rafmacalaba/datause-encoder-data huggingface dataset:
# **5. Conclusion**

This paper revisits the inverse plot-size productivity relationship and presents evidence to reject it. As in previous studies, the relationship is strong if yield measurement is based on selfreported production—the traditional approach in household surveys to estimate yields. Moreover, conventional explanations, such as unobserved household and plot characteristics, do not actually explain the IR. The relationship disappears and even reverses, however, if yields are measured with crop cuts, which is a standardized, objective method to measure yields. This suggests that the inverse _plot-size_ relationship is an artifact of systematic measurement error in self-reported production.

In theory, this result could also be explained by differences in labor input between large and small plots, since crop cuts measure the potential harvest (just before the harvest) and selfreports the actual harvest (after the harvest). If small plots are harvested more completely than large, an IR would be observed only for self-reported yields and not crop cuts. Although there is evidence of an inverse relation between plot size and family labor, the IR for self-reports remained significant even after controlling for labor input.

There is the possibility that self-reported yields include edges and crop cuts do not, and the higher yields at the edges contribute to the plot-size IR. Because we lack data on the shape of the plot, we cannot reject this possibility, although the observation of a positive association between plot size and crop cuts suggests that the edge effect does not drive the results. Our simulations of the required magnitude of the edge effect also lead us to doubt this as the explanation for the IR, at least based on our data from rural Ethiopia. Moreover, recent work on the IR in Uganda, measuring yields with self-reports, crop cuts (on subplots and on the entire field), and remote sensing, corroborates our findings, and also shows that the IR disappears when the entire field, rather than just a subplot, is crop-cut, which rules out the possibility that the edge effect explains the IR (Gourlay et al., 2017).

This leaves systematic measurement error in self-reported production as a remaining explanation for the plot-size IR. Suggesting that self-reported crop production in household surveys is measured with substantial error is not a particularly contested or questioned assertion. Presumably because this error is typically not considered to be systematically related to plot size, it has not been viewed as a causal source of the plot-size IR. But, the plot-size IR can readily be explained by measurement error if the error is negatively correlated with plot size. Our findings suggest that farmers substantially over-report production on small plots and underreport it on larger ones, thereby causing the IR.

18
Additionally, we analyze the group of full-time employees who remain employed and now earn the new minimum wage ("winners"). Part-time workers' data on the number of hours worked may not always be reliable. We assume that employees who earn less than the minimum wage are unaffected by the minimum wage increase. This assumption limits our data sample to 3,942,640 observations. Our focus is specifically on the lower end of the wage distribution, and we do not consider the potential impact on higher-wage workers due to measurement challenges arising from data limitations. For example, the minimum wage policy might increase employers' demand for high-skilled workers, substituting low-skilled labor. Furthermore, a minimum wage hike could exert upward pressure on wages linked or indexed to the minimum wage, particularly in the public sector. It is important to clarify that our analysis solely considers movement along the labor demand curve under various elasticities, excluding shifts in the demand curve.<sup>24</sup> Finally, extensive or intensive margin labor supply responses are not considered. These factors may have broader implications for employment, unemployment, and productivity.

**Moreover, given the lack of reliable estimates of labor demand wage elasticity for Romania, we base our microsimulations on wage elasticities estimated for other countries.** The literature suggests that the elasticity of labor demand, which reflects the responsiveness of firms’ employment decisions to wage changes, is high in Eastern European countries. We assume short- and long-run elasticities using results for Eastern Europe from a meta-analysis by Lichter, Peichl, and Siegloch (2013) (Table 4). We assume different elasticities for the skill level (the elasticity of demand for skilled labor is lower than the elasticity of demand for unskilled labor) and short-run and long-run (the elasticity of demand is higher in the long-run). Consistent with economic theory, firms’ labor demand responses are more limited in the short run than in the intermediate and long run. Moreover, firms' demand for low-skilled labor is generally more sensitive to wage rate adjustments than the demand for medium- or high-skilled workers, given the ease with which low-skilled tasks can be automated or outsourced to countries with lower income levels.

**Table 4. Wage elasticity labor demand**

||Short-term|Long-term|
|---|---|---|
|Skilled|-0.5|-0.8|
|Unskilled|-0.7|-1.0|

_<mark>Source: Lichter, Peichl and Siegloch (2013).</mark>_

> 24 Higher wages, constituting increased costs, may lead to adverse employment effects, particularly in the long term. Some of these effects might arise from businesses shutting down instead of laying off employees. However, comprehensively assessing the shift in the distribution of businesses proves challenging due to the absence of detailed firm-level data. A more in-depth examination at the firm level is necessary to properly consider and understand this phenomenon.

33
Figure 3: The proportion of farmers in the four types of irrigation institutions by the volumetric price of reservoir water <!-- Start of picture text --> No change / informal Manager<br>New smaller group Individual<br>0.00-0.03 0.03-0.06 0.06-0.09 0.09-0.12 0.12-0.15<br>Volumetric price of rearvoir water (Yuan/m3)<br>100<br>80<br>60<br>% of farmer 40<br>20<br>0<br><!-- End of picture text --> _Source:_ Authors’ analysis based on data sources discussed in the text.

31
**Figure 4. Average Share of Cash Crop Income by Region**

<!-- Start of picture text --> 70<br>60<br>50<br>40 1993<br>30 1998<br>20<br>10<br>0<br>NU RR NCC SCC CH SE MDTOTAL<br>Percent<br><!-- End of picture text --> In credit access, there has been a significant increase in the amount of credit used and an improvement in the structure of sources of borrowing. The ratio of total loans of households to their income has increased from 11 percent to 25 percent. The most important change has been in the role of formal banking institutions in supplying credit. The share of loans from Government banks in total household income have increased from 1 percent of income to 13 percent. The increases have been more dramatic in regions where the incomes have grown the fastest and where cash crop shares are large. The regional differences are significant and, especially in the South, the overall credit use have expanded from 5 percent to 30 percent of income and the share of credit from the formal banking systems have increased from 2 percent of income in 1993 to 18 percent. Since the real income in South has increased by 58 percent during this period, the expansion of formal credit has been very significant.

**Table 1. Real Income growth by Government Loan Status in both years**

||**Mean of**||**Shar**<br>**Cash**|**e of**<br>**Crop**|**Commerc**<br>**Ind**|**ialization**<br>**ex**|**Rea**<br>**Inc**|**l HH**<br>**ome**|
|---|---|---|---|---|---|---|---|---|
||**Growth**|**Share**|**Inco**|**me**|**(Agno**|**men)**|**(in 98**|**prices)**|
|**Status for Government Loan**|**Rates**|**of HHs**|**1993**|**1998**|**1993**|**1998**|**1993**|**1998**|
|No loan in either year|46|63|4|5|17|22|6,951|11,129|
|Loan in 93, No loan in 98|40|7|3|5|22|25|9,667|13,144|
|Loan in both years|50|8|8|9|25|31|9,453|14,829|
|No Loan in 93, Loan in 98|52|23|7|10|21|27|7,838|12,944|

14
# **I.  INTRODUCTION**

The international development community is increasingly recognizing the centrality of effective taxation to development.<sup>3</sup> The G-20, multilateral institutions, and the donor community want to ensure that their assistance to developing countries to reinforce their tax systems is effective, coherent, and well harmonized (OECD, 2011).

Tax systems exert a significant impact on investment decisions. On the other hand, higher tax revenues are important to lower the aid dependency in low-income countries. They also encourage good governance, strengthen state building and promote government accountability.

Effective tax systems are essential for both developing and developed countries. Given that budget deficits have been dramatically increasing in many countries following the introduction of large stimulus packages to promote economic growth in the face of the financial and economic crisis of 2008-2009, governments have been searching for possible ways of increasing tax revenues to finance public expenditures and narrow the deficit without much distorting economic activities.

The first step to understand public revenue systems is to establish some commonly agreed performance measurements and benchmarks. In this regard the paper deals with the concept and empirical estimation of countries’ taxable capacity and tax effort. This paper is the second part of Le, Moreno-Dodson, and Rojchaichaninthorn (2008) and intends to develop further country tax effort typologies and policy implications for fiscal revenue reforms.

Measuring taxation performance of countries is both theoretically and practically challenging. The actual tax collection-to-gross domestic product (GDP) ratio is generally interpreted as a measure of tax effort and used as the basis for cross country tax comparison. The use of such ratio is reasonable if one attempts to establish trends or to compare tax revenue performance across countries with similar economic structure and the same level of income. However, when used to compare the effectiveness in revenue mobilization across countries in different income groups, the tax-to-GDP ratio could provide a “completely distorted” picture due to different economic structures, institutional arrangements, and demographic trends. A number of tax economists have attempted to deal with this problem by applying an empirical approach to estimate the determinants of tax collection and identify the impact of such variables on each country’s taxable capacity. The development of a tax effort index, relating the actual tax revenues of a country to its estimated taxable capacity, provides us with a tempting measure which considers country specific fiscal, demographic, and institutional characteristics.

> 3 See World Development Report (1997), World Bank Global Monitoring Report (2005), The United Nations report on Financing for Development (2002), The UN Secretary-General’s Report to the Preparatory Committee for Financing for Development (2002).

2
the creation of “project entities,” land sales, local-level indebtedness, and persistent overstaffing raise serious questions about the sustainability of the reforms and about the central government’s approach that relies heavily on an increase in (mostly earmarked) transfers to address issues in agriculture and rural development. In the process, progress in some areas has only served to expose and highlight the need for further reforms and increased focus on the delivery of public services for the NSC.

The autonomy of the grassroots levels is extremely limited. Rather than strengthening the township and village levels to enhance their capacity to provide public goods and services to citizens, reforms have greatly weakened their positions as revenue sources and authorities are, de facto and de jure, transferred to the county. This stands in contrast to the national policy of strengthening village institutions such as _yishi yiyi_ and other democratic measures. However, these participatory institutions not only require political support, but – in order to be meaningful – also authority over financial resources.

The lessons we draw from the analysis in this report can be summarized as follows: First, the complex administrative and organizational structures impede resource flows and policy change. As the central government introduces new policies and increase resources devoted to rural areas, these changes have to be mediated by each successive level of government as well as all agencies before they reach the intended recipients. With so many layers and compartments, the process will hardly be straightforward.

Second, given this complex administrative structure, monitoring and supervision are of the utmost importance in keeping the reforms on course to achieve program objectives. This poses a tremendous challenge since the availability, reliability, and timeliness of information are at present weak. For example, it is impossible to track what public resources are spent on rural development by each government level. Since the statistical reporting system – on fiscal resources or the public services that they finance - does not delineate “rural” as a category, it is not possible to fully identify what government spends in rural areas or on rural citizens. Since 2005 the Ministry of Finance has introduced a new category called “sannong spending” in an effort to track what resources are devoted to the rural sector, but MOF officials emphasize that it is very much a work in progress.

60
# **Table 2. Changes in Pesticide Standards over Time by Importing Country**

||Number of<br>Observations<br>(at country‐HS|Share of<br>Observations<br>with Complete|Of Observat<br>(MRLs a|ions with Com<br>vailable in ev|Share with<br>plete Panel<br>ery year)|
|---|---|---|---|---|---|
||product‐<br>pesticide‐year<br>level)|Panel (MRLs<br>available in<br>every year)|Share with<br>No Change<br>in MRLs|Share with<br>Stricter<br>MRLs|<br>More<br>Lenient<br>MRLs|
|Argentina|90,084|49%|68%|27%|5%|
|ASEAN|5,193|75%|85%|11%|4%|
|Australia|11,508|46%|90%|4%|6%|
|Brazil|20,865|23%|63%|8%|29%|
|Canada|90,084|49%|97%|1%|2%|
|Chile|21,540|16%|42%|11%|46%|
|China|2,714|32%|88%|3%|8%|
|CODEX|19,674|16%|42%|12%|45%|
|Colombia|19,668|20%|82%|3%|23%|
|Dom. Republic|25,875|28%|47%|14%|39%|
|Egypt|87,314|93%|96%|3%|2%|
|European Union|88,333|87%|92%|4%|4%|
|Honduras|25,875|28%|47%|14%|39%|
|India|20,970|23%|54%|11%|40%|
|Israel|21,804|26%|71%|7%|22%|
|Japan|90,084|49%|86%|9%|5%|
|Republic of Korea|13,240|40%|87%|9%|4%|
|Malaysia|90,084|49%|67%|27%|6%|
|Mexico|10,585|49%|79%|9%|11%|
|Morocco|64,918|27%|71%|10%|19%|
|New Zealand|90,084|49%|96%|3%|1%|
|Panama|67,566|30%|65%|17%|18%|
|Russian Federation|10,490|1%|74%|18%|8%|
|Singapore|6,945|82%|93%|5%|2%|
|South Africa|90,084|49%|63%|4%|32%|
|Switzerland|67,619|20%|66%|18%|16%|
|Taiwan, China|5,862|67%|93%|6%|1%|
|Thailand|19,924|20%|58%|9%|33%|
|Turkey|65,225|18%|87%|7%|6%|
|Ukraine|3,385|100%|99%|0%|0%|
|United States|10,180|46%|75%|12%|13%|
|Total|1,257,776|45%|81%|9%|10%|

Note: ASEAN is the Association of Southeast Asian Nations.

31
**Table 7: Summary of Gender Wage Gaps and Minimum Wage Impacts from Table 8**

|Quantile of Logof Real Prod. Worker Wage:|10|30|50|70|90|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Panel A: Gender Wage Gaps(Women – Men)||||||
|No Primary|-0.621***|-0.386***|-0.369***|-0.086|0.249**|
|Primary|-0.225|-0.253***|-0.248***|-0.210***|<br>0.041|
|Junior|-0.103|-0.145*|-0.156**|-0.175**|-0.090|
|High+|-0.258|-0.106|-0.106|-0.176**|-0.318**|
|Panel B: Impacts of Minimum Wages on the G|ender Wage|Gap at Differ|ent Levels of|Educational|Attainment|
|No Primary|0.687|-0.061|-0.125|-0.462**|-0.453|
|Primary|0.475|0.431***|0.056|-0.157|-0.332**|
|Junior|0.569*|0.370**|0.379***|0.084|0.023|
|High+|0.329|0.316**|0.455***|0.577***|-0.331|

_Note:_ *** p<0.01, ** p<0.05, * p<0.1. Reported gap and minimum wage effect is calculated as effect on women minus the effect on men. Minimum wage, educational variables and share of female workers are demeaned.

37
||PPR<br>Title|Working Paper Series<br>Author|Date|Cuntact|
|---|---|---|---|---|
|WPS73|Taxation and Output Growth in Africa|Jonathan SkInner|August 1988|A. Bhalla<br>60359|
|WPS74|Fiscal Stabilization<br>and Exchange Rate<br>Instability: A Theoretical<br>Approach<br>and Some Policy Conclusions<br>Using<br>Mexican Data|Andrew Feltenstein<br>Stephen Morris|August 1988|A. Bhalia<br>60359|
|WPS75|Welfare Dominance and the Design of<br>Excise Taxation In the Cote d'lvoire|Shlomo Yitzhaki<br>Wayne Thirsk|August 1988|A. Bhalla<br>60359|
|WPS76|On the Shadow Price of a Tax<br>Inspector|Shlomo Yitzhaki<br>Yitzhak Vakneen|||
|WPS77|<br>Incentive<br>Policies and Agricultural<br>Performance in Sub-Saharan<br>Africa|Bela Balassa|August 1988|N. Campbell<br>33769|
|WPS78|Economists, Institutions<br>and Trade<br>Restrictions: A Review Article|J. Michael Finger|September 1988|<br>N. Artis<br>33731|
|WPS79|Quantitative<br>Appraisal of<br>Adjustment Lending|Bela Balassa|August 1988|N. Campbell<br>33769|
|WPS80|Emerging Issues of Privatization<br>and the Public Sector|Samuel Paul|September 1988|<br>E. Madrona<br>61711|
|WPS81|Reaching People at the Periphery:<br>Can the World Bank's Population,<br>Health, and Nutrition Operations<br>Do 1Better?|Richard Heaver|September 1988|<br>S. Ainsworth<br>31091|
|WPS82|Microeconomic Theory of the House-<br>hold and Nutrition<br>Programs|Dov Chernichovsky<br>Linda Zangwill|September 1988|<br>S. Ainsworth<br>31091|
|WPS83|Welfare Costs of U.S. Quotas in<br>Textiles, Steel, and Autos|Ja!me de Melo<br>David Tarr|September 1988|<br>C. Cabana<br>61539|
models are not capable of estimating economy-wide impacts of energy or climate change policies.

Researchers are working on linking the top-down models with bottom-up models so that they can capture technological details and at the same time produce economy-wide impacts of a policy. There are two ways to link top-down CGE models with bottom-up sectoral models. One way is hard linking of these two models which refers to directly reflecting detailed subsectors/technologies in the top-down model by disaggregating economic sectors/technologies in the social accounting matrices (SAMs), the main database for calibrating a CGE model. The resulting model is also referred to as a hybrid model (e.g., Dai et al. 2011; Dai et al. 2016; Dai and Mischke, 2014; and Schafer and Jacoby, 2006). However, the hard linkage is quite complicated due to the lack of data, and it is applicable only for limited technology, such as electricity generation. Studies such as Timilsina et al. (2018), Timilsina and Shretha (2006), Timilsina and Landis (2014) have applied this approach to model impacts of substitution of fossil fuel-based electricity generation with renewable energy-based electricity generation. The second way is understood as soft linking. It is an iterative process where the top-down model is run first to project key variables such as sectoral outputs and value-added, commodity prices and so on. These variables are then used in the bottom-up model, which then produces optimal energy supply or electricity supply systems depending upon whether the bottom-up model is an energy sector model or electricity model. The results from the bottom-up the model are then fed into the top-down model. A few rounds of iterations are carried out until both models converge. If the modeling assumptions are consistent between the two models, iteratively running the two soft-linked models can lead to a consistent equilibrium (Böhringer and Rutherford 2008, 2009). Several studies, such as Bukowski and Kowal (2010), Drouet et al. (2005), Schafer and Jacoby (2006), World Bank (2014), World Bank (2016), have applied this approach. In this study, we adopt the soft-linking approach to link the energy sector bottom-up model, TIMES, with a top-down CGE model.

As discussed above, the linkage of top-down models with bottom-up models is crucial for a more precise assessment of the economic costs of climate change measures and policies. However, there exists no study that illustrates how much the results of policy analysis would be different if the two models are linked and not linked. This study aims to illustrate the difference in the assessment of economic costs of climate change mitigation policies with and without linking top-down and bottom-up models. To our knowledge, this is the first study to illustrate the merits (or demerits) of linking top-down and bottom-up models for climate policy 3
# 2. Background

The DRC is the second largest country in Sub‐Saharan Africa, with an estimated population of about 80 million people. The estimated purchasing power parity–adjusted income per capita of $785 and Human Development Index score of 0.47 make the DRC one of the poorest and least developed countries in the world.

From a health system perspective, the DRC is organized into 26 provinces that are further divided into 516 health zones. Each health zone is further divided into catchment areas of about 10,000 inhabitants each. A catchment area typically has one health center providing a basic package of inpatient and outpatient services, including antenatal care and services for uncomplicated deliveries. Geographically large catchment areas have additional health posts that provide a more limited package of services. Some catchment areas are served by referral health centers, which have the capacity to provide a wider set of services. Most of the health zones have a single general referral hospital.

Government spending on health services is low, at an estimated total health expenditure of 4.3 percent of GDP (WHO 2014). Thus, public, private, and not‐for‐profit facilities rely on user fees for curative and preventive services. Many public sector health workers do not receive official salaries; they obtain remuneration based on user fees (Bertone, Lurton, and Mutombo 2016). These revenues are also used to cover other basic expenditure items, such as procurement of drugs and other supplies. At the time of the study, the health centers had significant autonomy in setting prices for various services. More recently, a flat‐fee policy within a health zone was introduced to reduce some of the observed variation in pricing.

Considering the high poverty rate, cost of services, and physical accessibility challenges in many of the provinces, a remarkably high share of women receives antenatal care and delivers in health facilities in the DRC. According to the latest estimates, 88 percent of women received at least some antenatal care, and 80 percent delivered at health facilities. However, several challenges remain: only 17 percent of women started receiving antenatal care in the first trimester, and only 48 percent received the recommended number of at least four consultations (Demographic and Health Survey 2013/14).

# 3. Data

Our data were collected in 133 health zones in 14 provinces between June 2015 and March 2016. The sample and geographical scope were purposively chosen for the evaluation of two performance‐based 5