rafmacalaba/datause-encoder-data huggingface dataset:
MINIMUM WAGES IN CAMBODIA 23

# 6.3. **Impact on household occupation and earnings.**

In this section, I use the same empirical framework to examine the impact of minimum wage hikes on participation and wage earnings at the household level.

Similar to the impact at the individual level, participation rates exhibit some adjustments just around the time of minimum wages hike, but the effect disappear in 2 quarters. In the quarter preceding the change, the probability of a household having a garment sector worker increases by 1.6 percentage points – 12 percent of the average participation rates (column (2), Table 3). But in the subsequent two quarters, participation in garment employment falls by 1.7 percentage points and participation in wage employment falls by 2.5 percentage points. These changes are consistent with the individual level results in Table 1. But, as with individual level participation, household participation in wage employment, and garment employment return to its pre-hike levels in the latter quarters.

For households with a garment sector worker, the number jobs performed by its member slightly increases and the total hours worked slightly falls. After 2+ quarters of minimum wage hike, number of jobs performed by members of household with a garment sector worker Table 3. Impact of minimum wage hikes at the household level

||HH has<br>wage worker|HH has<br>garment worker|num. of<br>jobs|Log(hours in<br>priv. sector)|Log(<br>HH wage)|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|1 quarter before|-0.005|0.016*|0.127|0.062**|0.080**|
||(0.015)|(0.009)|(0.083)|(0.029)|(0.034)|
|1 quarter before x|||-0.010|-0.044|-0.107**|
|Garment HH|||(0.120)|(0.034)|(0.047)|
|0-1 quarters after|-0.025**|-0.017**|0.023|0.001|-0.026|
||(0.011)|(0.007)|(0.057)|(0.021)|(0.022)|
|0-1 quarters after|||0.041|-0.000|0.017|
|x Garment HH|||(0.081)|(0.026)|(0.035)|
|2+ quarters after|0.006|-0.002|-0.011|0.014|0.000|
||(0.007)|(0.005)|(0.033)|(0.013)|(0.015)|
|2+ quarters after|||0.145*|-0.048**|-0.021|
|x Garment HH|||(0.085)|(0.020)|(0.026)|
|Average|0.533|0.131|3.275|5.667|13.317|

_Source_ : Author’s calculations from Cambodia SES 2007-2015. _Note_ : The table presents the estimate of _α_ 0 _, β_ 0 _, α_ 1 _, β_ 1 and _α_ 2 _, β_ 2 from Equation (3) – for columns (1), (2) – and Equation (6) – for columns (3) -(5) – estimated at the household level. The sample is restricted to households surveyed within 8 quarters of any of the minimum wage hikes. The sample is further restricted to households with at least one primary wageworker in the household for the last column. In addition to controls mentioned in the notes for Table 1, the estimates also control for garment sector specific time trend for each location, and garment sector specific global recession shock. Standard errors, clustered by survey year _×_ psu shown in parentheses. ***: p _<_ 0.01; **: p _<_ 0.05; *: p _<_ 0.1.
<!-- Start of picture text -->
-2.0%<br>2017 2018 2019 2020 2021 2022<br>-2.5%<br>-3.0%<br>-3.5%<br>-4.0%<br>-4.5%<br>-5.0%<br>-5.5%<br>-6.0%<br>baseline Cred: +10% REER: -10% Gov.Exp: -4%<br>Rel.Growth:-2% Inf.Vol: +0.01 best case worst case<br>Source: Authors’ calculations.<br>Projected Current Account Balance   (Percent of GDP)<br><!-- End of picture text -->

Notes: Cred.=Credit to private sector, REER= Real effective exchange rate (REER) , Gov.Exp= Public expenditures, Rel. Growth=Relative GDP growth, Inf.Vol.=Inflation volatility

**Lastly, we consider two final alternative scenarios** , outlined in Table 6, where we keep the values for all variables at their baseline values for 2018 and consider alternative paths for a subset of variables under each scenario.

**Under the “Domestic expansion” scenario, the credit growth recovers in 2019 and continues increasing at the moderate pace thereafter.** Real GDP growth contracts less in 2019 and is stronger by 0.5 percent relative to the baseline in each year (2020-2022). Depreciation of Lira is weaker in 2020, which together with higher inflation leads to smaller real exchange rate depreciation relative to the baseline in 2020 and subsequent RER appreciation. Inflation volatility remains high and elevated throughout the forecast horizon.

**Under the “Adverse world” scenario, oil prices continue to increase over the 2019-2022 period, albeit with the smaller pace in 2021-2022.** Terms of trade worsen, and the growth of trading partners is weaker by 15 percent relative to the baseline. Global uncertainty is assumed to increase more strongly relative to the baseline.

Table 6: Projections for different scenarios:

|Variable|2017|2018|2019|2020|2021|2022|
|---|---|---|---|---|---|---|
||Home Exp|ansion|||||
|Credit to private sector (in % of GDP)|84.90|76.90|77.00|80.00|83.00|85.00|
|Real effective exchange rate|73.36|62.36|57.99|56.00|60.00|65.00|
|GDP growth|7.43|2.60|-1.00|3.00|3.50|3.50|
|Inflation volatility|0.037|0.065|0.075|0.055|0.055|0.045|
|<br>|Adverse|World|||||
|Oil prices|54.40|71.07|71.03|73.00|76.00|79.00|
|Terms of trade|90.69|89.00|82.00|79.00|80.00|78.00|
|GDP growth partners|3.37|2.79|1.97|1.77|1.70|1.67|
|VXO|9.60|16.52|21.60|24.00|25.00|26.00|

**Figure 30 reports the estimates for two scenarios, which are presented as the deviation from the baseline projection.** Under the “Domestic expansion” scenario, the current account balance could deteriorate in each year

34
received cumulative debt relief greater than 50 percent of initial GDP over the period 1999-2003.

Table 1 provides some summary statistics on this measure of debt relief for the three periods. The first panel reports the incidence of debt relief across countries. Roughly half of the countries in our sample receive debt relief in each sub-period. The incidence of bilateral debt relief from Paris Club creditors has been fairly constant over time, while the proportion of countries benefiting from multilateral debt relief has increased sharply over time with the introduction and expansion of the HIPC initiative. The amount of debt relief has also increased over time and become quite substantial. For example, in 1999-2003, cumulative (discounted) total debt relief averaged to some 22% of the 1998 GDP of recipient countries, with the bulk coming from multilateral creditors.

While these figures suggest that debt relief has been considerable, it is also important to realize that other forms of aid have been substantially larger. In the last column of Table 1, we also report cumulative discounted aid flows for the same three periods. For the average country, total discounted aid over 1999-2003 was substantially bigger than debt relief, at nearly 40 percent of initial GDP.<sup>7</sup> In earlier periods this distinction is even more marked. In 1994-1998 for example total debt relief averaged only 15 percent of GDP while aid flows were over three times larger at 50% of GDP.

# **3.  Cross-Sectional Results**

We now turn to our results on the allocation of debt relief and other forms of aid across countries. Our point of departure is the literature on aid allocation, which has shown that a relatively small number of variables capturing both recipient characteristics as well as donor interests can explain much of the cross-country variation in aid receipts.<sup>8</sup> We then go on to ask how these and other variables predict the cross-country dispersion in debt relief. In the first three columns of Table 2 we report some benchmark aid allocation regressions for the three five-year periods spanned by our data on debt

> 7 The growing prominence of debt relief has complicated the measurement of aid, since published figures rarely clearly distinguish between debt relief and other forms of aid.  Roodman (2005) has compiled a careful set of estimates of aid flows net of debt relief that we rely on here. 8 See for example Alesina and Dollar (2000) and Alesina and Weder (2001).

6
Table D12: Ethnicity randomization checks, 2011 and after, 1

||(1)|(2)|(3)|
|---|---|---|---|
||Judge plur. Kalenjin|Judge plur. Kamba|Judge plur. Kikuyu|
|Pla. plur. Kalenjin|0.00864<br>(0.0108)|||
|Def. plur. Kalenjin|-0.0113<br>(0.0123)|||
|Pla. plur. Kamba||-0.000521<br>(0.00806)||
|Def. plur. Kamba||-0.00935<br>(0.00840)||
|Pla. plur. Kikuyu|||0.00681<br>(0.00987)|
|Def. plur. Kikuyu|||0.00469<br>(0.00970)|
|DV mean|0.100|0.125|0.303|
|Court-year FE|Yes|Yes|Yes|
|Ethnicity dummies||||
|Other controls|Yes|Yes|Yes|
|Observations|11838|11838|11838|

The regressions test whether female plaintiffs/defendants are more likely to be matched with judges of their own ethnicity than judges of other ethnicities.Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 2. Sample is restricted to the years 2011-2020. Other controls include case type dummies, a dummy for an appeal case, and variables for the numbers of defendants, plaintiffs, and judges. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown. Pla. = plaintiffs, def. = defendants, plur. = plurality, maj. = majority.

48
41

**Annex Table 2: Forty-seven Nation Survey of Firewalls* Applicable to Securities Underwriting and Dealing Activities**

|**Securities Activities**<br>**Permissible Without Fir**|**ewalls**|**Securities Activities Su**|**bject to Firewalls**|
|---|---|---|---|
|_Securities Activities_<br>_Permissible in the Bank_||_Securities Activities_<br>_Permitted Only in a_<br>_Nonbank Affiliate_|_Securities Activities_<br>_Permitted Only in a_<br>_Nonbank Affiliate_|
|Argentina'<br>Australia<br>Austria<br>(Bahrain)<br>Belgium<br>Bermuda<br>Cayman Islands<br>Chile|Luxembourg<br>Netherlands<br>New Zealand<br>Nigeria<br>Norway<br>Pakistan<br>Peru<br>Poland<sup>2</sup>|Bolivia<br>Brazil<br>Canada<br>Colombia<br>Indonesia<br>United States<sup>4</sup>|Czech Republic<br>Japan<sup>5</sup><br>Korea<sup>6</sup><br>Philippines<sup>7</sup>|
|Demnark<br>Estonia<br>Finland<br>France<br>Germany<br>Greece'|Portugal<br>Romania<br>Singapore<sup>3</sup><br>Spain<br>Sweden<br>Switzerland|||
|Hong Kong<br>India'<br>Ireland<br>Italy<br>Latvia|Turkey<br>United Kingdom<br>Uruguay<br>Venezuela'|||

***These "firewall" restrictions do not include so-called "chinese walls" which restrict the dissemination of non-public, confidential information between banking and securities operations.**

**'** Trading securities on the exchange limited to stock brokerage subsidiary.

- 2 Dealing in publicly traded securities limited to subsidiaries.

3Local banks are encouraged to conduct their nonbank business, whether financial or non-financial, through separately incorporated subsidiaries for better control, monitoring and accountability. Banks, however, are permitted to engage in securities activities directly without firewalls.

- 4Firewalls have been removed and replaced by eight "operating standards".

**5** There are number of firewalls in Japan, the most significant being that a securities subsidiary is prohibited from being a lead manager for a company with assets less than 500 billion yen if the bank is or has recently been the trustee on that or similar issues. In addition, loans by the bank to purchase securities underwritten by the securities subsidiary are prohibited, tie-in-sales are prohibited, sharing space in the same building is regulated, joint sales visitations are restricted (unless requested by the customer) and director and personnel interlocks are restricted. 6 Personnel interlock restrictions.

7Expanded commercial banks can engage in securities dealing directly and indirectly through a subsidiary, while regular commercial banks can engage in securities activities only through a subsidiary.
Figure 1 continued Attainment profiles for ages 15 to 19, by economic group: South America <!-- Start of picture text --> Bolivia 1993-94 Northeast Brazil 1991 Brazil 1996<br>0 8.81 8 --- 0.8<br>00.6-o0.6-006 08|D O 10 6 ----- 0-<br>0. 04-04<br>00 U.2 . . ) 1 0.2 0.2<br>0 . o - ! - o - 0 2 , 0 . 2<br>1 2 3 4 5 6 7 8 9 1 2 3 4 567891 2 3 4 5 6 7 8 9<br>Grade Grade Grade<br>Colombia 1990 Colombia 1995 i Peru 1991-92<br>0.8-- 0.8 - --<br>s 0.6 1< = 0.6 | 408o.6<br>0.4[ N -\<, \; ; I :; 0.4 E 0. . 0.4<br>0.2 -0.2 -0.2 - - -<br>0 - 0 _ __ _0 ____<br>1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 89 1 2 3 4 5 6 7 8 9<br>Grade Grade Grade<br>Peru 1996 [Northeast Brazil 19961<br>0.8 0.8 - -<br>0.6 e 0.6<br>0.4 0.4<br>0.2- -- 0.2 - .<br>O-- 0-<br>: 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9<br>Grade Grade<br>* Poorest . Middle A Richest<br><!-- End of picture text -->

14
The survey included the standard WFP food security module that captures food coping strategies and household information, as well as an access to basic services module inserted into the WFP survey by the World Bank. The module was designed to capture access to a number of key services and dimensions of welfare that are captured in the World Bank’s proposed multi‐dimensional poverty index. In particular, access to water, electricity, municipal sanitation services, and food were all captured in the survey.

Figure 5 demonstrates that immediately following the blockade, there was an increase in the reliance on all five food coping strategies captured by the WFP survey. The increase was most noticeable for households surveyed during the full blockade, but there was still a larger reliance on food coping strategies for those interviewed during the partial blockade. The most surprising part of this result is the speed with which the blockade affected household behavior, where the effects began the day after the onset of the blockade. However, it is not clear whether these results were driven by price increases, shortages of food, or anticipation of the blockade continuing. Regardless of the actual cause, these results can offer an explanation for why food insecurity is equally poor in the north of the country where supply chains are more disrupted, regardless of the level of violence.

In addition to investigating the effects of the blockade on access to food, the survey can also be used to identify how access to a variety of basic services was also affected. Importantly, the survey allows one to investigate the degree to which access to each of these basic and necessary services changed, as well as the degree to which these deprivations overlap. Although estimates of monetary poverty have dramatically increased following the start of the conflict (e.g., World Bank 2017), monetary poverty potentially has less meaning in a setting where nearly everyone is poor, and the vast majority of people are facing extreme challenges in sustaining life. However, each of these non‐monetary dimensions of welfare each has an important meaning in this setting, and the degree to which each of them overlaps is an indicator of the amount of stress faced by an individual household.

**Figure 5.  Food Coping Strategies Before and After the Blockade**

<!-- Start of picture text --> Average Number of Days Households Relied on<br>Food Coping Strategies<br>3.65<br>3.15<br>2.65<br>2.15<br>1.65<br>Less Expensive Borrow for Food Reduce Meals Limit Porti Restrict<br>Food Consumption<br>Before Blockade Total Blockade Partial Blockade<br><!-- End of picture text --> Source: WFP Mobile Phone Survey, November 2017.

16
the broad category of `investment' provisions. Specically, the DCRTA distinguishes between six types of investment provisions, which are listed in the rst column of Table 4. As before, we start with a dummy-variables specication, as in column (3) of Table 2, by sequentially replacing the dummy variable for DTAs with investment provisions ( _INVij,t_ ) with corresponding indicator variables for each of the six types of investment provisions from DCRTA. Our results appear in panel A of Table 4, and we see that they are all positive and signicant. In fact, some of the new estimates are identical to our estimate for the overall impact of DTAs with investment provisions. The simple, mechanical explanation for this result is that some of the subcategories of investment provisions (e.g., for `protection' and `liberalization') appear in all DTAs that cover investment. Thus, the indicator variables for such provisions are perfectly collinear with our main dummy variable ( _INVij,t_ ) from column (3) of Table 2. We do, however, obtain positive estimates for some provision types (e.g., covering `transparency') that are not perfectly collinear with _INVij,t_ .

Table 4: Estimates of the Eects of Alternative Investment Provisions on FDI

||A. In|dicator||B.|Depth||
|---|---|---|---|---|---|---|
|Provision Description|Est.|Std.Err.|Est.|Std.Err.|Est.|Std.Err.|
|(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Protection|0.295|0.108|0.378|0.169|-0.007|0.013|
|Liberalization|0.295|0.108|0.264|0.273|0.007|0.056|
|Transparency|0.185|0.108|0.461|0.164|-0.141|0.064|
|Regulation|0.289|0.108|0.532|0.159|-0.128|0.055|
|Dispute Settlement|0.297|0.108|0.553|0.298|-0.095|0.100|
|Scope and Denitons|0.295|0.108|0.287|0.392|0.002|0.072|

Notes: This table reports estimates of the eects of a series of DTA investment provisions on FDI over the period 1990-2011. Each row in each panel corresponds to a separate econometric model. The results in panel A are based on the specication from column (3) of Table 2 after replacing the dummy variable for DTAs with investment provisions ( _INVij,t_ ) with corresponding indicator variables for each of the six types of investment provisions from DCRTA. The results in panel B are based on the specication from column (4) of Table 2, where we also allow for dierential eects depending on the number of investment provisions (`depth') within each type. The dependent variable is always the value of FDI stock. The estimator is PPML. All estimates are obtained with three-year interval data and three-way xed eects, including origin-time, destination-time, directional pair xed eects, and all other control variables from columns (3) and (4) of Table 2, respectively. The estimates of all xed eects and controls are omitted for brevity. The standard errors in all specications are clustered by country pair. See text for further details.

Since, due to perfect or near-perfect collinearity, we could not obtain informative estimates of the eects of all types of investment provisions with an indicator-variable approach, 30
58 whether they can be replicated in developing countries. The experiences with credit extensions, especially to improve the maturity structure of debt and reach the SMEs, are extensive in both developed and developing countries. However, both the rationale for and effectiveness of those interventions are much more doubtful (see Caprio and Demirguc-Kunt, 1998; Beck and Demirguc-Kunt, 2006). As already discussed above, interventions through ownership of government institutions have also not been successful, overall.

Last but perhaps most importantly, governments can improve access by increasing competition in the financial sector. As financial institutions find their traditional business coming under competition, they seek out new lines of profitable opportunities, including lending to the SMEs and the poor. Given the right incentives, private sector can develop and make use of new technologies – like credit scoring – to reach the underserved segments. As already discussed above, foreign banks’ role in improving the competition environment and improving access is important. There is accumulating evidence that over time, foreign banks can enhance access. Indeed, multinational banks have been leading the way in expanding access all around the world.<sup>13</sup> Finally, should all countries follow these recommendations? While the general messages will not be dissimilar, the directions in which financial sector needs improvement in different countries will be based on their initial conditions (World Bank, 2001 and 2007). Furthermore, good policy making draws inputs from many sources, and research is only one such input. Implementation of policy requires complementing the results of research analysis with practitioner experience, hence tempering and tailoring this advice to individual country circumstances. In general, these reforms are likely to be most challenging for low-income

> 13 Studies have found that while foreign banks with small local presence do not appear to lend much to small businesses, large foreign banks in many cases surpass large domestic banks.  See for example, Sanchez et al. 2006.
# **I. Introduction**

People make mistakes. Local officials sometimes deny social grants to individuals and families who, under the terms of a program, are qualified to receive them. A teacher, in a pique of anger, might hit a student even though national legislation forbids corporal punishment. Health workers have forgotten appointments with patients who have walked long distances in poor conditions in hope of obtaining treatment. When these mistakes occur, it is important, as a condition of basic fairness and reciprocity, to facilitate the expression of complaints. Formal redress procedures in the social sector consist of official venues in which individuals can present their understanding of their entitlements, receive an attentive hearing, and be given an explanation or compensation. And formal redress procedures are useful, of course, not only for addressing mistakes, but in cases involving alleged corruption, negligence, or malfeasance.

Although there is a growing literature on institutional arrangements that amplify the voice of service users through added information, participation, and co-production, the topic of redress procedures has received less explicit attention.<sup>1</sup> This paper provides a preliminary assessment of the uses and limits of complaints procedures. (Although ―complaints‖ and ―grievance‖ redress procedures have somewhat different connotations, the terms are used interchangeably in this paper.) Section II summarizes some of the theoretical arguments involved and presents a simple conceptual framework. Section III presents design principles – some of which are in conflict with each other – for the development of redress procedures. Section IV examines three varieties of redress procedures – those within government and departments, independent institutions external to government agencies, and courts. Section V examines interventions on the demand side that are important for the functioning of redress procedures, including NGOs and CSOs, and the media. Section VI concludes.

Before going forward, a note on methods will be useful. Examples of redress procedures are interspersed throughout the paper. This set of examples – by no means comprehensive – is based on an online bibliographic review and conversations with development agency staff working in the social sectors. This review found few descriptions, and almost no thematic reviews or evaluations, of redress procedures in developing countries; as a result, OECD examples are provided where relevant. In addition, most available accounts of redress procedures in developing countries involved services outside the social sectors; and those, too, were included where relevant. This paper, then, begins to fill a gap in the developing country literature on basic service delivery.

# **II. What are redress procedures, and why are they important?**

> 1 For a summary, see Chris Stolk, ―User Involvement and Service Delivery,‖ 2011.

2