rafmacalaba/datause-encoder-data huggingface dataset:
# **Socioeconomic and Fiscal Impact of Large-Scale Gold Mining in Mali**<sup>**1**</sup>

Aly Sanoh World Bank Massaoly Coulibaly Groupe de recherche en économie appliquée et théorique JEL Classification: J16, J21, O13, O15, O18 Keywords: Local Impact, Natural Resources, Welfare, Governance

> 1 This is a background paper to the Socioeconomic Impact of Mining on Local Communities in Africa Project (P148422). The authors are grateful to Johannes Hoogeveen, Bryan Land, Gregory Smith, Michael Lewin, Punam Chuhan-Pole, Andrew Dabalen, Kristina Svensson, and Souleymane Traore.
## **Box 3. Korea’s Experience with LTV Regulation**

The Korean authorities implemented several prudential measures in view of the overheating in the real estate market in 2001–02. Between 2001 and 2002, the risk weight for mortgage loans was raised from 50 percent to between 60 and 70 percent. In November 2002, the minimum loan loss reserve coverage ratio for household loans classified as normal was raised from 0.5 percent to 0.75 percent, and for those classified as precautionary, from 2 percent to 8 percent. Since September 2002, the supervisory authority has used LTV ratio as the main policy instrument to constrain the growth in mortgage loans, as the above-mentioned indirect measures were considered insufficient.

The LTV cap was first lowered in September 2002 and twice more in 2003. By examining the apartment price movement in the speculative Seoul metropolitan area, together with the changing LTV cap and Bank of Korea (BoK) policy rate (see figure), it appears that lowering the LTV cap (in combination with other prudential measures, such as a DTI ceiling for certain types of borrowers, as well as other restrictions on granting mortgage loans and maturity extensions) had some positive effects in curbing the overheating housing market. It is interesting to note that monetary policy was expansionary during the same period due to concerns about economic weakness, which indicates the importance of macroprudential policy in addressing issues that may not be included in the defined objective of monetary policy.

### **Korea: Change in Apartment Price Index and LTV Cap in Seoul and Bank of Korea Policy Rate**

<!-- Start of picture text --> Apartment purchase price index, yoy % change, Seoul<br>80 10<br>LTV ratio cap for apartments, Seoul<br>70 BoK policy rate (right axis) 9<br>8<br>60<br>7<br>50<br>6<br>40<br>5<br>30<br>4<br>20<br>3<br>10<br>2<br>0 1<br>-10 0<br>Source : Chang (2010)<br>____________________________<br>Percent Percent<br>.1 7 7 7 7 7 7 7 7 7<br>1<br>200 2002.1 2003.1 2004.1 2005.1 2006.1 2007.1 2008.1 2009.1 2010.1<br><!-- End of picture text --> _Source_ : The author thanks Soon Taek Chang for sharing information on Korea‘s experience with LTV regulation, as well as his helpful suggestions for the text box. See Chang (2010) for a more detailed discussion.

# **IV. Conclusion**

This paper reviews a set of prudential measures that can be adopted by national authorities to deal with procyclicality and discusses issues in designing and implementing such measures. While it is impossible and also undesirable to provide a standard policy prescription for the diverse set of developing countries, such a review of potential measures will hopefully help provide a sound basis for further analysis and policy making. It is important to recognize that the 30
**Figure 6. Changes in share of workers among all individuals in a households between 2005 and 2012, by consumption per-capita deciles of 2005**

<!-- Start of picture text --> Rural areas Urban areas<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Consumption decile in 2005 Consumption decile in 2005<br>95% confidence intervals 95% confidence intervals<br>15 15<br>Change in share of workers 5 5<br>-5 -5<br>-15 -15<br><!-- End of picture text --> Source: IHDS, 2005 and 2012 Shifts out of agricultural employment into non-agricultural wage/salaried employment have been poverty-reducing and contributed to pro-poor growth. Households have been switching out of agricultural wage/salaried activities to non-agricultural wage/salaried activities; and both reduction in share of agricultural wage employment and increase in share of non-agricultural wage employment are more pronounced for the lower deciles (Figure 7). Since returns to work in the non-agricultural sector are much higher than in the agricultural sector (Figure A4, Appendix), it is easy to see how these labor market shifts have resulted in poverty reduction. Taken together, these results play a big role in the strongly pro-poor income growth pattern seen in the non-anonymous growth incidence curves in Figure 1 earlier.

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<!-- Start of picture text --> C. Time lost (students off-task)<br><!-- End of picture text --> <!-- Start of picture text --> A. Teaching<br><!-- End of picture text --> <!-- Start of picture text --> B. Teaching and Learning<br><!-- End of picture text --> <!-- Start of picture text --> A. Teaching B. Teaching and Learning C. Time lost (students off-task)<br>re} / ad \<br>+| !1 + rey \\<br>! w et<br>2° iI © /- - A)\<br>7 ! = 1 2 ‘<br>fa4 (fi | n BFa& 4 1 BO8c \hhVe<br>¢* f i f -. N fti r “ i\\<br>- eeaesaa _ Leenwi l e 77 i V ey mo symA<br>oO wollte of see ee ° eee<br>0} 2 4 6 8 1 0 2 A 6 8 1 0 2 4 Rs} 8 1<br>Share of Time Teaching Share of Time Teaching and Learning Share of Time Teaching and Learning<br>—- SDI -- Stallings © — Teach -—- SDI -- Stallings =— Teach —- SDI -- Stallings- Adj students off-task — Teach<br><!-- End of picture text -->
Table 1 reports the mean and standard deviation of the _employment growth_ measure across firm size with size measured as the initial size (three, two, or one year ago). There is positive employment growth across all firm sizes except for larger firms, which on average experience negative growth. The average firm level employment growth, according to our measure, is 12 %. Micro firms grew over the average, while small and medium growth was below average.

# **4. Variations in Objective Investment Climate Conditions by Firms**

We start our analysis by examining differences in the reported business environment conditions across firms for the different investment climate (IC) variables defined in Section 3 and summarized in Table 2. To do so, we estimate the following specification: _IC_ var _iableict_   1  2 _Smallict_  3 _Mediumict_  4 _L_ arg _eict_  5 _Foreignict_  6 _Exporterict_   7 _Matureijct_  8 _Olderijct_  9 _Governmentijc_  10 _Smallcityijc_  11 _Expandict_  1  12 _Contractict_  1  (1)  _c_ *  _j_  _ict_ To capture differences in the objective conditions facing firms of different sizes we include size dummies (micro-enterprises is the omitted category), with the size measured by number of employees at the time of the survey (period t). We also control for whether the firm is foreign or government owned, whether it is an exporter, whether it is located in a large city (or capital) or not (large city is the omitted), as well as age categories (young is the excluded category), all measured at period t.

We also include variables _Expandict-1_ and _Contractict-1 ,_ the rate of employment expansion or contraction in firm _i,_ country _c,_ between period t-1 and period t-3. The omitted category is that employment remained unchanged in the last period. The measures of contraction and expansion are thus lagged one period, to have them pre-determined, but the claims here are not causal. We are simply interested in describing the patterns across firms, examining whether there are differences in the reported objective characteristics across expanding and contracting firms. These coefficients will also provide information on whether endogeneity is likely to be an issue in the employment regressions.

Our focus is on within-country variations rather than across countries, therefore we include a full set of sector-survey interaction dummies (  _j_ *  _S_ ). This allows us to control 12
For non-provident fund schemes, participants are defined as active contributors plus pensioners, as defined in the coverage section. For the provident funds in Kenya and Nigeria only the number of affiliates (including those who were not actively contributing) was available. In both cases, the number of active contributors was assumed to be 50% the number of affiliates. This assumption was considered conservative based on the contributor/affiliate ratios found in Tanzania (16.2 %) and Ghana (44%) (see coverage section below). For provident funds which paid a lump-sum benefit, only new, same-year beneficiaries were counted as pensioners. In other words, past lump-sum recipients were excluded from the denominator of the ratio.

Information received since the publication of the report suggest that the administrative cost data for transition socialist countries such as China and Hungary are underestimated to a significant degree. This problem is due to the fact that much of the pension administration occurs within state-owned enterprises and therefore goes unrecorded. This seems to be especially true in the case of China but occurs in large State-owned enterprises in transition socialist economies as well. Throughout the transition economies, administrative costs related to collection of payroll taxes are rising along with the growth of the informal sector. The officially reported costs do not seem to include the costs borne by these firms some of which are required to keep records, pay benefits and determine eligibility.

Finally, it should be noted that the reported costs are generally for the main pension scheme and do not reflect the entire national costs of administering all publicly-managed pension schemes. These costs will be higher when multiple schemes exist and the per member costs may rise if those schemes are too small to take advantage of scale economies. Most countries have at least two publicly-managed pension schemes and some have many more. For example, the main pension scheme in Bolivia is supplemented by dozens of individual enterprise-level schemes whose administrative costs are also absorbed by the State.

Data and Sources. Administrative costs for Senegal were taken from the main pension scheme in that country (IPRES 1989) and are included in Annex Table 5 below (they did not appear in Averting). Most of the data were taken from statistical yearbooks of the administering agencies. Annex Table 5 shows the data presented in Appendix Table A.9 of the book. Average annual exchange rates for 23
# **Table 4: Maximum likelihood estimates of** **_Log_ Earnings function for teachers and non-teachers**

||Teachers l|og ear|nings|Non-teacher|s log e|arnings|
|---|---|---|---|---|---|---|
||_Ln_|_Wt_||_Ln_|_Wnt_||
|Independent<br>variable|Coefficient||Standard<br>error|Coefficient||Standard<br>error|
|Constant|9.516|***|(0.141)|12.114|***|(0.071)|
|Age|0.153|***|(0.006)|0.078|***|(0.003)|
|Age<sup>2</sup>|-0.001|***|(0.000)|-0.001|***|(0.000)|
|Dfemale|-0.010||(0.016)|-0.023|*|(0.012)|
|Drural|0.024||(0.021)|-0.119|***|(0.017)|
|D2002|0.021||(0.039)|0.066|**|(0.025)|
|D2003|0.225|***|(0.037)|0.265|***|(0.023)|
|D2004|0.162|***|(0.037)|0.331|***|(0.023)|
|D2005|0.229|***|(0.037)|0.291|***|(0.025)|
|D2006|0.300|***|(0.037)|0.416|***|(0.024)|
|D2007|0.368|***|(0.037)|0.511|***|(0.024)|
|D2008|0.405|***|(0.037)|0.538|***|(0.024)|
||Censored obs|=|33,489|Censored obs|=|6,530|
||Uncensored|||Uncensored|||
||obs|=|6,530|obs|=|20,934|
||Wald<br>2<br>χ(11)|=|3,228.82|Wald<br>2<br>χ(11)|=|3,937.5|
||Prob ><br>2<br>χ|=|0.000|Prob ><br>2<br>χ|=|0.000|

_*** p<0.01, ** p<0.05, * p<0.1._ For workers in non-teaching jobs, however, there are significant earnings differentials. Equally college-educated, a worker in rural areas earns 12 percent less.

Woman also earns less than man. Even though the earnings differential between man and woman is small (2 percent), it is statistically significant.

21
negative) as they should not be willing to register regardless of the reduction in adoption costs.<sup>17</sup> One exception would be if the treatment promoted the perception that e-filing was mandatory (We discuss this possibility in the section on mechanisms). Note that since _Taxp,t − Taxe,t_ is negative for these firms (since we expect that _CCp,t − CCe,t >_ 0 and _UPp,t − UPe,t >_ 0), they are likely to be evading tax in the paper system. As a result, we expect firms in this third category to have higher risk scores and thus to be less likely to e-file than firms in the first two categories.

# **5 Data**

We rely on three main sources of quantitative data. First, we use administrative data from the tax authority on firm characteristics (such as number of employees, legal status, industry and district), e-filing use, and tax behavior (such as monthly tax declarations and payments). These data include all 1,498 firms in the study sample as well as the additional 506 firms originally selected but that did not participate in the training.

The other two sources are the baseline survey of firms conducted prior to the trainings and the endline survey conducted about one year later. The surveys include information on firm characteristics and economic behavior, as well as experiences of firms with the tax administration process (such as compliance costs). All 1,498 firms in the study sample completed the baseline survey.

The endline survey differed from the baseline by including more questions and in that enumerators administered it in person at the firm’s premises (as opposed to the brief, self-administered baseline survey at the beginning of the training sessions). Eighty-four percent of the study sample (1,263 firms) completed the endline survey. Twelve percent of

> 17 Note that as _ACe →_ 0 (and _t →∞_ ), inequality (1) simplifies to: _E_ ( _CCp_ + _UPp_ + _Taxp_ ) _> E_ ( _CCe_ + _UPe_ + _Taxe_ ), which is never true for these firms.

17
# **I. Introduction**

Vietnam has set up poverty reduction as a major development policy. To achieve this goal, Vietnam has maintained an extensive public safety net and launched a large number of poverty reduction programs. These programs generally benefit from having precise information on where the poor are located, and on how poor they are, see e.g. Bigman and Fofack (2000) and Elbers _et al._ (2007).

The objective of this study is to estimate poverty and inequality for rural Vietnam at different levels of aggregation by combining the Vietnam Household Living Standard Survey (VHLSS) from 2006 and the Rural Agriculture and Fishery Census from the same year. We will produce estimates at the regional, provincial and district level, and will consider both expenditure and income based measures. The estimates are obtained by adopting the small area estimation method put forward by Elbers _et al._ (2003) (henceforward ELL), which has since been used to put poverty on the map in over 40 countries worldwide.

The information on all households provided by the census combined with the detailed information on selected households from the survey makes it possible to estimate poverty at levels of aggregation the survey alone does not allow for. The standard errors of our province level estimates are comparable to the standard errors of the region level estimates based on survey data only. The standard errors of our district level estimates are obviously larger, but still acceptable.

The use of the agricultural census denotes a modest variation on the approach of ELL, which conventionally uses a population census instead. The motivation for appealing to the agricultural census is that the population census is only available once every ten years. In Vietnam, the agricultural census is conducted every five years. This means that by alternating the population census with the agricultural census we are able to triple the frequency of poverty and inequality estimates at the small area level. The latter is important as it makes the small area estimation exercise a more suitable tool to monitor poverty and inequality over time, channel resources when and where they are most needed, and to evaluate poverty reduction initiatives across the different areas in Vietnam.

While replacing the population census with the agricultural census does not require any methodological changes, there are some differences worth noting. Most importantly, the agricultural census only allows us to provide estimates for rural Vietnam, where the population census covers both rural and urban areas. Also, the two different census data sets each have their own specific variables, in addition to a standard set of 2
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- December 2017, and November 2018 - February 2019. In each survey, we asked for up to a year of recall data on agricultural production; based on the timing of our surveys we therefore have production for all agricultural seasons from June 2014 through August 2018, with the exception of 2015 Dry (June - August 2015) and 2016 Rainy 1 (September 2015 - February 2016).

The household sample for the follow up surveys consists of all the baseline respondents, while the plot sample for the follow up surveys consists of the sample plots and most important plots. To maintain a panel of plots, we ran a “tracking survey”. This survey was triggered whenever a household’s sample plot or most important plot was sold or rented out to another household, or a household stopped renting in that plot if it was not the owner (“transacted”). Specifically, we tracked and interviewed the new household responsible for cultivation decisions on that plot to record information about cultivation and production, along with household characteristics when the new household was not already in our baseline sample. Data from this tracking survey is incorporated in all our plot level analysis, limiting plot attrition.

Attrition in our survey is low, and details on attrition are presented in Table A8. Only 6.0% (6.4%) of plot-by-season observations for sample plots outside the command area in our primary analysis sample (defined in Section 3.1) are missing during the dry season (rainy season). There are three sources of attrition: household attrition, plots transacted to other farmers that we were not successful in tracking, and plots rented out to commercial farmers who were based in the capital or internationally (from whom we were unable to collect agricultural production data). We do not find evidence of differential attrition of sample plots due to household attrition or plots transacted to other farmers that we did not track, however we do find access to irrigation causes an additional 6.4 - 10.2pp of plots to be rented out to a commercial farmer. We interpret the lack of data on these plots as biasing our primary estimates of the impacts of irrigation downwards, as these plots are cultivated with productive export crops, and we discuss attrition further in Appendix F.

# **2.3 Stylized facts**

To motivate our analysis of the impacts of hillside irrigation, we first introduce some stylized facts about irrigation in this context. Table 1 presents summary statistics for agricultural production from our four years of data, pooled across seasons.

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Table B10: Mechanism at play, controlling for investor concentration in source countries

|VARIABLES|(1)<br>CR5|(2)<br>CR5|(3)<br>CR5|(4)<br>CR5|
|---|---|---|---|---|
|Uncertainty (standardized)|-0.014|1.521***|1.411***|1.752***|
||(0.589)|(0.527)|(0.482)|(0.627)|
|Uncertainty _×_ Developing|1.420*<br>(0.721)||||
|FDI Concentration Source countries (standardized)|-0.666<br>(0.434)|-0.660<br>(0.469)|-0.500<br>(0.362)|-0.862<br>(0.652)|
|Observations|2,199|1,341|1,498|1,110|
|R-squared|0.823|0.819|0.835|0.797|
|Data Coverage|All countries||Developing||
|Minimum # of|||||
|Foreign Investors|3||1|10|

_Note:_ Each observation corresponds to a destination-year, represented as _it_ . “CR5” denotes the concentration ratio of the top 5 investing firms. Countries averaging fewer than 5 foreign investors are excluded from these calculations. “HHI” refers to the Herfindahl-Hirschman concentration index, calculated using the “CR5” sample. “log(# MNEs)” represents the natural logarithm of the number of foreign investors, and “log(K)” denotes the natural logarithm of the average capital expenditure. “Uncertainty” is based on the “T3” variable from the World Uncertainty database (Ahir et al. 2022), which counts the occurrence of the word “uncertainty” from Economist Intelligence Unit (EIU) reports for 143 countries since the 1950s. A higher value indicates greater uncertainty. Uncertainty in year _t_ is approximated as the logarithm of the average number of uncertainty-related words in EIU reports over the three years preceding the investment (standardized to have a mean of zero and a standard deviation of 1). The analysis includes country fixed effects and (world) region by year fixed effects. Countries are classified into 7 regions based on World Bank classifications. Destination-specific control variables include logarithms of population, GDP, the average exchange rate and a measure of concentration in the top 4 FDI sources (i.e., United States, Germany, United Kingdom, and France, weighted by their respective 2003 destination shares). Standard errors are clustered at the destination country. Standard errors are clustered at the destination country level. Significance levels: *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1.

35
**Table 7 Regression of Employment Growth Rates**

||All Data|Panel Firms|
|---|---|---|
|Small (10‐50)|‐0.14|‐0.149|
||(0.039)***|(0.083)*|
|Medium (51‐250)|‐0.155|‐0.233|
||(0.033)***|(0.068)***|
|Large (≥251)|‐0.114|‐0.191|
||(0.060)*|(0.079)**|
|Age|‐0.003|0.002|
||(0.001)**|(0.002)|
|Foreign|‐0.032|0.02|
||(0.072)|(0.096)|
|Govt|0.037|0.23|
||(0.045)|(0.041)***|
|Exporter|0.018|‐0.047|
||(0.033)|(0.052)|
|Const|0.321|0.184|
||(0.089)***|(0.125)|
|Obs<br>|668|416|
|R<sup>2</sup>|0.201|0.144|

Robust standard errors which are given in parentheses are clustered by region and 2‐digit industry for all data and by 2‐digit industry for the panel data. Additionally control for 2‐digit industry and region fixed effects. *** p<0.01, ** p<0.05, * p<0.1.

**Table 8 Fraction of Firms in Each Size‐Age Cell in Comparator Countries**

|||Turkey|||||Russia|||
|---|---|---|---|---|---|---|---|---|---|
|Employ\Age|1‐5|6‐15|>=16|Total|Employ\Age|1‐5|6‐15|>=16|Total|
|Micro|0.29|0.45|0.26|16,572|Micro|0.37|0.58|0.05|19,554|
|Small|0.15|0.54|0.31|17,710|Small|0.28|0.63|0.09|28,318|
|Medium|0.09|0.31|0.60|5,065|Medium|0.08|0.73|0.19|21,734|
|Large|0.06|0.45|0.49|1,932|Large|0.02|0.50|0.48|7,387|
|Total|7,945|19,511|13,822|41,279|Total|17,258|48,659|11,077|76,994|
|||Ukraine|||||Poland|||
|Employ\Age|1‐5|6‐15|>=16|Total|Employ\Age|1‐5|6‐15|>=16|Total|
|Micro|0.36|0.63|0.01|20,933|<=10|0.09|0.63|0.28|34,911|
|Small|0.24|0.58|0.19|16,489|11‐50|0.05|0.62|0.32|15,928|
|Medium|0.15|0.60|0.25|4,887|51‐249|0.04|0.46|0.50|5,920|
|Large|0.10|0.28|0.62|1,920|>=250|0.13|0.56|0.31|1,176|
|Total|12,297|26,218|5,713|44,229|Total|4,336|35,210|18,389|57,935|

16