rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 10570

# **Abstract**

This study assesses the short-term impact of fiscal policy, and its individual elements, on poverty and inequality in Cambodia as of 2019. It applies the Commitment to Equity methodology to data from the Cambodia Socio-economic Survey of 2019/20 and fiscal administrative data from various government ministries, departments, and agencies for the assessment. The study presents among the first empirical evidence on the impact of taxes and social spending on households in Cambodia. The study finds that: (i) Cambodia’s 2019 fiscal system reduces inequality by 0.95 Gini index points, with the largest reduction in inequality created by in-kind transfers from spending on primary education; (ii) while Cambodia’s fiscal system reduces inequality, the degree of inequality reduction is small in international comparison; and (iii) low-income households pay more in indirect taxes than they receive in cash benefits in the short term to offset the burden. As a result, the number of poor and vulnerable individuals who, in the short term, experience net cash subtractions from their incomes is greater than the number of poor and vulnerable individuals who experience net additions. Fiscal policy can deliver more net benefits to poor and vulnerable households through expanding social assistance spending. Cambodia has embarked on this expansion during the coronavirus pandemic, bringing it closer in line with comparators.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at wkaramba@worldbank.org; mmyck@cenea.org.pl; ktrzcinski@worldbank.org; ktong@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
measure. We reflect this by including an interaction term between the external military intervention variable and each of the polarization measures. The results, presented in columns (6) and (7) of Table 2, as well as those presented in columns (6) and (7) of Table 3 for the sample extended up to 2005,<sup>33</sup> suggest that ethnic polarization is a significant determinant of conflict incidence and that foreign intervention exacerbates the relationship between religious polarization and conflict. In other words, religious polarization combined with external military intervention is significantly and positively associated with civil war.

**Table 2 Logit Regressions for the Incidence of Civil Wars (PRIOCW)** **<u>(1965-1999)</u>**

||(1)<sup>1</sup>|(2)<sup>2</sup>|(3)|(4)<sup>3</sup>|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|LGDPPC|-0.28|-0.42*|-0.34|-0.38|-0.28|-0.36|-0.36|
|LPOP|0.34**|0.40**|0.38**|0.44***|0.39***|0.40***|0.40***|
|PRIMEXP|-0.90|-1.07|-1.71|-0.86|-1.77|-1.53|-1.49|
|MOUNTAINS|0.00|-0.00|-0.00|-0.00|-0.00|-0.00|-0.00|
|NONCONT|0.08|0.28|0.35|0.48|0.47|0.41|0.42|
|DEMOCRACY|0.07|0.03|0.11|-0.04|0.08|0.14|0.14|
|ETHFRAC|1.19*|0.17|0.12|0.04|-0.08|-0.35|-0.39|
|ETHPOL||**2.28****|**2.60*****|**2.11*****|**2.38****|**2.59*****|**2.76****|
|RELFRAC||||-4.45|-2.12|-1.89|-1.98|
|RELPOL||||3.28|1.92|1.32|1.36|
|Int_nh|||**1.52*****||1.53***|0.44|0.82|
|Int_nh ˟ RELPOL||||||**1.86**|**1.89***|
|Int_nh ˟ ETHPOL|||||||-0.71|
|Intercept|-5.82**|-6.29**|-7.00***|-7.54**|-7.71***|-7.15**|-7.23**|
|N|850|850|859|850|859|859|859|
|McFadden’s R<sup>2</sup>|0.101|0.123|0.169|0.135|0.176|0.182|0.183|
|McFadden’s Adjusted R<sup>2</sup>|0.079|0.098|0.142|0.104|0.142|0.146|0.144|

1 Refers to column 1 in Table 1 of Montalvo and Reynal-Querol (2005).

2 Refers to column 3 in Table 1 Montalvo and Reynal-Querol (2005).

3 Refers to column 8 in Table 1 of Montalvo and Reynal-Querol (2005).

> 33 We include data up to 2005 by adding one period to the sample employed in Montalvo and Reynal-Querol (2005). We have also added observations for Lebanon, which are not present in their sample. Similar results are obtained with and without Lebanon in the data sample. Results without Lebanon are available upon request from the authors.

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a proxy of participating in the training) and the list of students at risk, respectively. This information was collected in the last follow-up questionnaire, which was the only paper-based follow-up questionnaire and the one with the highest response rate. The proportion of schools with data on the reception of the guide was about 85 %, with no significant differences across control and treatment arms. For reception of the list of students at risk, the response rate was lower, with significant differences between the treatment and the control groups (the proportion of schools with data on this question was 77% and 55%, respectively). The difference may be driven by school principals in the control group not understanding the question (as they likely never heard about the list of students at-risk). The data on the take-up of treatment 3 comes directly from SIRE administrative records and is proxied by a dummy capturing whether the school principals logged into SIRE at least once in the month that followed the activation of the reminders on the platform.

# **Outcome data**

The main outcome indicator is a dummy variable capturing whether individual students who were enrolled in primary school in April 2018 had dropped out by April 2019. To construct this variable, we exploit the panel structure of our data—using the students’ identification numbers to track their academic trajectories through different years—with a variable reporting the enrollment status of the students during any given academic year (i.e., currently enrolled or dropped out). If a student is not found in the next year’s panel, we consider that the student has dropped out. In addition, potential errors in the linkage of students’ administrative records across years would also be interpreted as dropouts in our dataset. We do not expect these challenges would affect our identification strategy for two main reasons. First, even with these limitations, the SIRE data replicates well the dropout patterns observed in Guatemalan household surveys (Adelman et al, 2018). Second, there is no reason to believe that this potential measurement error would be different across randomly selected control and treatment groups. In short, we expect relatively small and random measurement error in dropout, creating (if anything) a small downward bias to our estimates.

# **Data on potential mechanisms**

The data on mechanisms was mostly collected via online questionnaires. There were some challenges with the response rates of the online questionnaires capturing the potential mechanisms, 17
# **Table A15. Heterogeneity for Employment and Work by Age, Addressing the Substitution Protocol’s Effects**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Wo<br>|men<br>|Me<br>|n<br>|
||Employed|Working|Employed|Working|
|LOA|0.052|0.069*|0.009|0.019|
||(0.039)|(0.038)|(0.034)|(0.029)|
||[0.154]|[0.058]|[0.806]|[0.501]|
|ESR|0.001|-0.022|-0.052|-0.052*|
||(0.039)|(0.039)|(0.036)|(0.030)|
||[0.987]|[0.551]|[0.161]|[0.084]|
|LOA x Youth|0.018|0.009|0.010|-0.031|
||(0.071)|(0.073)|(0.076)|(0.070)|
||[0.799]|[0.894]|[0.886]|[0.647]|
|ESR x Youth|0.031|0.041|0.139*|0.123*|
||(0.068)|(0.070)|(0.080)|(0.072)|
||[0.621]|[0.578]|[0.107]|[0.104]|
|LOA + LOA x Youth|0.071|0.077|0.019|-0.012|
||(0.059)|(0.062)|(0.066)|(0.063)|
||[0.223]|[0.198]|[0.751]|[0.847]|
|ESR + ESR x Youth|0.032|0.019|0.087|0.072|
||(0.057)|(0.060)|(0.072)|(0.064)|
||[0.552]|[0.755]|[0.235]|[0.293]|
|Observations|1,361|1,361|1,119|1,119|
|Outcome(Control Group)Mean|0.436|0.537|0.735|0.832|

_Notes:_ This table presents the treatment heterogeneity of including the LOA module (LOA) or enforcing self-reporting (ESR) by age for the reporting of employment or work status. We separate the samples by sex. We present the ITT estimates from Equation (2), where takes the value of 1 if the respondent is a youth between the ages of 15 to 24 years, as well as the total effect of each survey method. The vector iihss includes the indicator and the list of control variables selected using a double-LASSO procedure for DD iihss XX each outcome (see Table A3 in the Appendix for the outcome variables). All estimations include EA iihss DD (stratification variable) fixed effects. Each column is a separate dependent variable as defined in Section 3. The control mean refers to the mean of the control group for each outcome. Standard errors clustered at the household level are presented in parentheses, and the randomization inference _p_ -values are shown in brackets. *** _p_ <0.01, ** _p_ <0.05, * _p_ <0.1 52
**Table 10. Cross section, Telecom, regional dummies interactions.**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||OLS|OLS|2SLS|2SLS|
||pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|
|Constant|-2.577|-1.769|-0.034|2.113|
||(0.980)**|(0.913)*|(3.507)|(5.107)|
|GDPpc71|-0.001|-0.001|-0.001|-0.001|
||(0.000)***|(0.000)***|(0.001)|(0.001)*|
|prim_expen_70|-0.055|-0.063|-0.083|-0.084|
||(0.034)|(0.034)*|(0.097)|(0.090)|
|second_expen_70|0.002|0.003|0.004|0.002|
||(0.001)|(0.001)*|(0.006)|(0.005)|
|Invt_GDP7195|0.229|0.220|0.065|0.148|
||(0.043)***|(0.040)***|(0.197)|(0.113)|
|main7195|0.029|0.022|0.027|0.000|
||(0.007)***|(0.003)***|(0.025)|(0.000)|
|EA*main|-0.006||0.313||
||(0.005)||(0.339)||
|LI*main||-0.222||-1.188|
|||(0.066)***||(1.224)|
|MI*main||0.004||0.009|
|||(0.003)||(0.030)|
|Observations|51|51|44|44|
|R-squared|0.68|0.75|||
|Wu-Hausman F test,<br>p-value|||0.22|0.47|

Robust standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%. Regional dummies: EA (East Asia), LI (low income countries), MI (middle income countries). Instruments: see Table 3 and 4.

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2

# **1. Introduction**

An extensive empirical literature exists on residential water consumption in developed countries (see Hanemann 1998, Arbués et al. 2003, or Dalhuisen et al. 2003 for comprehensive surveys). Yet, few such studies exist for residential water consumption in developing countries. Most of the studies on residential water demand are mainly in the form of contingent valuation studies to derive willingness-to-pay for getting a house connection to a piped water network (North and Griffin 1997; Whittington et al. 2002; Pattanayak et al. 2006).

In most developing countries the quality of residential water consumption datasets often pose a problem, especially as metering is not a very common phenomenon. Yet, the market in which utilities operate in many of these countries is also startling different. In contrast to developed countries, where almost all households obtain water from the utility through a piped network, the market for residential water demand in many developing countries shows much more variation. Households may have a connection to the piped network and use exclusively water from their private tap, but they may also combine piped water with water collected from wells, public taps, or purchase water from vendors; or they may have no connection and rely exclusively on non-piped water. Little is known about households’ behavior in developing countries regarding the factors driving their choices and in particular the substitution/complementarity relationship between piped and non-piped water for piped households or the combination of non-piped water from different sources for non-piped households. As a result, policy decisions are often not well informed and it is usually assumed that residential water demand in developing countries mimics that of developed countries.

A more detailed knowledge of the structure of water demand of piped and non-piped households in developing countries can help to better understand consumer behavior. For planning purposes, it is essential to be able to predict the change in residential water demand for utility services that will result from any policy that would involve some change in tariffs and/or income for the household. As underpricing of piped water supply occurs often and makes tariff increases necessary to ensure the long-term sustainability of the service provision, understanding how customers might react to such price increases is of importance. Secondly, many households cannot expect to be connected to the piped network in the near
26 incentives. Bad inequality in this sense – inequality that is bad for incentives – will be condemned by the utilitarian measure of GDP per capita, because its elimination will increase social output. This is to be distinguished from inequality that is bad because it reflects disadvantage due to circumstances: as I have emphasized, eliminating this kind of inequality is not -- at least in the short run – synonymous with increasing total output or welfare.

The equal-opportunity approach, which focuses upon eliminating inequalities that are due to circumstances for which persons should not be held responsible, is both good ethics and also good policy – by which I mean it is policy supported by the majority of people in many countries. For we know from survey data that, globally, people believe injustice occurs when low incomes are due to bad luck as opposed to low effort. What differs across countries is the extent to which citizens attribute low incomes to bad luck as opposed to low effort: in Brazil, a much larger fraction believe poverty is due to bad luck than in the United States (and perhaps this reflects reality). Indeed, the popular moniker associated with equality of opportunity – it levels the playing field -- can be interpreted as a way of saying that disadvantages that some face due to circumstances beyond their control should be eliminated before the competition for economic goods begins.
effects on sales, of 63-71 million COP per month (USD $26,500-$29,900) in levels, or 9 to 10 percent in log terms. This treatment effect is not statistically significant compared to the control group (lowest p-value is 0.12 in column 1), but is statistically different from the individual treatment level effect post-intervention. The individual treatment effects have negative point estimates in level terms, and a point estimate close to zero post-intervention for the balanced panel for log sales.

# **6.3 Channels of Production Impact**

The results on employment and sales suggest that the group intervention increased the size of the firm, causing it to employ more people and sell more. In Table 7 we examine different channels through which this increase may have occurred. Column 1 considers the defect rate. Bloom et al. (2013) found quality improvements to be one of the first signs of improvement from better management in their Indian study. We only have defect data in 2017 for 78 of the firms in the study, due to many firms not keeping consistent records on defects. A first point to note is that the defect rates are low (which is one reason some firms do not record them): the control group has a mean defect rate of 0.025 and median rate of 0.007 in 2017, which compares to much higher defect rates in India (5 percent of output was scrapped, _after_ mending of defects was done). The result is that many of the auto parts firms do not have much scope to reduce defects, and we see treatment effects that are all very close to zero and statistically insignificant.

Columns 2 and 3 consider monthly inventories. In India, Bloom et al. (2013) found firms had excess inventory levels, which they reduced when management improved. Large stockpiles of inventories are less common in the auto parts sector, with some firms doing job work and producing upon request. Data are only available for half the sample of firms, due to some firms not keeping records, or changing the units in which they record inventories over time. The control mean level of inventories is equal in value to 1.4 months of mean sales. We see no significant change in inventories, with the sign of the coefficients changing between level and log specifications. However, the confidence intervals are wide, and include the 21 percent reduction in inventories found in Bloom et al. (2013), as well as increases in inventories of more than this magnitude.

Columns 4 and 5 consider energy costs, which are another input into producing more. The data here are consistent with the group treatment firms getting larger by using more inputs to produce 23
56

# **Figure A4.2: Canada - Net Government Debt as Share of GDP**

<!-- Start of picture text --> 90<br>80 Federal Provinces<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br>Sources: 1) Statistics Canada; 2) IMF GFS.<br>% of GDP<br><!-- End of picture text -->

# **Figure A4.4: Colombia - Gross Government Debt as Share of GDP**

<!-- Start of picture text --> 10 50<br>9 SNG CG 45<br>8 40<br>7 35<br>6 30<br>5 25<br>4 20<br>3 15<br>2 10<br>1 5<br>0 0<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br>Note: SNG=Subnational Government, CG=Central Government<br>Source: Ministry of Finance and Public Credit<br>Sovereign Debt as % of GDP<br>Subsovereign Debt as % of GDP<br><!-- End of picture text -->
_Table 4: Summary Statistics, Firm Characteristics, Matched Sample – State-Owned Banks_

||**N.**<br>**Obs.**|**Mean**|**St.**<br>**Dev.**|**p10**|**p25**|**Median**|**p75**|**p90**|**No.**<br>**countries**|
|---|---|---|---|---|---|---|---|---|---|
|Ownership type (foreign vs. domestic)|2,960|5.6|22.9|0|0|0|0|0|37|
|Exporter (exporters vs. non exporters)|2,962|21.6|41.1|0|0|0|0|100|37|
|size|2,992|1.9|0.8|1|1|2|3|3|37|
|Access to finance|2,695|17.4|37.9|0|0|0|0|100|35|
|Number of years firms operated without formal registration|2,963|0.6|5.6|0|0|0|0|0|37|
|Value of collateral needed for a loan (% of the loan amount)|1,269|183.0|129.7|88.9|100.0|150.0|200.0|<br>327.3|33|
|Proportion of loans requiring collateral (%)|2,904|74.1|43.8|0|0|100|100|100|36|
|Proportion of working capital financed by external sources (%)|2,869|39.1|31.1|0|10|35|60|90|37|
|Proportion of permanent full-time workers that are female|2,755|36.6|29.7|2.5|12.5|29.5|57|85.5|37|
|Proportion of working capital financed by banks (%)|2,869|25.7|26.0|0|0|20|40|60|37|
|Number of workers|2,933|108.3|224.3|7|12.3|31|104|267|37|
|Number of permanent full-time workers|2,933|107.1|224.0|6|12|30|103|265|37|
|Proportion of temporary workers (out of all workers)|2,747|2.6|8.2|0|0|0|0|7.1|37|
|Proportion of permanent workers (out of all workers)|2,747|97.4|8.2|92.9|100|100|100|100|37|
|Age (years)|2,952|20.4|16.3|6|10|17|25|40|37|
|Proportion of government/state ownership in a firm (%)|2,957|3.1|15.3|0|0|0|0|0|37|
|Proportion of private domestic ownership in a firm (%)|2,957|92.3|23.8|75|100|100|100|100|37|
|Proportion of private foreign ownership in a firm (%)|2,957|3.4|15.7|0|0|0|0|0|37|
|Legal status of the firm|2,987|2.4|1.0|1|2|2|3|4|37|
|Biggest Obstacle|2,695|9.0|4.9|1|4|11|14|14|35|
|Size Of Locality|2,880|3.6|1.2|2|3|4|5|5|36|
|Establishment Is Part of a Large Firm|2,954|1.8|0.4|1|2|2|2|2|37|
|Legal Status of The Firm|2,992|2.3|1.1|1|2|2|3|4|37|
|Competitors For the Main Product/Service in The Main Market|2,857|5.2|46.7|-4|-4|1|6|12|35|
|How Much of An Obstacle: Practices of Competitors in Informal Sector|2,992|0.4|2.8|0|0|0|2|3|37|
|Proportion Of Working Capital Financed from Private Commercial Banks|5|29.0|27.9|0|20|20|30|75|3|
|Establishment Has a Line of Credit or Loan from A Financial Institution?|2,992|1.0|0.0|1|1|1|1|1|37|
|How Much of An Obstacle: Access to Finance|2,992|1.4|1.6|0|0|1|2|3|37|

22
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without any provision on government procurement. The _TAPROC_ dummy captures instead country pairs that are involved in TAs that explicitly include a chapter on government procurement. In extensions, we also estimate the impact of single provisions on government procurement as collected in the Deep Trade Agreements (DTA) database (Mattoo et al., 2020). The variable _WTOGPA_ equals one for country pairs where both countries are members of the WTO GPA.<sup>17</sup> Among the 62 countries of our sample, 28 are EU members at some point in time. We isolate the distinctive role of the EU by adding a dummy for – EU membership. The TA variables are mutually exclusive i.e., the _TANOPROC_ and _TAPROC_ dummies are turned to zero for EU countries when the _EU_ indicator is equal to one.

We first compare estimates of the trade cost function across public and private markets, for goods and services. We focus on the estimates of the coefficient on the _SMCTY_ dummy as it captures the border effect and hence can used as a first indicator of bias towards local purchases. We then assess whether policy efforts to liberalize government procurement markets have increased cross-border flows. Specifically, we test if the coefficients on the TA and GPA dummies are positive and significant and if they are higher for government than for private flows. The estimated coefficients on these trade policy variables in eq (7) are likely to be biased because the specification does not fully control for unobserved time-invariant heterogeneity that can drive both the propensity to increase cooperation through various agreements and trade flows (Baier and Bergstrand, 2007). To control for this unobserved heterogeneity, we estimate the following specification: where the _γ_ ’s terms are bilateral fixed effects that capture unobserved and time-invariant determinants of trade costs.<sup>18</sup> Our interest is in the (partial) effect of trade agreements.<sup>19</sup> We isolate the effect of

> 17While the agreement entered officially into force in 1996, it was firstly singed in 1994. We thus assume that the countries that entered the agreement in 1996 were already _de facto_ members in 1995, the first year of our panel.

> 18Collinearity requires further restrictions on the set of bilateral fixed effects. As in Agnosteva et al. (2019), we suppress the time-invariant internal trade cost dummies so that the estimates of international time-invariant trade costs are relative to a geometric mean of the two countries’ internal trade cost: 1 _/_ 2<sup>�</sup> exp( _γij_<sup>_k,s_) =</sup> � _t_<sup>_s,k_</sup> _ij_<sup>_/_</sup> � _t_<sup>_s,k_</sup> _ii_<sup>_ts,k_</sup> _ij_ � .

> 19The effect of trade agreements directly implied by the estimated coefficients in eq (10) is ‘partial’ in

13