rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 3. Summary Statistics from Diaries:  Labor Supply and Sexual Activities**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|**Panel A. Averages**|Daily Average<br>Entire Sample|Daily Average<br>Round 2 Sample|Transaction Data:<br>All Clients|Transaction Data:<br>Regulars Only|Transaction Data:<br>Casuals Only|
|Participated in the Sex Sector|0.76|||||
|Income from Sex Work|686.84<br>(749.55)|||||
|Total Income (All Sources)|788.26<br>(778.89)|||||
|Number of Clients Seen|1.52<br>(1.12)|||||
|Number of Regular Clients Seen|0.54<br>(0.66)|||||
|Had Vaginal Sex|0.74|0.69|0.95|0.96|0.94|
|Had Anal Sex|0.22|0.18|0.24|0.19|0.26|
|Had Oral Sex|0.19|0.09|0.19|0.17|0.21|
|Had Unprotected Vaginal or Anal Sex|0.18|0.08|0.17|0.22|0.14|
|# Times Unprotected Vaginal or Anal Sex|0.42|0.12|0.27|0.41|0.20|
|_Round 2 Only _<sup>_1_</sup>||||||
|<br>Had Unprotected Vaginal Sex||0.07|0.07|0.10|0.05|
|Times Unprotected Vaginal Sex||0.10|0.08|0.11|0.05|
|Had Unprotected Anal Sex||0.02|0.02|0.01|0.02|
|Times Unprotected Anal Sex||0.02|0.02|0.01|0.02|
|Observations<br>|12526|5609|19041|6754|12112|
|Number of Women|192|91|192|192|192|
|**Panel B. Occurred at Least Once During**|**Sample Period**<br>(1)<br>|(2)<br>||||
||Entire Sample|Round 2 Only||||
|Vaginal Sex|1.00|1.00||||
|Anal Sex|0.82|0.72||||
|Oral Sex|0.71|0.50||||
|Unprotected Vaginal or Anal Sex|0.71|0.54||||
|Unprotected Vaginal Sex||0.47||||
|Unprotected Anal Sex||0.26||||
|Observations|209|108||||
|Number of Women|192|108||||

Note: Figures are calculated from self-reported daily diary data. The figures in Columns 1 and 2 are daily averages. Figures in Columns 3-5 are averages across all transactions (up to a maximum of 3 client transactions per woman per day). The number of observations in Columns 4 and 5 do not sum to the total in Column 3 due to reporting errors on client type. There are 192 total women in the sample, 108 of whom participated in Round 2. 1 Data on unprotected vaginal sex and unprotected anal sex are available in Round 2 only.
speed. But what would be the measures most promising to deliver this faster growth for lower income groups? Once identified, could they be ranked to assist the setting of priorities?

# **Figure 1**

**Extreme poverty rate projections, 2013-2030**

(percent of population) <!-- Start of picture text --> 30 30<br>23.8<br>23.8<br>25<br>20 18.0 20<br>15 13.8 13.8<br>13.0<br>10 10<br>5 3.0 3.0<br>0 0<br>2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030<br>GDP growth 3.3% (average 2011/14)<br>GDP growth 3.3% (average 2011/14)<br>GDP growth 2.5% (best spell 2005/09) GDP growth 3.6% (bottom 40% grows double the average)<br>GDP growth 7.2% (goal of 3% Ext. Pov.) GDP 2.5 % growth (best 2005/09 - bottom 40% grows double)<br><!-- End of picture text --> _Source_ : Barton-Dock and Singh (2015) _Note:_ The scenarios contemplate real GDP growth paths. The poverty rate projections are based on real per capita consumption obtained by applying a conversion factor of 0.87 to GDP growth (World Bank, 2014) and assuming a population growth of 1.4% per year (average of 2000-2012).

# **II. Review of the Literature**

While econometric exercises investigating determinants of GDP growth have been numerous, less is known about factors influencing household incomes at different segments of the income distribution. Empirical work by Dollar and Kraay (2002) and Dollar et al. (2016) broadly rejects the idea that other factors than mean income growth would influence the incomes of the poor, thus suggesting that growth would be mostly distribution neutral. Similarly, Deininger and Squire (1996), Chen and Ravaillon (1997), and Easterly (1999) suggest that growth does not have an impact on inequality. According to this literature, on average across countries, household income of the bottom 20 or 40 percent of the income distribution grows at almost exactly the same rate as mean household income.<sup>4</sup> As a result, the focus of research has been mainly put so far on identifying drivers of overall economic growth, with the idea that growth would benefit everyone including households at the bottom of the income distribution. As Stiglitz (2015) puts it, many economists argued that the best

> 4 See, inter alia, Lübker et al. (2002) and Amman et al. (2006) for a critical assessment of the Dollar and Kraay (2002) paper.

-4-
**13**

## **III. CONVERSION FACTORS and MULTIPLE EXCHANGE RATE PRACTICES**

**18.** Multiple exchange rates arise directly when separate groups of foreign<sup>exchange</sup> transactions are conducted at different exchange rates; they<sup>alsoariseindirectlywhena</sup> variety of taxes, subsidies, surcharges, or equivalent devices are applied<sup>to foreignexchange</sup> transactions. Multiple exchange rates are usually adopted to address<sup>externalpayments</sup> difficulties **by** attempting to relieve the pressure on<sup>different elementsof theexternal</sup> account. The most common features of multiple currency regimes are<sup>the maintenanceof</sup> a preferential rate for essential imports, often combined<sup>withdebtservicepayments;</sup> separate rates based on a distinction between capital<sup>andcurrenttransactions;'anda</sup> separate rate for some important<sup>currentinvisiblepayments.</sup>

# Effects of Multiple Currency Practices on Conversion Factors

**19.** The operation of a system of multiple exchange rates leads to effects very similar<sup>to</sup> the intended results of a system of tariffs and subsidies. The percentage<sup>differencebetween</sup> * For a broad historical review of country experiences with multiple exchange systems, the<sup>reasonsfor</sup> resorting to such system, their outcome, and the significance for the Fund<sup>intermsofitsjurisdiction and</sup> maintenance over the international exchange system, see the<sup>followingunpublished documentsof the</sup> International Monetary Fund: "Review of Experience with Multiple Exchange Rate Regimes", (SM/84/64), March **19,** 1984; "Review of Multiple Exchange Rate Regimes **-** Background Information", **_(SM/84/65),_**<sup>March</sup> 20, 1984; and "Multiple Currency Practices Applicable Solely to Capital Transactions", **_(SM/85/19),_** January **19,** **_1985._**

**"** For a discussion of issues arising from dual exchange rate markets for capital and current transactions,<sup>see</sup> Anthony Lanyi, "Separate Exchange Markets for Capital and Current Transactions," **IMF Staff Papers, Vol. 22** (Washington **D.C.:** International Monetary Fund, November **1975) pp.** 714-49.
TABLE 3. Distribution of Interstate Trade, 1991-94 _(percent)_

||_19_|_91_|_1_|_992_|_19_|_93_<br>|_19_<br>|_94_<br>|
|---|---|---|---|---|---|---|---|---|
||_Exports_|_Imports_|_Exports_|_Imports_|_Exports_|_imports_|_Exports_|_imports_|
|Country share of total i|nterstate trade||||||||
|Armenia|I.4|1.9|1.0|1.4|0.3|0.5|0.6|1.1|
|Azerbaijan|3.4|2.9|2.0|2.0|1.8|1.3|1.2|1.8<br>|
|Belarus|8.6|8.4|7.6|9.9|8.7|9.8|6.2|8.8|
|Estonia|1.4|1.2|0.6|0.7|1.0|1.0|1.7|1.2|
|Georgia|2.1|2.0|0.6|1.0|0.8|1.3|0.5|0.8|
|Kazakhstan|5.3|6.9|8.4|11.4|8.8|10.5|5.8|7.3|
|Kyrgyzstan|1.9|1.8|0.9|1.6|0.8|1.1|1.0|1.2|
|Latvia|2.2|1.8|1.8|2.2|1.7|1.9|1.5|1.9|
|Lithuania|3.5|2.6|2.0|2.9|2.6|3.2|3.4|3.7|
|Moldova|2.3|2.3|1.0|1.8|1.8|2.2|1.2|1.3|
|Russia|40.5|34.2|48.3|28.8|44.5|30.8|52.3|43.9|
|Tajikistan|1.3|1.8|0.4|0.8|0.3|0.6|0.5|0.7|
|Turkmenistan|2.4|1.5|2.4|1.9|4.9|2.6|3.7|0.9|
|Ukraine|18.5|25.1|20.7|29.8|16.0|26.8|16.4|22.2|
|Uzbekistan|_5.1_|5.8|2.5|3.8|5.9|6.5|4.2|3.2|
|Former Soviet Union|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|

**Trade with the rest of the world as a share of total trade**

|Armenia|1.8|15.0|14.1|24.6|19.2|54.2|16.3|23.5|
|---|---|---|---|---|---|---|---|---|
|Azerbaijan|5.1|15.1|59.1|43.4|35.8|34.3|47.5|31.0|
|Belarus|6.7|8.8|35.4|26.2|19.2|18.8|33.6|18.8|
|Estonia|1.3|6.4|62.2|63.5|57.5|65.5|56.0|75.5|
|Georgia|0.5|9.1|52.8|54.5|43.0|51.5|35.4|40.3|
|Kazakhstan|7.6|13.1|41.0|28.1|32.8|26.2|40.4|40.6|
|Kyrgyzstan|0.4|15.5|24.5|17.0|28.4|22.9|25.6|18.0|
|Latvia|2.1|9.9|48.8|47.2|43.9|34.3|51.0|47.1|
|Lithuania|3.6|7.1|52.5|35.4|42.8|30.4|42.4|45.4|
|Moldova|2.8|10.6|39.1|31.1|21.5|22.0|22.9|23.0|
|Russia|32.8|35.1|77.2|85.7|73.6|75.8|72.6|70.1|
|Tajikistan|10.9|13.9|54.3|43.5|69.0|65.4|65.2|54.9|
|Turkmenistan|2.3|14.4|65.0|57.0|40.0|46.1|22.8|48.8|
|Ukraine|14.6|15.6|53.3|46.1|52.6|33.9|45.6|36.4|
|Uzbekistan|8.4|12.7|58.1|52.9|41.3|36.5|39.3|50.5|
|Former Soviet Union|20.1|22.2|68.2|69.0|62.0|56.8|63.3|58.0|

_Source. Shares based upon data in current dollars in Table 1._
The following elasticity estimates have been employed:

# **Elasticity Assumptions**

||**Factors**|**Energy**|**Other goods**|
|---|---|---|---|
|**Substitution/**||||
|**Transformation**||||
|**Activities**|0.4|0.5|0|
|**Households**||0.25|0.5|
|**Armington**|||2.5|
|**Household Income**||1|1|

The SAM cost, revenue, or expenditure shares and the definitions of Uzawa-Allen Substitution/Transformation Elasticities provide the Own and Cross Price Elasticities employed in the model.

21
# **Table 2: Major Exporters of Iron Ore, 1950 – 1985 (Mn. Tons)**

|**Country**|**1950**|**1960**|**1970**|**1980**|**1985**|
|---|---|---|---|---|---|
|Brazil|0.9|5.4|27.9|79.0|92.3|
|Australia|0.0|0.0|44.5|80.4|88.3|
|USSR|3.2|15.4|34.9|46.9|45.0|
|Canada|2.0|17.3|39.3|39.0|32.2|
|India|0.6|8.8|18.8|26.2|28.8|
|Sweden|12.9|19.7|28.3|21.0|18.2|
|Liberia|0.0|3.1|22.6|17.4|16.2|
|South Africa|0.0|0.4|5.5|13.8|10.2|
|Mauritania|0.0|0.0|8.9|8.9|9.3|
|Venezuela|0.0|19.2|20.1|11.8|9.0|
|Peru|0.0|5.1|9.9|5.6|5.4|
|United States|2.6|5.2|5.3|5.8|5.0|
|Chile|2.6|5.2|10.1|7.8|4.8|
|France|7.6|27.2|18.7|8.7|4.6|
|Philippines|0.6|5.4|1.9|4.3|3.9|
|Norway|0.5|1.4|4.9|3.2|2.6|
|Other|12.1|19.9|19.6|13.8|3.0|
|**Total**|**45.6**|**158.7**|**321.2**|**393.6**|**378.8**|

Source: US Bureau of Mines

# **_Prices_**

According to Toweh (1989), iron ore is sold in a variety of types and grades on the world market. It is available as direct shipment lump ores, sinters, sinter feed (fines or concentrates used to manufacture sinter) and pellets. The price for iron ore is usually quoted on the basis of its iron (Fe) content on a dry basis. Such iron content of shipped ore could range from 52 to 68 percent.

Since the start of mining in Liberia in 1951, the nominal prices of iron ore have been relatively stable up to 1972. However, prices rose sharply following the onset of the first oil crisis in 1973/74. The sharp difference in the variability in prices between the two periods is seen from a comparison of the coefficient of variation of prices for the two periods. For example, the coefficient of variation for the first period (1951-1972) was 0.09 compared with 0.29 for the second period between 1973 and 1985. Overall, nominal prices have shown an upward trend over the entire period between 1951 and 1985. Real prices<sup>2</sup> have been much more variable than nominal prices over the first period (1951-1972), with a coefficient of variation of 0.13 compared with 0.09 for nominal prices. However, for the second period (1973-1985), there was much less variability in real prices (coefficients of variation for the real and nominal prices were respectively, 0.08 and 0.29). Overall, real prices for iron ore have shown a downward trend in contrast with the upward trend of nominal prices.

> 2 Prices deflated by the US Consumer price index (1982-1984 =100)

5
descriptive trends in human capital formation across both developed and developing countries. We also hope to enable analysis of factors correlated with and that have plausible causal links to the formation of human capital and economic growth. Finally, we hope this dataset can be useful for monitoring and evaluation of important policy goals.

Future iterations of this dataset will continue to expand coverage across countries and time as countries join existing assessments and by including additional assessments such as early grade reading and mathematics assessments. Moreover, we aim to build a dataset that enables overtime isolation of value-added learning by including variables which can account for various selection effects. This includes linking quality of education data to quantity of education data, as well as including measures of enrollment and retention across schooling levels. We also aim to enable further identification of the link between education quality and economic growth, by including variables such as comparable estimates on the returns to education. We hope this dataset, and future iterations, will enable a deeper understanding of mechanisms driving human capital formation, the link to development, and useful policy applications.

45
26 expected rate of return leads to a smaller impact of a MAT. But a high rrince vs Percent e Revenue Increase variance will typically also mean higher expected revenue; s the net effect of higher variance on the impact of a MAT is not clear a | _ priori. Brazilian data suggest that the higher rate of return effect _ _ _ _ **-04. -0 0 0.4 0.3 1.2** eventually overtakes the direct **LI"** Stwd **DtAdo** impact of more uncertainty: the sectors with the highest variance in fact face the smallest increase in their MERT after an introduction of a MAT (Figure 2). The reason becomes clear once we inspect the correlation between variance and average rate of return (Figure 1): the<sup>figure indicates a strong</sup> positive link between the two, as one would expect with risk averse<sup>investors.</sup>

# 5 Conclusions

Four main conclusions emerge from this analysis. First, uncertainty needs to play an explicit role in an evaluation<sup>of MAT proposals, and, in</sup> fact, of corporate taxation in general. We provide<sup>several examples, well</sup> within the parameter range of the Brazilian data<sup>analyzed also, where the</sup> option characteristics of the corporate tax<sup>itself completely dominate the</sup> impact of the various tax provisions on the MERT under full certainty. In particular, because of the absence of adequately<sup>indexed carry-over</sup> provisions, higher rate of return uncertainty<sup>significantly raises the tax</sup> burden for given expected rate of return.

Second, the MAT, with its simple tax code and in<sup>the end quite marginal</sup> impact on the MERT, is an appealing short cut to a comprehensive tax reform. We suggest that the revenue effects could be substantial<sup>in Brazil. This</sup> raises an intriguing possibility: in countries<sup>like Brazil, rate of return</sup> uncertainty is likely to be much more increased<sup>by macroeconomic uncertainty</sup> than by the introduction of a MAT, which in<sup>turn traces back to fiscal</sup> imbalances. If a MAT can contribute to reducing macroeconomic uncertainty by reducing fiscal imbalances, it will also<sup>contribute to lowering the MERT</sup>
Table 3: COVID-19 and Electricity Consumption in Non-Residential Sector in Rwanda

||||l|ogof Cons|umption||||
|---|---|---|---|---|---|---|---|---|
||Comm|ercial|Ho|tels|Health|Centers|Publi|c Works|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|DID (Month> March=0)|-0.075***<br>(0.002)|-0.073***<br>(0.002)|-0.067***<br>(0.027)|-0.069***<br>(0.029)|0.079***<br>(0.028)|0.086***<br>(0.029)|0.007<br>(0.035)|0.003<br>(0.036)|
|DID (Month> March=1)|-0.280***<br>(0.003)|-0.280***<br>(0.003)|-0.270***<br>(0.043)|-0.269***<br>(0.043)|0.004<br>(0.029)|0.004<br>(0.028)|0.013<br>(0.054)|0.013<br>(0.054)|
|DID (Month> March=2)|-0.178***|-0.173***|-0.179***|-0.177***|0.02|0.03|-0.077*|-0.076|
||(0.003)|(0.003)|(0.039)|(0.039)|(0.031)|(0.032)|(0.045)|(0.048)|
|DID (Month> March=3)|-0.131***<br>(0.003)|-0.132***<br>(0.003)|-0.146***<br>(0.04)|-0.146***<br>(0.04)|0.075***<br>(0.032)|0.074**<br>(0.032)|-0.063<br>(0.041)|-0.061<br>(0.041)|
|DID (Month> March=4)|-0.112***|-0.115***|-0.122***|-0.121***|0.061**|0.054*|-0.043|-0.04|
||(0.003)|(0.003)|(0.041)|(0.042)|(0.03)|(0.03)|(0.045)|(0.045)|
|DID (Month> March=5)|-0.101***|-0.091***|-0.108***|-0.105***|0.048|0.068**|-0.034|-0.033|
||(0.003)|(0.003)|(0.039)|(0.039)|(0.031)|(0.033)|(0.042)|(0.052)|
|DID (Month> March=6)|-0.084***|-0.086***|-0.123***|-0.122***|0.049*|0.043|-0.021|-0.019|
||(0.003)|(0.003)|(0.041)|(0.041)|(0.027)|(0.027)|(0.049)|(0.05)|
|DID (Month> March=7)|-0.056***<br>(0.003)|-0.057***<br>(0.003)|-0.087**<br>(0.039)|-0.085**<br>(0.04)|-0.018<br>(0.031)|-0.024<br>(0.031)|-0.001<br>(0.053)|0.003<br>(0.054)|
|DID (Month> March=8)|-0.050***|-0.060***|-0.110***|-0.112***|0.095***|0.072***|0.023|0.024|
||(0.003)|(0.003)|(0.043)|(0.047)|(0.028)|(0.029)|(0.057)|(0.054)|
|DID (Month> March=9)|-0.062|-0.063|-0.083|-0.08|0.098|0.094|-0.004|0.001|
||(0.003)|(0.004)|(0.043)|(0.044)|(0.027)|(0.028)|(0.06)|(0.062)|
|Climate Ctrls|No|Yes|No|Yes|No|Yes|No|Yes|
|Customer FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|District X Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean dep. Var|65.244|65.244|353.152|353.152|359.381|359.381|398.729|398.729|
|Obs|3188121|3188121|9014|9014|12167|12167|3471|3471|

Notes: Dependent variable is the log of monthly electricity consumption. DID (Month> April= _ω_ ) represents the DID estimates for the _ω_ months since the start of COVID lockdown (April, 2020). The DID estimates are derived from the expression _DID_<sup>_ω_</sup> = _β_<sup>ˆ</sup> 2020<sup>_ω−_</sup><sup><u>1</u></sup> 2 �2019 _k_ =2018<sup>_β_ˆ</sup> _k_<sup>_ω_.Climate Ctrls include the monthly average</sup> temperature and total precipitation . Standard errors clustered at district level in parenthesis. _∗_ Significant at 10 percent level _∗∗_ Significant at 5 percent level _∗∗∗_ Significant at 1 percent level 29
# **2. Literature Review: Housing Deficit and Adequate Housing**

Our paper contributes to the existing literature in several ways. First, it adds to the literature that estimates the housing deficit at country, regional, and global levels. Second, our work relates to the literature on housing adequacy that investigates (different dimensions of) adequate housing within and across countries relating them to socio-economic and health outcomes. Our work extends the literature by providing a comparable methodology that allows for a micro-founded analysis of housing adequacy across many emerging economies. In the following paragraphs, we discuss these two strands in more detail.

## **_2.1 Housing Deficit_**

The term ‘housing deficit’ is very prevalent in newspaper articles, academic literature, and policy papers. Yet, the term is rather ambiguous, relies heavily on assumptions that are seldom explicitly stated, and often lacks conceptual clarity (for an overview on the various definitions of housing deficit refer to World Bank (2020a)). Within academic literature, the term housing deficit usually refers to two separate but intertwined notions: (i) the quantitative housing deficit – commonly also referred to as housing backlog – measuring the numerical shortfall in housing units, and (ii) the qualitative housing deficit estimating how many HHs live in sub-standard houses based on the quality and condition of housing units and access to basic services (e.g., Bah et al. 2018; Bouillon 2012; UN-Habitat 2016; World Bank 2015).

Although both ideas refer to deficiencies in the housing stock, the conceptual differentiation between these two notions are often ambiguous and rarely explicitly stated outside formal publications. Traditionally, the _quantitative_ housing deficit is conceptualized as the difference between the number of HHs and the number of available dwellings. Over time, many authors added some qualifying aspects to this rather broad definition. In these expanded definitions, the quantitative housing deficit includes HHs that share a home with other families, HHs without access to individual housing, HHs that occupy homes of a very precarious nature, with severe structural deficiencies, or severely dilapidated and inadequate beyond repair (e.g., Habitat 2016; Olaya et al. 2017; Szalachman 2000). Often problematic in the estimation of the quantitative housing deficit is the absence of reliable (national accounts) data regarding the number of available dwellings and HHs. Hence, authors often rely on proxy estimations such as simple models of changes in household formation (e.g., Monkkonen 2013).

The _qualitative_ housing deficit broadly subsumes houses that are inhabitable or lacking basic quality standards. Definitions on these standards, however, vary significantly and the number and type of adequacy conditions included into estimations are seldom homogenous across studies. Many publications conceptualize the _qualitative_ housing deficit to include HHs with (some or all)
In spite of the strong criticisms, EPAs were not abandoned. Indeed, the number of publicly funded agencies increased over the course of the 1990s. More recently, the development literature has taken a slightly more positive view of the potential role of export promotion agencies in poor countries. The rationale underlying the criticisms of Keesing and Singer (1991, 1991a) was that the early failures of EPAs were mainly due to import substitution policies that made the job of EPAs very difficult. In the 1990s, that strong bias against exports vanished, and prominent development economists have adopted a more benign view of EPAs. For example, in a study of how governments can promote non-traditional exports in Africa, one of the main recommendations of G.K.Helleiner –who led the study– was to create an adequately funded EPA to help exporters overcome the costs and risks of entering unfamiliar and demanding international markets (Helleiner, 2002).

In terms of what type of program, institutional set-up, and financing is more likely to succeed, Alvarez (2004) provided evidence from a survey of 295 small-and-medium-sized sporadic and permanent exporters in Chile. While trade shows and trade missions did not affect the probability of being a successful exporter, a program of exporter committees showed a positive and significant impact. Such committees are composed of a group of firms with common objectives in international business, which cooperate on research, marketing and promotion.

Macario (2000) identified the policies that determine successes and failures in Brazil, Chile, Colombia, and Mexico. On the basis of interviews with successful exporters, she sets out various recommendations for export promotion agencies: they should be directed at firms with new products or who are entering new markets; they should emphasize cost-sharing to ensure that programs are used only by those truly dedicated to export; support should be given for a maximum of 2-3 years so that it does not turn into a subsidy; programs should be submitted to external evaluation; agencies work best when they are subject to a mix of public and private management.

The conclusion about private management is shared by the ITC (see for example ITC, 2000), and by much of the early literature (Keesing and Singer, 1991a 1991b, de Wulf, 2001).<sup>7</sup>

> 7The ITC also provides constantly updated information about best practices and advice on export promotion strategies (see http://www.intracen.org/instasptp), and their annual conferences are an useful tool for

6