rafmacalaba/datause-encoder-data huggingface dataset:
|**Table A2: OECD Development Centr**<br>**Data Provider**<br>OECD Development Centre<br> <br>|**e Afr**<br> <br>|**ican**<br>|**Econo**<br>|**mic O**<br>|**utloo**<br>|**k (AE**|**O)**|||
|---|---|---|---|---|---|---|---|---|---|
|**Description**<br>Multilateral organization he<br>**Website**<br>www.oecd.org/dev/aeo/<br> <br>|adqua<br>|rtered|n Paris|, Franc|e|||||
|**Data Source**<br>African Economic Outlook<br> <br>||||||||||
|**Type**<br>Expert Assessment<br>||||||||||
|**Respondents**<br>OECD Development Cente<br>**Frequency**<br>Annual since 1996<br>**Coverage**<br>African countries|r Staff|||||||||
|**Public Access**<br>Publicly available through<br>**Description**<br>Indicators are based on the<br>to "civil tensions" indicato<br>newspaper Marchés Tropic<br>from AFP in 2008. Total sc<br>distribution is highly skew<br>reported events. We con<br>(0,0.5,1) in each year.|African<br>freque<br>r. N<br>aux et<br>ores a<br>ed by<br>verted|Econ<br>ncy of<br>ewpap<br>Médit<br>re repo<br>a few<br>these|omic O<br>newsp<br>er repo<br>erranée<br>rted fo<br>countri<br>origina|utlook<br>aper re<br>rts are<br>ns bet<br>r each<br>es wit<br>l scor|report<br>ports o<br>taken<br>ween 1<br>countr<br>h very<br>es to|tables.<br>n inci<br>from<br>996 a<br>y and<br>high f<br>a three|<br>dents r<br>the w<br>nd 200<br>year, a<br>requen<br>-point|elated<br>eekly<br>7 and<br>nd the<br>cy of<br>scale||
|2008<br>**Voice and Accountability**<br>NA<br>..|2007<br>..|2006<br>..|2005<br>..|2004<br>..|2003<br>..|2002<br>..|2000<br>..|1998<br>..|1996<br>..|
|**Political Stability and Absence of Violence**<br>Civil Tensions<br>X|X|X|X|X|X|X|X|X|X|
|**Government Effectiveness**||||||||||
|NA<br>..|..|..|..|..|..|..|..|..|..|
|**Regulatory Quality**<br>NA<br>..|..|..|..|..|..|..|..|..|..|
|**Rule of Law**<br>NA<br>..|..|..|..|..|..|..|..|..|..|
|**Control of Corruption**<br>NA<br>..|..|..|..|..|..|..|..|..|..|
|**Country Coverage**<br>**48**<br>**Year of Publication**<br>**2008**|**36**<br>**2007**|**33**<br>**2006**|**33**<br>**2005**|**32**<br>**2004**|**26**<br>**2003**|**26**<br>**2002**|**26**<br>**2000**|**26**<br>**1998**|**25**<br>**1996**|

41
The composite health-promoting good _x_ is part of overall household consumption _c_ . Unless _x_ is inferior (in the sense that its consumption level decreases with total consumption _c_ ), then a recession that lowers family incomes will also lower consumption of these goods, say from _x0_ to _x1_ . By moving from point A to point C in Figure 2, child health outcomes in the family are reduced, and child health is expected to be pro-cyclical. If _public_ health expenditures also fall during the recession, this would shift the curve from _h0 (x, t, g)_ to _h2 (x, t, g)_ —in addition to the negative effect on health from declining private expenditures (A to C), there is a further deterioration caused by declining expenditures from the public sector (C to D). This second kind of recession leads to even more pro-cyclical health outcomes.

It may be, however, that decreases in the consumption of health-promoting goods and (possible) reductions in public health expenditures are offset by other effects. Health- _reducing_ goods such as smoking and drinking may also be normal, in which case consumption of them might decrease during recessions. Recessions also increase the time available to adult household members for health-promoting behaviors. If reductions in employment or hours worked free up time that can be used in better child care, the production function shifts upwards. If this increase is large enough to offset any effect from falling _g_ , then the curve could move from _h0 (x, t, g)_ to a position such as _h1 (x, t, g)_ , where child health outcomes are actually higher than before the crisis (comparing points A and B). Below we discuss the evidence on these changes, primarily based on data for the United States.

Finally, there may be changes in the composition of women giving birth. Dehejia and Lleras Muney (2004) provide a useful discussion of this, drawing on Becker (1965), Ben-Porath (1973), Ward and Butz (1980) and Heckman and Walker (1990). Dehejia and Lleras Muney (2004) argue that decreases in women’s wages, caused by an aggregate shock, have income and substitution effects on fertility. Children are time-intensive, so the substitution effect would result in women having more children during a crisis. On the other hand, the income effect reduces the demand for children. The relative importance of the income and substitution effects is likely to vary across households, with the income effect being more important for women who are credit constrained. The aggregate effect of a crisis on the composition of women is therefore ambiguous, and is an empirical matter. A number of papers using data from developing countries, including Paxson and Schady (2005), Baird, Friedman, and Schady (2007), and 8
a total of 76 of these firms, of which 39 were treated firms, 16 were control firms in treatment markets, and the remaining 21 were pure controls. As the results from adding these extra 76 are identical to the main estimates in the text we abstract from them.

# **5 Data**

We collected baseline data from October 1st to November 22nd, 2012. We then selected only those firms that declared not to be registered with the tax authority. It is important to reiterate that we tried to construct a census of firms within a given market; as explained earlier we used local knowledge as well as physical boundaries and an algorithm to define our notion of market. We collected a wealth of information on firm characteristics and business outcomes, i.e. the firm’s location (including GPS coordinates), the number of full time and part-time employees, amount invested in the last quarter, monthly sales and profits, the share of sales on credit, loans amount, sources (bank, micro-credit, money lenders, family and friends) and associated interest rates, length of contractual relationships with clients, share of sales to domestic market, interaction (if any) with different government agencies (including the NBR). We also collected information on the business owner’s durable consumption in the form of housing situation (owning versus renting), and value (either housing value or rent). We then collected the same information in our follow-up interviews, importantly we added a question to determine whether the business was visited by an NBR representative. Firms were re-interviewed in the follow up between September 7th and October 2nd, 2014.

As can be noted from Table 1 our sample of firms is mainly constituted by micro to small businesses (2 employees on average) in the trade sector (about 60 percent), while we have an almost even split of the remaining firms in the manufacturing and service sectors. Firm profits in the average month are about 15,000 Taka (BDT) (or 200USD) with average monthly revenues of about BDT 150,000.<sup>4</sup> The implied ratio of profits over revenues is about 10 percent and seems lower than for similar populations across several countries as summarized in McKenzie and Woodruff (2012). For example, in the US the average profit margins for the 20 most profitable firms ranges between 16.5% and 10.4%.<sup>5</sup>

> 4For the relevant period the exchange rate is about 75 Taka (BDT) per USD.

> 5Forbes (2011), “...The data are drawn from financial statements on nearly 300,000 companies, most with under USD10 million in annual revenue, and bucketed by five- and six-digit North American Industry Classification System codes. The figures were gathered between Jan 1, 2003 and Jan 1, 2011, to capture an entire business cy-

9
16

# **V. Large value payments in developed countries and the role of the central bank**

In developed countries which have large domestic and/or international money markets, timesensitive high value payments are typically sent over separate, secure, immediate payment, electronic interbank wire transfer networks. While the U.S. Fedwire network evolved from early telegraphic, final payment arrangements for settling interbank interregional consumer and business check payments using central bank offices, other networks-such as CHIPS (U.S.), CHAPS (U.K.), and BOJ-NET Uapan)evolved as a more secure and timely extension of bank clearing house check (non-final) payment arrangements.

The average value of a payment ?ver a wire transfer network is quite high, averaging **$4.** million (U.S. dollars) in Europe for 1993, $93.1 million for Japan, and $4.2 million for the U.S. The volume, value, anc4average value per wire transfer for 9 networks across 7 developed countries are shown in Table 2. The value of wire transfers in these countries ranges from 11 times the country's GDP to 100 times GDP. With such large values being transferred over wire transfer networks, the disruption of economic activity and/or risk of loss associated with the failure of participants on these networks is a major concern.

**Table 2. Selected large value wire transfer systems, 1993 (annual data)**

||||||**_Trac_**|**_ions_**||
|---|---|---|---|---|---|---|---|
|**_Country_**|**_System_**|**_Ownerl_**<br>**_manager_**|**_Settlemnet_**<br>**_2_**|**Volume**<br>_(millions)_|**_Value_**<br>_(US$_**_billon,)_**|**_Average value_**<br>_(US$millions)_|**_Value to_**<br>**_GDP_**|
|France|SAGITTAIRE|<br>CB|Net|3.9|19,204|4.9|15|
||TBF|CB|RTGS|12.0|52,412|4.4|42|
|Germany|EAF|CB|Net|10.9|78,158|7.2|41|
|Italy|ME|CB|Net|1.9|10,846|5.7|11|
|Switzerland|SIC|CB**_+_**B|RTGS|67.4|23,097|0.3|100|
|U.K.|CHAPS|B|Net|11.0|35,353|3.2|37|
|Japan|BOJ-NET|CB|Net & RTGS|<br>3.8|353,818|93.1|84|
|U.S.|Fedwire|CB|RTGS|69.7|207,630|3.0|33|
||CHIPS|B|Net|42.2|262,256|6.2|41|

**1.** CB - _Central Bank; B - Banks; 2._ Net - Multilateral net settlement; RTGS - Real time gross settlement _Source: BIS: Payment Systems in the C;-1O_ Countries, 1994

**_12_** _This figure_ represents an average _of payment values_ over _5 wire transfer networks: CHAPS (U.K.), SAGITTAIRE and TBF_ (France), _EAF_ (Germany), _and ME (Italy). The SIC network in Switzerland transfers both large and smaller value payments (with an average value of only $0.3 million) and was excluded because data are not reported separately for large_ value payments.

**_13_** _Japan is represented by BQ)-NET while the U.S. figure is an average of CHIPS and Fedwire._

**_14_** _Other large value wire transfer networks exist and are illustrated in Bank for Intemational Settlements (1994),_ Table _lob._
Policy Research Working Paper 6301

# **Abstract**

This paper explores a rich dataset of monthly firm-level data on the population of exporters of Lao PDR from 2005 to 2010. The survival analysis uses a discretetime logistic model based on firm-product-destination triplets while accounting for unobserved heterogeneity. It looks in detail at the role played by two important and related determinants of survival: experience and networks. These are particularly relevant for developing countries, where relevant export experience in firms is likely to be limited and networks leveraging it all the more important. The analysis reveals the positive impact of having prior experience with the export product and destination, experience with importing, as well as using a developed neighboring country as launch platform. Networks are found to be most relevant when they are most specific—the largest impact comes from province level aggregations of firms selling the same product in the same market during a particular month. A competing risks model was also investigated to discern the impact of these determinants on the likelihood of experiencing an upgrade to a superior product versus termination when a trade contract ends.

This paper is a product of the Poverty Reduction and Economic Management Sector Department, East Asia and the Pacific 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 author may be contacted at rrecord@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
(Senegal) (Figures 2a and 2b). From 2007 onward, transit through Abidjan started to recover but even by 2016, it was well below its pre-CIV crisis level for Burkina.<sup>7</sup> While the effect of political instability on maritime trade is evident from Figures 2a and 2b, volumes of trade that took place among neighboring countries are not recorded officially by routes. But the disruption of trade along the Abidjan route is expected to have similar adverse effects on the inland trade as well. It is clear from Figures 2a and 2b that shock to trade routes coincided with the CIV political crisis and the impact of shock lasted about 6 years before it started to recover. The shock was thus temporary but not too short-lived as to make no difference. We exploit this negative shock to transit through the BamakoAbidjan and Ouagadougou-Abidjan routes as a source of exogenous variation in market access concentrated in a particular region in Mali and Burkina Faso.<sup>8</sup>

# **(4) Conceptual Framework**

We develop a model that incorporates two distinguishing features of a poor developing economy. First, the production structures of poorer developing countries are dominated by agriculture. The share of non-agricultural employment in Mali and Burkina was less than 10 percent in the pre-CIV crisis period.<sup>9</sup> Second, roads are sparse and paved roads are rare. The high costs of transportation mean that not all areas trade with each other and pockets of autarkic subsistence economies are more of a rule than an exception.

# **(4.1) The Basic Set-up**

## **Geography and Endowment**

We take the location of cities and the transport network as given, and then examine how spatial distribution of population and sectoral employment shares respond to a shock to transport costs. We consider a country which consists of two cities: _K_ and _J_ . City _K_ uses

> 7For Mali, we do not have access to more recent transit data.

> 8There was a very brief period of political violence in 2011 in CIV that started in March 2011 and ended on April 11, 2011 with the arrest of Laurent Gbagbo by French forces.

> 9The share of manufacturing employment was less than 5 percent during the pre-CIV crisis period in both countries.

8
Table 2. Effects of the PEP Program on Fertility Decisions

||Dependent Var<br>|iable: Likelihood<br>|of havingchildren o<br>|f<br>|
|---|---|---|---|---|
||0 years of age<br>(1)<br>|1 year of age<br>(2)|2 years of age<br>(3)|3 years of age<br>(4)|
|_Panel A_: Estimates with baseline|and wave I||||
|PEP [=1]|-0.072***|-0.057***|0.007|-0.000|
||(0.017)|(0.016)|(0.005)|(0.003)|
|Observations|2,640|2,640|2,640|2,640|
|_Panel B_: Estimates with baseline|and wave II||||
|PEP [=1]|-0.006|-0.084***|-0.043***|0.001|
||(0.013)|(0.018)|(0.016)|(0.003)|
|Observations|2,640|2,640|2,640|2,640|
|_Panel C_: Estimates with baseline|,wave I and II||||
|PEP [=1]|-0.039***|-0.070***|-0.018*|0.001|
||(0.010)|(0.012)|(0.009)|(0.003)|
|Observations|3,960|3,960|3,960|3,960|
|_Controls in allpanels_|||||
|Wave FE|Yes|Yes|Yes|Yes|
|HH FE|Yes|Yes|Yes|Yes|
|Department_×_wave|Yes|Yes|Yes|Yes|
|Geographic sampling_×_wave|Yes|Yes|Yes|Yes|
|Pre-migration controls_×_wave|Yes|Yes|Yes|Yes|

_Notes:_ The table presents the estimates of the specification described in equation (1). Panel A presents results using data from the baseline and wave I, panel B shows results using data from the baseline and wave II, and panel C presents results stacking all the data together (baseline, wave I, and wave II). Department corresponds to the five departments in which the sample was collected and geographic sampling corresponds to the four geographic levels at which the sample is representative, including three main cities and a fourth group that accounts for nine smaller urban centers with prevalent migration from Venezuela. Pre-migration control variables include: (i) individual controls for the head of household (gender, age, and education); (ii) labor history for the head of household (probability of being employed, type of job, probability of having a written contract, and the time gap between the last job and the migration episode); (iii) household characteristics (number of children, household size, access to public services, owning dwelling, and having a smartphone); and (iv) networks prior to migration episode (had family and friends in Colombia, knew of job opportunities before migrating, and migrated for health-related reasons). Standard errors clustered at the household level in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

25
# **Severance Pay Compliance In Indonesia**

Vera Brusentsev, David Newhouse, and Wayne Vroman<sup>1</sup> _JEL codes:_ J32, J65 _Keywords:_ Severance Pay, Non-compliance, Indonesia, Labor Regulations

> 1 Brusentsev is Professor of Economics at Swarthmore College. Newhouse is a Labor Economist with the Social Protection Unit of the World Bank, and Vroman is a Senior Economist at the Urban Institute. This is a background paper for the Indonesian Jobs Report. We gratefully acknowledge Vivi Alatas, who provided indispensible leadership for the Jobs Report and was instrumental in adding of questions on severance pay to the Indonesian surveys. Lina Marliani and Ari Perdana provided invaluable research assistance. Finally, we thank Gordon Betcherman, Shubham Chaudhuri, Edgar Janz, Chris Manning, Pierella Paci, and Bill Wallace for helpful comments and support.
- 13 -

cities which began in this period, there was an increase in commuting from rural homes.

In the period since 1970, net migration has averaged 176,000 annually, although since 1980 it has declined to 146,000 people, due to the slowdown in the economy and lower investment levels in both industry and housing. During the 1970s, rapid growth in non-agricultural investments and output spurred rural-urban n.igration, _as_ did agricultural modernization, increased mobility of older farmers who handed over their land in return for pensions, the entry of Poland's baby-boom generation into the labor force, and the acceleration of housing investments.

To sum up, throughout the post-war period in Poland, a whole range of policies--industrial, agricultural, and urban--have had both direct and indirect effects on migration rates. In section V, we present the results of our model component of migration as a function of demographic variables, wages, and the housing shortage.

# Are Wage Rates Distorted?

The World Bank (1987) analysis of the Polish labor market finds that "in spite of goods shortages the household sector is more responsive to real wages than are firms", providing evidence that labor responds to wage variations.

The level of wage dispersion across aggregate economic sectors in Poland is high when compared to Western market economies and even when compared to other East European countries; by 1976, the latest year available for comparison, Poland had the most unequal earnings structure among its East European neighbors (Askansas and Levcik, 1983). A significant factor in this dispersion is the favored position of mining in the wage structure; the mining wage is almost twice that of the average manufacturing wage. Within the manufacturing sector, Poland's wage dispersion is lower than in Western market economies but still higher than in other East European countries, as indicated in Table 4.

Regional-level (voivodship) data on average monthly wages for 1985 in all sectors and in the industrial sectors is presented in Table 5. The coefficient of variation indicates that average regional industrial wages were more variable across voivodships than were average regional all-sector wages.

Our analysis of the relationship between wage rates and the housing shortage, presented in the next section, uses a model of wages as a function of a region's industrial mix and housing shortage. We found a strong and robust relationship between these factors.

# V. EMPIRICAL MODELS

In this section we test three behavioral models, designed to answer the following questions:
Table 5: Influence Asymmetry–Robustness to Channel-level Inequality

||Cens|us Farm Hous|eholds|Al|l Census Hous|eholds|
|---|---|---|---|---|---|---|
||Gini|Top 5% share|Landless prop.|Gini|Top 5% share|Landless prop.|
|_τ_ <sup>1</sup> _×_(<sup> ̄</sup>_LH −_<sup> ̄</sup>_LT_)|0.00540***|0.00520***|0.00534***|0.0109***|0.0108***|0.0109***|
||(0.00202)|(0.00201)|(0.00205)|(0.00299)|(0.00297)|(0.00295)|
|_τ_ <sup>1</sup> _× inequality_|-0.0302|0.0471|-0.00525|0.142*|0.0557|0.123**|
||(0.0893)|(0.0569)|(0.0659)|(0.0861)|(0.0524)|(0.0490)|
|_R_<sup>2</sup>|0.567|0.567|0.567|0.566|0.566|0.566|
|No. of Obs.|586,417|586,417|586,417|602,425|602,425|602,425|
|No. of clusters|892|892|892|909|909|909|
|No. of FOs|247|247|247|252|252|252|

_Notes:_ See notes to Table 4. Columns 1-3 should be compared to Table 4, panel (a), column 2; Columns 4-6 should be compared to Table 4, panel (b), column 2.

irrigators on channels along which large landowners most predominate at the _tail_ (log _θi_ two standard deviations below the mean) suffered essentially no erosion in water allocation following reform.

# **5.3 Robustness to channel-level inequality**

A large literature on collective action in commons management highlights the importance of heterogeneity among users (e.g., Ostrom 1990; Baland and Platteau 1997), although the effect of inequality on outcomes is often theoretically ambiguous (Bardhan and DaytonJohnson 2002). In the context of surface irrigation systems, Bardhan (2000) and DaytonJohnson (2000) find that the landholdings Gini coefficient is negatively associated with cooperation in water allocation and channel maintenance. Our concern here is that, if FOs along channels with, say, greater wealth inequality produce less cooperative outcomes, and if overall _channel-level_ land inequality is correlated with head-tail differences in landholdings, then our heterogeneity results in Table 5 may be spurious.

To deal with this concern, we construct channel-level measures of land inequality—Gini coefficient, share of land owned by top 5%, and proportion of landless—using the Agricultural 34
23 a In the absence of data, it is assumed the share of input tax/subsidy, domestic production tax/subsidy and border tax/subsidy payments for noncovered farm products are the same as those for covered farm products.

b All entries have been generated by dividing the producer subsidy equivalent of all (including NPS and ‘decoupled’) measures by the total agricultural sector’s gross production valued at undistorted prices.

c All entries have been generated by dividing the consumer tax equivalent of all measures by the total consumption value (at the farmgate level, valued at undistorted prices).

Source: Authors’ compilation based on data in Anderson and Valenzuela (2008) and Anderson and Croser (2009).