rafmacalaba/datause-encoder-data huggingface dataset:
effect of the information sheet on business sales or profits. We also accessed administrative follow-up data from the Brazilian Ministry of Labor and Employment and the Federal Tax Authority to investigate impacts on number of formal employees and business exit. We find no effects on these two outcomes either.

Finally, the follow-up survey asked firms who did not use SEBRAE services after the intervention for their reasons. Most firms in both the treatment and control group reported lack of time as the main reason for not using these services, but treatment firms were statistically significantly less likely to report that they were happy with their business’ performance (16 percent in the treatment group vs. 29 percent in the control group).

Our paper is related to recent randomized experiments that examine whether simply telling firms about the existence of best business practices (Dalton et al 2019) or benchmarking their performance helps them to improve by themselves (Grohmann, Menkoff, and Seitz 2022; Hou and Png 2022; Seither 2021). Although the overall evidence in these papers is mixed, the findings suggest that providing information can help improve firm performance at least in some settings, or when it is offered together with training. However, these papers don’t help explain why firms do not take-up more business assistance that already exists in the market. We test whether providing information on firms’ adoption of business practices increases their use of business services in the market, an intervention that is more scalable than also implementing training.

In a related study, Anderson and McKenzie (2021) gave small firms in Nigeria information on the market for professional business service providers and provider’s quality, without including information on firms’ business practices or past performance. Distributing this information on providers had no significant impact on the use of business services.

Our paper contributes to the literature by showing that lack of information on business practices is a reason why not more firms take up business services that exist in the market. When firms received information on how their business practices compared to other firms and on the benefits of improving practices, they were more likely to seek out business services that can help them improve their practices. However, after a few months, the control group started to use more business services, catching up to the treatment group. The information on business practices may thus have caused firms to speed up actions that they were going to take anyway. Eighteen months after the intervention, 51 percent of firms still did not use business services. Receiving the information made firms who did not use business services less happy with the business practices they were using, but firms claimed that they lack the time to use business services. Our findings thus highlight that time constraints might be 4
- Sabarwal, Shwetlena, Kanishka Kacker, and James Habyarimana. 2018a. " Better Than Most: Teacher Beliefs about Effort and Ability." _mimeo_ . World Bank.

- Sabarwal, Shwetlena, Unika Shreshtha, T.M. Asaduzzaman, and Deepika Ramachandran. 2018b. "School Leadership in Action: Vignettes from Bangladesh and Nepal" _mimeo_ . World Bank.

- Speybroeck, Sara, Sofie Kuppens, Jan Van Damme, Peter Van Petegem, Carl Lamote, Tinneke Boonen, and Jerissa de Bilde. 2012. 'The role of teachers' expectations in the association between children's SES and performance in kindergarten: A moderated mediation analysis', _PloS one_ , 7: e34502.

- Uwezo East Africa. 2014. "Are Our Children Learning? Literacy and Numeracy Across East Africa 2013." In. Uwezo East Africa at Twaweza: Uwezo East Africa

- Weisberg, Daniel, Susan Sexton, Jennifer Mulhern, David Keeling, Joan Schunck, Ann Palcisco, and Kelli Morgan. 2009. 'The widget effect: Our national failure to acknowledge and act on differences in teacher effectiveness', _New Teacher Project_ .

- World Bank. 2003. "World Development Report 2004: Making services work for poor people." Washington, DC: World Bank.

- ———. 2014. "World Development Report 2015: Mind, Society, and Behavior." Washington, DC: World Bank.

- ———. 2017. "World Development Report 2018: Learning to Realize the Education's Promise." Washington, DC: World Bank.

- Zerbe, Wilfred J, and Delroy L Paulhus. 1987. 'Socially desirable responding in organizational behavior: A reconception', _Academy of Management Review_ , 12: 250-64.

15
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from -2.59 to -0.69 (For a review see Sz ́ekely and Foster, 2001).

# **5.3 Simulated Impacts of Climate Change on Per Capita GDP and Poverty**

This section provides estimates of the poverty impacts of climate-driven changes in output. Thus far we have estimated the impact of historic temperature and precipitation on GDP per capita in each municipality. Now, we use IPCC’s temperature and rainfall projections for 2030 tailored for Mexico to build a different climate vector for each municipality, which then helped to obtain the percent change in output induced by climate change.

To calculate the effects of global warming on GDP in the study areas, we calculate the difference in GDP per capita at projected temperature and rainfall scenarios from the predicted GDP at the historical mean. Predicted impacts of climate change on output result from combining predicted coefficients in model (1) with climate projections.

Our estimates suggest that global warming is expected to decrease output (GDP per capita) in the sample by 1.45 percent, with municipality-specific estimates ranging from -2.25 to +5.86%.

How much will such output declines due to climate change affect poverty? To obtain the poverty impacts of climate change by 2030, we have projected changes on temperature and rainfall and then obtained their effect on GDP in equation (1). Then we simulate poverty rates in equation (2) for the new projections of GDP per capita under climate change in 2030.

In our sample, 48.1 million Mexicans live in asset poverty conditions, 25.2 million people in capacities poverty , while 18.6 million Mexicans are in a situation of food poverty.

Based on Table 1 estimates we simulate the impacts of climate change on poverty by projecting changes in temperature and rainfall. The impacts of climate change on poverty partially undermine the potential adaptive capacity of municipalities through economic growth and population mobility. Without climate change, higher population densities and prosperity would reduce poverty 34.15 percentage points between 2005 and 2030 (from 49.4% in 2005 to 15.25% in 2030), but once climate change is taken into account, poverty falls only from 49.4% to 17.68%. Poverty reduction gains would be 2.43 percentage points less once climate change is factored in. Climate change slows down the pace of poverty reduction. Monitoring changes in poverty over time is valuable, but we cannot determine conclusively whether, or by how much, warming contributes to poverty changes as long as other time-varying factors exist 14
# **2 Institutional Framework and Data Sources**

In 1995, Congress passed the DC School Reform Act allowing for the creation of charter schools in the District and instituting the DC Board of Education (BOE) as a charter authorizer. The Public Charter School Board (PCSB), created in 1996 as an additional, independent authorizer, has been the sole authorizing and supervising entity since 2006. Charters in D.C. are autonomous, non-profit institutions. They receive the same operational per-pupil funding as public schools. In addition, they receive a per-pupil facilities allowance. Since funding is fungible, henceforth “reimbursement” refers to total (operational plus facilities-related) per-student funding.

The Office of the Mayor has had direct authority over DCPS since 2007. DCPS includes multiple attendance zones for each grade level; middle- and high-school attendance zones are much larger than elementary school zones. At the “state” level, the overarching institution for public and charter schools is the Office of State Superintendent of Education (OSSE). In what follows, “total enrollment” refers to the aggregate over public, private and charter schools, and “total public enrollment” to the aggregate over traditional public and charter schools.

We focus on the 2003-2007 period in order to maximize data quality and comparability over time and across schools. In addition, 2007 marked the beginning of important changes in DCPS and hence constitutes a good endpoint for us.<sup>4</sup> Our data include school-level information on every public, charter and private school in Washington, D.C. for 2003-2007, neighorhood level information on school choice and distance traveled to school for 2003-2006, and block group-level information on child age, race, poverty status, and family income. Appendix A provides further details on the data.

While public and private schools have one campus each, many charters have multiple campuses. Hence, our unit of observation is a campus-year; “campus” is the same as “school” for single-campus schools.<sup>5</sup> We have 700, 228 and 341 campus-year observations for public, charter and private schools respectively. Our dataset includes regular schools; it excludes special education and alternative schools, schools with residential programs and early childhood centers. For each observation we have address, grade enrollment for kindergarten through 12th grade, percent of students of each ethnicity (black, white and Hispanic),<sup>6</sup> and percent of low-income (or “poor”) students, who qualify for free or reduced lunch. We also have the school’s thematic focus, which

> 4In 2007, Michelle Rhee began her tenure as chancellor of DCPS. She implemented a number of reforms, such as closing and merging schools, offering special programs and changing grade configurations, etc.

> 5A campus is identified by its name and not its geographic location. For instance, a campus that moves but retains its name is still considered the same campus.

> 6Since students of other races (mostly Asian) constitute only 2.26 percent of the total K-12 enrollment, for computational reasons we folded them into the white category.

6
# **<u>Table 8. Percentage of world migrants recorded as a bilateral movement between pairs of countries/regions (%)</u>**

||||||**D**|**estination co**<br>|**untries/r**|**egions**||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**ti/i f**|||**E15+**|**Australia+**||**Hih**||||||||
|**ounresregons o**<br>**origin**|**USA**|**Canada**|**EFTA**|**New Zealand**|**Japan**|**g**<br>**income**|**LAC**|**ECA**|**MENA**|**AFR**|**EAP**|**SAS**|**Total**|
|**USA**|**0**|0.16|0.34|0.04|0.02|0.03|0.43|0.04|0.05|0.03|0.15|0.02|**1.29**|
|**Canada**|0.54|**0**|0.10|0.02|0.00|0.01|0.02|0.01|0.00|0.01|0.01|0.01|**0.74**|
|**EU15+EFTA**|2.22|0.98|**5.59**|1.13|0.01|0.14|0.68|0.78|0.16|0.39|0.20|0.19|**12.47**|
|**Australia+New Zealand**|0.06|0.02|0.16|**0.23**|0.00|0.00|0.00|0.01|0.00|0.01|0.03|0.01|**0.55**|
|**Japan**|0.28|0.02|0.06|0.02|**0**|0.00|0.04|0.01|0.00|0.00|0.05|0.01|**0.50**|
|**High income MENA**|0.10|0.03|0.06|0.01|0.00|**0.12**|0.00|0.02|0.72|0.01|0.04|0.03|**1.14**|
|<br>**LAC**|10.22|0.36|1.45|0.05|0.13|0.10|**2.07**|0.17|0.08|0.14|0.14|0.25|**15.15**|
|**ECA**|1.25|0.39|4.75|0.26|0.00|0.92|0.07|**16.98**|0.33|0.34|0.18|0.41|**25.88**|
|**MENA**|0.47|0.17|2.85|0.10|0.00|1.49|0.04|0.16|**1.79**|0.28|0.05|0.12|**7.52**|
|**AFR**|0.41|0.12|1.58|0.10|0.00|0.25|0.02|0.11|0.18|**7.00**|0.03|0.16|**9.97**|
|**EAP**|3.32|0.71|1.09|0.63|0.54|0.48|0.06|0.14|0.14|0.09|**3.86**|0.27|**11.32**|
|**SAS**|0.83|0.31|1.13|0.12|0.01|2.66|0.02|0.13|2.07|0.14|0.37|**5.67**|**13.46**|
|**Total**|**19.71**|**3.25**|**19.14**|**2.72**|**0.74**|**6.22**|**3.45**|**18.56**|**5.53**|**8.44**|**5.10**|**7.15**|**100**|

Note: “EU15 + EFTA” includes Switzerland.

EU European Union EFTA European Free Trade Area MENA Middle East and North Africa LAC Latin America and the Caribbean ECA Europe and Central Asia AFR Sub-Saharan Africa EAP East Asia and the Pacific SAS South Asia 19
- 15 -

The second instrument follows the recent literature focused on the evaluation of infrastructure investment. Chandra and Thompson (2000), examining the impact of the U.S. interstate highways on earnings of firms, argue that the non-metropolitan counties served by highways received exogenous benefits, because the interstate highways first aimed at connecting metropolitan areas. Banerjee et al. (2012) also apply the same concept for the case of Chinese railways, calculating the distance from counties to straight lines connecting historic cities and ports, which can be arguably treated as exogenous.

Following these studies, a straight-line is drawn from Dar es Salaam, from where the construction of the Central Railway was started in 1905, to Kigoma, a port town situated on Lake Tanganyika, which was reached in 1914 (Figure 13). The railway construction was largely motivated for political and military reasons, especially between two European powers, Britain and Germany (Amin, Willetts and Matheson, 1986). Since other transport infrastructure development followed the railway construction, the areas between Dar es Salaam and Kigoma are considered to be more likely to receive “exogenous” benefits from transport investments.

Other explanatory variables included in **_X_** are summarized in Table 3. For obvious reasons, the firm location is likely to depend on the market potential, which is measured by the number of population. Labor market conditions also seem to be important (e.g., Procher, 2011). Both labor costs and quality are included (i.e., average wages and education attainment). The recent literature focuses on the impact of local taxation on firms’ locational choice of investment (e.g., Brulhart, Jametti and Schmidheiny, 2012; Mare and Graham, 2013). Local tax revenue data are available at least at the regional level in Tanzania (denoted by _RENT_ ). Since agrobusiness is one of the major industries in Tanzania. Presumably, the proximity to agriculture production areas is one possible advantage for agrobusinesses. The total value of agriculture commodities produced in each district is used as a measurement for this.
predict stock price responses to elections during the post-Suharto era, nor stock price responses to news about President Wahid’s impeachment.<sup>8</sup> In addition, we use data on the appointment dates of the Suharto appointed mayors from MartinezBravo et al. (2013). We supplement the data they have made publicly available with data we collected ourselves from the Government of Indonesia’s Official Directories at Cornell University. We also construct new data on entry regulation from presidential decrees issued in 1993, 1995, and 2000. We create a (stringent) entry regulation indicator variable that equals 1 if an industry is completely closed to investments or closed unless the firm in question meets certain conditions and zero otherwise. Industries that are reserved for small businesses are not considered as regulated.

# **4 The Impact of Political Turnover on Manufacturing Firms**

# **4.1 Characteristics of Politically Connected Manufacturing Firms**

Even though the 86 Suharto family firms account for only 0.4 percent of firms in our sample, they matter for macroeconomic performance. As Table 1 (panel A) shows, Suharto family firms accounted for 1.3 percent of jobs and exports, 2.9 percent of all imports, and 3.8 percent of output in 1996. The larger group of firms with broadly defined political connections (presented in panel B) comprised 1.1 percent of all firms, employed 4.4 percent of all manufacturing workers, and produced 15.8 percent of total manufacturing output. They also accounted for 5.0 percent of manufacturing exports and for 12.7 percent of all imports. Politically connected firms were thus among the larger firms and were oriented toward production for domestic consumption. By 2000 the aggregate contributions of politically connected firms had diminished somewhat. Suharto connected firms nonetheless remained important and continued to account for 13.4% of all output.

Table 2 shows the average characteristics of politically connected and nonconnected firms and compares the difference between these averages during the final years of Suharto’s tenure (1993–1996) and after his departure (2000–2009), presenting results for both Suharto family firms (panel A) and the broader group of politically connected firms (panel B). The average market share of Suharto family firms was almost seven times higher than that of nonconnected firms operating in the same five-digit industry (7% vs 1% respectively), and eight times larger when using the broader measure of connectedness. The

> 8 Additional support is provided by Leuz and Oberholzer-Gee (2006) who show that Suharto connected firms had difficulty re-establishing connections under the Wahid regime.

10
2 redistribution, which can be attained in two ways. In pay-as-you-go systems financed by worker contributions in the form of a payroll tax, there is a redistribution of income from the employed to the unemployed. Similarly, the design of benefit minimums and maximums can effect a redistribution; in a contributory scheme with flat rate contributions and capped benefits, workers earning higher salaries subsidize those earning lower wages. While it is preferable that the link between contributions and benefits be close and transparent, income redistribution is consistent with the equity objectives pursued by socialist governments such as those found in the transitional economies of Central and Eastern Europe (CEE), as well as Algeria. Unemployment insurance plays a crucial role in the transition to markets by facilitating industrial restructuring and labor reallocation. Most of the transitional economies have recently implemented UI systems with the goal of promoting labor shedding while providing income support to the affected population.

4. The Central and Eastern European systems. The various UI systems found in CEE have similar blueprints, which can be generalized as follows (see Table 1). Firms contribute a flat rate tax on the wage bill to a government-controlled Social Fund, which is responsible for financing both the payment of benefits as well as active labor market policies such as job

|<br>|**Table**1:**Charact**<br>**in Centra**|**eristics of Unemployment Ben**<br>**l and Eastern Europe and Alg**|**efit Systems**<br>**eria**<br>||
|---|---|---|---|---|
|**Country**<br>____________|**Duration of**<br>**benefie**<sup>**1**</sup>|**Replacement**<br>**rate**|**Minimum**<br>**_**<br>_<br>_<br>_<br>_|Maximum|
|Bulgaria|6-12 mo.|90-140% of MW|||
|Czech & Slovak<br>Republics|6 mo.|60% for 3 mo.<br>50% after<sup>3</sup>|-|3,000 CSK|
|Hungary|12 mo. max.|75% for 3 mo.<br>60% for 9 mo.|8,600 HFTs<br>8,600 HFT|18,000 HFT<br>15,000 HFT|
|Poland|12 mo.; 18 mo. in<br>crisis areas|36% of avg. nat'l wage (-<br>MW)|||
|Romania|9 mo.|60% for means-tested<br>populatiorn<br>70% for those w/ higher<br>education|||
|Slovenia|2 years max.|70% for 3 mo.<br>60% after|80% of MW|4 times MW<br>I|
|Algeria|i year min.<br>3 years max.|Declining replacement of MW-<br>weighted "reference salary"<sup>6</sup>|75% of MW|3 times MW|
|MW denotes min|imum wage.||||

IX After exhausting benefits, can receive a "social benefit" less than MW, e.g. in Hungary, Romania, Slovenia.

> 21 Replacement rate of average wage earned over the last year of employment, unless otherwise noted.

-/ 70% replacement if in retraining, benefits for 1 year.

4/ Eligible population earns < 50 % of MW.

5' Minimum wage in Hungary is 9,000 HFT (1993).

- -' For details, see para. 24 and Table 3.

Sources: Jackson, Koltay, and Biesbrouck, 1995; World Bank.
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