rafmacalaba/datause-encoder-data huggingface dataset:
ability to evade enforcement. In this case, more talented entrepreneurs may have more incentive to stay informal – consistent with what we see here.

This heterogeneity in the effect of tax registration seems a likely explanation for the differences in the effect of a NIT over the different methods. OLS averages the effect of a NIT over all firm sizes: the smaller and larger firms who have negative effects, and the middle-sized group of firms who have positive effects. As a result, the average over all these groups is close to zero. Propensity-score matching places more weight on the firms in the middle size group, since these firms are more likely to have similar propensity scores. The MLE and 2SLS estimates will estimate the effect for firms at the margin of becoming formal, for whom information and travel costs make a difference. These are unlikely to be the very small or largest firms, but rather a subset of the middle-sized group, for whom the gains to formality are very high indeed.

# **5.4. How does having a NIT change profits?**

Our treatment effects regression has found effects of a NIT on profits for some firms. In Table 7 we explore several avenues through which a NIT may change profits. Maximumlikelihood treatment effects regressions using log distance to the tax office as an instrument for a NIT are used, with our standard set of control variables, including firm size.

First and foremost, the main cost of registering for taxes is that we would expect firms with a NIT to be more likely to be paying taxes. The first two columns of Table 7 show this to be the case. Firms with a NIT are much more likely to pay taxes, and pay a larger share of their profits as taxes.

The main benefit of a NIT according to the firms themselves is the ability to attract more customers by issuing tax receipts. Columns 3 and 4 of Table 7 provide evidence for this positive effect. Having a NIT results in higher sales, and a greater likelihood of issuing tax receipts. This increase in customers is not coming through more contracting with the government, multinationals, or large firms: 97 percent of our firms make no sales to any

- 19 -
potential positive and negative effects on local collective action and governance. On one hand, emigration may provide an “exit valve”, allowing people to leave underperforming communities rather than staying to improve local institutions (Goodman and Hiskey, 2008; Hirschman, 1970; Sellars, 2019). On the other hand, remittances may enable more local investment (Escribà-Folch et al. 2015; O’Mahony 2013), and knowledge transfers may positively influence local governance and public goods provision (PérezArmendáriz and Crow, 2010; Pfutze, 2012). The net impact of out-migration on rural governance and public goods in China remains an important empirical question and an understudied topic.

A growing literature on public goods provision in rural China has focused on various formal institutions, such as village elections and fiscal policies, and informal ones like lineage groups, as determining factors, but lack attention to the impacts of out-migration. To explore the relation between migration and public goods provision, we use the 2003-2017 RCRE village survey data. The survey covers investment in eight public goods categories: agricultural land improvement, electricity, roads, irrigation and drinking water, ecological forest, elementary schools, immunization, and New Rural Cooperative Medical Insurance spending. A locally weighed regression (Figure 1) shows a negative correlation between per capita public goods investments (excluding New Rural Cooperative Medical Insurance) and the share of village labor force that has migrated out of the county. However, preliminary multivariate regression models do not yield a strong causal association. Further research is needed to understand this negative relationship and whether out-migration may play a causal role.<sup>23</sup>

**Split Families and the Left-Behind.** The movement of labor from low productivity agriculture to urban employment has been a key to China’s growth, contributing to poverty alleviation and consumption growth for poor rural households and communities. However, the impacts of migration extend far beyond these economic benefits. Nearly two-thirds of China’s roughly 150 million rural migrants are not joined by their families, with spouses, children, or parents staying behind in their home villages (National Bureau of Statistics, 2019). Despite a growing number of young migrant workers migrating with their children (Wang et al. 2019), 69 million rural children under 17 still grow up without one or both of their parents (UNICEF, 2018).

> 23 The shift in financing rural public goods to upper levels of government in the 2000s, further expanded under the 2008-2009 national economic stimulus plan, dramatically increased investment in rural infrastructure and social welfare (Michelson, 2012). The top-down pro-rural policies since 2004 seem to have boosted rural public goods investment.

20
- 39 -

Data on the value of production and net trade<sup>24</sup> by commodity for each year are from the Economic Planning Board (1964, 1984) except<sup>for data</sup> on agricultural production, vhich from our model definition is equal<sup>to</sup> labor income from employment in the rural sector. For each<sup>c omodity, the</sup> value of consumption is determined as the residual between<sup>the value of</sup> production and trade. The value of trade evaluated<sup>at world prices must,</sup> fcr general equilibrium consistency, satisfy trade balance,<sup>and a sc'.ing</sup> procedure incorporating the import data is used to ensure<sup>that condition</sup> holds.

The model also requires elasticity values for production<sup>and</sup> demand functions. We use values of 1.5 and -1.5. The unobservable parameter z, which measures the curvature of the utility function,<sup>we</sup> assume to be 1.5. Because of the potentially crucial nature of these values for model behavior, we use these values as our central set of values around which sensitivity analyses are performed.

To incorporate outward oriented growth policies into the model, data are also required on tariffs and export subsidies. Since agriculture is the only good that is imported in our model, we need tariff rates only on this product. We use the weighted average tariff rate on primary products (adjusted for rebates) in 1968 (the earliest period available<sup>to</sup> us) from Westphal and Kim (1977) and assume it to be be roughly equivalent to the tariff rate in 1962. For tariff rates in 1982, we use a<sup>simple</sup> average tariff rate on live animals and vegetable products in 1982 from World Bank (1987a).

> 241 Korea was a net Importer of manufacturing goods in both 1962 and 1982 and is treated as a net exporter of manufacturing goods in the model. We make the strong assumption that net exports of manufacturing goods in 1982 are given not by net trade in total manufacturing goods but rather in specific aggregate categories (consumpt:on and investment goods) of vhich Korea was a net exporter in 1982. In 1962, Korea was a net Importer in all specific aggregate categories (consumption, investment and raw material goods). Therefore, we use 1982 export data on the composition of trade to produce our 1962 microconsistent data set.
seems to suggest that public firms are more in compliance, but the results are weaker.

# **5.4 Age**

Another important policy question is whether firms follow environmental regulations with regards to the age of the firm (Table 6). Starting with a linear inclusion of the age variable, one does not find any significance regarding age. Given that this might be due to age affecting compliance in a non-linear way, one could look this by adding a quadratic term. The non-linear term results suggest that compliance increases with age in inspections and that these increases in compliance are decreasing. In self-reported cases, the opposite seems to be the case, with older firms self-reporting to be less in compliance than newer firms.

||(1)<br>Self mon.|(2)<br>Self mon.|(3)<br>Inspection|(4)<br>Inspection|
|---|---|---|---|---|
|Age|-0.205|0.117|0.738|0.873<sup>_∗_</sup>|
||(0.198)|(0.207)|(0.518)|(0.530)|
|Age<sup>2</sup>||-0.012<sup>_∗∗∗_</sup>||-0.002<sup>_∗∗_</sup>|
|||(0.002)||(0.001)|
|Private|-1.480<sup>_∗∗_</sup>|-1.528<sup>_∗∗_</sup>|3.728<sup>_∗∗_</sup>|3.896<sup>_∗∗_</sup>|
||(0.671)|(0.674)|(1.638)|(1.663)|
|Air category|1.089<sup>_∗∗∗_</sup>|1.022<sup>_∗∗∗_</sup>|-0.523<sup>_∗∗∗_</sup>|-0.524<sup>_∗∗∗_</sup>|
||(0.106)|(0.108)|(0.070)|(0.070)|
|Observations|4126|4126|3223|3223|
|Pseudo _R_<sup>2</sup>|0.190|0.208|0.108|0.109|

Standard errors in parentheses.<sup>_∗_</sup> _p < ._ 1,<sup>_∗∗_</sup> _p < ._ 05,<sup>_∗∗∗_</sup> _p < ._ 01 Table 6: Age Extending this analysis to the industry level (Annex Table) suggests that there are only a few industries in which age has a significant effect on compliance. In steel, it seems to increase compliance, but it reduces it in sugar. In the rest of the industries, the results are either non-significant or weak.

# **5.5 Voter Turnout and GDP**

Earlier literature suggests that public pressure is often more effective than the formal enforcement mechanism. This motivates one to explore the effect of civic activism on firms’ com17
gross income and 16% of gross agricultural income. This share only stands to increase as households get richer and urbanize, a trend already underway in many African countries.

Finally, taking a fully reduced form, we compared real per capita consumption levels (2011 PPP) among tree growing households and non-tree growing households controlling for district level effects (Table 5). As expected, tree cash crop growers were on average substantially better off (84% in Ethiopia, 19% in Nigeria, and 3% in Tanzania, though no difference was discerned in Uganda). Fruit tree growers were also better off in three of the five case countries (Ethiopia, Nigeria and Uganda), an important observation, given the potential for growth of Africa’s rising middle class. However, no positive effect was found for timber tree growing. Households with timber and fuelwood on their land appeared to be even worse off in Malawi. This result may be due to characteristics particular to the tiny (0.18%) subset of households reporting having such timber or fuelwood trees. Also a large proportion of Malawi’s territory includes areas where poverty and high forest cover overlap (Sunderlin et al., 2008).

# 3.4 Gender, Land, Proximity to Forest, and Country Factors Drive On‐Farm Tree Growing

Tables 6a, b and c present the estimated effects of different correlates on the adoption of and land allocation to trees on farms by tree type. The model had little explanatory power in explaining the adoption or land allocation to timber trees (Table 6c), but a number of clear generic patterns emerge when looking at the correlates of tree cash crops and fruit trees. First, adoption and land allocation to fruit trees and tree cash crops increased with the education level of the household head, but the adoption and land allocation to tree cash crops was about 5 percentage points less among female-headed households. This result is consistent with studies showing lower land tenure security among women in Africa (Berry, 1988; Schroeder, 1999). It was substantially less pronounced for fruit crops however, consistent with the higher nutritional value of fruit trees (Degrande et al., 2006; Mbow et al., 2014b). Fruit trees also tend to be more likely among households with older heads, consistent with other studies (Pattanayak et al., 2003).

Land endowments mattered for the area allocation to both fruit and cash crop trees (as well as for the adoption of fruit trees). Household size mainly affected fruit tree growing, suggesting that labor constraints are likely more binding for fruit than tree cash crops where use of hired labor is more common. Adoption of fruit trees is also less common among households with livestock.

13
SOUTH ASIA

# 1. **Estimatoe** and Projections

|TYa|Poputation|OP.<br>ratio|Pwrad|Groth<br>rate|W|co|TFt|oO<br>**Mlet**|Fmstes|lo<br>l|q5<br>1000|
|---|---|---|---|---|---|---|---|---|---|---|---|
|¶965|1058207|76.5|1985-89|2.29|34.9|11.9|4.66|56.8|56.9|102|136|
|1990|<br>1186528|74.7|1990-94|2.06|31.4|10.7|4.16|58.2|59.0|93|122|
|1995|<br>1314996|72.3|1995.99|1.85|28.2|9.6|3.67|59.8|61.3|82|106|
|2000|<br>14216|67.0|2000-04|1.73|26.2|8.8|3.32|61.5|63.6|71|90|
|2005|<br>1572428|61.3|2005-09|1.60|24.3|8.3|3.00|62.9|65.3|62|80|
|2010|<br>1703008|5f.9|2010-14|1.44|22.4|7.9|2.71|64.3|67.0|54|69|
|2015|<br>1¶30214|53.7|2014-19|1.33|20.9|7.6|2.54|65.8|68.8|46|58|
|2020|<br>156044|51.5|2020-24|1.23|19.7|7.4|2.43|67.3|70.6|37|48|
|2025|2079666|50.1|2025-49|0.89|16.4|7.6|2.20|71.2|75.2|21|27|
|2050|2596619|51.2|2050-74|0.45|13.7|9.3|2.09|75.8|80.6|8|10|
|2075|2905338|60.5|2075-99|0.21|12.7|10.6|2.07|78.7|84.1|5|6|
|2100|3062192|67.1|2100-24|0.11|12.1|11.0|2.06|80.8|86.9|3|4|
|2125<br>2150|3149368<br>3192437|70.7<br>72.5|2125-49|0.05|1'.9|11.3|2.06|81.7|88.1|2|3|

## 2. Age structure (percent): 1990 and 2025

10 8 6 4 2 0 2 4 6 8 10 <!-- Start of picture text --> 75+<br>70-74<br>65-69<br>60-64<br>55.59<br>50-54<br>45-49<br>40-44<br>35-39<br>30-34<br>25-29<br>20-24<br>1 5-1 9<br>10-14<br>5-9<br>0-4<br>1 8 6 4 2 0 2 4 6 8 10<br><!-- End of picture text -->

## 3. Distribution of population (percent): 1990 and 2025

<!-- Start of picture text --> sowv Cd U)<br>Jla^ 4<br>IN C17 Sr soJ'YwC42'UI<br><!-- End of picture text --> <!-- Start of picture text --> swt' sr-<br><!-- End of picture text --> 14
our findings suggest that, the discriminatory enforcement of property rights by the government in favor of connected firms will adversely affect the development of the private sector and the economy as a whole.<sup>30</sup>

# **References**

- Acemoglu, D., Johnson, S., Robinson, J., 2001. The colonial origins of comparative development: an empirical investigation. _American Economic Review_ 91(5), 1369-1401.

- Acemoglu, D., Johnson, S., Robinson, J. A. 2005. “Institutions as a Fundamental Cause of LongRun Growth,” in Handbook of Economic Growth (pp. 385–472). Amsterdam: North Holland.

- Acemoglu, Daron, Davide Ticchi, and Andrea Vindigni, 2011, Emergence and persistence of inefficient states, _Journal of the European Economic Association_ 9, 177-208.

- Acemoglu, D., D. Cantoni, S. Johnson, JA Robinson. 2011. “The Consequences of Radical

- Reform: The French Revolution.” _American Economic Review_ 101(7), 3286-3307.

- Acemoglu, Daron, Simon Johnson, Amir Kermani, James Kwak, and Todd Mitton, 2016, The value of connections in turbulent times: Evidence from the United States. _Journal of Financial Economics_ 121, 368-391.

- Akcigit, Ufuk, Salome Baslandze, Francesca Lotti. 2018. “Connecting to Power: Political Connections, Innovations, and Firm Dynamics.” NBER Working Paper 25136.

- Alston, Lee, Gary D. Libecap, Robert Schneider. 1996. “The Determinants and Impact of Property Rights.” _Journal of Law, Economics, and Organization_ 12(1), 25-61.

- Ang, James, Yingmei Cheng, and Chaopeng Wu, 2015, Trust, investment, and business contracting, _Journal of Financial and Quantitative Analysis_ 50, 569-595.

- Bailey, Warren, Wei Huang, and Zhishu Yang, 2011, Bank loans with Chinese characteristics: Some evidence on inside debt in a state-controlled banking system, _Journal of Financial and Quantitative Analysis_ 46, 1795-1830.

- Banerjee, Abhijit, Paul Gertler, and MaitreeshGhatak, 2002, Empowerment and efficiency: Tenancy reform in West Bengal, _Journal of Political Economy_ 110, 239-280.

- Berkman, H., Cole, R.A., Fu, L.J., 2010. Political connections and minority-shareholder protection: evidence from securities-market regulation in China. J. Financ. Quant. Anal. 45 (6), 1391– 1417.

> 30 There is an intriguing possibility that the positive investment response of connected firms may reflect coordinated response of local governments and connected firms—for instance, the local government may know that these connected firms would carry out their side of the bargain when the local government needs something done. This is an area that needs further research in the future.

36
BURKINA FASO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Information and communication technologies

Ampah, Mavis, Daniel Camos, Cecilia Briceño-Garmendia, Michael Minges, Maria Shkaratan, and Mark Williams. 2009. ―Information and Communications Technology in Sub-Saharan Africa: A Sector Review.‖ AICD Background Paper 10, Africa Region, World Bank, Washington, DC.

Mayer, Rebecca, Ken Figueredo, Mike Jensen, Tim Kelly, Richard Green, and Alvaro Federico Barra. 2009. ―Connecting the Continent: Costing the Needs for Spending on ICT Infrastructure in Africa.‖ AICD Background Paper 3, Africa Region, World Bank, Washington, DC.

# Irrigation

Svendsen, Mark, Mandy Ewing, and Siwa Msangi. 2008. ―Watermarks: Indicators of Irrigation Sector Performance in Africa.‖ AICD Background Paper 4, Africa Region, World Bank, Washington, DC.

You, L., C. Ringler, G. Nelson, U. Wood-Sichra, R. Robertson, S. Wood, G. Zhe, T. Zhu, and Y. Sun. 2009. ―Torrents and Trickles: Irrigation Spending Needs in Africa.‖ AICD Background Paper 9, Africa Region, World Bank, Washington, DC.

# Power

Briceño-Garmendia, Cecilia and Maria Shkaratan. 2010. ―Power Tariffs: Caught between Cost Recovery and Affordability.‖ First draft, February 2010.

- Eberhard, Anton, Vivien Foster, Cecilia Briceño-Garmendia, Fatimata Ouedraogo, Daniel Camos, and Maria Shkaratan. 2008. ―Underpowered: The State of the Power Sector in Sub-Saharan Africa.‖ AICD Background Paper 6, Africa Region, World Bank, Washington, DC.

- Foster, Vivien, and Jevgenijs Steinbuks. 2009. ―Paying the Price for Unreliable Power Supplies: In-House Generation of Electricity by Firms in Africa.‖ Policy Research Working Paper 4913, World Bank, Washington, DC.

- Rosnes, Orvika, and Haakon Vennemo. 2009. ―Powering Up: Costing Power Infrastructure Spending Needs in Sub-Saharan Africa.‖ AICD Background Paper 5, Africa Region, World Bank, Washington, DC.

- World Bank. 2005. Project Concept Document on a Proposed IDA Credit in the Amount of US$40.0 Million Equivalent to the Government of Burkina Faso and on a GEF Grant of about US$3.0 Million for a Energy Access Project. December 29, 2005

# Transport

Bofinger, Heinrich C. 2009. ―An Unsteady Course: Growth and Challenges in Africa’s Air Transport Industry.‖ AICD Background Paper 16, Africa Region, World Bank, Washington, DC.

- Bullock, Richard. 2009. ―Off Track: Sub-Saharan African Railways.‖ AICD Background Paper 17, Africa Region, World Bank, Washington, DC.

47
_Low-Income Countries’ Access to Private Debt Markets_

# **Figure 25 Average interest rate on new external debt commitments and GNI per capita, 2007**

<!-- Start of picture text --> Percent<br>10<br>Low-income cut-off<br>8<br>6<br>R 2 = 0.46<br>4<br>2<br>0<br>4 5 6 7 8 9<br>log(GNI per capita)<br>Source: World Bank Debtor Reporting System and World Development Indicators.<br>Years<br>50<br>40<br>30<br>20 R 2 = 0.38<br>10<br>Low-income cut-off<br>0<br>4 5 6 7 8 9<br>log(GNI per capita)<br>Average interest rate<br>Average maturity<br><!-- End of picture text --> _Source:_ World Bank Debtor Reporting System and World Development Indicators.

**Figure 26 Average maturity on new external debt commitments and GNI per capita, 2007**

_Source:_ World Bank Debtor Reporting System and World Development Indicators.

Low-income countries’ ability to access private debt market will depend on how well they can manage the risks entailed in borrowing on commercial terms. The debt sustainability framework developed by the IMF and the World Bank is designed to help ensure that debtors and creditors assess the risks effectively (IMF and World Bank 2004). Countries that are able sustain robust growth within a stable macroeconomic environment, while making progress on key reforms to improve governance and build sound institutions will be able to take on more private debt over time without endangering debt sustainability.

30
20

# Table 3: Descriptive Statistics

||Mean|Std. Dev.|
|---|---|---|
|_Not available to work during the survey week (of all population age_≥_15)_|||
|By reason (= 1, if not = 0)|||
|Having household, family duty|0.09|0.28|
|In school,student|0.09|0.29|
|Seasonal worker|0.01|0.10|
|Too young or too old|0.06|0.24|
|Illness, disability|0.02|0.13|
|Resting|0.01|0.09|
|Other reasons|0.03|0.18|
|Total not available to work during the survey week|0.31|0.46|
|_Determinants of work status_(= 1, if not = 0, except minimum wage)|||
|Province-level minimum wage (THB/day)|140.53|13.77|
|Non-municipal area|0.67|0.47|
|Female|1.5|0.5|
|Education - primary|0.62|0.49|
|Education - some secondary|0.17|0.38|
|Education - secondary|0.1|0.3|
|Education - vocational|0.05|0.21|
|Education - university|0.06|0.23|
|No. of children (age ≤ 5)|0.95|1.04|
|No. of children (age 6-14)|0.58|0.8|
|No. of elderly (age > 64)|0.31|0.6|
|Age (15-29)|0.36|0.48|
|Age (30-49)|0.4|0.49|
|Age (50-64)|0.16|0.36|
|Age (>64)|0.08|0.28|
|N(unweighted sample size)|4,770,735||

Source: Thai Labor Force Survey (1997 - 2005) Notes: N = unweighted sample size (individual respondents) for 32 quarters from 1997-2005. Mean and Std. Dev. are calculated using population weight given by the survey.
Proportion of Population in Food Insecurity <!-- Start of picture text --> Food Stress<br>35000K<br>— Food Crisis<br>30000K<br>25000K<br>Cc<br>2 20000K<br>&<br>|<br>oa<br>& 15000K<br>10000K<br>5000K<br>OK = ua<br>MO OO he Mm A AD AD AD Dm AD 9 oD DG 0 ON ON oD oD 2D 0D A OY OY A OD BD OD<br>PY 0” .6¥ 5” 6” 0” 6” 6” 0” 6” 6” 0” 6” 26” 5” 5” 10” 10” 6” 6” 6 Gh gk gh gk gh gk gh<br>gv re g’ “ g’ re 9” re 9” “ oY “ oY “ oY sv oY re oY re oY sv gv re oY sv g’ sv oY<br>FP PML ELM KLM LLM PM PHM LM PM LPM LM LM<br>Date<br><!-- End of picture text -->