WWt- 1|Constant|Wald<br>Test: p-<br>value(b)|L.R.<br>Elast.(a)|Sargan<br>Test|Autoco.<br>Test<br>(2nd o.)|No. Obs<br>(Plants)|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Base<br>Specification(c)|0.381(*)<br> <br>(0.095)|-0.244(*)<br>(0.015)|0.068(*)<br>(0.023)|0.041(*)<br>(0.007)|-0.006<br>(0.007)|-0.179(*)<br>(0.010)|--|-0.285|0.972|0.695|21014<br>_(1501)_|
|Time Variant|0.627|-0.169(*)|0.112|0.071(*)|-0.034|-0.176(*)|--|-0.153|0.660|0.811|21014|
|Specification|(0.475)|(0.043)|_(0.105)_|(0.027)|(0.039)|(0.021)|||||(1501)|
|Variable* T83|-0.533|-0.072|-0.104|-0.001|0.036|0.142(*)|0.442|-0.257||||
||(0.531)|(0.048)|(0.116)|(0.032)|(0.046)|(0.038)||||||
|Variable* T84|0.607<br>(0.572)|-0.009<br>(0.067)|0.193<br>(0.126)|-0.103(**)<br>_(0.045)_|-0.046<br>_(0.055)_|0.316(*)<br>(0.033)|0.159|-0.545||||
|Variable* T85|0.224<br>(0.853)|-0.068<br>(0.067)|0.029<br>(0.163)|-0.051<br>(0.038)|0.049<br>(0.048)|0.143(**)<br>(0.067)|0.199|-0.641||||
|Variable* T86|-0.707<br>(0.607)|-0.106(***)<br>(0.061)|-0.253(***)<br>(0.135)|-0.036<br>(0.034)|0.059<br>(0.045)|0.223(*)<br>(0.028)|0.043|-0.385||||
|Variable* T87|-1.108(***)<br>(0.663)|<br>-0.031<br>(0.077)|-0.193<br>(0.142)|-0.038<br>(0.034)|0.032<br>(0.046)|0.284(*)<br>(0.034)|0.111|-0.190||||
|Variable* T88|-0.596<br>(0.499)|-0.067<br>(0.059)|-0.153<br>(0.116)|0.007<br>(0.033)|0.054<br>(0.044)|0.244(*)<br>(0.025)|0.666|-0.286||||
|Variable* T89|-0.289<br>(0.568)|-0.110<br>(0.070)|0.025<br>(0.126)|-0.072(**)<br>(0.037)|0.006<br>(0.053)|0.214(*)<br>(0.028)|0.012|-0.215||||
|Variable* T90|-0.313<br>(0.568)|-0.130(***)<br>(0.075)|-0.060<br>(0.142)|-0.012<br>(0.036)|0.058<br>(0.043)|0.180(*)<br>(0.032)|0.243|-0.360||||
|Variable* T91|-0.694|-0.123(**)|_-0.125_|-0.036|0.038|0.205(*)|0.108|-0.286||||
||(0.501)|(0.061)|(0.121)|(0.033)|(0.042)|(0.023)||||||
|Variable* T92|-0.027<br>(0.558)|-0.138(**)<br>(0.067)|0.012<br>(0.140)|-0.004<br>(0.046)|0.057<br>(0.046)|0.215(*)<br>(0.024)|0.177|-0.457||||
|Variable* T93|-0.156<br>(0.888)|-0.025<br>(0.058)|0.038<br>(0.226)|-0.065(***)<br>(0.040)|<br>-0.022<br>(0.061)|0.176(*)<br>(0.044)|0.167|-0.085||||
|Variable* T94|0.981|-0.056|0.136|-0.027|-0.002|0.149(*)|0.930|-0.037||||
||(1.638)|(0.089)|(0.292)|(0.059)|(0.076)|(0.040)||||||
|Variable* T95|0.192|-0.069|0.012|-0.053|0.005|0.168(*)|0.780|-0.634||||
||(1.213)|(0.088)|(0.235)|(0.046)|(0.072)|(0.024)||||||
|Wald Test (d)|0.120|0.591|0.044|0.158|0.431|0.000||||||

Notes: GMM estimates with first-differenced data. All variables are assumed exogenous except for lagged employment. Instruments are based on second and further lags of employment and output. (*) Significant at the 1% level. (**) Significant at the 5% level. (***) Significant at the 10% level. (a) Best point estimates calculated as [(Ln WBt + Ln WBt- I )/ Ln Lt-1 I. (b) Wald Test of Joint Significance of the variables interacted with a given year dummy. (c) Time dummies were included but are here omitted (d) P. Values. Wald Test of Joint Significance of year dummies interacted with a given variable.
identify poor communities.<sup>18</sup> This task of identifying poor households is commonly known as “poverty mapping” in most studies on developing countries, since poverty estimates are usually graphed on a map at a lower-level administrative level (such as that of a district or a local community).<sup>19</sup> In other words, this case typically involves survey-to-census imputation, since only censuses can offer more disaggregated data than those available in a household survey. Put differently, survey-to-census imputation can often be regarded as some type of geographical imputation, which differs from the (mostly) temporal imputation offered with survey-to-survey imputation.

# _Method Description_

Similar to proxy means testing and survey-to-survey imputation methods, survey-to-census imputation methods also consist of two main steps. The first step is to estimate the household consumption model in Equation (2) using the more aggregated-level household survey. The second step is to combine the predicted coefficients β̂ and the distribution of the two error terms υ̂cj and εj obtained from the first step with the variables xij in the census to generate predicted consumption data at a more disaggregated level.

Elbers _et al._ (2003) is perhaps the first study that introduces a formal framework for surveyto-census imputation in economics. Building on this framework, Tarozzi and Deaton (2007) propose another condition where the conditional distribution of household consumption given xj is the same for both the survey and the census. This assumption ensures that the estimated parameters from the smaller areas (as representative in the survey) can be imposed on the data for the larger areas (as representative in the census). Recent developments have been proposed, mostly

> 18 See, e.g., <u>https://www.census.gov/srd/csrm/SmallArea.html.</u>

> 19 Another name for this topic in the statistical literature is “small-area estimation” (see, e.g., Rao and Molina (2015) for a recent textbook treatment).

18
Topalova, Petia **(** 2007) “Trade Liberalization, Poverty and Inequality: Evidence from Indian Districts.” In _Globalization and Poverty_ , ed. Ann Harrison, 291–336. Chicago: University of Chicago Press.

Topalova, P. (2010). Factor immobility and regional impacts of trade liberalization: Evidence on poverty from India. _American Economic Journal: Applied Economics_ , 2, 1–41.

38
three years is non-zero, with the maximum of these two amounts determining the level of advance payments.

Firms selling manufactured goods and certain service businesses, such as hotels and restaurants, are also liable for the monthly sales tax. The sales tax is eectively a VAT, with a recrediting mechanism for taxed inputs, but has a relatively narrow base that excludes most services (see Brockmeyer and Hernandez (2016) for details). Approximately 20% of income tax-liable rms are liable for the sales tax.

Instead of paying the regular income tax (and, where applicable, the sales tax), retailers in certain sectors and below certain size thresholds (i.e., those with annual purchases below 150 base salaries, net assets below 350 base salaries, or with less than six employees) can opt into a simplied regime. Under the simplied regime, a tax is levied on inputs at sector-specic rates that vary from 3% to 9.8%. Firms in this regime declare on a quarterly basis, and can claim credit for withholding by state institutions for the income tax, but not for withholding by credit card institutions for the sales tax. During the period we study, approximately 30,000 rms led their taxes under the simplied regime, while 360,000 rms led their income taxes under the regular regime. Firms can opt into or out of the simplied regime by submitting a D140 modication form, or deregister completely by submitting a D141 deregistration form.

Figure 1 shows the percentage of tax-registered rms that did not le a tax declaration in 2014 by rm and tax type. For the income tax, this share was 19% of corporations and 25% of the self-employed.<sup>13</sup> It is lower but still substantial for the sales tax19% for corporations and 14% for the self-employed. This proportion has been stable over the last ve years, and is based on a tax register that is updated on a regular basis. However, it is possible that the tax register contains inactive rms whose owners decided not to deregister, as deregistration requires a personal visit to the tax oce and the payment of any outstanding tax obligations. Even among rms covered by third-party information, i.e., shown to be economically active, 12% did not le their income tax for the 2014 tax year.

> 13The corresponding gures in Brockmeyer and Hernandez (2016) are comparable, albeit lower, as they use an algorithm based on tax declarations and registration/deregistration records to construct snapshots of the tax register at dierent points in time, but do not directly observe the tax register. The gures in this paper are the authority's own estimates based on all available data.

8
Table 12. Robustness test: Di¤erence-in-di¤erence estimates on the e¤ect on utilization at the control facilities when controlling for proximity to project facility.

|Dependent variable|Out-Patient<br>Services|Delivery|
|---|---|---|
|Speci...cation|(1)|(2)|
|Distance to closest project|-3.41|0.04|
|facility in 2005|(4.72)|(0.04)|
|2005|-142.1<br>(154.0)|0.54<br>(1.3)|
|Facility ...xed e¤ects|Yes|Yes|
|Observations|50|50|
|R<sup>2</sup>|0.75|0.91|

a. *** [**] (*) denote signi...cance at the 1 [5] (10) percent level. b. Robust standard errors in parenthesis.

57