rafmacalaba/datause-encoder-data huggingface dataset:
during the election year is 20 percent higher than in districts in which the incumbent is not seeking election. The election data at our disposal does not contain information on whether the incumbent district heads ran for election in the year of direct elections or not, but only on whether the newly elected district head was an incumbent or not (i.e. the successful incumbents only). In the 163 districts where direct elections were held between 2005 and 2007, slightly more than half (53 percent) had incumbents who were successful in getting elected.<sup>30</sup> The remaining 47 percent of districts likely included a sizeable number of districts where the incumbent did not contest the election.

Thus on the one hand, incumbents are more likely to increase their spending when they are contesting elections (Suharnoko et al., 2013) and on the other hand, some of the direct elections did not have incumbents contesting. Given this, any evidence from our data in favor of active manipulation of district spending in districts where direct elections were held, in the year of (or prior to) elections, is likely to provide only a _lower bound_ estimate of the extent to which district spending changed in the districts where incumbents (both successful and unsuccessful) ran for direct election.

To allow for the possibility of district spending changing in the year of election or the year before elections, we re-estimate an amended specification of equation (1), i.e.

where μm denotes the district fixed-effects as before, and εmt is an error term summarizing the influence of all other unobserved variables that vary across districts and over time. The set of comparison districts used is identical to that used in the earlier regressions, i.e. the districts that did not have direct elections until 2008 and later.<sup>31</sup> The main differences between the specification above and equation (1) are the following: (i) there are three different treatment groups of districts

> 30 Specifically, among the 101 districts where direct elections were held in 2005, 55 of the newly elected district heads were incumbents. Among the 45 (17) districts where direct elections were held in 2006 (2007), 24 (8) of the newly elected district heads were incumbents.

> 31 It should be noted that our control group may also be less questionable than the one used by Suharnoko et al. (2013), i.e., the set of districts where the incumbents were not seeking reelection.

17
This decomposition clarifies how partial analyses of productivity performance within individual sectors (such as manufacturing or agriculture) can be misleading when there are large differences in labor productivities ( _pi,t_ ) across economic activities. In particular, a high rate of productivity growth within an industry can have quite ambiguous implications for overall economic performance if the industry’s share of employment shrinks rather than expands. For example, if the displaced labor ends up in activities with lower productivity, economywide growth will suffer and may even turn negative.

Armed with the results of the decomposition, the authors of each of the chapters then use a variety of strategies to gain a deeper understanding of the country-specific factors that played a role in facilitating (or impeding) structural change. For example, in Chapter 1 of this book, Mitra and Ahsan use state-level data on employment shares by industry, tariffs, education, and labor regulations to explore the correlates of structural change across states in India.

# **Country Studies: Data and Measurement  Issues**

Here, too, in an effort to maintain consistency, all of the country studies use national accounts data and labor force statistics to compute measures of sectoral employment and value-added for nine broad sectors of the economy. The authors also draw on several complementary datasets to conduct more detailed analyses of the underlying correlates of structural change and withinsector productivity growth. Country-specific data appendixes appear at the end of each of the country chapters. These appendixes document the sources of data, as well as any inconsistencies in the data and how these were handled. Nevertheless, several measurement issues common to all of the studies warrant clarification.

**Informality.** A big question with national output and employment data in developing countries is how well they account for the informal sector. The coverage of the informal sector in national accounts data varies from country to country (Timmer and de Vries 2009). While all countries make an effort to track the informal sector, obviously the quality of the data can vary greatly. In contrast, employment shares are more likely to include the informal sector, because they are typically obtained from nationally representative household surveys (labor force surveys or population censuses). A failure to account for activity in the informal sector will lead to an underestimate of value-added in activities that are heavily dominated by informality, such as agriculture.

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coefficient implies that savings are 31 percent greater for M-PESA users, but the estimate is not statistically significant.

# **8 The Future of Mobile Savings**

We have shown that basic mobile savings (the use of M-PESA for savings) is common and presented some evidence that M-PESA increases savings. That M-PESA increases savings may seem surprising, given that it does not pay interest and thus functions as a mere money storage device. However, this finding is compatible with qualitative evidence on M-PESA; in the Plyler et al. (2010) focus group study, Kenyans highlighted the value of M-PESA as a place to store funds safe from the dangers of theft and inaccessible to other family members.

Another recent study of savings in Kenya not related to M-PESA provides additional evidence for the value of a simple storage mechanism. In a multi-armed experiment, Dupas and Robinson (2011) provided various opportunities to save for health expenditures through different technologies.<sup>8</sup> The simplest treatment was to provide participants with a simple metal box with a lock and key and a deposit slit in the top. Remarkably, just providing the box increased health savings by 68 percent. Based on follow-up interviews with participants, the authors conclude that this effect was due to ―mental accounting,‖ meaning that with the funds set aside, it was easier to refuse requests or limit consumption knowing that the funds were mentally allocated to savings. Respondents said they felt less obligated to share funds with others when they were in the box. Some also said that the hidden aspect of the box was important.

The reports from the qualitative work on M-PESA suggest that mobile money may serve a function similar to the box in the Dupas and Robinson study. Thinking of the M-PESA funds as reserved funds, as well as the fact that M-PESA funds are hidden from others, may make the system a preferred method of savings for Kenyans without easy access to other savings vehicles.

We have also shown that approximately six months after the introduction of M-KESHO, Kenya‘s heralded bank-integrated savings platform, use of the platform for savings was very rare. This result also seems surprising given the high rates of use of M-PESA for basic mobile

> 8 The Dupas and Robinson study was carried out in 2008, before takeup of M-PESA was widespread.

14
**Table 5 Dynamics of informal employment and unemployment in European countries, 2004-2009**<sup>a</sup>

||**Informal**|**Dependent**|**Info**|**rmal**|**Total Un**<br>|**employed**<br>|**LFS-**<br>|**Based**<br>|
|---|---|---|---|---|---|---|---|---|
||**Empl**<br> <br>|**oyment**<br> <br>|**Self-Em**<br> <br>|**ployment**<br> <br>|**Willing**<br> <br>|**to Work**<br> <br>|**Unem**<br> <br>|**pl. Rate**<br> <br>|
||Round 4<br>vs.<br>Round 3|Round 3<br>vs.<br>Round 2|Round 4<br>vs.<br>Round 3|Round 3<br>vs.<br>Round 2|Round 4<br>vs.<br>Round 3|Round 3<br>vs.<br>Round 2|Round 4<br>vs.<br>Round 3|Round 3<br>vs.<br>Round 2|
|||||**Souther**|**n Europe**||||
|Cyprus|-5.4<sup>*</sup>||0.0||-3.4<sup>**</sup>||-0.5||
|Greece<sup>b</sup>|-0.5||0.7||-2.7<sup>*</sup>||-0.9||
|Portugal|0.0|3.6<sup>***</sup>|1.5|-2.5|1.1<sup>***</sup>|-1.4<sup>***</sup>|0.1|0.9|
|Spain|-0.6|1.0<sup>***</sup>|-1.0|1.7<br>**Easter**|3.7<sup>***</sup><br>**n Europe**|-1.8|4.7|-1.8|
|Poland|-0.2|0.9|4.2<sup>**</sup>|-3.5<sup>**</sup>|-2.7<sup>*</sup>|-5.8<sup>***</sup>|-5.4|-5.8|
|Ukraine|0.2|0.5|1.1|2.0|-3.0|-2.1|2.7|-1.9|
|Slovenia|2.7<sup>**</sup>|-1.1|1.3|-1.5|-6.3<sup>***</sup>|5.2<sup>***</sup>|-1.3|-0.9|
|Bulgaria|-2.4<sup>**</sup>||0.5||-3.2||-2||
|Czech R.<sup>b</sup>|-0.8||1.7||-2.5<sup>**</sup>||-1.6||
|Slovakia|-1.0|0.3|1.6|-1.3|-4.6<sup>***</sup>|-6.2<sup>***</sup>|-2.6|-5.4|
|Russia|1.2||0.7||0.1||0.8||
|Estonia|-0.5|2.0<sup>***</sup>|1.8<sup>*</sup>|-1.6<sup>*</sup>|6.2<sup>***</sup>|-4.8<sup>***</sup>|4.9|-3.4|
|Hungary|0.6|-1.2|-1.1|-0.9|7.5<sup>***</sup>|2.4|2.2|0.4|
|Latvia|-2.4<sup>***</sup>||0.3||10.7<sup>***</sup>||11||
|||||**Wester**|**n Europe**||||
|Ireland|-6.9<sup>***</sup>|-2.0|-0.9|1.1|11.2<sup>***</sup>|1.7|8.1|0.1|
|UK|-0.4|-2.3|1.5|-1.0|1.2|-3.1<sup>**</sup>|0.7|0.8|
|Austria||1.3||0.4||-3.6<sup>***</sup>||-0.9|
|Switzerland|-0.5|0.8|-2.1|1.9|1.2|-1.1|0.5|-0.9|
|Netherlands|-1.7<sup>**</sup>|-1.0|2.2<sup>*</sup>|-0.6|-2.5<sup>***</sup>|-0.6|-0.9|-1.1|
|Germany|0.3|-0.5|0.3|1.1|-3.8<sup>***</sup>|-1.7|-2.2|-0.9|
|Belgium|0.0|-0.4|-1.2|-0.1|-1.0|-1.6|-0.7|-0.5|
|France|-1.1||1.4|**Norther**|0.1<br>**n Europe**||-0.7||
|Norway|-0.9|0.5|-2.1<sup>*</sup>|-0.2|-0.6|-2.9<sup>***</sup>|0.7|-1.2|
|Denmark|-3.9<sup>***</sup>|2.7<sup>**</sup>|0.1|1.0|0.3|-4.2<sup>***</sup>|-0.2|-1.3|
|Finland|0.4|-0.1|0.6|-0.6|-2.4<sup>**</sup>|-1.1|-0.6|-1.1|
|Sweden|0.2|0.1|-0.2|-0.4|-1.5<sup>*</sup>|-1.5|0.2|-0.9|

_Notes:_<sup><u>a</u></sup> The table presents the changes in estimated prevalence of informal employment and unemployment between ESS rounds: Round 4 (2008-2009), Round 3 (2006-2007), Round 2 (2004-2005).<sup>b</sup> For Greece and the Czech R., Round 4 is compared to Round 2 (rather than to Round 3). *, **, *** - estimates significantly different from 0 at the 10%, 5%, 1% level, respectively (not shown for LFSbased unemployment rates). _Source:_ Calculation with ESS data.

53
CCT programs in Latin America have been carefully analyzed—see for example, Schultz (2004) and Behrman, Sengupta and Todd (2005) on the PROGRESA program (now re-named Oportunidades) in Mexico, Bourguignon, Ferreira and Leite (2003) on the Bolsa Escola program (now re-named Bolsa Familia) in Brazil, and the reviews by Rawlings and Rubio (2005) and Das, Do, and Ozler (2005). In this paper we assess the impact of a similar program in a context with much lower income, lower enrollment rates, and weaker institutions.

As we show below, the JFPR scholarship program had a large, positive effect on the school enrollment and attendance of girls in Cambodia. Our preferred set of estimates suggest program effects on enrollment and attendance at program schools of 30 to 43 percentage points; scholarship recipients were also more likely to be enrolled at any school (not just program schools) by a margin of 22 to 33 percentage points. The impact of the JFPR program appears to have been largest among girls with the lowest socioeconomic status at baseline. The results we present are robust to a variety of controls for observable differences between scholarship recipients and non-recipients, to unobserved heterogeneity across girls, and to selective attrition out of the sample.

# **2. Data, variables, and program selection rules**

The main sources of data for the analysis in this paper are two: Application forms to the scholarship program, and data on school enrollment and attendance from an unannounced school visit. The application form contains 28 questions about parental education, demographic composition of the household, ownership of various assets, housing materials, and distance to the nearest secondary school; applicants were also asked to specify the name of the secondary school they would like to attend, with the understanding that this should be one of the 93 schools that were eligible for the scholarship program. These forms were completed by a girl in class or at home; some provisions were made for teachers to verify that the data that were provided were accurate.

We use the information on the application forms to correct for observable differences across girls. Two things are worth noting here. First, application forms were handed out at the primary “feeder schools” to the 93 secondary schools included in the scholarship program. In practice, selection of the feeder schools, and selection of the girls who were encouraged to fill out application forms by the primary school teachers appears to have been somewhat ad hoc. Second, almost 30 percent of the application forms were not filled out completely. Because we use the characteristics on the application form to correct for differences across girls, the sample for our analysis is limited to girls whose applications were complete. Appendix One suggests that the differences between the full sample of girls and the sample of girls with completed applications are generally small. Nevertheless, the fact that our data are not from a 3
Real growth has slowed following the completion of the oil pipeline constructions and a fall in oil production owing to technical problems as mentioned below. In 2005 non-oil GDP rebounded sharply, reflecting more favorable weather conditions for agriculture, subsequent to a drought in 2004 and recovery in cotton. In 2006, real GDP grew about 1.3 percent, reflecting higher non-oil growth offsetting a somewhat larger fall in oil production than envisaged. Real non-oil growth is expected to rise to about 6 percent by 2007, in part reflecting a higher level of government expenditure.

<!-- Start of picture text --> Chad GDP at Constant Prices<br>2002 - 2006<br>35<br>30<br>25<br>20<br>GDP at Constant price<br>15<br>Non-oil GDP<br>10<br>5<br>0<br>-5 2002 2003 2004 2005 2006<br>Years<br>Annual % change<br><!-- End of picture text --> Sources: Chadian authorities Bank and IMF.

Chad’ estimations of oil production were to increase steadily from 180,000 barrels per day (bpd) produced in July 2003 to 225,000 bpd by 2004 and remains steady through 2009. In late 2004 oil production reached the 225,000bpd, the maximum capacity of the pipeline, and the annual production totaled 61 million barrels. However, in early 2005, unexpected problems with the porosity of the subsoil led to excessive mixing of water with the oil deposits. The need to separate out the water reduced Doba oil production from 225,000 bpd in December 2004 to around 166,000 bpd in June 2005. Application of reinjection techniques to increase the yield of the Doba fields and the coming on-line of new two oil fields (Nya and Moundouli) helped to maintain oil production at around 173,000 bpd in 2005 and two new satellite fields (Maikeri and Timbre) are coming on stream by 2009 (IMF 2007b).

6
passenger vehicles (cars and two-wheelers) contributed 15% of directly emitted PM and taxis and three-wheelers about 9% of PM10 emissions.<sup>8</sup> What is being done to control emissions from transport in Mumbai and what remains to be done? As of 2002, all new commercial and non-commercial vehicles must obey Euro II emissions standards. To deal with highly polluting, older vehicles, all taxis over 8 years old are either to be retired or converted to CNG (as of 1/1/2003), as are highly polluting 3-wheelers over 8 years of age. All transport vehicles over 8 years old (except BEST buses) are to be retired or converted to CNG, effective 2/1/2004 (World Bank 2005).

Policies that are not included in the above list are requirements to reduce emissions from BEST buses, most of which are diesel, and policies to restrict the ownership and use of cars and two-wheelers. BEST diesel buses could be replaced with CNG buses or diesel buses with diesel oxidation catalysts (DOCs). Vehicle ownership and use could be discouraged by imposing a significant license fee on automobiles; ownership and use could also be discouraged by raising the tax on gasoline. If these policies were implemented in 2005, we would expect the share of emissions from taxis and three-wheelers to differ from those in row 4 of Table 1, assuming that the policies to retire or convert old taxis and three-wheelers are enforced. For this reason, we treat the contribution of taxis and three-wheelers to PM emissions as negligible in computing the share of emissions from different categories of vehicles. This implies that BEST buses account for 15.4%, cars 10.6%, and two-wheelers 5.6% of the PM10 emissions from transport.

> 8 According to NEERI the share of PM10 from transport is 32%.  It is 45% from industry, 18% from area sources, and 5% from building and road construction.

9
firms do tend to have poorer management practices for a sample of 10,000 public and private sector manufacturing firms across 35 economies. Lemos and Scur (2019) also find a similar relationship in a sample of 2,710 firms across 18 economies and implement an instrumental variable identification strategy for 912 firms.

We build on this literature by using unique data from the 2019 EBRD-WIB-World Bank Enterprise Surveys (WBES). This data has a special module on family-managed firms and political connections on top of the comprehensive information on internal and external operating environment of businesses found in the regular World Bank Enterprise Surveys. These surveys were rolled out across a similar timeframe (20182019) for developing and advanced economies across Europe, the Middle East and North Africa, and Mongolia. The module defines our sample and enables this study to make a unique contribution by examining the relationship between family managers and management practices for a set of economies, many of which have not been explored before. This study additionally contributes to the literature by leveraging the surveys to uncover firm characteristics and business environment factors that may strengthen or weaken the effects of family management on management practices.

For around 9,000 medium and large firms across manufacturing and service sectors in 41 countries, we largely confirm the findings of Bloom et al (2015) that family management is negatively correlated with the quality of management practices. This is an important finding given the differences of our sample – we explore both manufacturing and service firms, while Bloom et al (2015) only study manufacturing firms. Our sample also consists of firms in Eastern Europe and the Middle East and North Africa that are largely omitted in the Bloom (2015) sample.

The extensive nature of the WBES allows us to enrich the analysis in several ways. First, the surveys collected information on the share of managers that are family, providing some nuance on whether any family management or all family management is detrimental to management practices. We find evidence towards the latter. Second, the surveys allow for the exploration of whether certain factors either exacerbate or alleviate the negative relationship between family managers and the quality of management practices. These include a range of internal and external operating factors. Third, the surveys allow us to explore specifically what types of management practices are lacking in family-run businesses. Finally, the surveys provide information that describes the nature of family run businesses.

This study uncovers the following key findings. First, family management is negatively correlated with management practices, more so if all managers in the firm are from the same family. This is true in the 3
||**Ta**|**ble A.1.6 Panel B** (co|ntinued)|
|---|---|---|---|
|**Country name**|**On average, how**<br>**long does it take**<br>**to pay depositors**<br>|**What was the longest**<br>**that depositors had**<br>**to wait in the last 5**|<br>**Were any deposits not explicitly covered by**<br>**deposit insurance at the time of the failure**<br>**compensated when the bank failed (excluding**|
||**in full?**|**years?**|**funds later paid out in liquidation**|
||in months|in months|**procedures)?**<br>yes=1 no=0|
|Liechtenstein||||
|Lithuania|3|6|1|
|Luxembourg|3||0|
|Macedonia|||0|
|Malaysia||||
|Malta|||0|
|Mexico||||
|Netherlands|3|||
|Nicaragua|||0|
|<br>Nigeria|36|60|0|
|Norway|1|3|1|
|Oman||||
|Paraguay|3|12|0|
|Peru|2|0.25|0|
|Philippines|3|7|0|
|Poland|4|23|0|
|Portugal||||
|<br>Romania|3|3|0|
|Serbia & Montenegro|||1|
|<br>Slovak Republic|3|3|0|
|Slovenia||||
|Spain|3|3|0|
|Sri Lanka||||
|Sweden||||
|Switzerland||||
|Taiwan|||1|
|Thailand|1|4|0|
|Trinidad & Tobago|2||0|
|<br>Turkey|3||1|
|Turkmenistan|2|1|0|
|Ukraine|60|60|0|
|United Kingdom|6|6|0|
|<br>United States|0.1|||
|Venezuela|||0|
|Zimbabwe|6|||

Notes: The countries which did not provide answers for any of the survey questions listed here are excluded. Blank spaces indicate no response.

44
migrants were larger among the unskilled. A 10 percent rise in exports per worker raised the number of unskilled migrants by 5.2 people (panel a, columns 2–4). In contrast, the increase in the number of middle skilled and highly skilled migrants was only a fifth or less.

Table 9 shows the impacts among returnees from and emigrants to the United States in 2000– 10.<sup>10</sup> A rise in exports per worker by 10 percent raises the number of households with returning migrants from the United States by 14 on average. At the same time, it reduces the number of households with emigrants to the United States by about 9.

These internal and external patterns of migration also help explain the increase in the size of the working-age population experienced by municipalities when exports are growing. The size of the total population ages 14–55 in urban municipalities rose, especially among the unskilled, in response to export growth (table 8, panel d). While the size of the skilled working-age population increased by about 11.6 people when exports per worker rose by 10 percent in the average municipality, the size of the unskilled working-age population increased by about 82.1 people. This represents about 30 percent of the increase in the number of unskilled people in this age-group in the average municipality during the period. The migration response to higher exports implies that the estimated positive impacts on the local labor market are partially diluted because of the increase in the size of the local working-age population, particularly the unskilled.

A 10 percent increase in the growth of the export-to-worker ratio reduces the share of households receiving remittances from the United States by 0.18 percentage point (table 9, column 3). This represents almost 1.4 times the decline in the share of households receiving remittances during the period. This may be explained by the fact that the number of household workers abroad falls disproportionately as exports grow. An additional consumptionsmoothing mechanism may be important if, for example, more job opportunities reduce the need to rely on remittances.

# **5. Conclusion**

This paper provides new evidence on the impacts of exports on welfare in developing countries. Using a rich data set on exports, income distribution, labor market outcomes, and migration at the municipal level in Mexico in 2004–14, it offers new insights on why a

> 10 These data are not available for 2005 and 2015.

12