rafmacalaba/datause-encoder-data huggingface dataset:
improved toilet, or an electricity connection. The majority of households lack at least one of these amenities, from 70.1 percent in the largest city to 98 percent in rural areas. Homeownership rates are quite high on average at over 70 percent, much of which is concentrated in rural areas which has upwards of 85 percent of people owning their own homes compared to about half in urban areas.

Table 2 suggests in all cases rural areas are worse off in terms of shelter and infrastructure amenities compared to urban areas. Recent research in India corroborates this by showing that access to basic services for households depends on city size. The percent of households that had access to bathroom facilities in their home or drainage increased as city size increased. Additionally, drainage access decreased as households located further from the urban core, regardless of city size (World Bank 2013). According to the 2011 Census, India’s urbanization rate is 32 percent, which is comparable to the average urbanization level of the Sub-Saharan African countries studied here which averaged 36 percent across the countries examined in 2012.

The differences between the percentages of households that possess housing amenities also vary significantly across income quintiles; the difference in housing shortages between the first and fifth quintiles is especially stark. Shelter shortages disaggregated by income aids in identifying where types of housing shortages are concentrated among different population strata. Table 2 shows the average percentage of households in the first and fifth quintiles that have certain housing amenities and face shelter and infrastructure shortages. The difference between all the averages in these quintiles is significant at the 1 percent level, indicating a stark difference between housing for the richest and the poorest in SSA.

Housing conditions in SSA vary by sub-region, country, and type of urban area, with lower shortages being apparent in Middle Africa when compared to the Eastern and Western Sub-Regions. Figure 9 depicts a map of Africa with the 20 Sub-Saharan African countries analyzed highlighted in green. The UN classifies SSA into three sub-regions comprising of Western Africa, Eastern Africa, and Middle Africa. These sub-regions are characterized by a unique set of housing challenges, as summarized in Figure 9. Analysis of the SHIP data by region suggests that broadly, Eastern Africa suffers from the highest percentage of households in overcrowded conditions, and low number of households with permanent floors and access to a toilet. On the positive side, Eastern Africa has the highest percentage of households with electricity connections. Western Africa suffers from the most number of housing deficiencies and the highest percentage of households facing housing shortages. Middle Africa, on the other hand, has the least number of those facing housing shortages, but has the lowest percentage of households with electricity connections of all three sub-regions.

14
# **_1. Introduction_**

This paper examines the contribution of firm dynamics to overall productivity growth. It tests whether firm turnover is a prime driver of productivity growth through the shedding of unproductive firms and the entry of more productive firms, and whether turnover has additional indirect benefits through agglomeration externalities or through increasing the contestability of markets.<sup>1</sup> Of interest too is to look at whether there are trends in these relationships, particularly in the wake of policy reforms. We analyze the case of Morocco, using census data from 1985-2003. Trade liberalization efforts were begun in the 1980s, and a wave of privatization reforms followed in 1994. While barriers to entry remain, over time market institutions have improved. The patterns of technical efficiency (within firms) and allocative efficiency (between firms) do show rising trends over time. The direct contribution of turnover, on the other hand, appears to be muted. However, looking at indirect effects through increased contestability of markets and agglomeration spillovers indicates that turnover does contribute to aggregate productivity increases.

The empirical evidence on the contribution of firm turnover to aggregate productivity displays significant variation, partly attributable to the use of different methods – and to different countries. Baily, Hulten and Campbell (1992) for example examine the evolution of industrial productivity in the United States over the period 1963-87 and find that firm turnover provides a modest contribution to industry productivity growth. Similar evidence of a limited turnover effect on productivity growth

> 1 The subsequent task of exploring the drivers of firm turnover is undertaken in a companion paper (Hallward-Driemeier and Thompson 2009).

2
important with this measure (i.e. Hong Kong and Singapore have export/GDP ratios greater than one), countries with export shares greater than .75 were dropped. The export share remained robust in explaining investment.*% The export share was also found to be robust in the growth equation when investment was dropped. These results suggest an important two-link chain between trade and growth through investment. Interestingly, however, the theoretical ties between growth and trade typically seem to run through improved resource allocation and not through a higher investment share.

We also examined more direct measures of trade policy. Leamer (1988) uses the Hecksher-Ohlin-Vanek trade model to construct measures of "openness" and "intervention." The intervention index represents the deviation between the actual and predicted pattern of trade. The openness index represents the difference between the actual and predicted level of trade (as opposed to the pattern of trade). The openness index is constructed so that a higher value represents more “openness.” As Table 6-A indicates, neither Leamer's (1988) intervention or openness indexes is robustly correlated with average long-run growth rates when the I- variables are included in the regression. Both of Leamer’s indexes, however, are robustly, positively correlated with the investment share as depicted in Table 6-B. On the one hand this is not surprising because both of Leamer's indexes are highly (e.g., 0.70) and significantly (at the 0.01 level) correlated with the share of trade in GDP, which we found to be significantly correlated with the investment share. On the other hand, these results are difficult to 18 when the extreme bounds test is done on the subset of countries for which disaggregated tax data exist (over the 1974-1989 period), the coefficient on the export share becomes insignificant when the ratio of corporate tax receipts to GDP is included.

21
the proxy for formal sector labor productivity.

Social Security Tax-Employers (SSEmp): Social security (broadly defined) taxes as a share of the wage by worker. The model suggests that there may be different effects.

Employment Protection(Protection): An index of employment protection constructed by Gustavo Marquez that captures both the difficulty of laying off workers and the cost in terms of severance pay.'<sup>4</sup> Unfortunately, this leads to roughly a halving of the available observations and hence a separate set of regressions are run using this reduced sample.

# _3. 1. b Rise in the benefits to being self employed, or a reduction in worker taxes._ (T)

Social Securitv Tax-Workers (SSWorker): Social security (broadly defined) taxes as a share of the wage by worker. Ideally, we would have a measure of labor productivity in the self-employed sector. Unfortunately, this is not feasible as even in the OECD possible proxies, such as wages in the commerce or other services are not consistent across countries. However, social security tax incidence on workers does capture an important element of the relative attractiveness of each sector.

# _3.1. c Hiring Costs._

Education (Education): The share of the appropriate age group with secondary education. No cross country direct measures of hiring costs are available. However, public education is a public good that addresses exactly the externality identified in the efficiency wage model. The more firms have to train, the more they have to lose by workers moving to another firm or to selfemployment. We interpret any effect of this variable as working through training costs.

Real Interest Rate (Real Interest): The real interest rate affects the cost of training workers to raise future productivity as it would in the case of any other investment. We use the average of the 30-90 day borrowing rate deflated by changes in the CPI in most cases. While this is not generally the rate at which larger corporations borrow, it is none the less a rough indicator of the cost of investing in workers in the economy.

# _3. 1.d Other Variables:_

Duration of Unemployment Benefits: (U Benefits): Some share of the self-employed in Latin America would be found unemployed in the industrialized countries where unemployment 14 Chief Economist Office of the IDB.

14
# **4 Data and Methodology:**

# A. Data:

The details on the construction of the database, in particular the development of gross stocks of external assets and liabilities are in the appendix to this paper. The problem of negative stocks is inevitable since flows are accumulated for countries where there **is** no stock data, on the assumption that stocks were zero in the first available year. Fortunately, this problem is rare, and the adjustment required to ensure non-negative stocks was less than 0.3% of GDP in all cases. Starting with a database of over 200 countries, only those are retained that have population greater than one million and per capita GDP greater than $1000 in 1990. All variables are in 1990 US dollars.

The database has a mix of 167 developed and developing countries over a wide variety of geographical regions with a great deal of heterogeneity in their political and institutional environments<sup>2</sup> . A number of countries out of these are "new" and therefore with limited timeseries data was available. Eliminating countries with less than 20 years of data for all the relevant variables leaves the results unchanged. The rationale for eliminating countries with a population of I million or less in 1990 since very small economies raise specific empirical issues.<sup>3</sup> In addition, we use Bossone, Honohan and Long (2001) to distinguish countries with total M2 (money + quasi-money) less than 2 billion in 1998. In other words, if the financial system was approximately the size of the local credit union in that year, is the country more vulnerable to crises or not? Morley (1997) is used for the construction of the capital restnctions index and the political violence index. The construction of the crisis index follows the methodology of Glick and Hutchison (1999). In addition, various country-specific dummies are included, for example, political violence, rule of law, existence of black market premium on official exchange rate, current and capital account restrictions. There are several examples in the received literature on the use of such dummies. The inclusion of political variables in particular has several precedents in the literature. Other authors have controlled for this by including dummies for incumbent electoral victory or loss, change of government or of finance minister, legal or illegal executive transfer (Eichengreen _et al,_ 1995; Klein and Marion, 1994) and political uncertainty. Morley's indices (Morley, 1997) are used here since

> 2 Listed in the Appendix

10
# **References and resources**

Akinci, Ozge, Gianluca Benigno, Ruth Cesar Heymann, Julian di Giovanni, Jan J.J. Groen, Lawrence Lin, and Adam I. Noble. 2022. “The Global Supply Side of Inflationary Pressures.” Liberty Street Economics, FRB New York, https://libertystreeteconomics.newyorkfed.org/2022/01/the-global-supply-side-of-inflationary-pressures/ Arslanalp, S.; R. Koepke ; J. Verschuur. 2021. “Tracking Trade from Space: An Application to Pacific Island Countries”, IMF Working Paper No. 2021/225 (August 2021) https://www.imf.org/en/Publications/WP/Issues/2021/08/20/TrackingTrade-from-Space-An-Application-to-Pacific-Island-Countries-464345 Baltic Exchange. 2024. "BDI Baltic Exchange Dry Index"; London, United Kingdom Retrieved from Trading Economics: https://tradingeconomics.com/commodity/baltic Accessed: May 2, 2024 Flexport Research. 2024. "Ocean Timeliness Indicator", January 8, 2024 https://www.flexport.com/research/oceantimeliness-indicator/ Accessed: April 25, 2024 KPMG LLP and Association for Supply Chain Management (ASCM). 2022. "The KPMG Supply Chain Stability Index" 2022 https://kpmg.com/us/en/articles/2023/supply-chain-stability-index.html Accessed: April 25, 2024 Marine Traffic. 2023. MarineTraffic: Global Ship Tracking Intelligence. https://www.marinetraffic.com/en/ais/home/ Notteboom, T., A. Pallis and J-P Rodrigue (2021) “Disruptions and Resilience in Global Container Shipping and Ports: The COVID-19 Pandemic vs the 2008-2009 Financial Crisis”, Maritime Economics and Logistics. https://doi.org/10.1057/s41278-020-00180-5.

Rodrigue, J-P. 2020. “The Geography of Transport Systems”, Fifth Edition, London: Routledge. 456 pages. ISBN: 978-0367-36463-2. https://doi.org/10.4324/9780429346323.

Sea-Intelligence, "Press Release: Schedule reliability improves M/M in February 2024" (April 2024), Global Liner Performance (GLP) report https://www.sea-intelligence.com/press-room/254-schedule-reliability-drops-again-injanuary-2024 Shanghai Shipping Exchange. 2024. "Shanghai Containerized Freight Index" https://en.sse.net.cn/indices/scfinew.jsp, Accessed: April 25, 2024 S&P Global and Institute for Supply Management (ISM), 2024. US Composite Purchasing Managers’ Index (PMI) Retrieved on April 25, 2024 from https://tradingeconomics.com/united-states/composite-pmi S&P Global & Global eProcure (GEP). 2024. "Global Supply Chain Volatility Index" https://www.gep.com/newsroom/demand-at-asian-factories-rises-at-strongest-rate-in-over-2-years-improving-nearterm-growth-outlook-for-manufacturing-worldwide-gep-global-supply-chain-volatility-index , Accessed April 25, 2024 UNCTAD. 2024. “Liner Shipping Connectivity Index (LSCI)”, Geneva Switzerland, March 2024, https://unctadstat.unctad.org/datacentre/dataviewer/shared-report/8df8f52c-de5e-4090-84ea-5d428b94011f Verschuur, J., Koks, E.E. & Hall, J.W. 2022. “Ports’ criticality in international trade and global supply-chains”. Nat Commun 13, 4351 (2022). https://doi.org/10.1038/s41467-022-32070-0 19
**Figure 1: Credit Disbursement**

<!-- Start of picture text --> c 20000<br>2 1 5000 _<br>e 10000<br>2 5000<br>* ~07<br>1981 1983 1985 1987 1989 1991 1993<br>Fiscal Year<br>& - ADBP -- Coops -C-Comm Banks 0-Total<br><!-- End of picture text --> Lending by all agencies increased until 1987, and by the Agricultural Development Bank of Pakcistan (ADBP) until 1990, followed by stagnation. That, and the recent bailout of agricultural cooperatives by the government are all signs of serious problems in the rural financial system in Pakistan.

The formal credit sector lacks dynamism. Two institutions (ADBP, and the Federal Bank for Cooperatives) comprise virtually the entire formal sector. Both have failed to mobilize deposits, and rely on transfers to maintain lending capability. Deposits have been discouraged through interest rate ceilings and the availability of more a tractive goverunent bonds. Cheap financing from the public sector gave the state banks no incentive to mobilize deposits. Prudent financial regulation of the institutions is non-existent.

The 1972 Banking Reforms forced commercial banks to meet a target level of lending to the agricultural sector. Quotas were also introduced for lending by size of farm. Lending is conducted at non-economic rates, for non-viable projects and is directed to large farms, and the rural elite.

As in many developing countries, the credit system has poor enforcement and, Zo, many non-performing loans. Attempts at recovery can take years. Almost 30 percent of the US$100 million that farmers spend on tractors each year comes from loans that eventually turn bad. Thus distortions in the credit market have wider effects: in this case, they lower the price of tractors and introduce a major distortion in factor prices.

Collateral requirements are a major obstacle to small farmers. Amongst small farmers, credit access has increased much faster for owners and owners-cum-tenants than for tenant farmers. Nevertheless, there is a bizarre side to the use of land as collateral - foreclosures are non-existent. This obviously gives rise to perverse incentives.

The use of crops as collateral has been proposed as a partial solution to the lack of credit. This, however, exposes the bank to much additional risk - variability in the price of the crop and the chance that the purchaser might not pay for the crop. These risks are present for all crops, but are particularly pronounced for horticultural export crops. Banks do not have the technical

**1I**
|**Table 6: Poisson regressions for th**|**e number of pr**|**ojects in bilate**|**ral relations**|||||
|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|**Bilateral variables**||||||||
|Distance|-0.5900***|-0.6002***|-0.6165***|-0.5921***|-0.5878***|-0.5960***|-0.6171***|
||[0.061]|[0.060]|[0.060]|[0.062]|[0.060]|[0.060]|[0.059]|
|Colonial relationship|1.1699***|1.1558***|1.0550***|1.1714***|1.1726***|1.1840***|1.0545***|
||[0.263]|[0.265]|[0.221]|[0.265]|[0.265]|[0.261]|[0.220]|
|**Investor country variables**||||||||
|Net food imports per inhabitant|3.3056***|3.3913***|3.3203***|3.3733***|3.3758***|3.3843***|3.3208***|
||[0.368]|[0.354]|[0.373]|[0.357]|[0.351]|[0.351]|[0.372]|
|Population|0.7817***|0.7771***|0.7634***|0.7683***|0.7726***|0.7753***|0.7635***|
||[0.048]|[0.047]|[0.049]|[0.048]|[0.047]|[0.047]|[0.049]|
|**Host country variables**||||||||
|Food exports|0.0320|0.0345|0.0974***|0.0203|0.0604*|0.0477|0.0984***|
||[0.032]|[0.031]|[0.037]|[0.033]|[0.034]|[0.033]|[0.035]|
|Suitable non-forest land|0.4664***<br>[0.074]|||||||
|Suitable forest land|0.0320<br>[0.043]|||||||
|Max. possible output value non-<br>forest area||0.5162***|0.4830***|0.5307***|0.4904***|0.4909***|0.4846***|
|||[0.072]|[0.089]|[0.074]|[0.076]|[0.077]|[0.091]|
|Max. possible output value forest<br>area||0.0280|-0.0114|0.0050|0.0328|0.0302|-0.0118|
|||[0.041]|[0.043]|[0.041]|[0.040]|[0.041]|[0.043]|
|Yield gap|0.9486**|1.3042***|0.4959|1.3590***|0.9265**|1.0388**|0.5075|
||[0.376]|[0.404]|[0.519]|[0.442]|[0.472]|[0.439]|[0.511]|
|Land governance index|||-0.2082***<br>[0.049]||||-0.2136***<br>[0.059]|
|Weak investor protection||||-0.0013<br>[0.001]||||
|Control of corruption (%)|||||-0.0072*<br>[0.004]|||
|Political stability (%)||||||-0.0070*<br>[0.004]|0.0007<br>[0.004]|
|No. of observations<br>|25,704|26,838|20,223|25,515|26,838|26,838|20,223|
|Pseudo R<sup>2</sup>|0.269|0.265|0.261|0.260|0.266|0.267|0.261|

**_Notes:_** Variable in logs. Robust standard errors in brackets*** denotes significance at the 1% level, ** at the 5% level, and * at the 10% level. Constant included but not reported.

25
aggregates. One disadvantage is that migrants were sampled through neighborhood committees, so that unregistered migrants and those living in collective forms of housing (e.g., on construction sites or in dormitories) may be underrepresented. Thus, workers in the construction and manufacturing sectors (as well as some service sector jobs) are likely to be under-sampled.

We also analyze data from a nationally representative survey of over 1,600 manufacturing firms in China conducted by the Research Department of the People’s Bank of China in the fall of 2009 (hereafter People’s Bank Manufacturing Firm Survey (PBMFS)). The authors contributed an employment module that included questions on employment changes and the implementation of the new Labor Law. The surveys were conducted in 25 cities located in eight provinces, including 4 coastal provinces (Shandong, Jiangsu, Zhejiang, and Guangdong), one northeast province (Jilin), one central province (Hubei), one northwest province (Shaanxi), and one southwest province (Sichuan). The sampling frame for the PBC national firm survey includes all firms who have ever had credit relationship with any financial institution, which is likely to under-sample very small firms. The firm survey collected information on managers’ views of the impact of the Labor Contract Law on labor costs, training activity and costs associated with introduction of the new Law, and the number of employees and strictness of enforcement of labor regulations at four points in time which span the implementation of the new Law and the onset of the global economic crisis: December 2007, June 2008, December 2008, and June 2009.

# **3. How well was the law implemented?**

**_Assessments of Enforcement Strictness_** . We start by describing subjective assessments of enforcement of the Labor Law by workers and managers from the household and firm surveys. In the 2010 CULS, workers were asked whether implementation of the

7
Table 13: Productivity Decompositions and Policy Changes: By Firm Size _Panel A: Small Firms (<20 Employees)_

||Aggregate|Average|Reallocation|
|---|---|---|---|
||(1)|(2)|(3)|
|Final Goods Tariff|-.082|-.048|-.034|
||(.036)<sup>_∗∗_</sup>|(.037)|(.020)<sup>_∗_</sup>|
|Input Tariff|-.508|-.426|-.081|
||(.159)<sup>_∗∗∗_</sup>|(.148)<sup>_∗∗∗_</sup>|(.070)|
|FDI Reform|.052|.038|.014|
||(.021)<sup>_∗∗_</sup>|(.015)<sup>_∗∗_</sup>|(.014)|
|Delicensed|-.030|-.031|.001|
||(.022)|(.020)|(.013)|
|Obs.|10018|10018|10018|
|_R_<sup>2</sup>|.06|.076|.017|
|_Panel B: Medium-Sized Fir_|_ms(20-99 Employees)_|||
|Final Goods Tariff|-.079|-.057|-.022|
||(.029)<sup>_∗∗∗_</sup>|(.039)|(.025)|
|Input Tariff|-.464|-.496|.032|
||(.118)<sup>_∗∗∗_</sup>|(.136)<sup>_∗∗∗_</sup>|(.082)|
|FDI Reform|.024|.032|-.008|
||(.017)|(.018)<sup>_∗_</sup>|(.012)|
|Delicensed|.025|.036|-.011|
||(.019)|(.022)|(.015)|
|Obs.|10450|10450|10450|
|_R_<sup>2</sup>|.064|.066|.014|
|_Panel C: Large Firms(100_|_+ Employees)_|||
|Final Goods Tariff|-.016|-.008|-.008|
||(.033)|(.041)|(.025)|
|Input Tariff|-.627|-.653|.026|
||(.127)<sup>_∗∗∗_</sup>|(.140)<sup>_∗∗∗_</sup>|(.085)|
|FDI Reform|.079|.091|-.012|
||(.025)<sup>_∗∗∗_</sup>|(.025)<sup>_∗∗∗_</sup>|(.014)|
|Delicensed|.045|.014|.031|
||(.025)<sup>_∗_</sup>|(.025)|(.019)<sup>_∗_</sup>|
|Obs.|7993|7993|7993|
|_R_<sup>2</sup>|.06|.03|.011|

Each observation is a state-industry. Firms are classified into size categories based on the first year in which we observed them. Dependent variable names are given at the top of each column. “Aggregate” indicates market-share-weighted mean productivity, “Average” indicates unweighted mean productivity, and “Reallocation” indicates the covariance between market share and productivity. All specifications are fixed-effects analyses at the state-industry level, and include year dummies. Each observation is weighted by the total number of firms in the state-industry across all years, and standard errors are clustered at the state-industry level.

51