rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

The excessive liquidity in international capital markets during the 1990s posed several challenges to policymakers in emerging economies. Among numerous issues, the links between financial volatility and short-term capital flows were a particular source of concern. In addition, problems associated with moral hazard, fragility in financial intermediaries’ and corporate balance sheets, asset price bubbles, consumption booms and real exchange rate overvaluation were intimately connected to the boom-bust cycle in international capital flows to developing countries following the financial liberalization process.

In this context, it became evident that full capital account openness can be associated with increased macroeconomic volatility and limitations in degrees of freedom for domestic policy makers, particularly when capital flows are strongly pro-cyclical. These facts prompted a number of countries to (re)-adopt capital account regulations. Our interest at present lies in examining the imposition of price-based capital account management polices in Chile and Colombia since the early 1990s. The aims of those regulations included reducing the costs of sterilization policies and the appreciation of the real exchange rate associated with large capital inflows and increasing the autonomy of domestic monetary policy.

This paper intends to evaluate the effectiveness of those regulations in attaining some of these goals. In particular, we will concentrate on the effects of the reserve requirements on capital flows and on the real exchange rate. This task presents several difficulties. Cordella (2003) argues that those “taxes” on inflows may either increase or decrease the supply of flows depending on two counteracting forces. On the one hand, if those policies are successful in changing the composition of external liabilities and in reducing the vulnerability to crises, they could in fact generate larger long-term capital inflows, since risk-adjusted yields may increase. On the other hand, the direct decrease in returns from investing in the country (due to the tax) and the increase in the irreversibility of the investment have a negative impact on capital inflows.

We will analyze creditor-based data at a monthly frequency that has not been used previously to evaluate the capital account regulations. There are two main advantages in using this dataset. The capital flows recorded are arguably less likely to be subject to 2
# **Structure and Performance of the Services Sector in Transition Economies**

Ana M. Fernandes<sup>∗</sup> The World Bank

> ∗ Development Research Group, The World Bank, 1818 H Street, N.W., Washington, DC 20433, USA, email: <u>afernandes@worldbank.org. This paper was prepared as a background paper for the World Bank</u> flagship study _Path to Prosperity: Towards Faster Productivity Growth in Eastern Europe and Former Soviet Union_ . We thank Paloma Anos-Casero for comments. The findings and views expressed in this paper are those of the author and should not be attributed to the World Bank, its Executive Directors, or any of its members.
- _Β :_ Parameters of the equation

- _v_ : External shocks

- _u_ : Technical Efficiency ( _TE_ ) - _i_ : Firm index

## **_2._ 3** **_-_ Explaining Technical Efficiency**

A complementary approach, when having calculated Technical Efficiency ( _TE_ ), is to explain the reasons for firms’ diverse performances. Firms’ inefficiency can be explained by “exogenous” factors which affect either the technology of production, or the firm’s ability to transform inputs into outputs. In the literature, these factors have been estimated in two different ways. A simple method consists in estimating the stochastic production frontier, and in regressing the residual of the estimation (the Technical Efficiency, _TE_ ) on a vector of explanatory factors (z). This method is called the “Two Steps” procedure. Different estimation procedures can be used. The simplest way is to run an OLS regression. Another possibility is to apply a Tobit model, in order to address the question of the distribution of the efficiency. The “Two Steps” procedure presents, however, some shortcoming in separating the Technical Efficiency ( _TE_ ) from the production frontier. When some production frontier inputs (x) are explained by factors affecting efficiency, there is an issue of simultaneity<sup>14</sup> . Because the Technical Efficiency term ( _TE_ ) is correlated with the production frontier inputs (x), the likelihood estimation of the stochastic production frontier is biased, due to the omission of important explanatory variables.

In fact, a relatively new branch of the literature proposes to estimate the production frontier and the factors explaining inefficiency at the same time. This is the “One Step” procedure. In this case, the parameters of the equation (here _β_ and _δ)_ are simultaneously estimated by the likelihood estimation method. The stochastic version of the model can be written as follows: With

- _Y_ : Production - _X_ : Production factors - _Z_ : Factors explaining Technical Efficiency

_- v_ : External shocks _- u_ : Technical Efficiency - _β_ / _δ:_ Parameters of the equation - _i_ : Firm index

# **3- The ICA Firm Surveys: Data Limitations**

The World Bank Investment Climate ( _ICA_ ) surveys collect data on inputs and outputs, as well as on a large variety of quantitative and qualitative (perception-based) indicators of the investment climate. In building the database, we have tried to incorporate as much information as possible. We have integrated in our sample 23 countries which participate in the five main regions of the developing world: Sub-Saharan Africa ( _AFR_ ), East Asia ( _EAS_ ), South Asia ( _SAS_ ), Latin America and the Caribbean ( _LAC_ ), Middle East and North Africa ( _MENA_ , see list of countries in _Annex_ 1)<sup>15</sup> . In this sample, MENA is represented by 5 countries: _Algeria_ (2002), _Saudi Arabia_ (2005), _Lebanon_ (2006), _Morocco_ (2000, 2004) and 6
Table 1. **Evolution of Total Planned Commitments to Transport Projects in the World Investment commitment amounts, numbers of project commitments and average size of project commitments**

||**_Total_**||**Toll Roads**|||**Rail**|||**Airports**|||**Seaports**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||||||**Cumula**|**tive**|||||||
||**1985-2003**|**1985-95**|**1995-98**|**1998-03**|**1985-95**|**1995-98**|**1998-03**|**1985-95**|**1995-98**|**1998-03**|**1985-95**|**1995-98**|**1998-03**|
||||||_Num_<br>|_ber of projec_<br>|_ts committed_|||||||
|North America|**260**|**111**|**_-17_**|51|**16**|**_3_**|41|**15**|**_17_**|18|**15**|**_-6_**|-4|
|Europe|**297**|**67**|**_28_**|76|**34**|**_48_**|-21|**9**|**_17_**|19|**3**|**_6_**|11|
|Asia|**319**|**120**|**_16_**|-14|**96**|**_18_**|-30|**26**|**_34_**|-2|**30**|**_13_**|12|
|Other|**261**|**83**|**_67_**|-15|**27**|**_26_**|-10|**17**|**_17_**|-9|**21**|**_27_**|10|
|Total|**1137**|**381**|**_94_**|98|**173**|**_95_**|-20|**67**|**_85_**|26|**69**|**_40_**|29|
|Developed|**451**|**116**|**_3_**|135|**120**|**_-24_**|18|**20**|**_24_**|19|**3**|**_3_**|14|
|Developing|**686**|**265**|**_91_**|-37|**53**|**_119_**|38|**47**|**_61_**|7|**63**|**_37_**|15|
||||||_Total com_|_mitment amo_|_unt (in US$ bi_|_llion)_||||||
|North America|**111.5**|**34**|**_-8.1_**|31.2|**21.6**|**_12.1_**|11.5|**4.7**|**_0.2_**|2.9|**0.85**|**_0.97_**|-0.47|
|Europe|**240.1**|**53.1**|**_19.5_**|34.6|**47.4**|**_38.2_**|30.2|**6**|**_9.7_**|-1.09|**0.08**|**_5.06_**|-2.7|
|Asia|**250.7**|**83.6**|**_12_**|-21.6|**54.6**|**_26.1_**|15.3|**51.8**|**_25.2_**|-20.6|**19.8**|**_-5.4_**|9.9|
|Other|**81.6**|**19.4**|**_10.9_**|-9.8|**12.8**|**_11.1_**|13.4|**3**|**_3.5_**|7.09|**2.07**|**_3.07_**|5.07|
|Total|**683.8**|**190.1**|**_34.3_**|34.4|**136.4**|**_87.5_**|70.4|**65.5**|**_38.6_**|-11.6|**22.8**|**_3.7_**|11.8|
|Developed|**379.7**|**85.6**|**_4.8_**|59.4|**69.3**|**_39.9_**|81.7|**41.2**|**_7.1_**|-11.7|**.1**|**_1.3_**|.9|
|Developing|**304.1**|**104.5**|**_29.5_**|-56|**67.1**|**_47.6_**|-11.3|**24.3**|**_31.5_**|0.1|**22.7**|**_2.4_**|10.9|
||||||_Averag_|_e project size_|_(in US$ millio_|_n)_||||||
|North America|**429**|**306**|**_476_**|612|**1,350**|**_4,033_**|280|**313**|**_12_**|161|**57**|**_(162)_**|118|
|Europe|**808**|**793**|**_696_**|455|**1,394**|**_796_**|(1,438)|**667**|**_571_**|(57)|**27**|**_843_**|(245)|
|Asia|**786**|**697**|**_750_**|1,543|**569**|**_1,450_**|(510)|**1,992**|**_741_**|10,300|**660**|**_(415)_**|825|
|Other|**313**|**234**|**_163_**|653|**474**|**_427_**|(1,340)|**176**|**_206_**|(788)|**99**|**_114_**|507|
|Total|**601**|**499**|**_365_**|351|**788**|**_921_**|(3,520)|**978**|**_454_**|(450)|**330**|**_93_**|407|
|Developed|**842**|**738**|**_1600_**|440|**578**|**_-1663_**|4539|**2060**|**_296_**|-616|**33**|**_433_**|64|
|Developing|**443**|**394**|**_324_**|1514|**1266**|**_400_**|-297|**517**|**_516_**|14|**360**|**_65_**|727|

_Source:_ Public Works (2003) and authors’ calculations.
58

# **Appendix A: Generation of Aggregate Capital Stock**

Annual values of the country specific capital stock were generated from gross investment data using the perpetual inventory method: where _Kit_ is the level of the aggregate capital stock for country _i_ in year _t_

- δ is the depreciation rate, which is assumed to be have the same value for all countries for all time periods

_Iit_ is the level of gross investment for country _i_ in year _t_ Data on gross investment were obtained from the World Bank SIMA database as gross capital formation<sup>50</sup> in constant local currency units. We assumed a depreciation rate of 5 percent for all countries and all time periods, which implies that δ was set equal to 0.05. Note that before any capital stock series can be constructed, the initial value of the capital stock is required. To obtain these initial capital stock values, we employed the constant initial investment growth specification, detailed in Young (1995). Specifically, assume the period _t_ is the first period in which gross investment data for a country is available and also that _g_ is the value of the average growth rate of gross investment for the next 4 periods, that is periods _t_ +1 through _t_ +4. If we further assume that the capital stock had been growing at the same growth rate _g_ for infinite number of periods before period _t_ , then we are able to use the mathematical result for the sum of an infinite geometric series and thus it can be shown that the value of the initial capital stock (value of the capital stock in period 1) is given by: 1 − δ Note that < 1 is necessary in order to ensure that the convergence of the capital 1 + _g_ stock (infinite geometric) series

> 50 Gross capital formation (formerly gross domestic investment) consists of outlays on additions to the fixed assets of the economy plus net changes in the level of inventories.  Fixed assets include land improvements (fences, ditches, drains, and so on); plant, machinery, and equipment purchases; and the construction of roads, railways, and the like, including schools, offices, hospitals, private residential dwellings, and commercial and industrial buildings. Inventories are stocks of goods held by firms to meet temporary or unexpected fluctuations in production or sales, and "work in progress."  According to the 1993 SNA, net acquisitions of valuables are also considered capital formation.
Table 2: Event-study estimates of COVID-19 impacts on household business performance

||Monthly Suspensions|Monthly Closures|Weekly Work Hours|Monthly Income|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|**[Panel A] Aggregate efects**|||||
|Post Covid|7.380***|0.553***|-3.279***|-945.9***|
||(0.364)|(0.0473)|(0.142)|(42.82)|
|**[Panel B] Efects by month**|||||
|February|-1.988***|-0.250***|-3.583***|-511.4***|
||(0.309)|(0.0196)|(0.342)|(90.09)|
|March|2.646***|0.453***|0.864***|-384.6***|
||(0.383)|(0.0434)|(0.295)|(82.65)|
|April|29.70***|0.402***|-13.07***|-1,313***|
||(1.151)|(0.0410)|(0.355)|(74.84)|
|May|11.48***|1.596***|-4.116***|-1,885***|
||(0.553)|(0.288)|(0.314)|(106.8)|
|June|3.487***|0.442***|-0.835***|-750.8***|
||(0.372)|(0.0603)|(0.257)|(73.14)|
|July|1.886***|0.905***|-0.602***|-801.1***|
||(0.261)|(0.0649)|(0.230)|(86.55)|
|August|4.454***|0.320***|-1.806***|-812.6***|
||(0.458)|(0.0707)|(0.310)|(121.7)|
|September|-|-|-0.962***|-858.9***|
||-|-|(0.317)|(74.17)|
|Pre-COVID average|6.67|0.50|47.18|6362.68|
|Observations|355,080|371,635|419,252|418,824|
|Observation level|Commune|Commune|Individual|Individual|
|Month fxed efects|Yes|Yes|Yes|Yes|
|Commune fxed efects|Yes|Yes|Yes|Yes|
|Individual controls|-|-|Yes|Yes|

Note: Panel A shows the event-study estimates of the aggregate effect of COVID-19 on household business outcome variables. The coefficients are estimated following Equation 1. Panel B presents the estimated effect for each month after January 2020. The coefficients are estimated following Equation 2. Data on household business suspension and closure filings are obtained from the Household Business Tax Census. Data on individual work hours and income are obtained from the Vietnamese Labor Force Surveys. Monthly income is detrended from pre-COVID linear trend and residualized from individual observables including age, gender, educational level, and occupation level. Standard errors are clustered at the commune level and are presented in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

18
# **Data Deprivation: Another Deprivation to End**

Umar Serajuddin, Hiroki Uematsu, Christina Wieser, Nobuo Yoshida, and Andrew Dabalen* Key words: poverty, household survey, statistics, data deprivation JEL codes: C81, I32, O10

* World Bank, Washington DC. Email: userajuddin@worldbank.org, huematsu@worldbank.org, <u>cwieser@worldbank.org, nyoshida@worldbank.org, adabalen@worldbank.org</u>

** The authors acknowledge comments and suggestions from Ana Revenga, Haishan Fu, Peter Lanjouw, Grant Cameron, Neil Fantom, Ambar Narayan, Dean Jolliffe, and Amparo Ballivian. Juan Feng and Hiroko Maeda provided valuable inputs to the paper, especially in navigating the WDI database used in the analysis. The authors acknowledge that this paper benefited immensely from earlier studies conducted by Jaime Saavedra, Johan Mistiaen, and Christina E. Malmberg Calvo.  Also, the authors thank participants of the Development Data Directors Group meeting on December 4, 2014 and participants in a World Bank seminar for their comments. The findings and proposals of this paper do not necessarily reflect the views of the Group or the World Bank.
behavior, and the large number of small coffee traders which is viewed as an unneces‐ sary layer of trading activity. From only one exporter in 1990 (the Coffee Board), within a 3‐year period, 55 exporters were active in the sector, and a high of 78 were reached in 1995 (figure 3). More recently this number has declined to about 25, which has been at‐ tributed to as indicative of oligopsonistic behavior. However, most of the exporting firms that exited the industry represented only a small share of exports.<sup>**4**</sup> A Herfindahl index of market concentration constructed for the post‐1993 period shows a very low level of market concentration for most years (the index being well below 10 percent), and also no evidence of any upward trend (figure 4). This finding is consistent with an earlier study which examined coffee export concentration issues in Uganda in a more comprehensive manner (Nsibirwa 2002).

The large number of small traders involved in the sector is viewed by some as an unnecessary layer of trading activity which adds excessive costs to the industry. How‐ ever, this is unavoidable since the Ugandan coffee sector consists primarily of small‐ holders, thus necessitating several levels of aggregation before adequate coffee quanti‐ ties are collected to reach exporters’ processing facilities.<sup>**5**</sup> Robusta growers usually sun‐dry their red cherry coffee on the farm and then sell it in small quantities to the so‐called _kiboko_ traders who transport it to coffee mills for hulling (each _kiboko_ trader brings one to two bags of coffee to the milling factory). Most mills provide hulling services to _kiboko_ traders at a fixed fee. After milling, _kiboko_ traders sell the green beans to larger traders, the so‐called FAQ (fair average quality) traders, who sell it to exporters after accumulating larger quantities. Typically, export‐ ers require a minimum of 10 bags of coffee delivered to them, which explains the two layers of middlemen between farmers and exporters. To summarize, _kiboko_ and “FAQ” traders connect the 500,000 coffee growers—each of whom produces an average of 5‐6 bags of coffee—with the 15 exporters that account for 95 percent of Uganda’s coffee ex‐ ports—each of whom handles an average of 160,000 bags of coffee. (Prior to reforms the functions of _kiboko_ and “F.A.Q.” traders were performed by the cooperatives and the Coffee Board.)

# **5. Proposed Strategies**

A series of coffee expansion and promotion efforts have been proposed under the cabi‐ net‐approved Strategic Exports Program (SEP) initiative and by various donors. The SEP initiative envisaged considerable expansion of coffee production as well as in‐ creased investment in value addition enterprises, including product differentiation and access to new markets. One of the Program’s targets was to increase coffee production and exports from 3.15 million bags in 2001/02 to 12 million bags by the year 2007/08 (MFPED 2004, p. 15).

The most often proposed strategies are specialty coffee expansion, value addi‐ tion, coffee promotion, domestic consumption, and the establishment of a coffee auc‐

**_— 7 —_**
Reported weekly hours of those who are working are only slightly different between the short and detailed module for men, but hours are lower when reported by a proxy compared to self-reported, and this difference is larger for men than for women (7 fewer hours for men and 4 fewer hours for women). The short-proxy module also generates lower working hours than the other modules, and again the difference is larger in the case of men (almost 5 fewer hours) compared to women (4 fewer hours).

Daily earnings also differ across survey assignments, but following a different pattern. The detailed module produces higher average earnings for women but not men, but differences are not significant between the proxy and self-reported modules.

In Table 5, we turn to the distribution of main activities. Here we classify all subjects into four categories. Labor force participants are in agriculture or other sectors.<sup>17</sup> Those who declared having worked in the past 7 days can report that their main activity in the last week was domestic duties or no work. Participation in domestic duties, while not formally included in a labor force statistic, is commonly collected in surveys. This is usually done by including domestic duties as a possible answer to the question in what sector the main activity is; this approach is also followed in both the short and detailed modules. However, in the detailed module, like in most multipurpose modules, this question is preceded by the three screening questions on work in the last 7 days, which aim to find out the type of work of the respondent in more detail.

> 17 The non-agricultural sectors are too small to leave disaggregated. These include: mining/quarrying/manufacturing/processing, gas/water/electricity, construction, transport, buying and selling, personal services, education/health, and public administration. Buying and selling activities are the most frequently reported of these activities (4-7 percent, depending on the sub-group).

22
**Chart 3. Per capita consumption and real GDP growth, average 1984-93**

<!-- Start of picture text --> 6<br>2 -<br>0<br>]] JEhl:j<br>l Per capita consumption (in US$ X I00 GDP Rge anual in %<br>4<br>&~~~<br><!-- End of picture text --> Source: World Bank. IMF.

- Consumption figures include only 28 countries; GDP growth figures include 30 countries.

- **Growth rate ofthis region is negative due to its economic transformation.

In measuring the benefits of ODA debt relief we include the change in international reserves even though it is not directly linked to higher consumption and investment. In other words we consider the accumulation of international reserves to be one desirable use of foreign exchange (which needs to be financed) instead of it being a source of funds to finance other needs-such as imports of goods and services. We justify this approach by the low level of international reserves held by the countries considered during the sample period (Table 4). In fact, developing countries tend to maintain an average stock of foreign reserves in the range of 4-5 months of imports, mainly as a buffer to protect them against short-term liquidity shocks. Assuming that the average stock of foreign reserves held by other developing countries is near the optimum, it appears that African countries need to increase their own holdings, to make themselves less vulnerable to short-term liquidity shocks. Thus increasing African countries' stock of foreign reserves is a desirable (welfare-enhancing) outcome.

The use of the import bill capacity as defined above as an indicator of the benefits that accrue to the recipient countries when getting grants does not deny the existence of several other potential (although less straightforward) benefits, such as the reduction in indebtedness and its effects on creditworthiness, the strengthening of the fiscal stance (if public debt is being written off) and its impact on inflation, and so on.4<sup>3</sup> Nevertheless,

> 12 In other words we put the change in international reserves as an item "above the line," in contrast to the more common classification of this entry as a residual in balance of payments accounting.

> **3** There are other issues involved in analysis of grants and debt relief that we ignore in this paper. For example, the impact that different conditionality elements (imposed by donor countries) have on the recipient countries' mediumand long-term growth. Also, there are potential benefits accruing to the different bodies in donor countries; for

9
|**Appendix Table 1. Evolution of Tre**|**atment Inten**|**sity over Time**|||||
|---|---|---|---|---|---|---|
|**Variable**|**Total**|**Baskura**|**Enguli**|**Enkulal**|**Kentai**|**Zefie**|
|**Treatment 2009 (ha)**|||||||
|Soil & water cons. on cult. land|2,167|330|430|289|640|479|
|SWC on degraded land|347|0|318|28|1|0|
|Trees planted|56|0|56|0|0|0|
|**Treatment 2010 (ha)**|||||||
|Soil & water cons. on cult. land|9,730|1,104|3,047|2,517|1,631|1,429|
|SWC on degraded land|2,217|124|1,609|333|61|91|
|Trees planted|335|0|304|5|0|26|
|**Treatment 2011 (ha)**|||||||
|Soil & water cons. on cult. land|23,441|2,749|8,766|5,384|3,451|3,090|
|SWC on degraded land|7,405|653|3,425|819|1,422|1,085|
|Trees planted|945|149|343|113|145|194|
|**Treatment 2012 (ha)**|||||||
|Soil & water cons. on cult. land|33,379|4,353|11,635|7,438|4,634|5,319|
|SWC on degraded land|11,155|1,066|5,262|1,297|1,802|1,728|
|Trees planted|2,221|319|866|427|186|424|
|**Treatment 2013 (ha)**|||||||
|Soil & water cons. on cult. land|43,378|5,831|15,063|9,301|6,278|6,907|
|SWC on degraded land|18,434|2,692|7,679|3,349|2,066|2,648|
|Trees planted|3,466|606|1,177|734|336|613|
|**Treatment 2014 (ha)**|||||||
|Soil & water cons. on cult. land|48,710|6,507|16,942|10,214|7,369|7,677|
|SWC on degraded land|18,699|2,714|7,782|3,423|2,066|2,712|
|Trees planted|5,360|1,059|1,422|1,053|738|1,089|
|**Treatment 2015 (ha)**|||||||
|Soil & water cons. on cult. land|53,674|7,066|18,990|10,867|7,990|8,762|
|SWC on degraded land|18,960|2,726|7,862|3,509|2,066|2,796|
|Trees planted|6,783|1,215|1,565|1,600|905|1,499|

_Source:_ Project data.

29