rafmacalaba/datause-encoder-data huggingface dataset:
The results suggest that there are no consistent differences across treatment groups, which is what is to be expected given the random assignment.

# **Table A5. Balance Tuungane and Control**

|Variable|Q|Tuungane|Sd.|Control|Sd.|Diff.|(Se.)|N|
|---|---|---|---|---|---|---|---|---|
|Distance mine|QE13|20.98|42.46|25.03|59.73|-4.05|(3.86)|723|
|Distance police post|QE13|3.61|6.11|3.69|6.14|-0.08|(0.44)|777|
|Distance kingdom HQ|QE13|8.99|19.6|8.68|11.76|0.31|(1.16)|771|
|Ethnic heterogeneity|CQ13|0.32|0.27|0.33|0.28|-0.02|(0.02)|728|
|Religious heterogeneity|CQ14|0.52|0.19|0.52|0.2|0|(0.01)|724|
|Birth year former chief|CQ45|1936.43|19.62|1938.53|21.13|-2.1|(2.19)|347|
|Former chief democratic|CQ48|0.16|0.37|0.17|0.38|-0.01|(0.03)|653|
|Share in agriculture|CQ15|71.17|20.5|71.61|20.49|-0.43|(1.52)|724|
|Share in herding|CQ15|9.43|10.47|9.29|9.52|0.13|(0.77)|669|
|Share in commerce|CQ15|3.8|8.67|3.98|7.4|-0.19|(0.62)|669|
|Share in fishing|CQ15|6.13|12.76|6.05|12.2|0.08|(0.96)|673|
|Share in industrial|CQ15|0.13|0.97|0.17|1.42|-0.04|(0.09)|665|
|Share in mining|CQ15|3.29|8.71|3.5|8.32|-0.2|(0.66)|667|
|Share in other|CQ15|4.44|8.04|4.21|7.9|0.22|(0.63)|646|
|Share in other services|CQ15|4.41|4.66|4.13|4.55|0.28|(0.36)|665|
|Wells in 2006|CQ23|0.91|1.83|1.4|3.29|-0.49**|(0.2)|705|
|Schools in 2006|CQ24|3.5|5.04|3.51|4.48|-0.01|(0.36)|713|
|Clinics in 2006|CQ25|0.32|0.84|0.29|0.51|0.03|(0.05)|718|
|Churches in 2006|CQ26|2.18|2.5|2.55|2.67|-0.37*|(0.19)|716|
|Halls in 2006|CQ27|0.04|0.3|0.04|0.21|0.01|(0.02)|709|
|IDPs in 2006|CQ136|2.8|12.64|4.86|20.07|-2.06|(1.47)|533|
|IDPs returned in 2006|CQ137|5.4|24.45|3.91|16.04|1.49|(1.8)|518|
|Refugees in 2006|CQ138|0.57|3.78|0.97|7.29|-0.41|(0.5)|557|
|Refugees repatriated in 2006|CQ139|0.76|12.13|0.25|3.21|0.52|(0.72)|575|
|Share of male respondents|QF7|0.5|0.5|0.5|0.5|0|(0.01)|23567|
|Average age respondents|QF9|20.12|16.82|20.17|17.02|-0.06|(0.23)|22536|

Note: Question number responds to 2010-2011 survey (Humphreys et al. 2012). Tests of difference based on simple OLS regressions. * p<0.10, ** p<0.05, *** p<0.01 (two-tailed).

10
# **2 Context, data and the urban gradient**

This section introduces our two data sources: the Kagera Health and Development Survey (KHDS) and the 2002 Tanzania Population and Housing Census. Within each we demonstrate rising living standards from villages across towns to cities, which we refer to as the urban gradient. In KHDS the gradient is demonstrated for changes in migrant welfare in a panel data set, while in the census it is shown to hold cross-sectionally for all residents of these areas.

## **2.1 Kagera**

The migrants we study all originate from the Kagera region of Tanzania. Figure 1 provides a map showing where the region is situated within Tanzania. Kagera, being primarily dependent on agriculture and far away from the ocean, is badly connected to the global economy. It is typical of remote parts of Africa where policy prescriptions for economic growth are elusive (De Weerdt, 2010). Given small plot sizes (typically 3 or 4 acres per household) and relatively low-productivity agriculture, internal migration will be an important aspect of growth and poverty reduction for this region.

In the context of this paper it is useful to highlight some of the geographical specificities of the Kagera region, which may be relevant for the study of destination choice and in particular whether urban migrants end up in large cities or smaller towns. The top panel of Figure 1 shows the baseline villages from the KHDS survey where all our respondents originate (the survey is described in the next subsection) and the bottom panel shows the urban areas in the country, split into small towns (10-100k), big towns (100-500k) and cities ( _>_ 500k).

We see that Kagera lies at the periphery of the country and has, within its borders, only one large town (Bukoba) and no cities. The closest city is Mwanza on the southern shores of Lake Victoria, while Tanzania’s prime city, Dar es Salaam, is located at the other end of the country. In short, Kagera is a relatively remote region and that should be kept in mind when analyzing urban destination choices of migrants originating from Kagera.

4
Suárez and Delgado 2009). Specifically, Suárez and Delgado (2009) posit that, while the city’s residents have decentralized, employment opportunities are still concentrated in the center. In fact, according to the Population and Economic Censuses, there are more people working in the central city than residents living there, evidence that the city still has a monocentric structure (Suárez and Delgado 2009). Since more than 50 percent of employment in Mexico City is considered informal (Azuara and Marinescu 2013), Suárez and Delgado (2009) suggest that employment subcenters identified only considering formal employment could be biased and may not reflect the real urban structure of the city.

Notwithstanding the degree of monocentricity of a city, a well-connected city can provide high access to opportunities. However, in Mexico City, access to opportunities faces serious challenges given its size and limited infrastructure, leading to high congestion and frequent environmental contingencies due to concentration of atmospheric gases. In the last couple of years (2016-2017), more than a million vehicles were taken off the streets in order to reduce pollution in the city (Milenio 2016; Excelsior 2017). According to Romein et al. (2009), 80 percent of the daily trips use collective transportation modes (formal or informal) with the remaining 20% using private cars, including taxis. Different modes of transportation exist including private cars, semi-pubic buses (private concessions for public service), and public transportation (BRT, Metro, and LRT). Hence, a full characterization of accessibility in Mexico City must include both public (access to public transportation), and private accessibility (access to roads where private and semi-public modes of transportation operate).

Indicators of accessibility summarize the ease by which destinations of interest can be reached from a given location (Yoshida and Deichmann 2009). For example, they can be defined as the time households spend getting to job locations. Ideally one would extract this information from detailed origin-destination surveys, but such surveys are rarely available, and when available, they are only representative at an aggregate level, more often the municipal level.<sup>6</sup> Instead, access measures can be approximated by estimating the distance or time to reach destinations using a detailed geographically referenced data set of origins, destinations and the transport network. Gravity type models summarize average accessibility to a range of potential destinations such as employment subcenters with weights assigned

> 6 The last origin and destination survey of Mexico City was conducted by INEGI in 2007 (INEGI 2007).

5
# **ECONOMIC GROWTH**

Bahia was the preeminent region of Brazil in the 17<sup>th</sup> and early 18<sup>th</sup> centuries and until 1763, Salvador was the capital of Brazil. Sugar cane exports were the driving force in the economy. The mining boom and coffee boom in the Southeast in the 18<sup>th</sup> and 19<sup>th</sup> centuries moved the focus of Brazil’s economy southward. Bahia’s economic revival did not really occur until the advent of the so-called Brazilian miracle of 1967-73, when the military regime implemented reforms aimed at encouraging investment, reducing inflation, and restraining wage demands. Despite being a highly protected economy Brazil’s and Bahia’s GDP grew at an average annual rate of 8.5 percent and 10.5 percent respectively during 1965-80. In Bahia, the key source of growth was heavy manufacturing. The oil crisis of 1979 followed by the debt crises caused a downturn in Brazil’s growth and investment. Bahia’s economy was hard hit during 1986-92 as revealed by the low average annual GDP growth rate of 0.3 percent, slightly above half of the national rate (0.5 percent).

The Brazilian reform period set in the beginning of the 1990s and revived economic growth mainly due to the liberalization of the economy and a successful stabilization. Additionally, the federal government has maintained a tight monetary policy. Between 1992 and 2000, the Brazilian and _Bahiana_ economies grew at an average annual rate of around 3.1 percent. In 2001, GDP of Bahia accounted for 4.5 percent of the Brazilian GDP equivalent to R$48.9 billion.

Bahia’s agriculture, services, and manufacturing sectors grew at an average annual rate of 3.0 percent 1992-2000 (Table 2.3).<sup>5</sup> Bahia remains very much a dual economy where, on the one hand, agriculture contributes only 11 percent of GDP, accounting for 42 percent of employment, and manufacturing, and on the other hand, contributes 26 percent of GDP, but only 6 percent of jobs (World Bank 2001b).

> 5 Communications grew rapidly at an annual rate of 15 percent and transport, construction, and tourism expanded by around five percent per year. In agriculture the traditional crops (mainly cacao) experienced a decline, while new ones (soy, grain, tropical fruits, and  cellulose) emerged, along with expanded cattle ranching in South Bahia..

7
# **6. Climate simulations**

# **_Uniform climate change scenarios_**

In order to illustrate what the regressions imply for African animal husbandry, we examined some simple uniform climate scenarios where only one climate factor changes at a time across the continent. The purpose of these simulations is to understand how non-marginal changes in each climate component affect the results. We test increases in temperature of 2.5 ° C and 5 ° C, and changes in precipitation of -7%, +7% and +14%. The temperature changes are added to existing temperatures in each district. The precipitation changes are multiplied by the rainfall in each district. Assuming that climate changes will be uniform across the entire continent of Africa is not realistic, but the experiments provide important insights into how the model responds to climate changes.

Table 6 compares the baseline income with the predicted impact in each of five uniform climate change scenarios. For the small farms, warming of 2.5 ° C increases livestock net revenues by 25% and warming of 5 ° C by 58%. For the large farms, warming of 2.5 ° C reduces livestock net revenues by 22% and warming of 5 ° C by 35%. These outcomes are consistent with the marginal analyses reported in Table 5. A 14% increase in precipitation is expected to reduce livestock net revenues by about 10% for both small and large farms.

Table 6 also provides insights into whether these changes are due to changes in the stock of livestock owned or changes in the net revenue per animal. The increases in net revenue with warmer temperatures for small farms are due largely to changes in the number of livestock owned. Net revenues per animal actually fall slightly. The reductions in net revenue with higher temperatures for large farms, by contrast, are due to a combination of fewer animals per farm and reductions in net revenue per animal. The precipitation effects for all farms are due more to changes in the number of animals than to changes in the net revenue per animal.

# **_AOGCM climate scenarios_**

We also examined the climate change scenarios predicted by AOGCMs. We relied on a broad set of scenarios that predict a wide range of outcomes, consistent with the expectations in the most recent IPCC report (Houghton et al. 2001). Specifically, we used the A1 scenarios from the 16
as illustrated in Figure 10. The top panel displays on-time payment rates for the control group, distinguishing between women (in red) and men (in blue). Tax compliance increases with property valuation but is otherwise similar for men and women. The bottom panel reports the treatment effects for each quintile—i.e., the difference between treated and control units—alongside 95% confidence intervals. While women present homogeneous responses across quintiles, men exhibit a slightly negative gradient. Notably, in the lowest quintile, the personalized tax letter boosted timely payments of men by 8 percentage points—twice the increase observed for women.<sup>11</sup> Taken together, our findings indicate that men and women exhibit similar payment rates and responses to personalized tax letters. However, some of our evidence suggests that men might be slightly more sensitive to this type of intervention. For instance, men tend to respond more quickly to tax letters, though women eventually catch up. Additionally, men with lower-valuation properties tend to react more strongly to the intervention. Our findings thus somehow downplay the potential importance of gender-dependent strategies to enhance property tax compliance.

# **6 Conclusion**

The increased access to administrative data and tax authorities’ willingness to engage in randomized experiments offer a new opportunity to shed light on the differential effects of taxation by gender. In the context of property taxation in the municipality of Tres de Febrero, Argentina, we first document large gaps in property ownership by gender, particularly among high-value properties, consistent with other settings where these gaps were investigated. Nonetheless, administrative records show that compliance, measured by timely payment of property tax liabilities, is very similar across properties with male and female owners. Furthermore, we exploit a randomized trial aimed at improving compliance and show that reactions to an enforcement letter are similar across genders.

The overall absence of gender differentials in baseline compliance and response to the intervention is surprising. Surveys have shown that in contrast to men, women tend to think that the tax code is fairer, the likelihood of getting caught for evasion is greater, and they overestimate the penalties for evasion (D’Attoma, Volintiru, and Steinmo, 2017; Torgler and Valev, 2010). In terms of behavior, a number of tax compliance lab experiments have also shown women to be more compliant than men (e.g., see Chung and Trivedi, 2003; Spicer and Hero, 1985). López-Luzuriaga and Scartascini (2023), studying a similar setting of property tax in another city in Argentina, document that women are more compliant at baseline (but similar to our result, responses to an

> 11. As a validation, Figure C.9 shows no effects for the September 2020 pre-intervention bill.

14
with slower growth in social welfare); political instability as measured by revolutions (greater instability leads to slower growth in social welfare); and the share of agriculture in GDP (a higher share is associated with slower growth in social welfare). Interestingly, for each of these variables, the main effect operates through the relationship with growth in average incomes: the coefficients in the third column of each panel (which measure the partial correlations with growth) are much larger in absolute value than the coefficients in the third column (which measure the partial correlations with changes in equality). Consider for example the relationship between inflation and growth in social welfare. A one standard deviation increase in inflation reduces average annual growth in social welfare by between 1.8 and 2.2 percent per year, depending on which social welfare function we consider. Nearly all of this effect comes through lower growth in average incomes, which declines by 1.7 percentage points. This estimated coefficient is significantly different from zero in all of the models in which it appears.<sup>14</sup> The probability-weighted slope coefficient from the equality regression is negative, suggesting that high inflation is disequalizing on average. However, the estimated effect is much smaller than the estimated effect on growth, and is rarely statistically significant.

Another interesting case is the relationship between growth in social welfare and the share of agriculture in GDP, which is the one case among these four variables suggestive of a tradeoff. On the one hand, a higher share of agriculture in GDP is associated with substantially slower growth in average incomes, and this effect is significant in nearly all specifications. On the other hand, a higher share of agriculture in GDP is positively associated with subsequent growth in equality. However, this latter effect is once again much smaller than the effect on growth in average incomes, and moreover is never statistically significant at conventional levels. As a result, the growth effect dominates, and the effect of a larger share of agriculture in GDP on subsequent growth in social welfare is substantially negative.

It is however noteworthy that the remaining variables mostly have quite low posterior inclusion probabilities, and rarely show up as significant correlates of either growth in average incomes or growth in the relevant inequality measures. This is at least somewhat surprising, given that these variables were selected for their prominence in the empirical growth literature. However, there are at least two important differences between the empirical specifications here, and those in the majority of papers in this broader literature. The first is that in our work, growth in average incomes is measured as growth in average income or consumption from the underlying household survey. As we have noted earlier, there are substantial differences between these growth rates and measures of mean growth taken from

> 14 This finding is in part driven by a fairly small number of spells with quite high inflation rates.

25
Ghaus-Pasha, A. 2010. “Can Pakistan Get Out of the Low Tax-to-GDP Trap?” _The Lahore Journal of Economics_ 15: SE (September 2010): pp. 171-185 Hanson, G. 2010. “Sources of Export Growth in Developing Countries.” UC San Diego preliminary draft Hausmann, R., Hwang, J., & Rodrik, D. (2006). What you exprots matters. NBER Working Papers 11905, National Bureau of Economic Research, Inc.

Helpman, E., Melitz, M., and Y. Rubinstein. 2008. “Estimating Trade Flows: Trading Partners and Trading Volumes.” _Quarterly Journal of Economics_ , Vol. CXXIII:2 Hidalgo, C.A, Klinger, B., Barabasi A-L, Hausmann, R. 2007. “The Product Space and its Consequences for Economic Growth.” _Science_ **317** , 482-487 Hummels, D. and P. Klenow. 2005. “The Variety and Quality of a Nation’s Exports.” _American Economic Review_ , Vol. 95:3 Klinger, B. 2010. “(New) Export Competitiveness.” Harvard University, Center for International Development. Draft Lall, S. (2000). “The technological structure and performance of developing country manufactured exports, 1985-98”, Oxford Development Studies, 28(3), pp. 337–369.

Lederman, Daniel, and William F. Maloney, eds. 2007. Natural Resources: Neither Curse nor Destiny. Stanford University Press.

Meltiz, M. (2003). The impact of trade on intra industry reallocations and aggregate industry productivity. _Econometrica,_ Vol 71, No 6 , 1695-1725.

Rauch, J & Joel Watson, 1999. Starting Small in an Unfamiliar Environment, NBER Working Papers 7053, National Bureau of Economic Research, Inc.

Reis, J.G. and Thomas Farole, 2012. Trade Competitiveness Diagnostic Toolkit Siggel, E. (2006). International competitiveness and comparative advantage: a survey and proposal for measurement _. Journal of Industry, Competition and Trade_ 6:137-159.

World Bank. 2010. “Pakistan Trade Brief.” World Trade Indicators 2009/10: Country Trade Briefs. Washington, DC: World Bank. Available at http://www.worldbank.org/wti World Bank, forthcoming, _Pakistan: Reinvigorating the Trade Agenda_ , Washington DC.

49
# **Services Trade Liberalization and Regulatory Reform: Re‐invigorating International Cooperation** *

Bernard Hoekman (World Bank and CEPR) Aaditya Mattoo (World Bank)

**Keywords** : services regulation, trade liberalization, international negotiations, trade agreements, development assistance, aid for trade, knowledge platforms, WTO, GATS

**JEL classification** : F1, F2, F5, L80

* This paper draws on research that has been supported by the Global Trade and Financial Architecture project and the Multi‐Donor Trust Fund for trade. We are grateful to Paul Brenton, Nora Dihel, Abdel‐ Hamid Mamdouh, Jean‐Christophe Maur and Sebastian Saez for inputs on the some of the elements of the proposal for a knowledge platform that is discussed in this paper, to Rolf Adlung and Patrick Messerlin for helpful comments and to participants in the Cordell Hull Institute meeting on services in Sydney, December 11‐12, 2010 and the Second Research Conference on Empirical Issues in International Trade and Finance, Indian Institute of Foreign Trade, New Delhi, December 16‐17, 2010. The views expressed are personal and should not be attributed to the World Bank.
**Table 1: Basic stylized facts on urban water and sanitation access in developing countries**

||**Urban a**<br>**improved**|**ccess rates to**<br>**water sources**|**Urban a**<br>**sa**|**ccess rates to**<br>**nitation**|
|---|---|---|---|---|
||**Access rate as %**<br>**pop (2015)**|**Average growth rate**<br>**in access rates**<br>**(1991-2015)**|**Access rate as**<br>**% pop (2015)**|**Average growth rate**<br>**in access rates**<br>**(1991-2015)**|
|**East Asia & Pacific**|96.9|0.09%|74.9|1.8|
|**Europe & Central Asia**|98.5|0.07%|93.7|0.4|
|**Latin America & Caribbean**|97.3|0.15%|80.6|1.0|
|**Middle East & North Africa**|94.9|-0.05%|89.7|1.0|
|**South Asia**|95.3|0.26%|64.6|0.9|
|**Sub-Saharan Africa**|86.8|0.18%|29.7|0.9|

Source: Authors’ computation based on World Bank Development Indicators data The most obvious institutional weakness explaining the issues is a lack of fiscal capacity. What the table does not show is that when the private sector does not deliver, the public sector needs to step in with subsidies. Since 2008, this has been a problem. Almost all countries have seen fiscal constraints reduce governments’ ability to take on the investment responsibility to reduce the gap between demand and supply in the sector in the poorest regions for water, and across regions for sanitation. A related explanation is that investment financing prospects have not met with optimistic predictions. For almost 30 years, the assumption has been that the private sector would replace the public sector as a source of financing for WSS. In practice, this assumption slowed total investment because public sector cuts were not compensated by private investment (e.g. Estache and Fay (2011). The hardest hit by the slowdown in investment were the poorest countries, despite the strong political support for investment in the sector resulting from the adoption of the MDGs in 2000.

An additional driver of underinvestment in WSS is the fact that the responsibility for investment decisions in the sector is often a mandate shared across various levels of government. With uncertain local commitment to respect weakly enforceable joint or interrelated contractual obligations (i.e. little accountability), shared mandates lead to a moral hazard issue resulting in service under-provision. A recent case study of the shared responsibility for water pollution in the state of Sao Paulo (Brazil) shows that enforcement of commitments works only reasonably well when there is political alignment between the various government levels sharing the mandate for sanitation (i.e. when they all belong to the same party or the same coalition). In other cases, investment in water treatment is lower than 13