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23
Table 1. Continued.

|I________________|_________<br>World|Developed||of whic|Major**G**<br>h:|eographic D<br>g p c|**e**stinations (%)<br>tn<br>Eastern|<br>Socialist|_________Mo<br>Developing|of which:|
|---|---|---|---|---|---|---|---|---|---|---|
|Exporting Country|**(S**million)|<br>Countries|Europe|North<br>America|Japan|Others|Europe|Asia|Countries|Developing<br>Africa|
|Sierra Leone|145.4|96.5|59.1|36.2|1.3|_|_|-|1.5|1.3|
|Sonalia|81.0|61.1|60.2|0.4|0.5|-|-|0.5|38.4|1.0|
|South Africa, Rep.|17,052.0|80.4|55.2|12.4|10.8|2.0|0.1|-|15.3|6.1|
|Sudan|554.0|35.4|26.1|3.2|6.0|-|9.0|1.6|53.8|1.1|
|Togo|306.4|52.0|38.2|13.0|0.2|0.6|_6.5_|0.1|41.4|19.2|
|Uganda|152.1|90.5|76.5|10.6|3.3|0.1|0.4|-|9.1|7.1|
|Tanzania, United Rep.|404.0|68.7|59.4|4.5|4.5|0.3|1.8|0.8|30.7|7.1|
|Zaire|853.0|84.5|54.8|22.2|7.4|0.1|1.2|1.2|11.2|5.4|
|Zambia|1,347.5|65.7|34.5|1.6|29.1|0.4|-|12.3|21.8|11.9|
|Zimbabwe|1,467.6|73.4|44.1|7.3|5.5|16.6|0.6|1.8|23.9|17.2|
|MEMO ITEM|||||||||||
|All Sub-Saharan Africa|54,657.2|80.6|51.2|22.1|5.6|1.4|0.9|0.7|15.4|7.5|
|All Developing Countries|708,947.0|63.1|25.5|24.0|12.0|1 .3|3.1|3.6|27.2|2.6|

l________ Note: The shares shown in Table I may not sum to 100 since some sub-Saharan countries trade has not been fully allocated to proper country destinations. That is, some exports are classified as going to 'not elsewhere specified" (n.e.s.) destinations, 'not elsewhere classified" (n.e.c.) destinations, or to bunkers and ships stores.

Source: Compiled from United Nations COMTRADE records or United Nations Conference on Trade and Development, Handbook of International Trade and Development Statistics 1992, (New York: United Nations, 1992).
6 demand and supply sides can mitigate the movements described above. Job creation measures, for example, such as public works schemes, wage subsidies, and tax incentives for private firms, will rotate the VS curve up and left, by rendering vacancies more productive. Similarly, requiring beneficiaries of unemployment insurance to participate in job training improves the pool of those who can fill vacancies, which also has a positive effect on the supply of vacancies. Switching to the demand side, the model captures the effects of active employment measures that improve the efficiency of the matching function x _(u, v)._ Measures to improve the exchange of information at employment offices regarding vacancies and training programs, as well as better monitoring of the unemployed, and retraining programs to eliminate mismatch (a common problem in state-controlled industries which relied on obsolete technology) will effectively cause the UV curve to shift inward toward the origin. The model therefore derives policy prescriptions in terms of active employment policies because of their positive influence in mitigating long-term unemployment.

12. Moving beyond a simple static version of the model, Burda incorporates some dynamic complexity in the process of job search and employment decisions for workers and firms. Evidence from both OECD and CEE countries suggests that a significant portion of labor market movement comes from job-to-job switches, without passing through unemployment. The model incorporates on-the-job search into the unemployment function, assuming that a fraction H of the pool of employed workers actually look for a job while working, such that the equilibrium unemployment condition becomes u= s(1-u)= _(u/(u+I (I-u))_ * (x (u+fI (I-u),v)) = _0,_ for LiE **(0,1).** Because job-search is procyclical, fewer workers look for jobs as unemployment worsens (i.e. [1' < 0, and II" < 0). The resulting UV curve is not strictly convex, and can be upward sloping for high levels of _u,_ as in Figure 2. The intuition for this result lies in the fact that high u means a smaller employed labor force, and therefore fewer effective job searchers, Figure 2 such that vacancies are greater. There is no concurrent improvement in the matching V function, because high u causes congestion, i.e. _dx/du <_ 0. On the supply side, the VS curve can be totally differentiated to yield _dv/du >_ 0 initially, i.e. for u=0, but _dv/du_ turns negative assuming that HI is sufficiently responsive to high levels of _u._ Firms' incentive to offer vacancies diminishes at high rates of unemployment because there are fewer on-the-job seekers, such that vacancies become less "productive". Therefore, when job search behavior of workers in considered, multiple equilibria are possible, as depicted in I Figure 2.

<!-- Start of picture text --> V<br>\<br>VS<br><!-- End of picture text -->
**Figure 3. OECD Employment Protection Legislation (EPL) Index and Level of Development**

<!-- Start of picture text --> VEN<br>MEX<br>ARG<br>HND<br>COL<br>PER<br>URY<br>BOL<br>PRY ECU BRB PAN CHL<br>BRA<br>JAM<br>NIC BHS<br>DOM<br>SLV CRI<br>GTM<br>8 9 10 11 12<br>Log of GDP PC PPP<br>Log OECD measure of regulation (v3) Log OECD measure of regulation (v3)<br>Fitted values Fitted values<br>1.5<br>1<br>.5<br>Log OECD measure of regulation (v3)<br>0<br><!-- End of picture text --> Source: Authors using IADB-OECD (2016) While the ILO’s EPlex indicator does not appear to be significantly related to level of development, the OECD-IADB EPL measure presets intriguingly contrasting relationships. The restrictiveness of employment protection legislation in OECD-member countries (excluding Mexico and Chile) is inversely related to level of development. This relationship is most likely reflecting labor policy reforms in several European countries since 2000 (most notably, in Germany, Spain and Italy) which have generally loosened constraints on firms’ choices (Gill, Packard and Koettl, 2013).

However, among the countries of Latin America and the Caribbean, the level of restrictiveness of employment regulation is positively related to level of development. For these countries, the observed relationship may reflect geographical differences of timing of transition of from mainly agrarian economies to industrial and service economies, and employer-employee relationships becoming more common. This step in the structural transformation of an economy is likely to increase societies’ interest in regulating the labor market. The figure may also reflect the relative wealth in the Southern part of the region which underwent this structural transformation and urbanization earlier, and where larger waves of migrants from Europe settled, bringing with them social norms and political and regulatory institutions mainly from Portugal, Spain and Italy.

13
department, but were eventually mainstreamed in the relevant sectoral units. These are global public goods that would never have happened without initiatives by researchers and strong institutional support. Indeed, I doubt if there would have been any of this progress without strong institutional support—going well beyond the immediate needs of individual practitioners.

However, research could do more to help expand the tool kit routinely employed by policy makers and analysts in deciding what policies and programs to implement and how to assess their performance. Theoretical work often points to new tools, some more useful than others. Choosing wisely is important. Researchers can have great value to practitioners in demonstrating the usefulness of new analytic tools in real applications. Over the years, the Bank’s researchers have developed numerous software programs that have facilitated data analysis throughout the Bank and in client countries.<sup>6</sup> There is also much to be done in making data-collection efforts (including administrative data) more relevant as evaluative tools. There has been a longstanding need to develop better survey modules on program participation and access to public facilities. The design of routine surveys could also be more useful for evaluative research. For example, by allowing only partial rotation of samples at each survey round one can create longitudinal observations that can facilitate the construction of baseline observations for impact evaluations.<sup>7</sup>

# **Making evaluations more useful for practitioners**

The archetypal formulation of the evaluation problem aims to estimate the average impact on those to which a specific program is assigned (the participants) by attempting to infer the counterfactual from those to which it is not assigned (non-participants). While this is an undeniably important and challenging problem, solving it is not sufficient for assuring that evaluation is relevant to development practice. The box outlines 10 steps toward making evaluation more relevant. It is acknowledged that there are some difficult problems ahead in following these steps, and that more research is needed; the main aim here is to point in useful directions for future work.

> 6 See, for example, the “Poverty Analysis Toolkit” produced by the Bank’s research department <u>(http://econ.worldbank.org/programs/poverty).</u> 7

> For example, the evaluation by Galasso and Ravallion (2004) of the government’s safety net response to the macroeconomic crisis in Argentina exploited this property of the labor force survey; otherwise, no baseline data would have been possible given that one could not (of course) delay the intervention to collect baseline data.

9
The results in Table 2 are also suggestive of the presence of learning-by-exporting effects.

However, as argued in Section 1, a dummy to identify entrants into export markets does not accurately capture their exposure to export activities. In Table 3, we present estimates of Equation (6) using both OLS with industry dummies and fixed plant effects. The fixed effects or within estimates are obtained by subtracting from each variable<sup>_x_</sup> _it_<sup>its plant-specific mean over time</sup><sup>_x_</sup> _i_ . before estimation by OLS.<sup>15</sup> All regressions include year dummies. Since conventional F-tests reject the null hypothesis of no fixed effects, we focus on the results with fixed effects, although we also present OLS results for comparison. Interestingly, in all cases age has an unexpected sign, indicating that productivity decreases as plants get older. In contrast, the cumulative output index has always the expected positive sign. These results suggest that it is the intensity of exposure to production activities, and not the mere passage of time, that contributes to learning-by-doing.<sup>16</sup> Our measures of export experience are positive and significant in all regressions in Table 3. In the fixed effects regressions, plant TFP increases 4.8% for each additional year of export experience and 2.1% for an increase of one standard deviation (about 10) in the cumulative exports index.

Two counterarguments can be made to the proposition that export experience increases productivity. A first counterargument is that although plants typically experience a boost in measured TFP during the years when they export, this boost may not reflect a true productivity increase but merely a higher utilization of existing factors in response to the increased demand

> 15 We also estimated random effects specifications but found that often the Hausman test rejected the exogeneity of the regressors with respect to the random plant effects, making the fixed effects specification more appropriate. 16 Olley and Pakes (1996) also find that age is inversely associated with plant productivity. Levhari and Sheshinski (1973) find that average workers’ age is insignificant when average workers’ experience is included in the production function.

22
Demirgurc-Kunt, Asli, Leora Klapper, and Georgios A Panos, “Saving for old age,” The World Bank, 2016.

Gourinchas, Pierre-Olivier and Jonathan A Parker, “Consumption over the life cycle,” Econometrica, 2002, 70 (1), 47–89.

Harris, Christopher and David Laibson, “Dynamic choices of hyperbolic consumers,” Econometrica, 2001, 69 (4), 935–957.

Hu, Yu-Wei and Fiona Stewart, “Pension Coverage and Informal Sector Workers: International Experiences,” OECD Working Papers on Insurance and Private Pensions 31, OECD Publishing January 2009.

Laibson, David, “Golden eggs and hyperbolic discounting,” The Quarterly Journal of Economics, 1997, 112 (2), 443–478.

La Porta, Rafael, and Andrei Shleifer, “Informality and development,” Journal of economic perspectives, 2014, 28(3), 109-26.

Loayza, Norma, Klaus Schmidt-Hebbel and Luis Servén, “Saving in developing countries: An overview,” The World Bank Economic Review, 2000, 14(3), 393-414.

Lydall, Harold, “The life cycle in income, saving, and asset ownership,” Econometrica: Journal of the Econometric Society, 1955, 131-150.

Shorrocks, Anthony, “The age-wealth relationship: A cross-section and cohort analysis,” The review of Economics and Statistics, 1975, 155-163.

Mitchell, Olivia and James Moore, “Can Americans afford to retire? New evidence on retirement saving adequacy,” Journal of Risk and Insurance, 1998, 371–400.

Nakajima, Makoto and Irina Telyukova, “Housing and saving in retirement across countries,” in “Contemporary issues in microeconomics,” Springer, 2016, 88–126.

De Nardi, Mariacristina, Eric French, and John Jones, “Why do the elderly save? The role of medical expenses,” Journal of political economy, 2010, 118 (1), 39–75.

Mahmood, Naushin, and Zafar Mueen Nasir, “Pension and social security schemes in Pakistan: Some Policy Options,” No. 22211. East Asian Bureau of Economic Research, 2008.

<mark>Paxson, Christina. “Saving and growth: evidence from micro data,” European Economic Review, 1996, 40(2), 255-288.</mark> 21
**Table 7: Modeling Profits in the Nonfarm Enterprise Sector**

||**coef**||**sd**|
|---|---|---|---|
|Household size|-0.005||0.009|
|_Characteristics of the household head_||||
|Age of household head|0.033***||0.007|
|Age of household head<sup>2</sup>/1000|-0.396***||0.080|
|Household head is a male|-0.483***||0.051|
|Schooling of household head<br>|0.050*||0.026|
|Schooling of household head<sup>2</sup>/1000|-3.671||2.424|
|**_Location_**||||
|Rural town|-0.072||0.088|
|**_Distances_**||||
|Distance to all weather road|0.041**||0.019|
|Distance to the food market|-0.041||0.025|
|**_Seasonality_**||||
|Activities seasonal?|-0.037||0.042|
|**_Activities_**||||
|-Hotels and restaurants|0.281***||0.090|
|-Retail trade via stalls and markets|0.075||0.085|
|-Services (services, manufac.|-0.229***||0.086|
|-Whole sale trade|0.596***||0.124|
|-Transport services|0.180||0.195|
|-Manufacturing (excl: grain milling, food|-0.180***||0.059|
|-Grain milling|0.110||0.254|
|-Others (specialized services)|0.210||0.861|
|-Retail (not stalls/mkts)|0.177**||0.080|
|**_Region_**||||
|Tigray|0.359***||0.081|
|Amhara|-0.069||0.062|
|SNNP|-0.347***||0.059|
|**_Base of operation_**||||
|-home, inside residence|-0.065||0.076|
|-market|0.528***||0.089|
|-shop|0.521***||0.098|
|-road|0.150||0.142|
|-mobile|-0.208||0.166|
|-other|0.101||0.170|
|Constant||||
|R2||0.264||
|Adjusted R2||0.256||
|Number of observations||2,474||

_Note:_ Standard errors are clustered by enumeration area. Significance at the 10%, 5%, and 1% level is indicated by *, ** and ***, respectively. The omitted reference category for base of operation is ‘home, outside the residence’. The omitted reference category for activities is “food and beverages”. The omitted region is Oromia.

-28-
as can be observed in the bottom panel of Figure 4.<sup>12</sup> Optimal routes also evolved with the construction of the separation barrier wall. Construction of the barrier wall started during the Second Intifada and it continued throughout the period of analysis.<sup>13</sup> Figure 5 provides two examples of optimal route changes between 2006 and 2012: Qalqiliya to Hebron (left panel map) and Qalqiliya to Ramallah (right panel maps).

## _Temporal variation in travel times_

Changes in optimal routes generally yield changes in travel times. But travel times may also change when optimal routes have not, namely when the time it takes to traverse the checkpoints located along the optimal routes have changed (without in fact altering the optimal routes). In Figure 6 we plot the kernel density of optimal travel times between all origin-destination pairs for two points in time (2006 and 2010). The third density, depicted in bars, corresponds to a hypothetical world where all road closure obstacles have been removed. Our calculations demonstrate that obstacles have greatly increased the travel times between origins and destinations in the West Bank, and that these travel times are subject to sizeable fluctuations over time as obstacles are intermittently reconfigured. For example, traveling from Hebron in the southern West Bank, we calculate that an obstacle-free journey to Nablus would have taken 2 hours and 17 minutes. In 2005-2008, however, the same journey would have taken around 7 hours. In 2009 travel times declined to “just” 4 hours and 40 minutes, and to less than 4 hours in 2010.

# **3.3 Market access over time and across space**

Estimates of travel times _Tijt_ are combined with population count data _Pjt_ to obtain estimates of market access _MAit_ , following eq. (1), for all localities _i_ and times _t_ . The distance decay parameter for our main specification is set at _θ_ = 40. Other values for _θ_ will be considered as part of the sensitivity analysis (see Sections 5.2 and 6.2).

We compute market access on an annual basis. This matches the frequency with

> 12The figures show that in 2006 and 2007 the obstacles in place forced traffic to take a longer route compared to later years when these obstacles were removed. In 2006-2007, commercial traffic traveled via the Huwwara or Awarta commercial checkpoints south of Nablus. In 2009, the Beit Iba checkpoint was dismantled which opened up the North-West route out of Nablus towards Tulkarm. This eliminated the need to travel via the Jit checkpoint.

13As of July 2011, approximately 437 kilometers of the barrier wall was completed, 58 kilometers was under construction, and 213 kilometers was planned and not yet under construction (UNOCHA, 2011). As a result, 71 of 150 settlements are on the Israeli side of the separation barrier. Upon completion of the planned separation barrier, part of the total barrier (15%) is near the 1949 Armistice Line (Green Line) or within Israel, while the remainder of the total barrier (85%) is within the West Bank.

15
**<mark>Table 4. Changes in Productivity, Profitability, and Labor Earnings Associated with Firm Death</mark>**

||Log(Labor Earnings + 1)|Log(Labor Productivity)|Log(Profitability+1)|
|---|---|---|---|
|Panel A: Full Sample||||
|Firm Death|-1.8567***|-0.1829***|-0.2095***|
||(0.0666)|(0.0313)|(0.0318)|
|Observations|15,187|7,927|7,261|
|Panel B: By Cause of Death||||
|Making a Loss|-1.5123***|-0.1774***|-0.2207***|
||(0.1109)|(0.0591)|(0.0579)|
|Illness or Family Care|-3.3645***|-0.1828**|-0.1990***|
||(0.1051)|(0.0769)|(0.0635)|
|Better Opportunities|-0.1911|-0.1642|-0.0739|
||(0.1650)|(0.1232)|(0.1040)|
|Observations|13,114|6,035|5,661|

Note: Robust standard errors, clustered at the firm level, in parentheses. Labor earnings are the sum of wage earnings plus profits in the month preceding a given survey wave. Labor productivity is measured as business sales in the month preceding a given survey wave divided by the number of employees +1. Profitability is measured by business profits in the month preceding a given survey wave.

**Table 5. Cause of Firm Death and Main Activity of Owner After Firm Death**

|Reason for closure|Percent|Frequency|Working<br>for a wage|Looking<br>for a job|Operating<br>a different<br>business|Housework<br>looking after<br>children|Other|Total|
|---|---|---|---|---|---|---|---|---|
|Making a loss|40.55|590|30.92|7.96|18.26|17.54|25.32|553|
|Sickness|13.54|197|8.13|2.50|10.63|46.25|32.50|160|
|Care for family|12.37|180|5.29|1.76|4.12|71.18|17.65|170|
|Better wage job|6.53|95|95.56|1.11|2.22|1.11|0.00|90|
|Better business<br>opportunity|4.88|71|10.77|1.54|75.38|6.15|6.15|65|
|Other|22.13|322|19.51|6.27|18.12|33.10|23.00|287|
|Total|100.00|1,455|342|71|228|392|292|1,325|

Note: Data for columns (1)-(2) from BENINFORM, KENYAGETAHEAD, MALAWIFORM, NGYOUWIN, SLKFEMBUSTRAINING, SLKINFORMALITY, SLLSE, SLMS, and TOGOINF. The category “Other” includes 3.09% of businesses, which had to close because of a catastrophe (e.g. fire or burglary), 1.51% of business which were shut down by government, a remaining 1.72% of owners who discontinued their businesses because either the business was taken from them, they married, or migrated. The remaining 15.81% of owners who closed their businesses either did not specify this other reason more in detail or closed because of any other miscellaneous reason, which could not be coded into one of the above categories (for instance because of a lack of workers or inputs, or a machine broke down).

Data for columns (3)-(8) from BENINFORM, KENYAGETAHEAD, MALAWIFORM, SLKFEMBUSTRAINING, SLLSE, SLMS, and TOGOINF.

37
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