rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 8749

# **Abstract**

This paper studies the effects of a large increase in the minimum wage on the destruction of formal firms, and the associated impacts on employment, wages, and informality in a developing economy. It examines the ramifications of a 33 percent nominal increase in the minimum wage that took effect in Turkey in 2016. It utilizes an exceptionally rich, employer-employee-linked dataset that shows the wage distribution within firms, which enables to measure the degree of firms’ minimum-wage exposure, and to estimate causal effects using a difference-in-differences approach. The paper shows that raising the minimum wage substantially increased the destruction of formal firms, leading to a fall in the number of formal firms in the economy. Effects are concentrated among small firms with low levels of productivity; highly productive firms are unaffected. The minimum-wage increase had negative effects on formal employment that largely originated through firm destruction, rather than through cuts in formal employment within surviving firms. Among workers who lost jobs in firms that ultimately exited, only a minority had obtained new employment a year later. The minimum-wage increase was accompanied by a rise in inactivity and unemployment, rather than growth of informal employment, suggesting that workers who lost formal employment mostly failed to find new jobs one year after the minimum wage hike, even informally. While the higher minimum wage had large, positive effects on the wages of formally employed workers, limited effects on the wages of informal workers are reported.

This paper is a product of the Social Protection and Jobs Global Practice. 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 authors may be contacted at lbossavie@worldbank.org; mmakovec@eorldbank.org; or aacar@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
We test these theoretical predictions using market-level panel data on producer prices for millet, sorghum, and cowpea in Niger between 1999 and 2008. We find that mobile phone coverage reduces the spatial dispersion of producer prices by 6 percent for cowpea, a semi-perishable cash crop in Niger. These effects are larger for more isolated markets and during certain periods of the year. We find no effect of mobile phone coverage on producer price dispersion for millet or sorghum or on producer price levels. Nevertheless, mobile phone coverage reduced intra-annual producer price variation for cowpea by 6 percent.

We use farmer-level data to better understand the correlations between mobile phone coverage and farmers’ marketing behavior. While farmers were more likely to use information technology as a search tool, mobile phone ownership was not correlated with changes in farmers’ marketing behavior or the sales prices that they received, consistent with Fafchamps and Minten (2012) and Aker and Ksoll (2013). Yet Tack and Aker (2014) find evidence that mobile phone coverage increased traders’ search behavior over time, suggesting that most of the reductions in search costs might have accrued to traders during this period.

Our paper makes two contributions. First, the results speak to a substantial economic literature showing that information is crucial for the effective functioning of markets, both from a theoretical (Stigler 1961; Reinganum 1979; Stahl 1989) and an empirical perspective (Autor 2001; Brown and Goolsbee 2002; Jensen 2007; Aker 2010; Goyal 2010). Most of this work has focused on consumer markets, with a more recent extension to producer prices (Jensen 2007, Muto and Yamano 2009; Goyal 2010; Fafchamps and Minten 2012). However, few of these studies assess the impact of information technology on agricultural producer price dispersion, as our work does.

Second, much of the existing literature concentrates on the impact of information technology on a single good (Jensen 2007; Aker 2010; Goyal 2010). To our knowledge, only Muto and Yamano (2009) and Nakasone (2013) assess the impact of information technology on perishable and non-perishable commodities. Our study is able to measure the impact of information technology on multiple goods with varying perishability and over a long time period.

3
COST RECOVERY, EQUITY, AND EFFICIENCY IN WATER TARIFFS IN AFRICA

**Figure 8B. Comparison of connection costs with GNI per capita**

<!-- Start of picture text --> SEEN<br>AdeM Quelimane<br>AdeM Pemba<br>AdeM Nampula<br>Adem Maputo<br>ADeM Beira<br>Electrogaz<br>ONEA<br>CRWB<br>FCT WB<br>SONEB<br>NWC South Darfur<br>SODESI<br>Dire Dawa<br>SDE<br>WASA<br>NWC Khartoum<br>KIWASCO<br>NWSC<br>LWSC<br>AWSA<br>Katsina WB<br>Windhoek<br>Johannesburg<br>eThekwini<br>Drakenstein<br>SWSC<br>NWASCO<br>DAEASCO<br>ADAMA<br>DUWASA<br>MWSA<br>Tygerberg<br>Kaduna<br>ELECTRA<br>NWC Upper Nile<br>Oshakati<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 110%<br>Connection Cost as % of GNI Per Capita<br><!-- End of picture text --> _Source:_ AICD WSS Survey Database, 2007.

# Cross-subsidies for water and wastewater consumers

Many African utilities are responsible for providing wastewater services in addition to water supply. Wastewater charges are either included in the water bill or imposed separately in the form of a fixed or block structure. For the utilities that enforce a block tariff structure, it is possible to compare water and wastewater prices at similar levels of consumption. The consumers of wastewater services are subsidized by water users (figure 9). In ONEA and ONAS, particularly, the water charges are several orders of magnitude higher than the wastewater charges.

17
Jäckle, A., Lynn, P., Sinibaldi, J., Tipping, S. (2013). The effect of interviewer experience, attitudes, personality and skills on respondent co-operation with face-to-face surveys. Survey Research Methods, Vol.7, No.1, pp. 1-15.

Johnson, W.T., Delamater J.D. (1976). Response effects in sex surveys. Public Opinion Quarterly; 40: 165–81 Kalton, G. and Schuman, H. (1982). The effect of the question on survey responses: A Review. Journal of the Royal Statistical Society. Series A (General), Vol. 145 (1), 42-73 Kane, Emily W., and Macaulay, L. J. (1993). Interviewer gender and gender attitudes. Public Opinion Quarterly 57:1–28 Karlan, D. and Zinman, J. (2012). List randomization for sensitive behavior: An application of measuring use of loan proceeds. Journal of Development Economics 98, 71-75 Laajaj, R. and Macour. K. (2017). Measuring Skills in Developing Countries. World Bank Policy Research Working Paper 8000.

Lenski, G. E., and Leggett, J.C. (1960). Caste, class, and deference in the research interview. American Sociological Review 65:463–467 Lupu, N., and K. Michelitch. 2018. Advances in Survey Methods for the Developing World. Annual Review of Political Science 21: 195-214.

McKenzie and Rosenzweig, (2012). Preface for symposium on measurement and survey design. Journal of Development Economics, vol. 98, 1-2 Randall, S., Coast, E., Compaore, N. and Antoine, P. (2013). The power of the interviewer: a qualitative perspective on African survey data collection. Demographic research, 28 (27). 763-792.

Reese, S.D., Danielson, W. A., Shoemaker, P.J., Chang, T. and Hsu, H. (1986). Ethnicityof-Interviewer effects among Mexican-Americans and Anglos. Public Opinion Quarterly, vol. 50 (4), 563–572 Schaeffer, N. C. (1980). Evaluating race-of-interviewer effects in a national survey. Sociological Methods and Research. 8: 400-419.

Schaeffer N.C., Dykema, J., Maynard, D.W. (2010). Interviewers and interviewing. In Handbook of Survey Research, Ed. Marsden, P.V. and Wright, J.D., pp. 437–70. Bingley, UK: Emerald Group Schuman H. and Converse J.M. (1971). The effects of black and white interviewers on black responses in 1968. Public Opinion Quarterly, vol. 35. 44–68 Serneels, P. M., Beegle, K. G., and Dillon, A. S. (2016). Do returns to education depend on how and who you ask? Policy Research Working Paper Series 7747, World Bank.

23
ECCAS’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE No countries in ECCAS have made progress **Table 2.12. Changes in market share of major regional** toward achieving international standards in air **carriers, 2001–07 (%)** safety, making this an area ripe for further regional collaboration. In some cases, problems Market share (%) 2001 2004 with air transport safety have been an Air Service undesirable consequence of market Hewa Bora Airways 0.6 liberalization (figure 2.13). Regional TAAG Angola Airlines 10.2 12.4 cooperation could help to improve oversight and safety by pooling scarce human resources and Trans Air Congo (TAC) 1.5 increasing regulatory independence—a process Ethiopian Airlines 1.1 that is under way. With support from France, Star Equatorial Airlines CEMAC member countries already belong to Cameroon Airlines 32.7 46.7 the Agence pour la Sécurité de la Navigation Air Gabon 24.6 32.4 Aérienne en Afrique et à Madagascar Air Afrique 14.6 (ASECNA), which provides air traffic safety Market concentration measures services. There is scope to broaden and deepen Concentration of top 3 71.9 91.5 this kind of collaborative arrangement. A _Source:_ Bofinger 2009. Cooperative Development of Operational Safety and Continuing Airworthiness Project (COSCAP) has been planned for the CEMAC region.

**Table 2.12. Changes in market share of major regional carriers, 2001–07 (%)**

|Market share(%)|2001|2004|2007|
|---|---|---|---|
|Air Service|||25.2|
|Hewa Bora Airways||0.6|22.9|
|TAAG Angola Airlines|10.2|12.4|16.9|
|Trans Air Congo (TAC)||1.5|8.9|
|Ethiopian Airlines|1.1||8.8|
|Star Equatorial Airlines|||6.9|
|Cameroon Airlines|32.7|46.7||
|Air Gabon|24.6|32.4||
|Air Afrique|14.6|||
|Market concentration measures||||
|Concentration of top 3|71.9|91.5|65|
|_Source:_Bofinger 2009.||||

An important factor of air safety is the vintage of the airline fleet. Unlike in southern Africa and other regions, the Central African airline fleet was not significantly renewed between 2001 and 2007. Here, the percentage of the airline fleet classified as ―Western old‖ has remained relatively steady at 36–37 percent. In fact, the number of planes classified as ―new‖ or ―somewhat new‖ decreased between 2001 and 2007 from 42 percent to 35 percent (figure 2.14).

Airport charges in the CEMAC region are extremely high by international standards—30 to 40 percent higher than the charges at Fraport in Frankfurt, a commonly used international benchmark. Gabon and Cameroon, two CEMAC countries for which data are available, record extremely high airport charges.

# **Box 2.1. Airport concession in Cameroon: A rare phenomenon in Africa**

Few public-private partnerships (PPPs) have been implemented in Africa to improve the overall performance of the airports. Cameroon is one of the few African countries with one such PPP, a 15-year joint management contract involving shared risk between the public and private sector. The contract was comanaged by the Aéroports de Paris (34 percent) and government of Cameroon (24 percent), along with other carriers and a bank, from 1993 to 2008; the agreement covered 7 of Cameroon’s 14 airports.

_Source:_ Bofinger 2009.

30
fixed effects, regressions with year and country fixed effects together, regressions with a one-year time lag and country fixed effects, and regressions with a one-year time lag and country and year fixed effects.

For the purposes of this paper we focus on four primary livelihood indicators as our outcome variables: (1) GDP growth, (2) agricultural GDP value added, (3) industrial GDP value added, and (4) poverty headcount ratio at national poverty line (% of population). The data used for this study covers the period 1961 – 2003.

We also perform these regressions using both fixed effects and random effects. The specifications for these regressions are shown below.

# _Country Fixed effects_

Using fixed effects with a linear model, we de-mean the variables to remove the time-invariant unobserved characteristics that are correlated with the other regressors in the equation. In these regressions, such time-invariant characteristics include, for example, the geographical characteristics of a country.

We begin with a basic fixed effects regression using the panel data for all 133 countries included in our sample.

where _Yit_ represents a livelihood measure of country _i_ at time _t, X it_ represents climate measures for country _i_ at time _t, αi_ is the fixed effect and therefore represents the sum of all time-invariant aspects of country _i_ , and ε _it_ represents time-variant factors, which are typically not known by the countries before the time period occurs. We can also add controls for other variables, such as mean annual temperature and precipitation for country _i_ . The unobserved country effects are coefficients on dummy variables for each country.

10
Table 5.1b: Age, Unemployment and Mortality (IV with controls)

|VARIABLES|45-54|55-64|65-74|75-84|>85|
|---|---|---|---|---|---|
|Instr. Total Job Losses|0.00253|0.0961|-0.0166|-0.0849|0.0894|
||(0.139)|(0.0942)|(0.0880)|(0.0804)|(0.0851)|
|Constant|-0.0971|0.108|0.118*|0.0711|0.227***|
||(0.0682)|(0.103)|(0.0664)|(0.0589)|(0.0612)|
|Controls|Yes|Yes|Yes|Yes|Yes|
|Observations|2,213|2,213|2,213|2,213|2,212|
|R-squared|0.210|0.142|0.245|0.360|0.275|

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

# **5.2 By gender**

Disaggregated by gender, we observe that mortality among females and male is insignificantly affected by unemployment. Ruhm (2015) also documented that the pro-cyclicality of mortality is also decreasing for both males and females, but stronger for males.

Table 5.2: Gender, Unemployment and Mortality (IV)

|VARIABLES|Fe|male|M|ale|
|---|---|---|---|---|
|Instr. Total Job|0.273*|0.0131|0.394**|0.0299|
|Losses|||||
||(0.146)|(0.0517)|(0.159)|(0.0584)|
|Constant|-0.0152|0.0955**|-0.0129|0.0785*|
||(0.0148)|(0.0407)|(0.0173)|(0.0456)|
|Controls|No|Yes|No|Yes|
|Observations|3,047|2,213|3,057|2,213|
|R-squared|-0.081|0.335|-0.199|0.376|

Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1 20
Nevertheless, the unpredictability of monsoon rains left colonial India highly exposed to famines, given its dependence on rain-fed agriculture. By reducing internal transportation costs, the arrival of the railroad networks could potentially allow food to be moved around the country as a means of improving food security. Burgess and Donaldson (2010) investigate the issue of how trade changes the weather-to-death relationship, by employing a colonial era Indian district database for the period 1875 to 1919. They show that the arrival of railroads in Indian districts dramatically limited the ability of rainfall shocks to cause famines in the colonial era. On average, before the arrival of railroads, local rainfall shortages led to a significant rise in the index of famine intensity. But after a district gained railroad access, the effect of local rainfall shortages on famine intensity was significantly muted. Burgess and Donaldson (2010) suggest that investments in transportation infrastructure like railroads that enable internal trade can play an important part in improving food security.

Chen et al (2016) study the heterogeneous effects of inter- and intra-city transportation infrastructure on the growth of 219 Chinese cities for the period 1999 - 2012. The long-term contribution of inter-city infrastructure in the form of highways or railways accounts for 6 percent of city growth, that of intra-city public transit being 2 percent. The core cities are found to benefit more from these infrastructures than peripheral cities. The impacts of transport infrastructures on economic growth are heterogeneous across regions, with more productive impacts in the west. Policy-wise, cities seem to benefit from intra-city transit in the short run, but most of the long-term benefits are brought by inter-city transportation facilities. In addition, infrastructure investments are most productive in core cities at the expense of peripheral cities. It is important to consider these trade-offs between regional inequality and efficiency while designing investment strategies.

**Table 14: Survey of selected papers on the development impact of railroads for freight**

|**Author**|**Level of**<br>**Analysis**|**Country/**<br>**Region**|**Indicator of**<br>**Development**|**Main Findings**|
|---|---|---|---|---|
|Jedwab &<br>Moradi (2016)*+|Cell|Sub-Saharan<br>Africa|Output|Colonial railroads had large and durable effects on the level and spatial<br>distribution of economic activity, even after they were replaced by<br>roads.|
|Donaldson<br>(2018) *|District|India|Income|Colonial railroads decreased trade costs and increased domestic and<br>international trade, boosting real agricultural income by 16% on<br>average.|
|Burgess &<br>Donaldson<br>(2010)*|District|India|Food security|Colonial railroads greatly reduced incidence of famine due to drought<br>affecting rainfed agriculture, by allowing food to be traded internally.|

41
# **Annex I. Standardizing Household Surveys to 2005**

The periodicity at which developing countries undertake nationally representative household surveys is very irregular, with some countries having one every 2, 5 or even 10 years and others not having any at all. This irregularity of survey years results in a very low number of countries for which a survey is available for a particular year. To be able to work with the income/expenditure distributions of a relatively large sample of countries (73) and anchor them to a given year (2005) we had to use some assumptions. We collected surveys for years _circa_ (usually before) 2005 and assume that the distribution between the survey year and 2005 remained constant; additionally --and perhaps more importantly-- we assumed income/expenditures for all households grew at a rate equal to the growth in private consumption reported in national accounts (NA). Numerous studies discuss the appropriateness of NA information as an indicator of changes in household incomes (see the debalte between Bhalla, 2003 and Ravallion, 2003). Although we acknowledge the limited relationship between consumption from NA (basically a residual) and the disposable incomes/expenditures of households, we believe that in the absence of household survey information it is better to “gross-up” the survey’s income using NA information rather that assuming that zero growth occurred between the survey year and 2005. This “grossing-up” process often overestimates actual growth in household incomes (Ravallion, 2003). This bias in household incomes (keeping distribution constant) will result in an _underestimation_ of the changes in the poverty deficit reported in this paper. Finally, we believe that the magnitude of this bias is small since the discrepancies between growth in survey mean and private consumption from NA will have an effect over a limited number of year, i.e. the difference between survey year and 2005 (usually 2 or 3 years).
assumption. They are homogeneous of degree one in the first two moments, respectively, of the weather distribution. The homogeneity assumption for the weather variable is similar to most specifications of stochastic output in the theoretical literature on agricultural risk utilizing the expected utility framework (see Feder 1977). We have also for simplicity assumed that there is one source of stochastic variability in profits. It is straightforward to consider multiple weather shocks, and we test for these below. With one source of profit variability, r measures the riskiness of the asset portfolio.4 The mean of consumption is given by (4) Me = /n.

The mapping of the standard deviation in profits to that of consumption depends on what is assumed about capital market constraints. If assets cannot be sold and borrowing is not possible then a. = _l,_ as is assumed in farm risk studies (e.g., Just and Pope, 1978 and 1979; Antle, 1987 and 1989). On the other hand, if farmers are fully insured against income fluctuations, _oa_ a °, as is assumed in, for example, studies of savings based on the permanent income hypothesis (e.g. Wolpin, 1980; Paxson, 1992). Most likely, the situation is somewhere in between these extreme cases. Moreover, the sensitivity of consumption variability to ex post profit variability may depend on the total level of asset holdings, for which there may be a limited market and which may serve as collateral for loans. Rosenzweig and Stark (1989), for example, report that the association between the variances of intertemporal profits and consumption was significantly lower for Indian farmers with greater inherited wealth, and Binswanger and Rosenzweig (1986) report that wealthier Indian farmers were substantially heavier borrowers. Both of these studies utilized the same survey data to be employed in this study. Accordingly, we express the relationship between consumption and profit variability as influenced by total wealth: 6
Higher Wages for Relief Work Can Make Many Of the Poor Worse Off Recent Evidence from Maharashtra's "Employment Guarantee Scheme" by Martin Ravallion Gaurav Datt and Shubham Chaudhuri*

# Table of Contents

|1.|Introduction|1|
|---|---|---|
|2.|Maharashtra's<br>"Employment Guarancee Scheme" Before and<br>After May 1988|3|
|3.|Did the Process of Employment Determination<br>Change<br>Significantly?|6|
||3.1<br>A Model of Monthly Attendances<br>on the EGS, 1975-88<br>3.2<br>Tests for a Structural Break in the Model After<br>Wage Increase|6<br>10|
||3.3<br>An Estimate of the Extent of Rationing After the<br>the Wage Increase|11|
|4.|Does the EGS Now Guarantee Employment?|13|
||4.1<br>The Effect of the Number of Works in Progress<br>on Employment|13|
||4.2<br>The Effect on Agricultural Wages|19|
|5.|Conclusions|21|
|Note|s|25|
|Ref|erences|29|
|Tab|les|31|
|Fig|ures|36|

* The authors are grateful to the Government of Maharashtra for providing generous access to their records on the "Employment Guarantee Scheme" and related data, and to Seema Arora for able research assistance. For their comments on this paper, our thanks go to Roger Slade and Dominique van de Walle. This research has been supported by the World Bank's Research Committee under RPO 675-96.