rafmacalaba/datause-encoder-data huggingface dataset:
competition was always there. But initially, firms run into niches and avoid domestic competitions. Early in the transition process, there aren’t many domestic firms that can challenge foreign competition. As the economies mature, however, there is more successful home-grown competition, and so domestic competition indicators heat up. High quality imports were always there, but high quality domestic production is a new phenomenon. Another factor is that the industrial structure is changing rapidly in the TEs: the share of industry is falling while services are growing rapidly. Manufacturing firms face more competition from abroad while many services are non-tradables.

# **IV. Finance and financial constraints**

Access to external finance has long been regarded as an important aspect of the business environment crucial for the creation, survival and performance of firms. The entrepreneurship and finance literature in the developed market economies has long emphasized the existence of financial constraints implying the inability of firms to raise external financing in order to fund all desired investments (e.g., Evans and Jovanovic (1989), Fazzari, Hubbard and Petersen (1988)). It may be argued that financial constraints facing firms in the transition countries are much more severe than in the developed market economies, by virtue of the fact that financial markets did not exist during the era of central planning. On the other hand, things may not be as simple as they appear at the first glance: it is well known that state-owned firms before the start of transition operated under so-called soft budget constraints. The footprints of these SBCs have been found well after the start of the transition process. For example, using data from Poland, the Czech Republic, Bulgaria and Romania covering 1994-1999, Konings, Rizov and Vandenbussche (2003) found greater financial constraints for firms in Poland and the Czech Republic than for firms in the less-financially-developed countries of Bulgaria and Romania. Such a cross-country pattern is explained by the persistence of SBCs in Bulgaria and Romania.

Despite a growing number of studies in the field, few authors provide a comprehensive picture of the financial development of and financial constraints in the transition region.

28
Figure A14: Specification Charts for Variance of Daily Temperature <!-- Start of picture text --> Ethiopia Malawi<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.4 .5<br>.2 0<br>0 -.5<br>* Weather Product * -.2 * Weather Product * -1<br>MERRA-2 -.4 MERRA-2<br>ERA5 ERA5 -1.5<br>CPC CPC<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18<br>Niger Nigeria<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>3 1<br>2<br>1 .5<br>0 0<br>* Weather Product MERRA-2* -1-2 * Weather Product MERRA-2* -.5<br>ERA5 ERA5<br>CPC CPC<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18<br>Tanzania Uganda<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.5 .4<br>.2<br>0 0<br>-.2<br>* Weather Product * -.5 * Weather Product * -.4<br>MERRA-2 MERRA-2 -.6<br>ERA5 ERA5<br>CPC CPC<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18<br><!-- End of picture text --> _Note_ : The figure presents specification curves, where each panel represents a different country, with three different models presented within each panel. Each panel includes 18 regressions, where each column represents a single regression. Significant and non-significant coefficients are designated at the top of the figure. For each Earth observation product, we also designate the significance and sign of the coefficient with color: red represents coefficients which are negative and significant; white represents insignificant coefficients, regardless of sign; and blue represents coefficients which are positive and significant.

62
evident since 1997. From this year on, we find that China accumulated more FCS than Latin American countries only in the manufacturing sector. Even here, U.S. stocks in China did not grow faster than in Chile or Costa Rica. At the same time, stocks in India increased more than in Latin America in both periods. This was true both for stocks originating in the OECD and in the U.S., but their growth were less significant than that of China between 1990 and 1997.

After controlling for shocks emanating from source countries and bilateral distance between source and host countries, the OECD data suggest that the significant change in Latin America’s FCS relative to China occurred between 1990 and 1997. However, even this econometric analysis is silent with respect to any substitution effects that might have affected Latin America’s FCS positions. That is, further econometric analyses are needed to directly test the hypothesis that changes in Chinese and/or Indian FCS positions were associated with changes in Latin American FCS levels, as has been attempted by Eichengreen and Tong (2005) and Cravino, Lederman, and Olarreaga (forthcoming), among others. In any case, the data and the findings of this paper suggest that the threat from China and India in terms of FDI might be the dog that did not bark.

17
Table 12. Africa subsample: SMEs vs. Non-SMEs

|||SME Interaction|||SMEs|||Non-SMEs||
|---|---|---|---|---|---|---|---|---|---|
||||LP|||LP|||LP|
|Dep. Variables|TFP|log LP|<br>Growth|TFP|log LP|<br>Growth|TFP|log LP|<br>Growth|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|FemHead|0.078|-0.065|-0.006|-0.439**|-0.075|0.010|0.098|-0.128|-0.020|
||(0.163)|(0.123)|(0.031)|(0.165)|(0.089)|(0.024)|(0.172)|(0.126)|(0.031)|
|FemHead*SMEs|-0.423*|-0.044|0.016|||||||
||(0.227)|(0.165)|(0.041)|||||||
|Foreign|0.658**|0.688***|0.106***|0.271|0.469***|0.025|0.610*|0.683***|0.116***|
||(0.228)|(0.099)|(0.021)|(0.379)|(0.132)|(0.028)|(0.299)|(0.109)|(0.023)|
|OwnLargest|-0.333*|-0.632***|-0.114***|-0.668|-0.606**|-0.142**|-0.187|-0.580***|-0.078*|
||(0.171)|(0.173)|(0.037)|(0.407)|(0.244)|(0.054)|(0.122)|(0.174)|(0.044)|
|Small|0.110|-0.125|-0.042*|||||||
||(0.161)|(0.099)|(0.021)|||||||
|Middle|0.012|0.131|-0.001||||-0.008|0.111|-0.004|
||(0.141)|(0.092)|(0.014)||||(0.140)|(0.091)|(0.015)|
|Large|0.182|0.551***|0.065***||||0.167|0.461**|0.050**|
||(0.179)|(0.171)|(0.016)||||(0.221)|(0.163)|(0.023)|
|Age6_10|0.372**|0.139**|0.010|0.386|0.113*|0.022|0.437|0.160|-0.016|
||(0.160)|(0.058)|(0.012)|(0.318)|(0.059)|(0.020)|(0.306)|(0.131)|(0.030)|
|Age10plus|0.377**|0.218**|0.016|0.180|0.139*|0.022|0.566*|0.313*|-0.001|
||(0.144)|(0.083)|(0.015)|(0.258)|(0.069)|(0.026)|(0.271)|(0.150)|(0.030)|
|Exporter|0.170*|0.025|0.021|0.070|-0.180|-0.012|0.158|0.077|0.027|
||(0.082)|(0.204)|(0.022)|(0.101)|(0.212)|(0.035)|(0.092)|(0.161)|(0.017)|
|Initial LP|||-0.144***|||-0.151***|||-0.138***|
||||(0.010)|||(0.009)|||(0.010)|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|1,329|5,279|4,500|556|2,623|2,147|773|2,656|2,353|
|Adjusted R<sup>2</sup>|0.142|0.279|0.622|0.136|0.210|0.646|0.157|0.317|0.603|

Notes: *, **, *** represent statistical significance at the 10, 5, and 1 percent levels. Heteroskedasticitycorrected standard errors clustered at the country level in columns.

SMEs is defined as the permanent employees three years ago is less than or equals to 10, and non-SMEs above 10 which is the median in Africa subsample In Africa, the share of female-headed firm amongst small firms is 13.4 percent and amongst large firms is 7.9 percent).

42
Policy Research Working Paper 9466

# **Abstract**

The coronavirus disease 2019 (COVID-19) and the attempts to limit its spread have resulted in profound economic impacts, and a significant contraction in the global economy is expected. This paper provides some of the first evidence on the socioeconomic impacts of and responses to the pandemic among households and individuals in Sub-Saharan Africa. To do so, reduced-form econometric methods are applied to longitudinal household survey data from Ethiopia, Malawi, Nigeria, and Uganda—originating from the pre-COVID-19 face-to-face household surveys and from the novel phone surveys that are being implemented during the pandemic. The headline findings are fourfold. First, although false beliefs about COVID-19 remain prevalent, government action to limit the spread of the disease is associated with greater individual knowledge of the disease and increased uptake of precautionary measures. Second, 256 million individuals—77 percent of the population in the four countries—are estimated to live in households that have lost income due to the pandemic. Third, attempts to cope with this loss are exacerbated by the inability to access medicine and staple foods among 20 to 25 percent of the households in each country, and food insecurity is disproportionately borne by households that were already impoverished prior to the pandemic. Fourth, student-teacher contact has dropped from a pre-COVID-19 rate of 96 percent to just 17 percent among households with school-age children. These findings can help inform decisions by governments and international organizations on measures to mitigate the effects of the COVID-19 pandemic and reveal the need for continued monitoring.

This paper is a product of the Development Data Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at tkilic@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
As lockdowns are lifted, the constraint on only doing home-based work eases. Therefore, among industries not amenable to home-based work, those with low face-to-face interactions with consumers, such as in manufacturing, might see workers return to their jobs more easily. The risk of physical proximity with co-workers along assembly lines can potentially be addressed through personal protective equipment and other relevant precautions undertaken by factory managers. Industries intensive in face-to-face interactions with consumers, however, may be slower to recover as consumers will likely remain apprehensive of going to restaurants or stores that are frequented by many people.

**Figure 3: Lower paid jobs are less amenable to home-based work**

<!-- Start of picture text -->
1.10<br>Computer and<br>1.00 Mathematical<br>Legal<br>0.90<br>Education and Training Management<br>0.80 Business and Financial<br>Operations<br>Arts, Entertainment, and<br>0.70 Sports<br>Office and<br>Administrative Support<br>0.60 Life, Physical, and Social<br>Science<br>0.50 Architecture and<br>Engineering<br>0.40 Community and Social<br>Service<br>Sales<br>0.30 Personal Care and<br>Service<br>Protective Service<br>0.20 Building and Grounds ...Transportation and<br>Food Preparation and  Material Moving Installation,  Healthcare Practitioners<br>0.10 Serving Related Farming Maintenance, and Repair<br>Production Construction and Extraction<br>0.00 Healthcare Support<br>0 2 4 6 8 10 12<br>Income Deciles<br>Less to more home-based Work<br><!-- End of picture text -->

Source: Authors’ calculations based on Dingel and Neiman (2020) and the US. Bureau of Labor Statistics.

Furthermore, occupations where home-based work is less feasible and face-to-face interactions are necessary tend to be concentrated among vulnerable groups. Occupations that are more amenable to home-based work are largely concentrated among higher wage deciles. This includes legal, management, and computer-related jobs. In contrast, occupations that are less amenable to home8
MOZAMBIQUE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE conceded to the Dutch company Cornelder. In 2003 the ports of Maputo and Matola were conceded to a consortium that included the consortium Maputo Port Development Company (MPDC), formed by the UK’s Mersey Docks and Harbour Company, which secured a 15-year concession with a right to a 10-year extension. Then in 2005, the operation of the Nacala port was conceded to the RITES Ltd. and IRCON International consortium for a 15-year period as part of the concession of the Beira Corridor. The same year, Cornelder was awarded the concession for the Port of Quelimane. In all three of the latter projects, CFM has an equity stake of 49 percent, of which 16 percent is reserved for offloading government projects.

# **Achievements**

Between 1999 and 2008 Mozambique increased the use of its ports capacity. The amount of 20-foot equivalent units (TEUs) shipped daily grew 43 percent over this period, from 207 to 297 TEUs. From 1999 to 2008 the number of ships calling at the ports increased by around 16 percent, from 1,353 to 1,574. Similar growth was registered in the number of tonnes shipped per day, which increased from 2,280 in 1998 to 3,658 in 2008 (table 6).

In particular, Mozambique’s port demand rose strongly in the period 2005–08. In 2008, 11.64 million metric tonnes were handled compared to 9.98 in 2005, with the Port of Maputo representing around 65 percent of the market (table 7). The number of containers handled grew by 40 percent from 158,287 TEU in

**Table 6. Traffic in Mozambique’s ports**

|**Ships calling**<br>**on ports**|**Tonnes/ship/**<br>**day**|**TEUs/ship/**<br>**day**|
|---|---|---|
|1999<br>1,353|2,280|207|
|2008<br>1,574|3,658|297|
|Percent increase (%)<br>16|16|43|
|_Source:_CFM annual reports.<br>_Note:_TEU = 20-foot equivalent unit.|||

2005 to 225,419 in 2008. The market share of the Beira port over this period of time went up from 20 percent in 2005 to 38 in 2008, making it the port that handled the highest number of containers.

**Table 7. Cargo and containers handled in Mozambique’s ports**

||Total|Maputo|Beira|Nacala|Quelimane|Pemba|M.da Praia|
|---|---|---|---|---|---|---|---|
|Cargo handled (1,000 metric t|onnes)|||||||
|2005|9,982|6,360|2,428|878|244|63|10|
|2006|10,683|6,666|2,746|952|219|85|14|
|2007|11,079|6,858|2,915|1,108|86|97|16|
|2008|11,637|7,406|2,991|1,054|66|100|20|
|Containers handled (TEUs)||||||||
|2005|159,287|57,511|35,000|32,310|9,704|5,244|215|
|2006|171,216|65,390|34,965|34,184|8,753|7,976|645|
|2007|194,247|63,764|71,167|44,870|4,870|8,244|1,332|
|2008|225,419|74,792|85,716|49,770|4,172|9,295|1,674|
|_Source:_CFM 2006; 2009.<br>_Note:_TEU= 20-foot equivale|nt unit.|||||||

17
Policy Research Working Paper 8422

# **Abstract**

This paper defines institutions, presents their basic characteristics, and discusses forces for institutional change. It draws on a wealth of research and study by scholars in different fields and follows from the flagship 2002 World Development Report on this topic, including relevant new research since then that illuminates key issues. The definition of institutions includes rules and organizations (specifically rules guiding peoples’ actions). The paper emphasizes the diversity of institutions that can effectively perform similar functions, the arguments for and against standardization of institutions across specific transactions or groups, and the implications of these choices. It highlights the relationship between informal and formal institutions.

It discusses forces for institutional change influencing all economies, ranging from external shocks (whether they be economic or weather-related shocks or driven by wars) to internal dynamics, such as those following population growth or compositional changes, and those following technological innovations. Many of these forces have a two-way relationship with institutions: they are affected by them and, in turn, influence whether and when they change. A special section is devoted to institutional transplants and their effectiveness. The paper concludes with a discussion of whether and under what conditions institutional change can be externally directed and the lessons for aid donors.

This paper is a product of the 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://www.worldbank.org/research. The author may be contacted at rislam@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
Löfgren’s general approach is fairly representative of a large number of trade and poverty studies carried out over the past decade. One variant is to address in greater detail how external shocks affect the _total_ income distribution of a country. For this purpose it is necessary to postulate a distribution of income for each representative household type (as in Adelman and Robinson) or to work at the level of individual workers (as in Cogneau and Robilliard, and Cockburn). If the distribution of income within a given group of households is estimated _a priori_ , it can be used in conjunction with the general equilibrium model to assess the impact of exogenous shocks on income distribution and poverty. In these studies, it is typically the case that mean and total income levels for a household group are endogenous, while higher moments of the distribution are fixed.

In an interesting and useful synthesis paper, Decaluwé, Patry, Savard, and Thorbecke (1999) consider this basic approach and provide further refinements. One innovation is to model the withingroup income distribution with a flexible Beta distribution instead of the restrictive log-normal or Pareto distributions that are commonly used. This permits them to adapt the shape of the income distribution to observed socio-economic characteristics of each household type, and it is shown that these distributions vary markedly across household groups. In turn, an endogenous poverty line is developed based on the cost of obtaining a subsistence level of consumption. Although no empirical results are presented, these innovations are ripe for use in conjunction with the new, increasingly detailed household-level data becoming available, and thus could shed additional light on the behavior of poverty following an external shock to the economy.

Cogneau and Robilliard (2000) offer a fundamentally different approach to trade, poverty, and income distribution modeling. They assess the impact of different growth strategies on poverty in Madagascar by embedding an econometrically estimated labor allocation model based on 4,508 households _within_ a general equilibrium framework.<sup>**7**</sup> The combination of micro-simulation and general equilibrium facilitates the modeling of a country’s overall income distribution, since it is no longer necessary to _a priori_ assume an income distribution for each household type. In addition, the

> 7 Two other studies that incorporate large numbers of actual households into a relatively standard GE model are Gørtz, Harrison, Nielsen, and Rutherford (2000), and Rutherford, Tarr, and Shepotylo (2003).  The latter study works with more than 50,000 households in a CGE model of Russia.

15
and create a comprehensive list of the names and whereabouts of each original household member for a follow-up interview.

Out of the initial 1985 sample of 399 households, a total of 324 households could be identified and located in the course of the listing operation. The total sample for this study includes 296 households.<sup>19</sup> Full histories of NTX adoption and membership in the cooperative were collected, together with detailed information on the socioeconomic situation of the households. Recall methods were also used capture trends in asset positions, as well as to collect full land transaction histories.

As mentioned, more than 20 years from the onset of the NTX boom in the area, the agricultural configuration and socio-economic make-up of the communities appear to have changed quite dramatically. Many farmers have succeeded in continuing to grow NTXs, but many more have abandoned cultivation. Others have entered production significantly later, with mixed success. Figure 1a shows the survivor function for the NTX adoption decision. The survivor function indicates the probability for a unit to survive beyond some time _t_ or, in other words, the share of households that still have not adopted at a given time _t_ . Most farmers in the sample adopt snowpeas (82 percent),<sup>20</sup> and the vast majority of these adopt within the first few years of exposure, partly thanks to the incentives provided through a vibrant cooperative in terms of inputs, technical assistance and market support. By 1985, 65 percent of the sample, or 80 percent of ever-adopters, had already adopted. However, as can be seen in Figure 1b which presents the survivor function for the withdrawal decision – i.e. the percentage of

> 19 Of the original 399 households, neither the name of the household head nor address information were available for about 20 cases, thus we had to exclude these households from the listing operation, bringing down the number of potential panel households to about 380. In approximately 50 cases, only households of the children who had left the original households were available, and in a few cases, the male heads of original households appeared to have changed. Only the original households with original male heads are used for this study. Further data cleaning eliminated households with insufficient or suspect information, yielding a final adoption sample size of 296. The sample used for withdrawal totals 242 and excludes one household that adopts in the final year of observation, as the model does not allow for adoption and withdrawal within the same period.

> 20 This percentage rises to 85% when “incidental adopters” who adopted for two years or less are included.

17