rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 6572

# **Abstract**

This note presents the results of an empirical analysis of firm-level productivity growth in Russia’s manufacturing sector during the period 2003–08 using a rich Amadeus database as well as the recent EBRD/World Bank Business Enterprise and Performance surveys (BEEPs). The results show that productivity grew steadily between 2003 and 2008, with an annual growth rate averaging 4 percent over the period, showing no signs of a slowdown from the previous period after the 1998 crisis. Firm characteristics such as size, location, age, and the structure of firm ownership are important determinants of productivity, as evidenced by positive effects of scale economies (large firm effect), agglomeration (Moscowcity effect), private ownership, and a firm’s industry dominance. Supplemental analysis of the quality of infrastructure—water, electricity, transport, and the internet—using BEEPS data show that infrastructure quality gaps reduce firm productivity with water supply gaps having the largest impact.

This paper is a product of the Poverty Reduction and Economic Management Department, Europe and Central Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The authors may be contacted at zbogetic@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
_Africa’s Macroeconomic Story_

# **III. Africa’s Recent Growth Experience from a Historical Perspective**

# **_Stylized Facts_**

Africa’s growth experience is often characterized with reference to three sub-periods. Rapid growth in the first 13 years following independence (1961-73), was followed by a 20-year period when per capita real GDP declined by 0.8 percent per year on average (Table 1). Growth then recovered in the mid-1990s, averaging 1.8 percent over the past 18 years (1995-2012) and is projected to increase to 2.5 percent over the next few years (2013-15).

# **Table 1: Real GDP Growth in Developing Countries**

_Percent_

||**1961-73**|**1974-94**|**1995-2012**<sup>**1**</sup>|**2013-15**<sup>**2**</sup>|
|---|---|---|---|---|
|All Developing Countries|5.7|3.4|5.3|5.7|
|ex. China and India|6.0|2.7|3.7|4.2|
|_Regional_|||||
|East Asia & Pacific|5.2|7.8|8.3|7.7|
|Europe & Central Asia|6.7|1.0|3.7|4.0|
|Latin America & Caribbean|6.0|3.3|3.1|3.8|
|Middle East and Africa|5.5|2.6|4.1|4.3|
|Sub-Saharan Africa|4.8|2.0|4.3|5.1|

_Source:_ World Bank _Global Economic Prospects_ January 2013.

_Notes:_

1. Estimated for 2012.

2. Projected.

Comparing growth rates across sub-periods presumes that the transition dates between low and high growth regimes are known a priori. An alternative approach is to apply a smoothing algorithm (the Hodrick-Prescott filter) to capture the trend component,<sup>3</sup> which does not require any assumptions about transition dates. This leads to similar results (Figure 1). The trend growth rate declines from just over 2 percent in the 1960s, to around -1 percent 1980s and then recovers to just over 2 percent again by the mid-2000s.

> 3 Hereafter _trend_ growth refers to the trend component generated using the Hodrick-Prescott filter.

9
_5_ Table 2: **Parallel** Market Excbange Rate Premium (percent)

|Country|1974-80|1981-86|1990-91|
|---|---|---|---|
|Sub-Saharan adusters la<br>Extremely**hig**premiums in 1990-91||**-**<br>1<br>|16.|
|<br>Mauritania<br>.<br>Mozarbique<br>Sierra Leone<br>Tanzania<br>Zambia|62.3<br>468.5<br>21.0 /b<br>125.7<br>117.3|121.5<br>2,110.8<br>49.4<br>248.8<br>46.3|166.6<br>62.6<br>104.4<br>74.5<br>149.7|
|M.|37.7 /c|43.7|I<br>47.5|
|Medium premium<br>Burundi<br>The Gambia<br>Malawi<br>Nigeria<br>Uganda<br>Zimbabwe|20.3 Ic<br>-5.4 /c<br>74.5<br>63.3<br>728.3<br>102.2|24.1<br>13.8<br>53.6<br>232.7<br>190.0<br>81.3|20.9<br>21.3 Id<br>29.4<br>25.1<br>24.6<br>23.5|
|Low premium<br>Ghana<br>Guinea<br>Guinea-Bissau<br>Kenya<br>Madagascar|276.6<br>130.7<br>..<br>12.5<br>19.3 Ic|1,098.2<br>655.2<br>59.7<br>15.1<br>42.0|3.4<br>7.6<br>-2.3/d<br>7.3<br>7.1_Id_|
|Mean<br>Excluding Mozambique<br>Median|140.9<br>119.1<br>68.9|299.2<br>186.0<br>59.7|45.5<br>44.4<br>24.6|
|Other**adjusting**countries||||
|<br>Argentina<br>Bolivia|_94.5_<br>8.3|32.8<br>-136.2|42.4_Id_<br>1.5 Id|
|Costa Rica<br>Indonesia<br>Mexico|9.2<br>2.4<br>1.7|204.7<br>4.2<br>13.9|15.9 Id<br>2.6 /d<br>6.8 /d|
|Morocco<br>Philippines<br>Thaiand-|5.3<br>7.1<br>-06|6.0<br>12.3<br>-22|13.1 Id<br>7.1 /d<br>20 /d|
|<br>Turkey<br>Venezuela|.<br>25.7<br>0.0|.<br>9.6<br>110.3|.<br>2.4 /d<br>5.2 /d|
|.Mean<br>Medianm|15.3<br>6.2|52.8<br>13.1|9.9<br>6.0|

..Not available Note: The parallel market exchange rate premium is calculated as the parallel market exchange rate minus the official exchange rate (in domestic currency at the end of the period), multiplied by 100.

a. Data are only for the countries with flexible exchange rates b. Data are for 1979-80 c. Data are for 1976-80 d. Data are for 1990 Sources: "Adjustment in Africa Reforms, Results and the Road Ahead". Policy Research Department, The World Bank, 1993.
# **Table 17: The employment situation of non-Japanese and** **_Nikkeijin_ from Latin America (as of June 1, 2003)**

||non-Japanese who<br>come from Latin||(Of those)_Nikkeijin_<br>from Latin America||
|---|---|---|---|---|
||America|Composition ratio||Composition ratio|
|The total number of direct labor|61,172|100.0%|55,193|100.0%|
|<br>(by industry)|<br>|<br>||<br>|
|Manufacturing industry|51,980|85.0%|47,444|86.0%|
|Service sector|4,524|7.4%|3,708|6.7%|
|Wholesale and retail trade|669|1.1%|571|1.0%|
|Restaurant, lodging industry|271|0.4%|205|0.4%|
|Instruction, learning assistance service|189|0.3%|80|0.1%|
|Others<br>|3,539<br>|5.8%<br>|3,185|5.8%<br>|
|The labor who takes up employment with a business establishment<br>which primarily performing labor dispatch or contracting business|38,542|63.0%|35,469|64.3%|

Source: Japanese Ministry of Health, Labor, and Welfare.

39
# (b) Blow up of lower segment

<!-- Start of picture text --> 0 10 20 30 40 50<br>6000<br>4000<br>citations<br>2000<br>0<br><!-- End of picture text --> Cumul. number of articles (most-cited first) <!-- Start of picture text --> Harvard<br>Berkeley<br>LSE<br>Oxford<br>Princeton<br>WB<br>Yale<br><!-- End of picture text --> 17
- Barrett, A., & Kelly, a. E. (2012). The impact of Ireland’s recession on the labour market outcomes of its immigrants. _European Journal of Population/Revue européenne de Démographie 28.1_ , 91-111.

- Becker, S. O., & Ferrara., A. (2019). Consequences of forced migration: A survey of recent findings. _Labour Economics 59_ , 1-16.

- Betts, A., Stierna, M. F., Omata, N., & Sterck, O. (2021). Social Cohesion and Refugee-Host Interactions: Evidence from East Africa. _Unpublished Working paper. Commissioned as part of the “Preventing Social Conflict and Promoting Social Cohesion in Forced Displacement Contexts” Series. Washington, DC: World Bank Group._

- Blanco, C., Meneses, F., & Villamaizar-Chaparro, M. (2021). Why Student Aid Matters? Roadblocks to the Transition into Higher Education for Forced Migrants in Chile. _Unpublished Working paper. Commissioned as part of the “Preventing Social Conflict and Promoting Social Cohesion in Forced Displacement Contexts” Series. Washington, DC: World Bank Group._

- Bratsberg, B., Raaum, O., & Røed, K. (2018). Job loss and immigrant labour market performance. _Economica 85.337_ , 124-151.

- Daniele, G., Martinangeli, A. F., Passarelli, F., Sas, W., & Windsteiger, L. (2020). Fear and Loathing in Times of Distress – Causal Impact of Social and Economic Insecurity on Anti-Immigration Sentiment. _Working Paper of the Max Planck Institute for Tax Law and Public Finance_ .

- Danzer, A. M., & Dietz, B. (2018). Migrants’ well-being during the global financial crisis: Economic and social predictors. _Journal of comparative economics 46.3_ , 770-787.

- de Berry, J., & Roberts, A. (2018). _Social Cohesion and Forced Displacement, A Desk Review to Inform Programming and Project Design._ The World Bank Group.

- Dipoppa, G., Grossman, G., & Zonszein, S. (2021). Locked Down, Lashing Out: Situational Triggers and Hateful Behavior Towards Minority Ethnic Immigrants. _SSRN Working Paper_ .

- Dustmann, C., Glitz, A., & Vogel, T. (2010). Employment, wages, and the economic cycle: Differences between immigrants and natives. _European Economic Review 54.1_ , 1-17.

- Gourinchas, P.-O., & Obstfeld, M. (2012). Stories of the twentieth century for the twenty-first. _American Economic Journal: Macroeconomics 4.1_ , 226-65.

- Hatton, T. J. (2016). Immigration, public opinion and the recession in Europe. _Economic Policy 31.86_ , 205246.

- Heckman, J., Ichimura, H., Smith, J., & Todd, P. (1998). Characterizing selection bias using experimental data. _Econometrica, 66_ (5), 1017–98.

- Heckman, J., Todd, P., & Ichimura, H. (1997). Matching as an econometric evaluation estimator: Evidence from evaluating a job training program. _Review of Economic Studies, 64_ (4), 605-54.

- Isaksen, J. V. (2019). The impact of the financial crisis on European attitudes toward immigration. _Comparative Migration Studies 7.1_ , 1-20.

16
but two (Belgium and Spain in 2007-2008) show dominance of the intensive margin, and in more than half of this group the robust export growth is fully explained by the intensive margin.

**Table 5: Decompositions of Export Growth across Countries**

|||**Change in Total**<br>**Exports**<br>**Between 2006**<br>**and 2007**<br>**(bn USD)**|<br> <br>**Contribution of**<br>**the Intensive**<br>**Margin in 2006-**<br>**2007**|<br>**Contribution of**<br>**the Extensive**<br>**Margin in 2006-**<br>**2007**|<br>**Change in Total**<br>**Exports**<br>**Between 2007**<br>**and 2008**<br>**(bn USD)**|<br> <br>**Contribution of**<br>**the Intensive**<br>**Margin in 2007-**<br>**2008**|<br> <br>**Contribution of**<br>**the Extensive**<br>**Margin in 2007-**<br>**2008**|
|---|---|---|---|---|---|---|---|
|**ALB**|**Albania**||||0.27|60.9%|39.1%|
|**BEL**|**Belgium**|48.45|104.5%|-4.5%|20.81|45.2%|54.8%|
|**BFA**|**Burkina F.**||||0.01|-331.2%|431.2%|
|**BGD**|**Bangladesh**|-4.06|103.6%|-3.6%|6.01|73.9%|26.1%|
|**BRA**|**Brazil**|22.53|81.3%|18.7%|36.82|114.0%|-14.0%|
|**BWA**|**Botswana**|0.55|98.5%|1.5%|-0.70|120.8%|-20.8%|
|**CHL**|**Chile**|9.86|27.6%|72.4%|2.40|0.3%|99.7%|
|**CMR**|**Cameroon**||||0.37|0.0%|100.0%|
|**CRI**|**Costa Rica**|1.11|86.6%|13.4%|0.11|455.3%|-355.3%|
|**DOM**|**Dominican Republic**|0.29|-475.8%|575.8%|0.54|248.7%|-148.7%|
|**ECU**|**Ecuador**|0.78|44.3%|55.7%|1.42|73.1%|26.9%|
|**EGY**|**Egypt**|2.57|99.4%|0.6%|3.74|113.9%|-13.9%|
|**ESP**|**Spain**|35.94|106.7%|-6.7%|17.19|41.7%|58.3%|
|**EST**|**Estonia**|1.71|117.7%|-17.7%|1.71|51.2%|48.8%|
|**GTM**|**Guatemala**|0.85|73.7%|26.3%|0.77|53.0%|47.0%|
|**IRN**|**Iran**|0.94|232.4%|-132.4%|2.85|129.8%|-29.8%|
|**JOR**|**Jordan**|0.20|30.1%|69.9%|1.53|81.4%|18.6%|
|**KEN**|**Kenya**|0.66|149.9%|-49.9%|0.88|123.1%|-23.1%|
|**KHM**|**Cambodia**||||1.41|110.9%|-10.9%|
|**LAO**|**Laos**|-0.05|295.0%|-195.0%|0.56|75.3%|24.7%|
|**LBN**|**Lebanon**|||||||
|**MAR**|**Morocco**|2.33|99.2%|0.8%|4.86|99.7%|0.3%|
|**MEX**|**Mexico**||||11.67|148.6%|-48.6%|
|**MKD**|**Macedonia**|0.79|77.2%|22.8%|0.30|83.0%|17.0%|
|**MLI**|**Mali**|-0.32|133.3%|-33.3%|0.23|85.9%|14.1%|
|**MUS**|**Mauritius**|-0.15|89.8%|10.2%|-0.01|-1921.2%|2021.2%|
|**MWI**|**Malawi**|0.22|223.8%|-123.8%|0.03|123.5%|-23.5%|
|**NER**|**Niger**|||||||
|**NIC**|**Nicaragua**|0.20|72.9%|27.1%|0.29|88.8%|11.2%|
|**NZL**|**New Zealand**|3.75|109.3%|-9.3%|2.19|90.0%|10.0%|
|**PAK**|**Pakistan**|1.20|66.4%|33.6%|2.51|87.7%|12.3%|
|**PER**|**Peru**|3.87|82.7%|17.3%|2.12|16.7%|83.3%|
|**SEN**|**Senegal**|0.14|90.5%|9.5%|0.29|72.9%|27.1%|
|**SLV**|**El Salvador**|0.46|55.3%|44.7%|1.02|93.2%|6.8%|
|**TUR**|**Turkey**|20.10|61.6%|38.4%|22.07|95.0%|5.0%|
|**TZA**|**Tanzania**|0.26|3.4%|96.6%|0.77|82.0%|18.0%|
|**UGA**|**Uganda**||||-0.09|316.5%|-216.5%|
|**YEM**|**Yemen**|0.06|124.3%|-24.3%||||
|**ZAF**|**South Africa**|9.62|96.2%|3.8%|12.26|73.6%|26.4%|
|**Medi**|**an - all countries**||90.5%|9.5%||84.5%|15.5%|
|**Medi**|**an - countries in both**|||||||
|**sub-p**|**eriods**||90.5%|9.5%||82.5%|17.5%|

Notes: the terms of the decomposition shown in the table are based on measures in the country-year level dataset (file CY.dta).

27
One particular feature the LECSs, VHLSSs and ENAHOs share is a rotating panel design, which collects panel data for a subset of each survey round between two adjacent years. Around one third and one half of the households in the first round are repeated in the next round for the LECs and VHLSSs respectively, and the corresponding repetition ratio for the ENAHOs is around one quarter. This combination of both cross-sectional data and panel data in one survey provides an appropriate setting for us to implement our procedures on the cross section components, and then validate our estimates against the true rates from the panel components for each country. For the BLSMSs and the PSIDs, there is no rotating panel design and so we use the panel halves, pretending that these are cross sectional data. To ensure comparability between estimates based on the panel and cross section components, we use household weights with our estimates for the ENAHOs and population weights for the remaining surveys.<sup>25</sup> We use income and household consumption as a household welfare measure respectively for the US and all the other countries.<sup>26</sup> Appendix 3 provides a more detailed description of these surveys and other data quality checks.

> 25 There can be both pros and cons with using true panel data versus a survey with a rotating panel design for validation purposes. On one hand, a rotating panel design may be more suitable since actual panel surveys usually have a smaller sample size than those of cross sections, and a reasonably large sample size is required to obtain accurate estimates for the cohort-level simple correlation coefficient as well as for our asymptotic results. On the other hand, an important requirement for using rotating panel surveys is that the data from the cross section component be similar to those from the panel component. For Peru, the household-weighted headcount poverty rates based on the actual panel component are around 5 percent lower than those based on the cross section component, and the population-weighted estimates are even more different. Thus while the Peruvian data are not perfect for validation purposes, we believe it is still useful to show estimates for this country using household weights. But note that this difference will introduce more noise into our estimates.

26 The PSID also has some information on household consumption but this measure is not commonly used to measure poverty and is much less comprehensive than those for other surveys.

29
line. In Panel B of Figure 7, the reduction (measured in percentage points) is higher in 2009 when considering both the international and the official poverty lines. But if we measure these differences as a proportion of poverty, the reduction is higher in 2017 under both lines. It is difficult to assess the effect using these indicators, so we also estimated the effectiveness index and we have similar results for both lines. Vertical efficiency was higher in 2009 whereas the spillover effect was lower. As a result, the poverty reduction efficiency decreased between 2009 and 2017. However, the poverty gap efficiency increased.

**Figure 8.** Evolution of Poverty rate through different concepts. 2009 and 2017.

**Panel A.** Headcount ratio

<!-- Start of picture text --> 0.350 0.331<br>0.312<br>0.294<br>0.300<br>0.250<br>0.190<br>0.200 0.173<br>0.158<br>0.150<br>0.100<br>0.050<br>0.000<br>Pre-fiscal income Net market income Disposable income<br>Official line 2009 Official line 2017<br>Panel B. Change of Headcount ratio<br>From Net to Disposable<br>From pre-fiscal to Net income income From pre-fiscal to disposable<br>0.030<br>0.019 0.018<br>0.020<br>0.010<br>0.000<br>-0.010<br>-0.020 -0.014<br>-0.018<br>-0.030<br>-0.032<br>-0.040<br>-0.037<br>Official line 2009 Official line 2017<br><!-- End of picture text --> Source: Authors’ estimates based on ECH (2017, 2009), INE and Fiscal Accounts and CEQ Methodology.

# **V. Conclusions**

This paper attempted to update the impact analysis of fiscal policies in Uruguay for 2017, as well as compare those results with previous studies. Uruguay shows a strong social compact, at least 25
# **6.2 Heterogeneity in Responses**

This section highlights the significant heterogeneity in response to energy price hikes across various firm attributes by estimating Equation 4.

**Firm Size.** Considering three categories of firm-sizes: large (more than 50 employees), medium (between 20 and 49) and small (fewer than 20),<sup>14</sup> we draw the following conclusions on the heterogeneity in responses based on the results presented in Table 2: _First_ , the overall positive correlations of fossil fuel price changes with firm upgrading and productivity are primarily driven by large firms (panel I). In response to the withdrawal of fossil fuel subsidies, large firms upgrade both machinery _and_ ICT equipment, consistent with the results from Oman, Mexico and Indonesia (Amann et al., 2021; Cal`ı et al., 2022). _Second_ , the aggregate negative productivity effects observed for electricity price hikes are primarily driven by small and medium-sized firms (panel II). Large firms seem to be unperturbed by electricity price increases, both in terms of their productivity and profitability. However, large firms also observe negative correlations of fossil fuel price hikes with profit margins. _Third_ , firms of all sizes are sensitive to energy prices and substitute away from the energy source whose price increases and towards the alternative source. Intra-fuel elasticities do not notably differ by firm size (panel III).

**Foreign Ownership.** We differentiate between nationally-owned (domestic) and foreign firms.<sup>15</sup> Results from Table 3 suggest that although investment correlates positively and significantly with fossil fuel fuel hikes for both firm types, the magnitude is generally higher for foreign firms (panel I). It is not surprising, then, that fuel price increases correspond more strongly to productivity increases for foreignowned firms than domestic ones (panel II). Furthermore, electricity price hikes are negatively and significantly correlated with domestic firms’ productivity and profit margins. Regarding substitution and absorption effects, foreign firms’ substitute more strongly in response to increases in fossil fuel prices (panel III) while they sharply reduce employment following electricity price hikes (panel IV).

> 14We provide summary statistics for the size cut-offs used in the analysis in Table B.4. In ENIA the classification by firm size varies between the raw and combined data sets and complicates the firm matching algorithm described in Appendix C slightly; see Table C.7 for more information on the respective cut-offs in the two ENIA data sets.

> 15We define a firm as _domestic_ if it records a foreign capital formation of zero in all periods; see Table B.4 panel II for the corresponding summary statistics.

16
Table S1.9: EFFECTS ON OUTSIDE-VILLAGE TRANSFERS OF ULTRA-POOR HOUSEHOLDS

||Transfer<br>in past 1|s received<br>2 months|Transfe<br>in past 1|rs given<br>2 months|Net t<br>in past|ransfers<br>12 months|
|---|---|---|---|---|---|---|
||(Yes=1)|(BDT)|(Yes=1)|(BDT)|(Yes=1)|(BDT)|
||(1)|(2)|(3)|(4)|(5)|(6)|
|_Panel A:_|||_Within-Fam_|_ily Transfe_|_rs_||
|Treatment|-0.010|-63.842|0.013***|8.436|-0.011|-74.894|
||(0.007)|(54.182)|(0.004)|(39.976)|(0.007)|(49.939)|
|Mean in control|0.157|401.939|0.024|106.703|0.154|295.236|
|Adjusted R-squared|0.046|0.023|0.012|0.000|0.044|0.014|
|Observations|13464|13464|13464|13464|13464|13464|
|_Panel B:_||_O_|_utside-Fa_|_mily Transfe_|_rs_||
|Treatment|-0.117***|-86.216***|-0.001|36.538**|-0.116***|-123.587***|
||(0.019)|(30.892)|(0.004)|(16.085)|(0.019)|(33.826)|
|Mean in control|0.317|238.713|0.027|25.410|0.313|213.303|
|Adjusted R-squared|0.113|0.006|0.021|0.000|0.112|0.005|
|Observations|13464|13464|13464|13464|13464|13464|

**Source:** Author’s analysis based on original survey data. **Notes:** The table reports ITT estimates based on specification (1) estimated with OLS. The sample includes observations from ultra-poor households surveyed at the midline and endline surveys. All specifications control for the baseline level of the outcome, survey wave and subdistrict (strata) fixed effects. Standard errors are clustered at the BRAC branch office level (unit of randomization). All monetary values are measured in Bangladeshi Takas, deflated to 2007 prices using the annual CPI index published by the Bank of Bangladesh. In 2007, 1USD=69BDT nominal and 1USD=18.5BDT at PPP. *** (**) (*) indicates significance at the 1% (5%) (10%) level. **The dependent variables in Panel A refer to transfers received from or given to households who live outside the village but are part of the first-degree family (parents, siblings, children, parents-in-law or siblings-in-law) network of the respondent. In contrast, the dependent variables in Panel B refer to transfers received from or given to households who live outside the village and are not part of the first-degree family network of the respondent’s household.** In particular, the outcome variable in column 1 is a dummy variable (DV) equal to 1 if the respondent’s household received any transfers (in cash or in kind) during the 12 months preceding the survey; in column 2 it is the monetary value of transfers received during the past 12 months; in column 3 it is a DV equal to 1 if the respondent’s household gave any transfers to another household during the past 12 months; in column 4 it is the monetary value of all transfers given by the respondent’s household during the last 12 months; in column 5 it is a DV equal to 1 if the respondent’s household was a net transfer receiver in the past 12 months; in column 6 it is the monetary value of transfers received minus transfers made by the respondent’s household in the past 12 months.

9