rafmacalaba/datause-encoder-data huggingface dataset:
exchange rate changes between today and yesterday ( _∆Xt11-13_ ), a higher probability that the BCRP will intervene in the future by buying foreign currency to avoid drastic appreciation of the sol against the USD decreases the volatility of the sol. In contrast, an expectation of higher participation by the central bank to defend the sol against the USD, or strong sol depreciation, causes higher volatility of the expected changes in the exchange rate (again on _∆Xt11-13_ ). This effect is almost twice as strong as the effect of buying foreign currency to avoid appreciation of the sol. These results might explain why the statistical standard deviation of the expected changes in the exchange rate within one day seem to be smaller than the expected changes in the exchange rate between today and the previous day. The latter period coincides with the periods in which the central bank participation in the forex is not expected.

Figures 5 and 6 show the conditional standard deviation of the change in the log of the Sol/USD, _∆Xt11-13 and ∆Xt13-11_ , respectively, together with the net interventions.

**Figure 5. EGARCH(1,1) Conditional Standard Deviation of** _∆Xt11-13_ **and net BCRP interventions**

**Figure 6. EGARCH(1,1) Conditional Standard Deviation of** _∆Xt13-11_ **and net BCRP interventions**

<!-- Start of picture text --> Interbank Rates. 2004 - 2009 Interbank Rates. 2004 - 2009<br>9 500 2.5 500<br>8<br>7 250 2.0 250<br>6<br>1.5<br>5<br>0 0<br>4<br>1.0<br>3<br>2 -250 0.5 -250<br>1<br>0 -500 0.0 -500<br>3200 3400 3600 3800 4000 4200 4400 3200 3400 3600 3800 4000 4200 4400<br>StandDevSol/USD BCRP NetInterven StandDevSol/USD BCRP NetInterven<br>% Change mill USD % Change mill USD<br><!-- End of picture text --> 25
NIGER’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE You, L. 2008. _Irrigation Investment Needs in Sub-Saharan Africa. Appendix 2 Country Results_ . Washington, DC: World Bank.

- You, L., C. Ringler, G. Nelson, U. Wood-Sichra, R. Robertson, S. Wood, G. Zhe, T. Zhu, and Y. Sun. 2009. ―Torrents and Trickles: Irrigation Spending Needs in Africa.‖ AICD Background Paper 9, Africa Region, World Bank, Washington, DC.

- World Bank. 2009c. ―Niger Agro-Pastoral Export and Market Development Project.‖ Project Information Document (PID), World Bank, Washington, DC, March 26.

- ———. 2009d. _Development de l’irrigation au Niger: Diagnostic et Options Strategiques. Revue Sectorielle de l’irrigation_ . Washington, DC: World Bank.

# Power

- AfDB (African Development Bank). 2005. ―Results-Based Country Strategy Paper—Republic of Niger. 2005–2009.‖ July 2005. http://www.afdb.org/fileadmin/uploads/afdb/Documents/Project-andOperations/Niger_Eng.pdf.

- Briceño-Garmendia, Cecilia, and Maria Shkaratan. 2010. ―Power Tariffs: Caught between Cost Recovery and Affordability.‖ First draft, February 2010.

- Eberhard, Anton, Vivien Foster, Cecilia Briceño-Garmendia, Fatimata Ouedraogo, Daniel Camos, and Maria Shkaratan. 2008. ―Underpowered: The State of the Power Sector in Sub-Saharan Africa.‖ AICD Background Paper 6, Africa Region, World Bank, Washington, DC.

EIU (Economic Intelligence Unit). 2010. _Niger Country Report. November 2010 Report_ . London: EIU.

- Foster, Vivien, and Jevgenijs Steinbuks. 2009. ―Paying the Price for Unreliable Power Supplies: In-Hoe Generation of Electricity by Firms in Africa.‖ Policy Research Working Paper 4913, World Bank, Washington, DC.

- GTZ (Deutsche Gesellschaft für Internationale Zammenarbeit). 2009. _International Fuel Prices 2009_ . 6th ed. Eschborn, Germany: GTZ.

- NIGELEC (Societe Nigerienne d’Electricite). 2009. _Analyse des Etats Financiers Exercice 2009_ . Niamey: NIGELEC.

- Rosnes, Orvika, and Haakon Vennemo. 2009. ―Powering Up: Costing Power Infrastructure Spending Needs in Sub-Saharan Africa.‖ AICD Background Paper 5, Africa Region, World Bank, Washington, DC.

- World Bank. 2006. ―Niger Investment Climate Summary 2006.‖ Africa Region Note No. 30, World Bank, Washington, DC, August 2006.

- ———. 2007b. _Implementation Completion and Results Report (IDA-31300 IDA-3130A) on a Credit in the Amount of SDR 14.0 Million ($ 18.6 Million Equivalent) to the Republic of Niger for a Privatization and Regulatory Reform Technical Assistance Project_ . Report No: ICR0000425, World Bank, Washington, DC, June 29.

46
# **Figure 25: Relative Wages, Labor Demand and Labor Supply**

<!-- Start of picture text --> A. Relative Labor Supply by Education of Monthly B. Relative Log Hourly Wages by Education of<br>Wage Earners, 1991 – 2004 Monthly Wage Earners, 1991 - 2004<br>2.0 5 0.8<br>0.6<br>1.5 4 0.4<br>0.2<br>1.0 3<br>0.0<br>0.5 2 -0.2<br>-0.4<br>F91 A91 F92 A92 F93 A93 F94 A94 F95 A95 F96 A96 F97 A97 F98 A98 F99 A99 F00 A00 F01 A01 F02 A02 F03 A03 F04 A04<br>0.0 F91 A91 F92 A92 F93 A93 F94 A94 F95 A95 F96 A96 F97 A97 F98 A98 F99 A99 F00 A00 F01 A01 F02 A02 F03 A03 F04 A04 1 Primary to Less than PrimaryUpper Secondary to Lower Secondary Lower Secondary to PrimaryVocational to Upper Secondary<br>University to Vocational<br>Primary to Less than Primary Lower Secondary to Primary<br>Upper Secondary to Lower Secondary University to Vocational<br>Vocational to Upper Secondary (RHS)<br>C. Relative Demand by Education of Monthly Wage D. Relative Demand by Education of Monthly<br>Earners, 1991 – 2004, σ=1 Wage Earners, 1991 – 2004, σ=2<br>2.5 2.5<br>2.0 2.0<br>1.5 1.5<br>1.0 1.0<br>0.5<br>0.5<br>0.0<br>0.0<br>-0.5<br>-0.5<br>-1.0<br>-1.0 F91 A91 F92 A92 F93 A93 F94 A94 F95 A95 F96 A96 F97 A97 F98 A98 F99 A99 F00 A00 F01 A01 F02 A02 F03 A03 F04 A04<br>F91 A91 F92 A92 F93 A93 F94 A94 F95 A95 F96 A96 F97 A97 F98 A98 F99 A99 F00 A00 F01 A01 F02 A02 F03 A03 F04 A04 Primary to Less than Primary Lower Secondary to Primary<br>Primary to Less than Primary Lower Secondary to Primary Upper Secondary to Lower Secondary Vocational to Upper Secondary<br>Upper Secondary to Lower Secondary Vocational to Upper Secondary University to Vocational<br>University to Vocational<br>E. Relative Demand by Education of Monthly Wage F. Relative Demand by Education of Wage<br>Earners, 1991 – 2004, σ=3 Earners, 1991 – 2004, σ=2<br>2.5 2.5<br>2.0 2.0<br>1.5 1.5<br>1.0 1.0<br>0.5<br>0.5<br>0.0<br>0.0<br>-0.5<br>-0.5<br>-1.0<br>-1.0 F91 A91 F92 A92 F93 A93 F94 A94 F95 A95 F96 A96 F97 A97 F98 A98 F99 A99 F00 A00 F01 A01 F02 A02 F03 A03 F04 A04<br>F91 A91 F92 A92 F93 A93 F94 A94 F95 A95 F96 A96 F97 A97 F98 A98 F99 A99 F00 A00 F01 A01 F02 A02 F03 A03 F04 A04<br>Primary to Less than Primary Lower Secondary to Primary<br>Primary to Less than Primary Lower Secondary to Primary Upper Secondary to Lower Secondary Vocational to Upper Secondary<br>Upper Secondary to Lower Secondary Vocational to Upper Secondary University to Vocational<br>University to Vocational<br><!-- End of picture text --> 60
environment that recorded, for each project under study, raw project data associated with each criterion. Additional calculations were made in cases where data were not readily available at the project level.

# 4.1.3. Step 3: Calculation of the Indicators

The approach to calculate the composite SEI and FEI is two‐fold and includes (a) identifying the criteria to be included in each composite indicator, along with their units of measurement, and (b) specifying a method to estimate or assign weights to the criteria involved in calculation of the SEI and FEI. In this section, the criteria selected as inputs to the SEI and FEI are described, along with the selected weighting methods.

The preliminary list of criteria presented in Table 2 was further revised by two experts in the water sector: a local consultant (former Additional General Manager of National Water Supply and Drainage Board) and an international water expert. The preliminary criteria were also contrasted against current data availability. A final list of criteria that resulted from this process is shown in Table 2 below.

_Table 2. Final criteria selected_

||SEI (8)||FEI (2)|
|---|---|---|---|
|‒|New beneficiaries/users per $ invested|‒|Benefit‐cost ratio|
|‒|Jobs created (direct) per $ invested|‒|Existing water resource yield|
|‒|Poverty level (in area of intervention)|‒|Non‐revenue water (%)|
|‒|Bacterial quality of existing water|||
|‒|Water‐borne diseases|||
|‒|Continuity of supply|||
|‒|Existing safe water coverage|||

# 4.1.3.a.  Input Criteria for Social and Environmental Index (SEI)

This section describes the seven input criteria for the SEI: SEI 1. Beneficiaries: For each project, the criterion 'beneficiaries' measures the number of new direct project beneficiaries per million dollars invested. All 28 projects to which the IPF framework is applied are being developed in areas that usually have some existing National Water Supply and Drainage Board (NWSDB) scheme serving a portion of the population in those areas. The new projects would connect these existing beneficiaries as well as some previously untapped portions of the population, who are the new beneficiaries. Projects with a greater number of new beneficiaries are expected to have higher SEI scores. This criterion considers only new beneficiaries, as it aims to determine the additional social benefits that are generated by the implementation of the project. However, the benefits to existing beneficiaries are accounted for in the benefit‐cost analysis under the FEI. The information was obtained from the project's pre‐feasibility studies shared by NWSDB.

15
Behaviour of Firms 30

-

only a quarter of firms as a strong constraint in 1992, with over 60% citing current revenues as the primary factor. This is consistent, of course, with wllat we know about the excess wage tax rule and its general ineffectiveness over 1992. And as expected, we find that real wage tax payments by firms decline slightly across most size classes for the period 1992.Q1-3 over 1991.Ql-3.

# 6: Fnrm-Provided Benefits

It has long been realized that benefits provided through the firm were an important component of labour income. Nevertheless, it has been far from clear what the value of such benefits were, particularly given the large non-monetary component. The matter acquires yet more significance in the light of demands for local authorities and other institutions to take over the benefits programmes hitherto run by firms. This section provides both an overview of the type of benefits provided by firms and then attaches costs and/or income flows to those benefits. In this way, we hope to provide some measure of the share of benefits costs in total labour charges from the cost perspective of the firm while also picking up the effective distribution of labour income. In addition, we provide a more extended discussion of one - crucial component of benefits the housing programmes run by a significant minority of firms in the survey.

But first a simple listing of the types of benefits provided by firms is given in Table 10. It can be seen that a significant proportion o, the total labour force continues to have entitlements to child care, paid vacations, housing and holiday homes. We can make a distinction over mandatory benefits - such as maternity or child care allowances - and discretionary benefits. It is clear that the latter are both varied and pervasive across all firm size classes. Further, there is a positive association between firm size, as measured by employment, and range of benefits. The absence of housing programmes among several of the largest firms may be unrepresentative. Bigger samples - such as the ILO survey of 500 firms - point to housing programmes as an almost defining feature of larger firms **12.** This is more likely to be true for firms that act as locally dominant employers. Here, the functions of local authorities appear to be commonly arrogated.

Among smaller firms with less local labour market dominance, the evidence suggests that firm provided benefits may not necessarily exceed those covered by local authorities. Indeed, in our sample for housing, child care and health facilities, firm expenditure at local level was comparable or in excess of local authority or non-firm expenditures in only 10-30% of cases, depending on the function (see 12 See Standing (1992a)
# **Table 2. Summary statistics, firm level, 2005**

||all<br>manufacturing<br>(1)|estimation<br>sample<br>(2)|
|---|---|---|
|revenues|1.36|13.32|
||(0.15)|(1.27)|
|avg. annual earnings/worker|7.06|10.01|
||(0.14)|(0.09)|
|employment|17.38|108.59|
||(0.29)|(7.51)|
|age of frm|15.74|25.08|
||(0.32)|(0.86)|
|number of establishments in Portugal|1.17|1.83|
||(0.01)|(0.11)|
|fraction exporter|0.15|0.62|
|fraction importer|0.14|0.61|
|N (frms)|45031|2522|

Notes: Table reports averages across firms, weighting firms equally. Values of revenues (sales plus income from provision of subcontracting and other services) are in millions of 2000 euros. Average annual earning per worker are in thousands of 2000 euros. Standard errors of means in parentheses. Estimation sample contains 2639 firms in 2005; a small number of firms could not be linked to the manufacturing census.
Table 10 Estimation of different pass-through rates using growth rate in national accounts measures and growth rates in household survey estimates available in PovcalNet

|||Poo<br>|led<br>|||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|VARIABLES|All|No High Income|Lower Middle<br>Income|Consumption|Sub-Saharan<br>Africa|
|GDP<br>(growth rate)|0.869***|0.894***|0.693***|0.754***|0.715***|
||(0.0392)|(0.0503)|(0.0775)|(0.0579)|(0.169)|
|Observations|1,751|1,184|502|696|150|
|R-squared|0.219|0.210|0.138|0.196|0.107|
|||Compa|rable|||
||(1)|(2)|(3)|(4)|(5)|
|VARIABLES|Pooled|No High Income|Lower Middle<br>Income|Consumption|Sub-Saharan<br>Africa|
|GDP<br>(growth rate)|0.876***|0.911***|0.722***|0.756***|0.718***|
||(0.0384)|(0.0491)|(0.0772)|(0.0507)|(0.146)|
|Observations|1,403|933|391|525|150|
|R-squared|0.271|0.270|0.184|0.298|0.139|
|||Poo|led|||
||(1)|(2)<br>|(3)<br>|(4)|(5)<br>|
|VARIABLES|All|No High<br>Income|Lower Middle<br>Income|Consumption|Sub-Saharan<br>Africa|
|GNI|0.737***|0.768***|0.761***|0.791***|0.694***|
||(0.0375)|(0.0485)|(0.0837)|(0.0605)|(0.122)|
|Observations|1,542|1,019|428|558|106|
|R-squared|0.200|0.198|0.162|0.235|0.235|
|||Compa|rable|||
||(1)|(2)|(3)|(4)|(5)|
|||N Hih|L Middl||SbSh|
|VARIABLES|All|o g<br>Income|ower e<br>Income|Consumption|u-aaran<br>Africa|
|GNI|0.836***|0.910***|0.754***|0.751***|0.684***|
||(0.0385)|(0.0500)|(0.0815)|(0.0503)|(0.120)|
|Observations|1,266|828|339|437|106|
|R-squared|0.272|0.286|0.202|0.338|0.236|

Note: Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Each coefficient is from a different regression conducted using different samples. Each regression uses growth rates from national accounts (GDP/GNI per capita) and growth rates in average household consumption from survey-data available in PovcalNet to estimate a discount factor that accounts for the difference in growth rates between these two data sources. Different samples are used in each regression. Pooled samples use any survey-based estimate available in PovcalNet and focus on different income groups, welfare measure or region. Comparable samples use only comparable survey-based estimates available in PovcalNet (see PovcalNet’s comparability dataset, available at: <u>https://development-data-hub-s3public.s3.amazonaws.com/ddhfiles/506801/povcalnet_comparability.csv ).</u> 35
varied from 11.6% o 13.7%. The railways sector is somewhat unusual in that low middle income countries show the highest cost of capital.

**Figure 4:  Estimated weighted average cost of capital by sector according to  income levels (1998-2002)**

<!-- Start of picture text --> Cost of Capital<br>(by sector grouped according ro income levels)<br>UMC<br>LMC<br>LIC<br>10.0% 11.0% 12.0% 13.0% 14.0% 15.0% 16.0%<br>Energy Water Railw ays Ports<br><!-- End of picture text --> It may be useful to point out that even if the cost of capital levels seems to be fairly high by comparison to the costs of capital observed in similar industries in OECD countries, these numbers benefit from the high leverage of the companies noted earlier which maintains the relative importance of the cost of equity low in comparison to the cost of debt. Indeed, the average cost of equity estimated for the sample (19.2%, 14.5% and 13.8% respectively for LIC, LMC and UMC), is significantly larger than the cost of capital (15.4%, 11.8% and 11%, respectively). Figure 5 summarizes the estimates of the cost of equity across sectors for the three main income groups. The cross sectoral differences observed for the cost of capital are however confirmed with the highest cost of equity for all sectors, except railways, higher than 19% and above 22% for ports in LICs.

**Figure 5:  Average Cost of equity by sector (1998-2002)**

<!-- Start of picture text --> Cost of Equity<br>(by sector grouped accourding to income levels)<br>UMC<br>LMC<br>LIC<br>10.0% 12.0% 14.0% 16.0% 18.0% 20.0% 22.0% 24.0%<br>Energy Water Railw ays Ports<br><!-- End of picture text --> 12
# I. Introduction

Reducing poverty is one of the major objectives of economic policies in both developed and developing countries. There are many ways to go about achieving this task, each of which has associated costs. To the extent that assistance does indeed reach the poor overall poverty will be reduced, or even eliminated if the funds allocated for this purpose are large enough. However, it is not always easy to identify the poor directly. Given that governments have limited resources it is important that assistance is not mistakenly given to the nonpoor, who may attempt to gain access to benefits by misrepresenting their income status. The task of ensuring that poverty assistance actually reaches the neediest is often referred to as the targeting issue." Targeting benefits to reach the poor can be done in many ways. This paper is limited to targeting in the form of transfers (money, food stamps, rations, etc.) given directLy to households which are identified as likely to be poor. The relevant questions here are: 1. To which households should one give these transfers, given that reliable information on incomes is difficult to obtain? 2. How much money (or stamps, or rations) should be given? The answers to these questions depend crucially on the information available. This paper preeants a method that uses data from household surveys to increase the efficiency of targeted assistance.

The paper is organized as follows. The next section discusses the theoretical issues involved in targeting. Section III provides a method of / Recent theoretical papers on targeting include Besley and Kanbur (1988), Kanbur (1987), Nicholas and Ze_khauser (1982), Ravallion (1988), and Ravallion and Chao (1988).
improvement plans is assumed between the two groups).<sup>4</sup> The Matching method is used to control for pre-SBM differences, correlated with non-randomness of the SBM implementation between the two groups in the sample.

Our work is closely related to an earlier study by Khattr et al. (2012). They used the initial implementation stage of SBM during the TEEP period, when the government initially planned three batches by divisions to implement SBM (thus, enabling a pipeline method). Their empirical setting is characterized by two important features: (i) in reality, lags between batches were short for some reasons related to the actual project implementation, and (ii) it was at the very initial stage of SBM in the country. Their estimate of an increase in the total test scores from two-year exposure to SBM is 1.45 points, which is quite small probably due to the factors mentioned above.

In contrast to the above study, Yamauchi and Liu (2012b) evaluated the impact of the whole intervention package of TEEP (including SBM) on test scores. Their analysis does not attempt to purge out SBM impacts from the other components, so the impact estimate includes the SBM impacts. The estimation used the sample of schools from the macro-region of Visayas where treated and control divisions are geographically scattered. Based on double differences combined with propensity score matching, the impact is around an increase of score by 4.5 to 5 points over two years.<sup>5</sup> The paper is organized as follows. The next section reviews recent literatures on SBM. Section 3 describes our empirical setting, especially TEEP and non-TEEP divisions, and discusses why I restrict our sample to non-TEEP and non-city division schools. Section 4 describes our data. The data come from three sources: (i) national school census data, (ii) SBM fund data, and (iii) national achievement test score

> 4 Schools are required to set up school improvement plan (SIP) for the coming 5 years as well as more specific annual improvement plans (AIP) that describes their plan for a school year. Based on significance of their SIP/AIP, SBM grants were allocated to schools.

> 5 Interestingly, the simple double difference estimate (without matching) is similar to that of Kattr et al. (2012) for SBM.

3
# _Overall results_

To provide more detail on legal and regulatory barriers to investment, the GIC Survey 2019 asked respondents about the degree to which various legal and regulatory issues presented obstacles to their operations. Such issues covered issues ranging from cumbersome investment approval processes to restrictions on expatriate staff.

Cumbersome investment approval processes and operational restrictions are the most often cited regulatory barriers for FDI in surveyed MICs (Figure 2). Fifty-six percent of respondents list cumbersome investment approval processes as moderate or major obstacles to operations, and 44 percent cite price, technology, or product restrictions as moderate or major obstacles. The salience of these top two concerns holds across most host countries and sectors. In addition, these findings are consistent with prior work that also finds approval processes and restrictions on prices, technology, or products to be a significant obstacle for foreign affiliates (Mistura and Roulet 2019; UNCTAD 2019).

# _Heterogeneity across investor types_

Compared with manufacturing affiliates, services affiliates perceive joint venture requirements to be more significant obstacles on average, as evidenced by the positive and statistically significant (at the p<0.10 level) coefficient on the dummy of services in our preferred specification (Table 16, Column 6). Affiliates in the services sector also perceive expatriate restrictions to be more significant obstacles on average (see the positive and significant—at the p<0.10 level—coefficient in Table 18, Column 6).

**Figure 2. Salience of legal and regulatory obstacles (% of respondents)**

**Question:** To what degree are the following factors an obstacle for your company to operate in this country?

<!-- Start of picture text --> Investment approvals 26% 30%<br>Price, tech, or product restrictions 16% 28%<br>Foreign investment limits 16% 22%<br>Expat restrictions 13% 24%<br>JV requirements 15% 20%<br>Local sourcing requirements 12% 21%<br>R&D investment requirements 10% 21%<br>Minimum investment requirements 8% 19%<br>0% 20% 40% 60% 80% 100%<br>Major obstacle Moderate obstacle<br><!-- End of picture text --> _Source_ : Computation based on the 2019 GIC Survey.

_Note:_ JV = joint venture; R&D = research and development; tech = technology.

14