rafmacalaba/datause-encoder-data huggingface dataset:
movements reflect within country changes and not the addition or subtraction of particular observations.<sup>13</sup>

# _The exchange rate_

We first look at the real exchange rate. Figure 5 shows movements in the real exchange rate, exchange rate misalignment, and volatility. Export surges are preceded by a significant currency depreciation, especially in the case of developing countries where we observe that, on average, there is a depreciation of about 30 percent before the surge.

How does the depreciation leave the exchange rate relative to its equilibrium level? To answer this question we estimate exchange rate misalignment adjusting for the Balasa-Samuelson effect. Specifically, using data from the Penn World Tables, we regress the real exchange rate at PPP on real per-capita GDP and calendar-year fixed effects, and the extent of misalignment is measured as the difference between the log of the real exchange rate at PPP and the log of the fitted value from the regression (as in Frankel 2006, Cheung, Chinn, and Fujii 2007, and Rodrik 2007). The middle panel of Figure 5 shows misalignment. In developing countries a move toward undervaluation tends to precede surges. At the onset of the surge, the exchange rate tends to be about 10 percent undervalued and by the end of the surge it is about 20 percent undervalued.

Exchange rate volatility is measured as the standard deviation of the monthly nominal rate as recorded in the IFS IMF Statistics. As shown in Figure 5, it declines before the surge, especially in developing countries. Together these graphs suggest that a steady and large real depreciation stimulates the export surge.

> 13 Table A4 lists the number of countries with data for all of the relevant years for each variable, which are included in each of the graphs.

13
|**Province**|**Table 1**<br>**Geogr**<br>**Pro**|**0. Pove**<br>**aphic**<br>**file**|**rty Rates**<br>**Concen**<br>**Pro**|**by Regio**<br>**tration**<br>**file**|**n and Wi**<br>**Uncon**<br>**Profile (**|**thin Regi**<br>**ditional**<br>**normal)**|**on**<br>**Uncon**<br>**Pro**|**ditional**<br>**file**|
|---|---|---|---|---|---|---|---|---|
||**Urban **|**Rural **|**Urban**|**Rural**|**Urban**|**Rural**|**Urban **|**Rural**|
|Sierra|9.4|22.3|5.9|36.5|12.5|49.8|12.8|52.6|
|Costa|19.2|32.4|16.6|43.6|23.0|48.6|24.2|51.0|
|Oriente|23.6|59.4|12.0|43.6|16.5|68.7|16.3|67.6|

_Source:_ Own calculations based on ECV Survey.

For the Oriente, characteristics and returns each explain around 50 percent of the difference in log-welfare ratios between urban and rural sector (Figure 2). In the Sierra and Costa, characteristics explain approximately 30 percent and 34 percent of the differentials, respectively.

**Figure 2. Decomposition of Welfare Differences (logaritms) Within Regions**

<!-- Start of picture text --> Oriente<br>Costa<br>Sierra<br>0% 20% 40% 60% 80% 100%<br>Returns Characteristics<br><!-- End of picture text --> _Source:_ Own calculations based on ECV Survey.

The urban-rural composition of the Sierra and Costa are similar, with 65 percent of the households in urban areas in the case of Sierra and 75 percent in the case of Costa, while Oriente is mainly rural, at 61 percent approximately. In terms of poverty, Costa is the lagging region in the urban sector while Oriente is behind in the rural area.

The comparison between Oriente and Costa on the one hand and Sierra and Costa on the other in urban areas reveals that urban welfare differences are mostly due to returns, rather than characteristics. In the former case, concentration plays a role also, but in spite of the fact that Oriente has better characteristics than Costa, which yields a difference in the concentration profile, it is smaller than the difference in the returns. In the second comparison, both regions have similar characteristics, which lead to a differential only in returns (Figure 3). In the rural sector, both geographic and concentration effects explain the disparities in poverty rates between Sierra and Oriente. Characteristics explain the main differential between Costa and Oriente, although there is a difference in returns as well (Figure 4).

15
G. Gbetibouo, S. Jain, A. Mahamadou, S. El-Marsafawy, S. Ouda, M. Ouedraogo, I. Sène, D. Maddision, N. Seo and A. Dinar. 2006. “Will African Agriculture Survive Climate Change?” _World Bank Economic Review_ 20(3): 367-388.

- Kurukulasuriya, P. and R. Mendelsohn. 2007.. “Modeling Endogenous Irrigation: The Impact Of Climate Change On Farmers In Africa” World Bank Policy Research Working Paper 4278, Washington DC.

- Kurukulasuriya, P. and R. Mendelsohn, 2008a. “Crop Switching as an Adaptation Strategy to Climate Change” African Journal Agriculture and Resource <u>Economics</u> (forthcoming).

- Kurukulasuriya, P. and R. Mendelsohn, 2008b. “A Ricardian Analysis of The Impact of Climate Change on African Cropland” African Journal Agriculture and <u>Resource Economics</u> (forthcoming).

- Leary, N. A. 1999, “A framework for Benefit-Cost Analysis of Adaptation to Climate Change and Climate Variability”, _Mitigation and Adaptation Strategies for Global Change_ **4** : 307-318.

- McFadden, D. L. 1981. “Econometric Models of Probabilistic Choice”, in D. McFadden, _Structural Analysis of Discrete Data and Econometric Applications_ MIT Press, Cambridge.

- McCarthy, J., O. Canziani, N. Leary, D. Dokken, and K. White (eds.) 2001, _Climate Change 2001: Impacts, Adaptation, and Vulnerability_ , Intergovernmental Panel on Climate Change Cambridge University Press: Cambridge.

- Mendelsohn, R. "Efficient Adaptation to Climate Change", _Climatic Change_ **45** (2000) 583-600.

- Mendelsohn, R., A. Dinar and A. Sanghi. "The Effect of Development on the Climate Sensitivity of Agriculture", <u>Environment and Development Economics</u> **6** (2001) 85-101.

- Mendelsohn, Robert, Pradeep Kurukulasuriya, Alan Basist, Felix Kogan, and Claude Williams. 2007. “Measuring Climate Change Impacts with Satellite versus Weather Station Data.”, _Climatic Change_ 81: 71-83.

- Mendelsohn, R. and S.N. Seo 2007, “An Integrated Farm Model of Crops and Livestock: Modeling Latin American Agricultural Impacts and Adaptations to Climate Change”, _World Bank Policy Research Series Working Paper4161_ , Washington DC, USA.

- Moll, H. A.J., “Costs and Benefits of Livestock Systems and the Role of Market and Nonmarket Relationships”, _Agricultural Economics_ 32, (2005),181-193.

- Nin, A., S. Ehui, and S. Benin 2007, “Livestock Productivity in Developing Countries: An Assessment”, in R. Evenson and P. Pingali (eds), Handbook of Agricultural Economics, Volume 3, North Holland, Oxford, UK. pp 2467-2532.

- Oklahoma State University, 2007. Breeds of Livestock, Available at htttp://www.ansi.okstate.edu/breeds.

19
The  set  of  firm  level  variables  available  in  our  dataset  allow  us  to  appropriately  control  for these  differences  between  exporters  and  domestic  firms  in  identifying  the  heterogeneous impact  of  trade  policy  on  wages  of  these  firms.

# **3.   Firm-­‐Level   Analysis**

We  begin  our  analysis  at  the  firm  level  to  ensure  the  comparability  of  our  results  with  those of  the  existing  literature  based  on  firm-­‐level  data  and  to  highlight  the  importance  of introducing  worker  and  match  heterogeneity  into  the  analysis.  To  this  end,  we  aggregate the  matched  employer-­‐employee  data  to  the  firm  level  and  estimate  the  following specification:

> where   the   dependent   variable, !"# _!_ $ ,   is   the   logarithm   of   average   wages   at   the   firm   level _"#_ for   firm _j_ at   time _t. !"!_ denotes   the   level   of   protection   in   sector _k_ in   which   the   firm   operates, and _!"#_ is   an   indicator   variable   equal   to   one   if   firm _j_ reports   a   positive   dollar   value   of _$%_ exports   at   time _t_ and   zero   otherwise.   The   level   of   protection   at   the   sector-­‐level   is   measured by   both   tariffs   and   the   effective   rate   of   protection   (ERP).   We   use   the   latter   measure   in   our main   specifications,   since   in   an   environment   in   which   Brazilian   firms   face   declines   in   both final   goods   and   intermediate   input   tariffs,   the   ERP   is   a   more   appropriate   measure   of protection   faced   by   domestic   firms.   In   each   specification,   we   include   an   interaction   term between _!"!_ and _!"# $%_ to   allow   for   changes   in   protection   to   have   differential   effects   on exporters   and   firms   serving   only   the   domestic   market.

As  we  noted  earlier,  the  post-­‐liberalization  period  in  Brazil  coincided  with  a  period  of  an appreciation  of  the  currency,  the _real_ ,  making  Brazilian  goods  less  competitive  on international  markets,  while  making  imported  goods  cheaper  in _real_ terms.  Failing  to incorporate  such  fluctuations  in  exchange  rates  into  our  analysis  could  bias  the  estimated effect  of  liberalization  on  wages.  Henceforth,  in  each  specification,  we  also  include  an 11
with a profit incentive, and (iii) better governance seems to be associated with technology adoption only in EU-8 countries.<sup>1</sup> Our results also indicate that access to international knowledge - through FDI or exports – is positively correlated with ISO certification and web-use, which can suggests the relevance of international technology spillovers.

The paper is organized as follows. Section II describes the conceptual framework. Section III describes the empirical strategy and the data. Section IV presents the main results and Section V summarizes the main conclusions.

# **II. Conceptual Framework**

We assume that firms make a profit-maximizing cost/benefit assessment of different alternatives when deciding whether to adopt new technology. A firm decides to adopt a particular new technology if the corresponding expected net payoffs are larger than those of the alternatives, including the alternative of not adopting it. Besides the cost of the technology itself, different investment climate factors may affect the expected net payoffs from the adoption of modern technology. Without the ambition of completeness, in the rest of this section we draw on a wide literature to lay out three groups of investment climate factors that may affect those payoffs. In doing so, we implicitly define a set of hypotheses that will subsequently be tested using firm-level survey data from the ECA region.

# **_II. 1 Complementary Inputs: Skills, Finance, and Physical Infrastructure_**

A firm’s access to complementary inputs can affect both the adjustment costs as well as the benefits that the firm derives from the adoption of new technology. First, both theoretical models and empirical evidence document how low _labor skills_ can delay a firm’s modern technology adoption due to the inability to operate advanced equipment - which tends to be skill-biased - or because learning is technology-specific and there is a high cost of retraining workers (Alesina and Zeira, 2006; Berdugo et al., 2005; Navaretti et al., 2001). Furthermore, evidence from industrial countries suggests that in settings where labor market institutions do not allow wages or internal training to offset high hiring and firing costs, the latter reduce firms’ incentives for innovation and for the adoption of new technologies, and lead to lower productivity performance (Scarpetta and Tressel 2004). The lack of highly qualified _managerial capacity_ (or entrepreneurship) can also constrain the firm’s adoption of

> 1 EU-8 countries are the countries that joined the European Union on May 1st 2004: the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, the Slovak Republic, and Slovenia.

- 3 -
remain elevated in importing countries, with some close to levels that have in the past been associated with episodes of financial stress (World Bank 2016a). Rising leverage tends to increase the probability of capital flow reversals (Mendoza 2010; Elekdag and Wu 2011) and make recessions more severe in the presence of financial stress (Claessens, Kose and Terrones 2012).

**_Risks associated with deteriorating fundamentals in EMDEs._** Macroeconomic fundamentals in EMDEs have weakened in recent years (Figure 18). While EMDEs continue to grow faster than advanced economies, the growth differential has narrowed. And credit risks have increased as sovereign ratings have been downgraded in a context of a growing stock of EMDE bonds maturing in the near term, raising roll-over risks. All these factors have reduced the relative attractiveness of EMDE assets. This could limit potential gains and increase risks associated with exceptionally favorable financing conditions in NIRP countries. EMDE growth has slowed for five consecutive years, reaching in 2015 its slowest pace since the global financial crisis. The slowdown has been significantly more pronounced than previously expected, reflecting a combination of external and domestic headwinds.

Weak global trade and declining commodity prices weighed heavily on EMDE prospects since 2012. Disappointing global trade growth has been attributed to a combination of factors, including anemic growth in advanced economies, weak global investment, slowing expansion of global value chains and limited progress on trade liberalization. The sharp drop in commodity prices has been an aggravating factor for commodity exporters (Baffes et al. 2015). From their peak in 2011 to the first quarter of 2016, commodity prices dropped by 33 percent for agriculture products, 53 percent for metals, and 65 percent for energy. As a reflection of these negative terms-of-trade shocks, commodity exporters account for 90 percent of EMDEs experiencing a protracted slowdown in recent years.

Beyond external factors, the slowdown also reflected domestic headwinds, including the unwinding of cyclically strong, policy-supported growth in the post-crisis period, as well as rising supply side constraints, demographic pressures, and lower productivity growth (World Bank 2016b; Didier et al. 2015). Moreover, fiscal and monetary policy space has narrowed, particularly in commodity exporters. Bouts of policy uncertainty and erosion of policy buffers also contributed to dampen confidence and activity. Investor concerns about growth prospects and rising vulnerabilities have been reflected in lower credit ratings, particularly among commodity exporters. Against this backdrop, NIRP is not expected to play a substantial role in boosting capital flows to EMDEs as earlier unconventional monetary policies did in the post-crisis period.

39
consumption per capita, as have the past international “$1 a day” poverty counts. Although the general presumption is that there is rather little scope for economies of size in consumption for poor people, Lanjouw and Ravallion (1995) have questioned that presumption. Mean household size tends to be higher in rural than urban areas of developing countries, so introducing an allowance for economies of size in consumption will narrow the urban-rural differential in mean living standards. We expect that this would also hold for poverty measures.

Naturally the surveys are scattered over time. We estimate the poverty measures for four years spanning the range of the data, namely 1993, 1996, 1999 and 2002. We call these the “reference years.” To estimate regional poverty at a given reference year we “line up” the surveys in time using the same method described in Chen and Ravallion (2004). The latter paper also describes our interpolation method when the reference date is between two surveys.

The urban population data are from the latest available issue of the WUP in 2006 (UN, 2005). As noted in the introduction, there are undoubtedly differences in the definitions used between countries, which we can do little about here.<sup>17</sup> The WUP estimates are based on actual enumerations whenever they are available. The WUP web site provides details on data sources and how specific cases were handled; see <u>http://esa.un.org/unup/.</u> Using the household survey data, we could also draw urban population shares from each survey’s internal sample weights. We found that these two sets of weights differ for some countries. This was mainly a problem in the data for Sub-Saharan Africa (SSA). To test robustness we re-calculated the estimates for SSA using the survey-based urban population shares (giving results more consistent with Chen and Ravallion, 2004). The rate of decline over time is somewhat higher using the census shares, but the difference is modest.<sup>18</sup>

> 17 In some cases, the WUP made adjustments to assure consistency over time, but there do not appear to have been any adjustments between countries.

> 18 For 1993, 1999 and 2002 the headcount indices for SSA were 51.28, 49.19 and 46.93% using census shares as compared to 51.42, 49.75 and 47.64% using the implicit weights from the survey data.

13
BOTSWANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Transport

Botswana is part of the north-south road corridor, the most extensive in the region, which links the country to Zimbabwe, Mozambique, Zambia, the Democratic Republic of Congo, Malawi, Tanzania, and to the country’s largest trading partner—the Republic of South Africa—with the busiest port in Africa. One of the corridor’s main legs stretches from the Port of Durban in South Africa, via Johannesburg, to Francistown on the eastern border of Botswana, and on to Lusaka in Zambia and Lubumbashi and Kolwezi in the Democratic Republic of Congo. In addition, the north-south corridor provides Botswana with links to other important corridors such as the TransKalahari, Beira, Lobito, Dar es Salaam, and Nacala corridors (Curtis 2009).

Botswana Railways’ single main line runs along the north-south corridor and currently transports only freight. It serves to provide access to the South Atlantic coast when connecting to the Trans-Kalahari railway. The total distance from key coal deposits to the port of Walvis Bay is 1,480 kilometers (km), of which 880 km is now a gap that would require new construction to be operational (Bullock 2009).

Botswana’s landlocked condition makes it particularly vulnerable to logistical inefficiencies, crossborder charges, and other bureaucratic procedures. Not surprisingly, the country experiences very high export and import costs<sup>1</sup> by comparison to the Sub-Saharan African average and its neighbors (figure 5). To put this logistical challenge and cross-border burden in perspective, we might note that Botswana still ranks very well—52 among 183 countries<sup>2</sup> in 2010—with regard to the overall ease of doing business in the country. Yet the country is ranks 151st (out of 183 countries) in the ―trading across borders‖ category. Constraints on trade imposed by transport deficiencies, bureaucracy, and other factors in effect neutralize some of the benefits of the overall ease of doing business in the country.

> 1 Includes the costs for documents, administrative fees for customs clearance and technical control, customs broker fees, terminal handling charges, and inland transport.

> 2 www.doingbusiness.org/data/exploreeconomies/botswana.

9
enterprises on average, female-owned enterprises may be further away from the margin of formalizing and less likely to respond to the assistance.

To draw the sample for the baseline, we stratified the listing data by location and gender of the business owner and identified 3,600 firms that complied with one of the following criteria: (i) had at least one worker contracted outside of family members and business owners, (ii) were operating in a fixed location with more than one person working in the business, (iii) were at the 25th percentile of revenues or above. These criteria were developed in collaboration with government officials to focus on firms that were larger and more likely to both be the target of government formalization efforts, as well as potentially more likely to benefit from formalizing. We were targeting 3,000 firms for the baseline but started with 3,600 given the risk of not finding firms (the listing exercise collected limited contact information) or finding that they registered in the meantime or were incorrectly listed as not registered.<sup>11</sup> Through this process, we completed a detailed baseline survey for 3,002 informal firms, of which 1,195 were female-owned and 1,494 were from Lilongwe.

The baseline survey was done between December 2011 and April 2012. The baseline survey collected information on the characteristics of the firm and owner, including their usage of financial services and finance, their financial literacy and knowledge about business registration processes, and the financial performance of their business.

# **3.2 Summary Characteristics of the Sample by Gender**

Appendix Table 1 compares and further discusses the baseline characteristics of our sample by gender. Over 70 percent of the firms in our sample were in the retail sector, including selling groceries (21 percent of total), selling agricultural produce (10 percent), selling animal produce (10 percent), and hardware shops (8 percent). The focus on retail was particularly pronounced for men, while women were more prevalent in services (35 percent for women versus 14 percent for men).

> 11 This process requires attempting to visit all 3,600 firms at least once and keep tracking through a protocol those not found in the first attempt. The work would be concluded when at least 3,000 informal firms were interviewed for baseline.

9
**Developed countries vs. developing countries**

<!-- Start of picture text --> a. Regulaions for starting business/new operations<br>o. Terrorism$ 4.5 ilb. Price controls<br>n. Corruption 3C c. Regulations on foreign trade (exports, imports)<br>m.Crime and theft d. Financing<br>1. General uncertainty on costs of regulations e. Labor regulations<br>k. Inflation f. Foreign currency regulations<br>j. Safety or environmental reguations g. Tax regulations and/or high taxes<br>i. Policy instability h. Inadequate supply of infrastructure<br>LDC<br>| OECD<br><!-- End of picture text -->

As can be seen from the above figure, index values for 12 out of 15 obstacles are higher in developing countries. The only three obstacles receiving higher quantitative values in developed countries can be easily classified as they are related to different types of regulations: question 12.a: regulations for starting new business question 12.e: labor regulations, and question 12.j: safety or environmental regulations.

# **Degree of troublesomeness**

The following graph shows the number of countries together and in each region where 50 or more percent of surveyed firms gave scores of 4 or higher to each of the considered obstacles. The leader was question 12.g - tax regulations and/or high taxes. For example, in all ten surveyed CIS countries at least 50 percent of surveyed businessmen think that tax regulation and/or high taxes are an obstacle. Overall in 49 out of 69 surveyed countries, taxes and tax policies were considered an obstacle by more than 50 percent of surveyed businessmen.

The second highest obstacle is corruption (question 12.n). A majority of businessmen in 35 countries considered it a hurdle for doing business. Of these 35 countries, 15 are in Africa. In none of the developed countries surveyed was corruption found to be an obstacle by a majority of respondents .

28