rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 5156

# **Abstract**

Recent studies have shown that not only exporters but also importers perform better than firms that do not trade. Using a detailed firm level dataset from 43 developing countries, I show that there are persistent differences in evolution of firms when they are grouped according to their trade orientation as: two-way traders (both importing and exporting), only exporters, only importers, and non-traders. Extending the existing models of firm evolution in open economies by incorporating importing decision, I show that: i) globally engaged firms are larger, more productive, and grow faster than non-traders; ii) two-way traders are the fastest growing and most innovative group who are followed by only-exporters; iii) estimating export premium without controlling for import status is likely to overestimate the actual value by capturing the import premium; and iv) R&D investment contributes to growth of traders significantly more than to non-traders. Finally I show the robustness of the findings by providing evidence from the panel data constructed from the original dataset and controlling for variables that are likely to affect firm growth.

This paper—a product of the Enterprise Analysis Unit, Finance and Private Sector Development Department—is part of a larger effort in the department to explore the relation between international trade and growth. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at mseker@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
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# **4.2 Productivity**

We start this section by defining our productivity measure, and continue by showing the productivity dispersal by locations and gender. We then identify the determinants of rural enterprise productivity using a Heckmann selection model and panel data analysis.

## **4.2.1 Productivity Measure**

To study the productivity of non-farm enterprises, we calculate labor productivity, a partial productivity measure, defined as, As average monthly sales is not available for all countries, we take total sales during the last month of operation in Malawi and Nigeria, and average gross revenues in Uganda. Once labor productivity is calculated, we take the log of labor productivity for our estimates.

We are aware that is productivity measure is an approximation, and imprecise for a number of reasons. First, we do not take into account output or profit due to a lack of data. Second, we do not have information about the time-use of workers, and do not know how many hours of work are allocated by each individual to the enterprise. And third, we assume that the self-reported variable itself is imprecise, as most enterprise are informal and do not always record sales or revenues, but provide rough estimates of the business volume. Due to data availability, this productivity measure is however the best approximation that is currently at hand for this study.

## **4.2.2 Dispersal**

Figure 1 shows the dispersal of labor productivity by location and gender in Nigeria and Uganda. The kernel density estimates confirm our expectations that urban enterprises are more productive than rural enterprises, with a more pronounced productivity gap for Uganda, and that enterprises with a male owner are more productive than enterprises with a female owner.

Although urban enterprises are on average more productive than rural enterprises, the simple dichotomy between rural and urban areas may be of limited usefulness for policy decisions due to a large variation in Africa’s economic geography between deep rural, small towns, and major urban areas. The potential importance of secondary towns and rural agglomerations has generally been underestimated. As Christiaensen et al. (2013) point out, productivity might be higher in metropolitan areas, but not all inhabitants from rural areas are or will be able to access opportunities in these areas. Using data from Tanzania they find that only one in seven people who escaped from poverty did so 19
who witnessed death in the genocide experience a larger reduction in depression from the employment intervention. Columns 3-4 present the parallel results for baseline depression, but we observe no obvious pattern. Columns 5-6 examine heterogeneity by extroversion, where we likewise observe no obvious pattern, with reassuringly no evidence that introverted individuals are harmed by the outward-facing work.

# **5.7.5 Benchmarking main effects**

**Benchmarking the employment impact** How do our employment impacts compare to alternative anti-poverty programs or targeted psychotherapy programs? Ridley et al. (2020) perform a meta-analysis of the mental health impacts of multi-faceted anti-poverty interventions (e.g., livestock transfer, business training, employment, etc.) and find an average effect of 0.2 SD per $1,000 PPP in cash transfers (or 0.024 SD per $120 PPP); the effects we document are substantially larger. Singla et al. (2017) perform a meta-analysis of the mental health impact of targeted psychotherapy programs in low and middle-income countries and find an average of a 0.49 SD reduction in depression and PTSD, against which the impact of employment that we document are smaller. Perhaps most relevant is a study of a year-long psycho-education program directed at Rohingya refugee women residing in the same set of camps as those in this study (Islam et al., 2021). The program provided weekly sessions of in-person psychoeducation and parental counseling and yielded a 0.15 SD reduction in depression severity. In comparison, the employment program we consider generates a 0.24 SD reduction in depression severity.

**Benchmarking the cash impact** A meta-analysis of the mental health impacts of cash transfer programs by McGuire, Kaiser, and Bach-Mortensen (2020) approximates that transfers which double consumption generate a 0.12 SD improvement in mental health, and transfers of $120 PPP are likewise associated with a 0.12 SD improvement in mental health. An alternative meta-analysis by Ridley et al. (2020) estimates impacts of cash that are substantially smaller: $1,000 PPP cash transfer generates an average mental health impact of 0.12 SD; a linear interpolation implies that our $120 PPP transfer would yield a 0.014 SD impact on mental health. Our cash transfer, which is valued at $120 PPP and at least a doubling of daily consumption, exhibits a 0.06 unit (though imprecise) change in our psychosocial index of standardized mental health outcomes, falling in between these two meta-analyses.

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14 production and consumption at undistorted prices. Table 5 shows that Nigeria and Sudan are the two countries that dominate the region’s contributions, with Sudan becoming more important over time (as its WRI series trends upwards). Ethiopia accounts for up to 10 percent of the focus region’s welfare reduction. The last column of Table 5 reports country contributions to the decline in the regional WRI from its value of 44 percent in 1975–79 to its value of 27 percent in 2000–04. Once again, Nigeria and Sudan dominate the overall reduction, together accounting for around 80 percent of the fall in the WRI. However, Uganda, Cameroon, Senegal and Madagascar have a slightly offsetting effect on the regional fall in the WRI over that period.

It is useful to compare the TRI and WRI series reported above for all covered products, with those for just covered tradables in Africa.<sup>5</sup> In Table 6, it can be seen that the TRI and WRI for all covered products is significantly lower than that for covered tradables. This is because nontradables account for a large share of the gross value of production and consumption. The TRI estimates for all covered products are roughly half, and WRI estimates are roughly two-thirds, what there were with nontradables included.

Another point to note from Table 6 is that the country sample matters for the reporting of TRI and WRI results. For comparison, we report the results from Lloyd, Croser and Anderson (2010), which computed TRI and WRI series for an alternative sample of African countries in the Distortions to Agricultural Incentives database. Their sample is the same 19 countries in this paper, with the addition of Egypt and South Africa, and excluding the five cotton countries of Benin, Burkina Faso, Chad, Mali and Togo, because these countries only have one covered tradable product (cotton). In general, the 19 focus countries in this study have higher TRI and WRI 5-year averages. This is driven by the exclusion of Egypt and South Africa, which had low country-level TRI and WRI estimates. The exception to the general pattern is the time period 1985–89, where the Lloyd, Croser and Anderson (2010) estimates are higher, owing to very high protection in Egypt in that five-year average period (when international food prices collapsed just as Egypt raised its previously very low domestic food prices).

5 Note that Lloyd, Croser and Anderson (2010) report measures for covered tradables only.
|**Table 7: Channels of Production Impact**|Defect|Inven|tories|Energy|Costs|Labor Productivi<br>|ty Export at<br>|Log|
|---|---|---|---|---|---|---|---|---|
||Rate|Levels|Logs|Levels|Logs|LogSales/Worke|r<br>all|exports|
|Individual Treatment*During Intervention|‐0.008|‐63|‐0.185|544|‐0.079|0.016|0.018|‐0.116|
||(0.008)|(75)|(0.224)|(926)|(0.063)|(0.046)|(0.019)|(0.211)|
|Individual Treatment*Post Intervention|‐0.008|‐78|0.118|1430|‐0.038|‐0.024|‐0.009|‐0.108|
||(0.005)|(180)|(0.268)|(1079)|(0.159)|(0.054)|(0.026)|(0.195)|
|Group Treatment*During Intervention|0.000|79|0.049|1718**|0.155|‐0.003|‐0.017|‐0.271|
||(0.004)|(103)|(0.189)|(831)|(0.094)|(0.049)|(0.026)|(0.170)|
|Group Treatment*Post Intervention|‐0.005|28|‐0.169|1072|0.156|‐0.033|‐0.039|‐0.114|
||(0.005)|(121)|(0.259)|(821)|(0.145)|(0.059)|(0.028)|(0.137)|
|Sample Size (N*T)|3879|3875|3849|5121|5121|5591|8904|1983|
|Number of Firms|78|76|76|97|97|100|159|96|
|P‐value: Individual=Group During|0.400|0.199|0.332|0.379|0.063|0.762|0.251|0.586|
|P‐value: Individual=Group Post|0.600|0.652|0.350|0.761|0.422|0.897|0.311|0.978|
|Control Mean in 2017|0.025|554|5.150|8564|8.063|1.771|0.212|9.602|
|Notes:<br>Regressions control for firm and time fixed<br>the proportion of production that is faulty;<br>real (December 2017) pesos. Labor produc<br>variable that takes value one if the firm exp<br>USD value of the amount exported in the m<br>Standard errors clustered at the firm level.|effects, a<br>inventori<br>tivity is de<br>orted dir<br>onth, and<br>*, **, and|nd are re<br>es are in<br>fined as<br>ectly abr<br>is condi<br>*** den|stricted to<br>millions of<br>log real sal<br>oad in the<br>tional on e<br>ote signific|samples w<br>real (Dece<br>es (in milli<br>past month<br>xporting ta<br>ance at th|ith data av<br>mber 2017<br>ons of peso<br>, and zero<br>king place.<br>e 10, 5, and|ailable in Decemb<br>) pesos; energy co<br>s) per worker. Ex<br>otherwise; Log ex<br> <br>1 percent levels r|er 2017. Defe<br>sts are in tho<br>port at all is a<br>ports is the lo<br>espectively.|ct rate is<br>usands of<br>dummy<br>g of the|

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percent, respectively. In developing countries, the 7-quarter moving average of the growth rate of output in a typical recession is 0.4 percent compared to -2.3 percent in an extreme recession. The corresponding ...gures for a typical and extreme expansion are 1.6 and 7.7 percent, respectively. As will become clear below, important policy implications will arise from comparing typical and extreme business cycle conditions, particularly regarding (i) the expansionary e¤ect of government spending under alternative recessionary environments, and (ii) how the e¤ectiveness of austerity packages may crucially depend upon the severity of the recessionary environment.

One of the main novelties of this paper is to assess potential asymmetric e¤ects on output of government spending depending on whether government spending is increasing or decreasing. Before interacting this new dimension with the stance of the business cycle, we ...rst evaluate in Section 3.3 whether the size of the ...scal multiplier may depend on whether government spending is going up or down (without considering the stance of the business cycle). For this purpose, we modify our linear speci...cation (1) splitting each variable depending on whether forecast errors have positive (FE<sup>GP OS</sup> ) or negative (FE<sup>GNEG</sup> ) values:<sup>13</sup> Finally, we look into the possible interaction between recession/expansion and government spending going up or down in Section 3.4 (for typical expansions and recessions) and in Section 4 (for extreme expansions and recessions). For this purpose, we modify our linear speci...cation

> 13 In other words, �Y P OS (�Y NEG) equals �Y if FEG > 0 (FEG < 0) and zero otherwise. Similarly, �G<sup>P OS</sup> (�G<sup>NEG</sup> ) equals �G if FE<sup>G</sup> > 0 (FE<sup>G</sup> < 0) and zero otherwise.

11
# **Figure 2 Average MSE of Poverty Measures for LAC Countries**

<!-- Start of picture text --> 10.00<br>8.00<br>6.00<br>4.00<br>2.00<br>0.00<br>2017 2018 2019 2020<br>Old Model New - Rescaling New - No Rescaling<br><!-- End of picture text --> Source: Own estimations based on SEDLAC.

The difference between actual and estimated poverty gap values is presented in Figure 3. The poverty gap measures the ratio between the shortfall of the household per capita income from the poverty line and the poverty line. In general, results suggest that in the pre-COVID-19 pandemic scenario, the different 'model's variations overestimate the poverty gap. However, the magnitude varies once again with the country. Countries such as Bolivia and El Salvador have higher levels of overestimation in the poverty gap, while the measure is underestimated in Brazil.

Like in the poverty incidence case, the MSE for the poverty gap indicates that all the proposed models' variations predict better in the short run with very little difference between the _old model_ and the _new model with rescaling_ (Figure 4). This means the variations succeed at identifying the poor and estimating the intensity of poverty in the most immediate years. Yet, the performance of the _new model without rescaling_ is poorer. However, in the medium run, the less flexible variations ( _old model_ and _new model with rescaling_ ) fall behind _the new model with rescaling_ since the trend of their error increases exponentially over time. This way, both poverty headcount and the poverty gap indices suggest that, in the short run, there is little difference among the analyzed model variations. Even the simplest one (the _old model_ ) brings reliable results. Still, in the middle run, it is necessary to incorporate more flexibility in the income structure to capture the changes in poverty and vulnerability measures. All of this is subject to country-specificities, as shown before.

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13 Table 2. Macroeconomic Indicators: Africa and Latin America (Model results in parenthesis)

||Af<br>1978-81|rica<br>1986-89|Latin<br><br>1978-81|America<br>1986-89|
|---|---|---|---|---|
|GDP growth per annum|2.9<br>(3.0)'|2.8<br> <br>(2 .6)b|3.7<br>(4.4)|2.4<br> <br>**(t**<br>1<br>I)b|
|Investment/GDP|22.7<br>(21.6)|18.1<br>(15.5)|22.3<br>(22.6)|17.3<br> (14.8)|
|Current account/GDP|-4.8<br>(-4.6)|-5.9<br>(-5.4)|-4.6<br>(-3.3)|-5.2<br>(-7.4)|
|External debt/exports|.89<br>(1.07)|3.59<br> <br>(1.89)|1.16<br>.99)|<br>3.35<br>(2.89)|
|Interest payments/exports|3.4<br>(3.7)|11.1<br>(15.1)|9.9<br>(9.5)|24.3<br>(42.5)|
|Inflation rate per annum (CPI)|20.9<br>(16.4)'|22.1<br> **( 1 8 . 3 )b**|21.1<br>(30.5)'|271.3<br> (3 7.5)b|
|Real exchange rate<br>index (1980-<br>1.00)|1.02<br>(1.00)|1.46<br> <br>(1.09)|1.01<br>(1.00)|<br>1.14<br>(1.08)|

Notes: Average values are unweighted averages. Unless otherwise indicated, figures in parenthesis in the 1978-81 (1986-89) columns are period 1 (7) results.

a. Compounded 7-year average annual growth rates for the no-shock simulation.

b. Compounded 7-year average annual growth rates for the shock simulation.

SQurce: World Bank country data.
-4-

a GNP growth rate of 6.7 percent and despite reductions in poverty levels in all provinces (Malik 1991).5 This stuay uses a preduction function approach to ask whether the nutritional status of children in rural communities in Pakistan is responsive to changes in household food availability at the margin. Similarly, it evaluates the role of morbidity in explaining children's weights and heights. In doing so, it also evaluates the degree to which household income accounts for differences in observed nutritional status and the degree to which other factors such as education and public health contribute to the nutritional outcome.

# **MODEL** AND ECONOMETRI **C** ONSIDERATIONS

The model employed here is based on the standard Beckerian model of household utility in which utility is derived both from purchased goods and home produced goods including health and nutrition. As the model is now well known (see, for example, Schultz [1984]), there are only a few features that need recapitulation for the current study. In particular, it is important to recall that the household must include both budget and household technology constraints in its optimization process. Thus, the derived demands for inputs into the production of household goods such as nutrition reflect both the marginal productivity of those inputs and the household's budget constraint. Assume that household utility is 5 Anand and Kanbur (1990) report on a mirror image relationship, finding that expenditures on social programs had a greater effect on improvir,g mortality in Sri Lanka than did investments aimed at raising income.
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**Table 4: Factors affecting the probability to have a conflict on plot**

||**1999-200**|**1 period**|**Poole**|**d data**|
|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|
|Land price per acre by village (log)|0.002<br>(0.17)|0.002<br>(0.20)|0.002<br>(0.42)||
|Tenure status: Kibanja on customary (Dummy)|-0.100*<br>(2.52)|-0.089*<br>(2.27)|||
|Tenure status:Kibanja on Mailo (Dummy)|-0.008<br>(0.19)|-0.001<br>(0.02)|||
|Age of household head (Log)|-0.134*<br>(2.40)|-0.160**<br>(2.82)|-0.056**<br>(2.92)||
|Distance from home greater than zero (Dummy)|-0.166**<br>(4.99)|-0.158**<br>(4.83)|||
|Plot size (Log)|-0.013|-0.013|||
||(0.74)|(0.76)|||
|Length of plot ownership (Log years)|0.009|0.010|0.009|-0.001|
||(0.36)|(0.39)|(1.39)|(0.19)|
|Plot rented in (Dummy)|-0.089|-0.105*|||
||(1.61)|(2.03)|||
|Plot purchased (Dummy)|-0.054|-0.059|||
||(0.79)|(0.86)|||
|Female-headed household (Dummy)|0.113**<br>(2.79)||||
|Married female head (Dummy)||0.004<br>(0.06)|||
|Separated female head (Dummy)||0.515**<br>(3.38)|||
|Widow female head (Dummy)||0.125*|||
|||(2.53)|||
|Conflict pending in 1996-1998 (Dummy) δ|||0.052*<br>(2.55)|0.050*<br>(2.41)|
|Conflict pending in 1999-2001 (Dummy)φ|||0.167**<br>(7.19)|0.162**<br>(7.03)|
|Observations|52|8|15|15|
|Log likelihood|112.52|120.52|126.48|117.44|
|Pseudo R-square|0.23|0.25|0.15|0.13|
|**Equality test of the parameters**<br>|||δ =φ|δ =φ|
|χ<sup>2</sup>(1)|||31.76|30.63|

Note: Variables on households and farm assets and regional dummies are included but not reported. Absolute value of z statistics in parentheses * significant at 5%; ** significant at 1% 22
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