rafmacalaba/datause-encoder-data huggingface dataset:
**Table 7:** Effects of Turnovers on Public Goods Provision

||**Public Goods Index**|**Citizen**|**survey**|**Village head survey**|**Balance**|
|---|---|---|---|---|---|
||_Podes_2021|Access|Quality|Take Action|_Podes_2014|
||(1)|(2)|(3)|(4)|(5)|
|New village head|0.503<sup>_∗_</sup>|0.073<sup>_∗∗_</sup>|0.208<sup>_∗∗_</sup>|0.165<sup>_∗∗_</sup>|-0.058|
||(0.216)|(0.037)|(0.090)|(0.071)|(0.341)|
||[0.263]|[0.043]|[0.102]|[0.086]|[0.419]|
|Observations|378|8848|8846|443|375|
|Control mean|0.23|0.78|-0.028|0.76|0.018|
|Robust p-value|0.053|0.039|0.014|0.046|0.823|
|<br>MSE-opt. bandwidth|18.7|15.2|14.9|24.9|19.0|
|Effective obs.|161|3479|3427|241|161|

_Notes_ : This table reports RD estimates of _γ_ in equation (1). Units of observation are villages in columns 1, 4 and 5, and citizens in columns 2 and 3. In column 1, the dependent variable is a standardized index of local public service provision constructed using the 2021 Podes data. The index has the following components: drinking water, sewage, garbage collection, street lighting, paved roads, public transit, kindergartens, primary schools, village maternities ( _polindes_ ), and community health centers ( _puskesmas_ ). The index is standardized to have mean 0 and standard deviation 1. In column 2, the dependent variable is an index of access to local services constructed using our citizen survey data. The index ranges between 0 and 1 and is constructed as the average of binary variables denoting access to each type of service, where 1 denotes that the service is accessible in the village. In column 3, the dependent variable is a standardized index of service quality based on citizens’ ratings of service quality for each service on a 5-point Likert scale. In column 4, the dependent variable is an index of village governments taking action on a public service, constructed as the average of binary variables indicating that the village government took action or made investments to improve each type of service, where 1 denotes that such action was taken. The indices in columns 2–4 each have the following components: water, garbage collection, paved roads, electricity, kindergartens, primary schools, and community healthcare. In column 5, the dependent variable is a standardized index of local public service provision constructed using the 2014 Podes data, and serves as a balance check. The 2014 index has the same components as in column 1, with the exception of garbage collection, which was not collected in 2014, and a different coding for street lighting, which was categorized into two groups in 2014 ( _no lights_ , _any lights_ ), compared to three groups in 2021 ( _no lights_ , _some lights_ , and _many lights_ ). See Section 5 for details. * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Conventional standard errors in parentheses and robust bias-corrected standard errors in brackets. In columns 2–3, standard errors are additionally clustered by village. Significance levels are based on the robust bias-corrected standard errors (clustered by village in columns 2–3).

39
proportionately high concentration of firms within the same industry increases possibilities for collective action to lobby regulators or bid-prices of intermediate products.

There is considerable theorizing on localization economies in the works of Marshall (1890), Arrow (1962), and Romer (1986). They argue that cost-saving externalities are maximnized when a local industry is specialized (often called MAR externalities), and their models predict that externalities predominantly occur within the same industry. Therefore, if an industry is subject to MAR externalities, firms are likely to locate in a few cities where producers of that industry are already clustered. Examples of highly localized industries are ubiquitous. Semiconductor and software in Silicon Valley and automobile in Detroit are classic cases in point. Later, Porter (1990) also emphasized the importance of dynamic externalities created in specialized and geographically concentrated industries.

There is an extensive empirical literature supporting the positive effects of localization economies (Henderson 1988, and Ciccone and Hall 1995). In a recent study of Korean industry, Henderson et ai. (1999) estimate scale economies using city level industry data for 1983, 1989, and 1991-93, and find localization economies of about 6 to 8 percent. However, while industry concentration provides many benefits, some of these may be offset by costs from enhanced competition between firms for labor and land causing wages and rents to rise, as well as higher transport costs due to congestion. Therefore, the net benefits of own industry concentration may be marginal for sectors with low skilled labor and standardized technologies.

There are several ways of measuring localization economies. These include own industry employment in the region, own industry establishments in the region, or an index of concentration, which reflects disproportionately high concentration of the industry in the region in comparison to the nation. We use own industry employment in the district to measure localization 8
if wages are US$100 per labor efficiency unit, and we have 100 labor efficiency units because we have two workers and an efficiency level of 50, then the wage per worker is US$5000. With 100 workers at efficiency level 1, the wage per worker is US$100. Equations [3] and [4] may thus be re-written as _wi_ * _U_   _iU wiU_   _iU_ ( _RMAiU_ )1/  and _wi_ * _R_   _iR_ ( _RMAiR_ )1/  respectively, where _RMAiU_ denotes region _i_ 's level of RMA in the urban sector and _RMAiR_ its level of RMA in the rural sector. _RMAiU_ is basically equal to the weighted sum of real aggregate income levels across all regions, including region _i_ , where the weights are determined by the cost of transporting urban goods from _i_ to each region. _Ceteris paribus_ , _RMAiU_ can therefore be expected to increase if real incomes in surrounding regions increase or if costs of transportation fall. Regions with high levels of nominal urban wages will be those which are well-connected to other regions which have high levels of real income. A similar interpretation applies to _RMAiR_ except that it is the cost of transporting rural goods from _i_ to each region which is now important.

Efficiency levels are defined relative to the minimum observed level of labor efficiency in the _U U U_ urban sector. Hence, for the urban sector,  _i_  _Ai_ / _A_ min , whilst, for the rural sector, _R R U U_  _i_  _Ai_ / _A_ min , where<sup>_A_</sup> _i_<sup>denotes a region's absolute level of labor efficiency and</sup><sup>_A_</sup> _i_<sup>the</sup> minimum observed level of labor efficiency in the urban sector. The presence of these parameters in equations [3] and [4] imply that non-NEG, as well as NEG, forces will be important in determining the geography of both urban and rural wages.

# _3.2. Methodology for Estimating  Impacts_

Our methodology consists of five stages, involving the two key wage equations – equations [3] and [4] – and an assessment of the fit of the model's short-run equilibrium solution to Chinese data for 2007. In the **first stage** , we use GIS techniques to construct two _N_  _N_ travel time matrices, _TIME After_ and _TIME Before_ . The _i_ - _j_ th element of the matrix _TIME After_ ,<sup>_t_</sup> _ijAfter_ , measures the optimal travel time by road between regions _i_ and _j_ based on a digital representation of the 2007 Chinese road network, which includes the NEN. Likewise _TIME Before_ measures optimal travel times excluding the NEN, thus providing the basis for our counterfactual scenario. For both networks, travel time is invariant to the direction of travel, _x x x_ i.e. _t ji_  _tij_ where _x_  { _Before_ , _After_ }. For simplicity, we also assume that<sup>_t_</sup> _ii_  0 for  _i_ . In 13
**<u>Figure 7. Distribution of Bank Loans and Advances in March 2006</u>**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>above $US 1,000<br>70%<br>between $US$500.01 and $1,000<br>60% dollars<br>between US$200.01 and $500<br>50%<br>dollars<br>40% between US$100.01 and $200<br>dollars<br>30% below US$100 dollars<br>20%<br>10%<br>0%<br>By number of accounts By volume<br><!-- End of picture text --> Source: Data from commercial banks in Zambia In terms of volume, at the end of 2005 73% of the total loan portfolio of banks was composed of loans granted to private firms and the remaining 27% by loans granted to individuals and households. In the case of loans to private firms, 69% of credit is concentrated in the following sectors: agriculture, forestry and fishing, wholesale and retail trade and manufacturing.

**Figure 8. Distribution of Bank Credit** **<u>to Private Firms in Zambia in 2005</u>**

<!-- Start of picture text --> 8%<br>3%<br>3% Agriculture, forestry, fishing<br>3% Wholesale and retail trade<br>35%<br>Manaufacturing<br>5%<br>Transport<br>Mining<br>9%<br>Real estate<br>Restaurants<br>Construction<br>16% Other<br>18%<br><!-- End of picture text --> In Zambia, bank credit to private firms is not just scarce, but also extremely expensive. The average annual interest rate on loans was 48% in 2005 (as compared to 20% inflation rate). Interviews with selected banks revealed that only large firms borrow at rates below the average (prime rate was 20% in 2005). The few small and medium enterprises (SMEs) in Zambia which are able to borrow from banks pay the average annual lending rate. Given the exorbitant level of existing lending rates, most loans have a short-term 16
the baseline data. The baseline data for Nigeria reveal that imported machinery and transport equipment comprise nearly 40 percent of the Nigerian government’s consumption and, more generally, total imports make up over 70 percent of Nigerian government consumption. In contrast, domestically produced goods, such as fresh fruits and vegetables, comprise over 85 percent of rural households’ consumption. The rise in domestic demand causes land and labor prices in Nigeria to rise relative to the prior three scenarios and contributes to an overall rise in prices in the Nigerian economy seen in the GDP price index presented here. Scenario 4 does best from an income redistribution perspective to be discussed in section 4.5.

<!-- Start of picture text --> GDP price index<br><!-- End of picture text --> <!-- Start of picture text --> GDP quantity index<br><!-- End of picture text --> <!-- Start of picture text --> 0.66<br>0.56<br>0.18 0.17<br>0.13<br>0.10 0.11 0.10 0.10<br>0.08<br>‐0.02<br>‐0.08<br><!-- End of picture text -->

**Figure 6: Changes in GDP Indices (%).**

Next we examine changes in real spending – private and government consumption and savings.<sup>14</sup> Results show that real government expenditure increases by 18.4, 10.2, 5.6 and 5.5 percent under the four scenarios, respectively, which is driven by the increases in government income of 7.2, 4.0, 2.3 and 2.2 percent. Government income increases are primarily due to the reduced outlays on fuel subsidies. The increasing government income under Scenarios 3 and 4, despite the redistribution of the fuel subsidy to production subsidies or rural households, is explained by increases in the government revenue from taxes on intermediate input usage and private consumption.

> 14 Absorption is total private household consumption, government spending and investment.  Absorption is often used as a measure of welfare as it takes into account welfare derived from the provision of government services as well as private consumption and investment, although it also gives equal weight to all components and all households.  In this case we replace investment with savings as domestic savings reflects domestically owned investment.

18
<!-- Start of picture text --> Minibus Routes<br>Value<br>| High: 70<br>Ci 9 re<br>ia ay = By J<br>> - ppSy.a ote Sx \ ca? oe we- L-<br>+ Oh eseA aeeS en,i)<br>a 4 Sy~ li ¥<br><!-- End of picture text -->

<!-- Start of picture text --> s<br>te) 2S 3 10<br>LitoKilometersiii ii _t<br>Esri, HERE, Garmin, © OpenStreetMap contributors, and the GIS user community<br><!-- End of picture text -->
- 6 -

their formal designation, would have to deal with other matters as well and hence their capacity to deal with WTO matters diluted. However, the use of the measure itself, counting heads, tends to introduce an offsetting, small country bias: At the limit, each country represented has no less than one representative. A very small country with a minute proportion of world trade would still have a representative - and an influence disproportionate to its trade share -- while a country like the US or a group of countries like that of the EC whose trade or GNP may be many hundreds or even thousands of times larger would not be expected to have Missions of proportionate size.<sup>3</sup> But of course, numbers and formal representation and chairmanships do not tell the whole story. Informal arrangements work along with and sometime supersede the formal committee structure and arrangements. Information on these informal processes of developing consensus is hard to document. In some cases, the groups involved and their membership is a matter of public record. In other cases, they are not, and the analysis presented here has had to rely on personal interviews with the information provided on condition of no attribution.

Finally, it was not possible to undertake a systematic analysis of institutional constraints and requirements for effective representation and participation in the WTO for all developing countries. The main new data presented on this issue are based on the "Trade Related Technical Assistance Needs Assessments" prepared by a number of Least Developed Countries in the context of the High Level Meeting on Integrated Initiatives for Least Developed Countries' Trade Development held in Geneva in October 1997.

3It was also thought that Mission size would also be affected by a country's geographical proximity to Geneva. Were this to be an important factor, it would tend to bias downward the Mission size of developed, European countries. It turns out, that if that bias exists, it does not show in the statistics, - as the European countries are substantially represented in Geneva both through their national missions and, for EC matters, through the EC. On the other hand, the distance of most developing countries from Geneva means that participation in the activities of the organization by representatives from their capitals is quite costly and raises the importance of having effective representation located in Geneva.
41 change ... [A] country which is isolated is ... by contrast unlikely to absorb new ideas quickly ... " (1955, p. 178).

There are a number of ways in which Lewis' insight can be formalized at the microeconomic level. One possibility is to postulate a "learning-bylooking" process of the type proposed by Findlay (197?).29 According to that framework the mere association with newer commodities and technologies increases the efficiency with which innovations are absorbed. In this section, however, I will not try to provide the microfoundations of this absorption process, but I will derive a simple aggregative that captures Lewis' insight regarding the connection between trade orientation and growth.30 The model itself is a simple adaptation of the Nelson-Phelps (1965) study on the relation between human capital and growth.

Let's start with an aggregate production function of the form assumed in equation (3) (which is reproduced here for convenience): from where we have already shown that the rate of growth of real output can be expressed as: (where the notation used in Section IV has been maintained).

Contrary to traditional growth models we will consider the rate of technological change as endogenous. More specifically, we will assume that

> 2 9 A 1984 study by Dalham, Ross-Larson and Westphal provide microeconomic empirical support to this general view. Rodrick (1988) develops optimizing models that yield to opposite results.

30The formulation of such a micro model would have to be based on some of the new and rich literature on North-South trade.
<!-- Start of picture text --> 1<br>0,8<br>0,6<br>0,4<br>0,2<br>0<br>-0,2<br>-0,4<br>-0,6<br>-0,8<br>-1<br>Kingdom Korea Italy Lithuania Malta Spain Colombia Belgium Portugal AfricaSouth Denmark Latvia Canada Brazil Greece Uruguay France Australia Mexico Honduras United States Austria Costa Rica Japan Germany Peru Sweden Bolivia Philippines Norway Argentina Chile<br>United<br><!-- End of picture text --> _Source:_ Min. Economy, INDEC. _Notes:_ the cyclical components of GDP and tax rates have been estimated using the Hodrick-Prescott filter. A positive (negative) correlation indicates a countercyclical (procyclical) tax policy.

However, as previously mentioned, traditional taxation in Argentina is less representative due to the significant role played by a wide range of heterodox taxes, as depicted in **Figure 14** . Unusual sources of revenue, which are typically more distortive, contribute to almost one-third of taxation in Argentina. Unfortunately, the availability of data poses a constraint when attempting to expand the analysis to include all sources of taxation, including these heterodox taxes. Specifically, the turnover tax presents extreme complexity as it is collected in different ways by each of the 24 provinces. Furthermore, it varies across economic sectors, and historical data availability is limited. Consequently, it becomes challenging to establish a single historical series to measure the effect of cyclicality. However, for export duties and financial transaction taxes, both collected at the federal level, readily available information is accessible.

Export duties and financial transaction taxes in Argentina exhibit a negative correlation with the economic cycle. Historically, export duties were initially implemented as emergency measures to address the inflationary impact of significant peso depreciation, typically during balance of payment crises. In line with this, export duties were reintroduced in 2002 following a sharp devaluation of the peso. However, unlike previous crisis episodes, export duties have remained a regular source of tax revenue even after the recovery from the 2002 crisis, persisting for more than two decades. The structure of export duties is complex, with varying tax rates for each product or sub-product. Notably, export duties differ across sectors and even within agricultural products. For the purpose of this study, an export tax rate index was constructed, assigning weights to each product based on its contribution to total exports. **Figure 15** illustrates significant changes in export duties over the years. These fluctuations in tax rates demonstrate a negative correlation with real GDP (-0.26), suggesting a procyclical behavior ( **Figure 17** ). However, the correlation is weak and not statistically significant. Similarly, the financial transactions tax was initially introduced as an emergency tax during the 2001-2002 crisis but has since become a regular tax with a constant rate of 1.2 percent. With minimal changes since its implementation, the correlation with the economic cycle is very weak, albeit negative (-0.12), as it was introduced during the economic recession of 1999-2002.
# **Figure 6: Illustrative decomposition of nominal lending interest rate and changes between 2007 and 2017**

<!-- Start of picture text --> %<br>15<br>13<br>11 Macro<br>9<br>Bank<br>7<br>Business<br>5<br>Expected<br>mean<br>3<br>EMDEs<br>(constant)<br>1<br>Error<br>-1<br>Nominal rate Macro Bank structure Business Other Nominal rate<br>2007 conditions 2017<br><!-- End of picture text --> _Note:_ Average nominal lending interest rate across all EMDEs in the sample (50 countries) for 2007 and 2017. “Other” captures country specific effects and estimation error.

Source: Authors’ calculations.

**Table 4: Illustrative decomposition of nominal lending interest rate and changes between 2007 and 2017 by region**

||<br>||Perce|nt||||
|---|---|---|---|---|---|---|---|
|Region<br>2017|<br>Observed<br>Rate|Estimated<br>Rate|Macro|Bank|Business|Other|Estimation<br>Error|
|**EMDEs**|**11.0**|**12.1**|**2.2**|**3.2**|**0.3**|**6.4**|**-1.2**|
|EAP|6.4|6.6|1.5|3.6|0.3|1.3|-0.2|
|ECA|8.8|10.5|2.2|3.1|0.3|4.8|-1.7|
|LAC|16.9|15.9|1.9|2.8|0.3|10.9|0.9|
|MENA|8.0|8.6|1.1|2.7|0.6|4.2|-0.6|
|SA|11.6|10.9|4.5|2.8|-0.1|3.7|0.7|
|AFR|11.1|12.2|3.3|3.9|1.3|3.7|-1.1|
|||Di|fference 201|7 vs. 2007||||
|**EMDEs**|**-2.7**|**-0.8**|**-0.3**|**0.2**|**-1.0**|**0.3**|**-1.6**|
|EAP|-2.3|-1.9|-0.5|0.2|-1.6|0.0|-0.5|
|ECA|-4.0|-1.5|-0.1|0.3|-1.2|-0.5|-2.5|
|LAC|-0.6|-0.6|-0.4|0.0|-0.7|0.5|0.0|
|MENA|-0.6|1.5|-0.3|-1.3|-0.5|3.6|-2.1|
|SA|-0.8|0.0|0.4|0.5|-1.7|0.7|-0.8|
|AFR|-2.2|3.6|0.3|0.2|0.1|2.9|-5.8|

Note: Average nominal lending interest rate in 2017 by region. “Other” captures constant and country specific effects. Source: Authors’ calculations.

44
exploring whether decision-making in the household is more authoritarian or inclusive. Another solution to reducing spousal disaccord may be to reduce the chances of differing interpretations by anchoring respondents’ frames of reference for what it means to be a decision-maker and to make a decision by using vignettes or more specific questions. While, to the best of our knowledge, the impact of such vignettes on reducing gender gaps in reports of decision-making has not yet been tested, it has shown to be promising in other contexts. For example, Montgomery (2017) shows that anchoring response scales for life satisfaction questions reverses gender differences in life satisfaction worldwide.

Enumerator effects may also be of concern in study design, especially when asking sensitive questions. Indeed, we find a correlation between the gender of the enumerator and women’s survey responses, where female respondents are more likely to report that their spouses are involved in decision-making when interviewed by women. This correlation is small and does not negate other findings of the survey, and thus we are cautious making any recommendations on the use of same-gender enumerators, particularly as we are unable to state whether statements given to female enumerators are more or less accurate than those given to male enumerators. We do, however, suggest including controls for enumerator gender when analyzing survey data that could be sensitive to enumerator effects.

While our data suggests that men and women draw on different definitions of decision-making when answering survey questions, future research should confirm this finding through measurement experiments and further unpack it through qualitative work on perceptions of decision-making and household dynamics. Details about how both men and women perceive decision-making and what it means to be a decision-maker may be more easily gathered in qualitative research rather than a structured spousal survey. The results from the qualitative work can be used to design better quantitative surveys. Further research is also recommended to evaluate whether our findings on the mechanisms behind spousal disagreement extend to other regions or contexts. Finally, qualitative or quantitative work on decision-making may wish to pay particular attention to the ways in which assets and relative bargaining power affect disagreement, given our finding that households with female agrarian reform beneficiaries see higher contention over the decision-maker. Future work should further investigate the ways in which decision-making is viewed within the household, particularly by women.

26